more information - www.cambridge.org/9780521875011 Behavioral Neurology & Neuropsychiatry Behavioral Neurology & Neuropsychiatry Edited by David B. Arciniegas, MD The Michael K. Cooper Professor of Neurocognitive Disease, Director of the Neurobehavioral Disorders Program, and Associate Professor of Psychiatry and Neurology at the University of Colorado School of Medicine C. Alan Anderson, MD Professor of Neurology, Emergency Medicine, and Psychiatry at the University of Colorado School of Medicine, and Staff Neurologist at the Denver Veterans Affairs Medical Center and Christopher M. Filley, MD Professor of Neurology and Psychiatry, and Director of the Behavioral Neurology Section at the University of Colorado School of Medicine, and Neurology Service Chief at the Denver Veterans Affairs Medical Center Managing Editor T. Angelita Garcia CAMBRID GE UNIVERSIT Y PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Mexico City Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/ 9780521875011 c Cambridge University Press 2013 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2013 Printed and bound in the United Kingdom by the MPG Books Group A catalog record for this publication is available from the British Library Library of Congress Cataloging in Publication data Behavioral neurology & neuropsychiatry / edited by David B. Arciniegas, C. Alan Anderson, Christopher M. Filley. p. ; cm. Behavioral neurology and neuropsychiatry Includes bibliographical references and index. ISBN 978-0-521-87501-1 (hbk.) – ISBN 0-521-87501-3 (hbk.) I. Arciniegas, David B. (David Brian), 1967– II. Anderson, C. Alan. III. Filley, Christopher M., 1951– IV. Title: Behavioral neurology and neuropsychiatry. [DNLM: 1. Brain Diseases – psychology. 2. Behavior – physiology. WL 348] 616.8 – dc23 2012030788 ISBN 978-0-521-87501-1 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Every effort has been made in preparing this book to provide accurate and up-to-date information which is in accord with accepted standards and practice at the time of publication. Although case histories are drawn from actual cases, every effort has been made to disguise the identities of the individuals involved. Nevertheless, the authors, editors and publishers can make no warranties that the information contained herein is totally free from error, not least because clinical standards are constantly changing through research and regulation. The authors, editors and publishers therefore disclaim all liability for direct or consequential damages resulting from the use of material contained in this book. Readers are strongly advised to pay careful attention to information provided by the manufacturer of any drugs or equipment that they plan to use. Contents List of contributors vii Foreword xi M. Marsel-Mesulam Preface xiii 1 Introduction 1 Christopher M. Filley, C. Alan Anderson, and David B. Arciniegas Section I – Structural and Functional Neuroanatomy 184 14 Praxis 199 Kenneth M. Heilman 15 Visuospatial function 214 Doron Merims and Morris Freedman 2 Behavioral neuroanatomy 12 C. Alan Anderson, David B. Arciniegas, Deborah A. Hall, and Christopher M. Filley 3 Cerebellum 32 Jeremy D. Schmahmann 4 White matter 47 Christopher M. Filley 5 Frontal-subcortical circuits David G. Lichter 6 Arousal 88 C. Alan Anderson, Christopher M. Filley, David B. Arciniegas, and James P. Kelly 16 Executive function David B. Arciniegas 225 17 Comportment 250 Michael Henry Rosenbloom, Oliver Freudenreich, and Bruce H. Price 18 Emotion 266 David B. Arciniegas 59 7 Sleep 98 Martin L. Reite 8 Attention 115 Joshua Cosman and Matthew Rizzo 9 Motivation 134 Brian D. Power and Sergio E. Starkstein 10 Perception and recognition 144 Benzi M. Kluger and Gila Z. Reckess 11 Memory 161 Felipe DeBrigard, Kelly S. Giovanello, and Daniel I. Kaufer 12 Language 174 Mario F. Mendez 13 Affective prosody Elliott D. Ross 19 Personality 299 Sita Kedia and C. Robert Cloninger Section II – Neurobehavioral and Neuropsychiatric Assessment 20 Neuropsychiatric evaluation Fred Ovsiew 21 Neurological examination Stuart A. Schneck 310 319 22 Assessment for subtle neurological signs 333 Igor Bombin, Celso Arango, and Robert W. Buchanan 23 Mental status examination David B. Arciniegas 344 24 Neuropsychological assessment C. Munro Cullum 394 v Contents 25 Forensic assessment 406 Hal S. Wortzel and Robert L. Trestman 26 Structural neuroimaging 415 Robin A. Hurley, Deborah M. Lucas, and Katherine H. Taber 27 Advanced neuroimaging 430 Deborah M. Little, David B. Arciniegas, and John Hart, Jr. 28 Electroencephalography 442 Lauren C. Frey and Mark C. Spitz 29 Advanced electrophysiology Donald C. Rojas 459 30 Neurotoxicology 474 Christopher M. Filley 31 Neuropathological assessment 485 B. K. Kleinschmidt-DeMasters, Katherine L. Howard, Steven G. Ojemann, and Christopher M. Filley Section III – Treatments in Behavioral Neurology & Neuropsychiatry 32 Principles of pharmacotherapy Jonathan M. Silver vi 33 Rehabilitation and pharmacotherapy of cognitive impairments 511 David B. Arciniegas, Hal S. Wortzel, and Kimberly L. Frey 34 Pharmacotherapy of emotional disturbances 543 Steven L. Dubovsky 35 Pharmacotherapy of behavioral disturbances 566 Thomas W. McAllister and David B. Arciniegas 36 Psychotherapy 587 Lynne Fenton and Robert Feinstein 37 Environmental and behavioral interventions 604 Laura A. Flashman and Thomas W. McAllister 38 Procedural interventions 627 C. Alan Anderson and David B. Arciniegas Index 649 498 The color plates are to be found between pp. 224 and 225. Contributors C. Alan Anderson, MD Professor of Neurology, Emergency Medicine and Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA; Staff Neurologist, Denver Veterans Affairs Medical Center, Denver, CO, USA Celso Arango, MD, PhD Head, Adolescent Unit, Department of Psychiatry, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Adjunct Associate Professor of Psychiatry, Maryland Psychiatric Research Center, University of Maryland, Baltimore, MD, USA David B. Arciniegas, MD The Michael K. Cooper Professor of Neurocognitive Disease; Director, Neurobehavioral Disorders Program; Associate Professor of Psychiatry and Neurology, University of Colorado School of Medicine, Aurora, CO, USA Joshua Cosman, PhD Postdoctoral Fellow, Department of Neuroscience, University of Iowa, Iowa City, IA, USA C. Munro Cullum, PhD Professor of Psychiatry and Neurology & Neurotherapeutics; Director of Neuropsychology; Pam Blumenthal Distinguished Professor of Clinical Psychology, The University of Texas Southwestern Medical Center, Dallas, TX, USA Felipe DeBrigard Graduate Student, Department of Philosophy and Coginitive Neuroscience of Memory Laboratory, The University of North Carolina at Chapel Hill, NC, USA Steven L. Dubovsky, MD Professor and Chair, Department of Psychiatry, The State University of New York at Buffalo, Buffalo, NY; Adjoint Professor of Psychiatry and Medicine, University of Colorado School of Medicine, Aurora, CO, USA Igor Bombin Department of Psychology, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, University of Oviedo, Oviedo, Spain; Reintegra Foundation, Centro de Rehabilitación Neurológica, Spain Robert Feinstein, MD Professor of Psychiatry and Vice Chairman for Clinical Education and Evidence Based Medicine, Integration, Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA Robert W. Buchanan, MD Professor of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA Lynne Fenton, MD Assistant Professor, Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA C. Robert Cloninger, MD Wallace Renard Professor of Psychiatry; Professor of Genetics and Psychology; Director, Center for Psychobiology of Personality, Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA Christopher M. Filley, MD Professor of Neurology and Psychiatry; Director, Behavioral Neurology Section, University of Colorado School of Medicine, Aurora, CO, USA; Neurology Service Chief, Denver Veterans Affairs Medical Center, Denver, CO, USA vii List of contributors Laura A. Flashman, PhD Professor, Department of Psychiatry, Dartmouth Medical School, Hanover, NH, USA Katherine L. Howard Coordinator, Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA Morris Freedman, MD, FRCP Head, Division of Neurology and Director, Behavioural Neurology Program, Baycrest Hospital; Professor, Faculty of Medicine (Neurology) and Director, Behavioural Neurology Section, Division of Neurology, University of Toronto, Toronto, ON, Canada Robin A. Hurley, MD Professor of Psychiatry & Behavioral Medicine and Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA Oliver Freudenreich, MD Associate Professor of Psychiatry, Harvard Medical School; Director, First Episode and Early Psychosis Program, Massachusetts General Hospital, Boston, MA, USA Kimberly L. Frey, MS, CCC-SLP, CBIS Instructor, Neurobehavioral Disorders Program, Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA Lauren C. Frey, MD Assistant Professor, Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA Kelly S. Giovanello, PhD Assistant Professor, Department of Psychology Biomedical Research, Imaging Center, The University of North Carolina at Chapel Hill, NC, USA Deborah A. Hall, MD, PhD Assistant Professor, Department of Neurology, Rush University Medical Center, Chicago, IL, USA John Hart, Jr., MD Medical Science Director, Center for Brain Health, Jane and Bud Smith Distinguished Chair, Cecil Green Distinguished Chair, Professor, Behavioral and Brain Sciences, UT Dallas; Professor, Neurology & Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA Kenneth M. Heilman, MD The James E. Brooks Jr. Distinguished Professor of Neurology and Health Psychology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA viii Daniel I. Kaufer, MD Assistant Professor, Department of Neurology, and Director, UNC Memory Disorders Program, University of North Carolina at Chapel Hill School of Medicine, NC, USA Sita Kedia, MD Assistant Professor, Department of Pediatrics, Children’s Hospital Colorado and University of Colorado School of Medicine, Aurora, CO, USA James P. Kelly, MD Director, National Intrepid Center of Excellence, Bethesda, MD, USA B. K. Kleinschmidt-DeMasters, MD Professor and Head of Neuropathology, Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA Benzi M. Kluger, MD, MS Assistant Professor, Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA David G. Lichter, MB, ChB, FRACP Clinical Professor of Neurology and Psychiatry, The State University of New York University at Buffalo, Buffalo, NY, USA Deborah M. Little, PhD Assistant Professor of Neurology and Rehabilitation Anatomy and Cell Biology, University of Illinois College of Medicine at Chicago, Chicago, IL, USA Deborah M. Lucas, MD Radiology Preceptor, W.G. “Bill” Hefner VA Medical Center, 1601 Benner Avenue, Salisbury, NC 28144, USA List of contributors Thomas W. McAllister, MD Millennium Professor of Psychiatry and Neurology; Vice Chair for Neuroscience Research, Department of Neurology, Dartmouth Medical School, Hanover, NH, USA Mario F. Mendez, MD, PhD Professor of Neurology and Psychiatry & Biobehavioral Sciences, UCLA School of Medicine; Director, Neurobehavior Unit, Greater Los Angeles VA Medical Center, Los Angeles, CA, USA Doron Merims, MD Neurologist, Movement Disorders Unit, Department of Neurology, Tel-Aviv Sourasky Medical Center, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel Steven G. Ojemann, MD Associate Professor and Director, Stereotactic and Functional Neurosurgery, University of Colorado School of Medicine, Aurora, CO, USA Fred Ovsiew, MD Professor of Clinical Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago IL, USA Brian D. Power Clinical Lecturer, School of Psychiatry, University of Western Australia, Australia Bruce H. Price, MD Assistant Professor of Neurology, Harvard Medical School; Chief, Department of Neurology, McLean Hospital, Belmont, MA, USA Gila Z. Reckess Graduate Student, Department of Clinical and Health Psychology, University of Florida, FL, USA Martin L. Reite, MD Clinical Professor, Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA Matthew Rizzo, MD Professor of Neurology, Engineering and Public Policy; Director, Division of Neuroergonomics; Director, University of Iowa Aging Mind and Brain Initiative, Department of Neurology, University of Iowa, Iowa City, IA, USA Donald C. Rojas, PhD Associate Professor, Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA Michael Henry Rosenbloom, MD Health Partner Specialty Center, Center for Dementia and Alzheimer’s Care, St. Paul, MN, USA Elliott D. Ross, MD Professor, Department of Neurology, The University of Oklahoma College of Medicine; Director, OKC VAMC Center for Alzheimer’s and Neurodegenerative Disorders, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Jeremy D. Schmahmann, MD Professor of Neurology, Harvard Medical School; Director, Ataxia Unit; Cognitive and Behavioral Neurology Unit; Laboratory for Neuroanatomy and Cerebellar Neurobiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA Stuart A. Schneck, MD Professor Emeritus, Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA Jonathan M. Silver, MD Clinical Professor, Department of Psychiatry, NYU School of Medicine, New York, NY, USA Mark C. Spitz, MD Professor of Neurology, Director, Comprehensive Epilepsy Center, Medical Director, Clinical Neurophysiology Laboratory, University of Colorado School of Medicine, Aurora, CO, USA; Staff Neurologist, Denver Veterans Affairs Medical Center, Denver, CO, USA ix List of contributors Sergio E. Starkstein, MD, PhD, FRANZCP Winthrop Professor, Fremantle Hospital, The University of Western Australia, Crawley, WA, Australia Katherine H. Taber, PhD Research Health Scientist, Salisbury VAMC; Assistant Co-Director, MIRECC Education Component x Robert L. Trestman, PhD, MD Professor, Department of Psychiatry, University of Connecticut Health Center, Farmington, CT, USA Hal S. Wortzel, MD Assistant Professor of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA; Director, Neuropsychiatric Consultation Services, Denver Veterans Affairs Medical Center, Denver, CO, USA Foreword Practitioners of Behavioral Neurology & Neuropsychiatry should consider themselves privileged to have been assigned the uniquely challenging task of treating the most complex disorders of the most complex organ in the body. The facts and figures are quite daunting. The cerebral cortex alone contains 40 billion neurons crowded into 30 square feet of surface area. Each neuron makes thousands of contacts with other neurons. At these contact points, known as synapses, information flows from one neuron to another at a rate of approximately 100 times per second. The total number of neural contacts on the surface of the brain alone is 40 followed by 12 zeros, a number that is as large as the number of all the stars in our galaxy. This complexity is not devoid of order. A distinctive principle of brain function is the regional variation of specializations – different parts of the brain have different responsibilities. Some of these job descriptions defy common sense. What kind of engineering logic would have made memory for recent events, a faculty essential for all aspects of behavior, critically dependent on a tiny part of the temporal lobe known as the hippocampus? Why is language, a faculty that permeates all aspects of thought, critically dependent on only one hemisphere? The past 150 years have allowed us to accumulate mountains of facts at a continuously accelerating rate. The classic case reports of the late nineteenth and early twentieth centuries, the advent of new methods for tracing structural and chemical neuroanatomy, single cell recordings in behaving primates, and the modern revolution in neuroimaging are some of the engines that powered this growth. The next revolution will arise when these facts are linked to explanatory the- ories of brain function, theories that can explain, in some principled way, how patterned synaptic activity can transform muscle contractions and sensory input into memories, words, and actions. While we wait for new insights to emerge, we have patients that need our help. In many instances, the diseases we see are irreversible. Few of our patients can be restored to former states of normalcy and many turn out to have relentlessly progressive neurodegenerative disorders. However, the practitioner in this field needs to understand (and believe) that “incurable” diseases are nonetheless “treatable.” Characterizing the neurobehavioral parameters of the disease, specifying the chief deficit that undermines daily activities, educating the patient and family, identifying appropriate resources for rehabilitation, and the judicious use of pharmacotherapy are some of the modalities that allow the informed practitioner to make a real difference in patient care. This volume covers key theoretical and practical topics in Behavioral Neurology & Neuropsychiatry. A special strength is the section on treatment. Edited by three leaders in this field, this volume will allow seasoned practitioners as well as novices to have a better understanding of this complex area of medicine and to take better care of their patients. M. Marsel-Mesulam, MD Director, Cognitive Neurology and Alzheimer’s Disease Center; Ruth Dunbar Davee Professor of Neuroscience, Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA xi Preface The recent merger of behavioral neurology and neuropsychiatry into a single medical subspecialty, Behavioral Neurology & Neuropsychiatry (BN&NP), represents a substantive rapprochement between the parent disciplines from whence its trainees and practitioners hail. This event occasions an approach to brain– behavior relationships that transcends the traditional perspectives of neurology and psychiatry, and creates an enduring context for the combined study of the psychiatry of neurology and the neurology of psychiatry. Most importantly, it identifies a cadre of clinicians dedicated to caring for persons and families affected by all manner of brain disorders producing cognitive, emotional, behavioral, and sensorimotor disturbances. The clinical approach of subspecialists in BN&NP reflects a materialist philosophy of mind that regards mental events as brain events and, by extension, mental disorders as brain disorders. Critical evaluation of the relationships between psychological and neurobiological phenomena is encouraged, as are revisions or elimination of concepts or theories that do not comport with modern neuroscience. Reciprocal interactions between neurobiological, psychological, social, and environmental factors and their influences on neuropsychiatric health are appreciated, and they are considered relevant to understanding brain–behavior disorders and their treatments to the extent that they affect brain structure and/or function. This philosophy precludes guild-based division of brain disorders into neurological and psychiatric types, and requires subspecialists in BN&NP to employ a comprehensive clinical approach that blends and adds to the historically distinct methods of neurology and psychiatry. For more than a decade, our BN&NP faculty group at the University of Colorado School of Medicine has applied this philosophy and approach to our daily practice. As BN&NP clinicians, teachers, and educators, we are an integrated transdisciplinary faculty group who bring a uniform body of subspecialty knowledge and skills to the clinic and bedside despite differences in our primary training backgrounds. Our model informed the work of the Joint Advisory Committee on Subspecialty Certification of the American Neuropsychiatric Association and the Society for Behavioral and Cognitive Neurology, their creation of the BN&NP training curriculum adopted by the United Council for Neurologic Subspecialties [1], and that organization’s development of BN&NP fellowship program accreditation standards and practitioner certification processes [2] – all endeavors to which the editors of this volume and many of its chapter authors contributed substantively. This book complements and extends those efforts by translating the philosophy and clinical approach used in our clinical and teaching efforts into a compendium of the concepts and principles of BN&NP. It draws naturally from information presented in the many volumes in this field that we have studied, and it extends and complements the writings of the many great teachers from whom we have learned. It is unique, however, in its specific focus on BN&NP and its organization around a peer-reviewed and nationally accepted curriculum for training in this subspecialty as well as its development by editors and authors committed to the growth of this area of clinical practice and study. Where material offered in this book imitates or critiques prior works, our aim – both as editors and as contributors – has been to ensure that its presentation honors the sources from which it derives, reflects the best traditions of scholarship, and serves to advance our field. Designed as a primer of concepts and principles in BN&NP, rather than an exhaustive survey of neurobehavioral disorders, the text divides into three parts: Structural and Functional Neuroanatomy (Section I), Clinical Assessment (Section II), and Treatment (Section III). Part I begins with an introduction to the history and current practice of BN&NP. Chapter 2 offers an overview of essential behavioral neuroanatomy, including brain–behavior xiii Preface relationships associated with the brainstem, cerebellum, diencephalon, subcortical structures, limbic and paralimbic areas, white matter, and neocortex. The next several chapters provide detailed reviews of the neuroanatomy of frontal-subcortical and limbic-subcortical circuits, white matter, and the cerebellum and the neurobehavioral functions these systems support. Subsequent chapters in Section I discuss brain–behavior relationships from the vantage point of neurobehavioral functions (i.e., cognition, emotion, and behavior) and the neuroanatomy on which they are predicated. Given the importance of executive function and emotion in BN&NP, extended discussions of the history of ideas on these subjects and current views of their phenomenology and neuroanatomy are offered in Chapters 16 and 18. The principles of clinical assessment presented in Section II draw on the brain–behavior relationships described in Section I, and their application to everyday practice facilitates construction of subspecialty-relevant clinical histories and examinations. Chapter 20 outlines an approach to obtaining a neuropsychiatric history that is complemented by the bedside examination techniques and standardized assessments presented in Chapters 21–25. Recognizing that neuropsychiatric problems sometimes result in civil and criminal legal entanglements, special consideration is given to forensic neuropsychiatric assessment in Chapter 25. Current and emerging uses of neuroimaging, electrophysiologic, and other laboratory measures that may inform clinical evaluation and/or treatment planning are considered in Chapters 26–31. Throughout Section II, emphasis is placed on interpreting clinical symptoms, signs, and syndromes in terms of the neural processes underlying them, considering but not relying upon conventional (i.e., Diagnostic and Statistical Manual of Mental Disorders-based) psychiatric diagnoses, and avoiding dichotomization of clinical conditions into strict “psychiatric” or “neurological” types. The chapters comprising Section III of this volume describe treatments in BN&NP and the principles of their application to the care of patients and families affected by neurobehavioral disorders. The breadth of the clinical problems encompassed by BN&NP requires expertise in environmental, behavioral, rehabilitative, psychological, social, pharmacologic, and procedural interventions. The evidence base for the treatment of many neurobehavioral disorders is evolving rapidly, and their popularity often waxes and wanes xiv in short order. Nonetheless, the principles of treatment remain relatively stable and consistently applicable across the many conditions for which subspecialists in BN&NP are consulted and the various settings in which they practice. We therefore have limited consideration of condition-specific treatment issues throughout this volume and offer such only when their discussion illustrates the application of, or an exception to, a principle of treatment in BN&NP. We developed this volume over several years, constantly weighing the need to publish timely information against the necessity of producing a principlesfocused, stylistically coherent, and enduringly useful volume. The carefully selected group of international experts contributing to this work made these tasks easier. Their diverse training and practice backgrounds provide readers with a broad set of perspectives on the subjects addressed and their collective effort on this volume exemplifies the type of transdisciplinary collaboration that defines BN&NP. For chapters focused on subjects within the externally acknowledged expertise of our faculty groups, as well as for those topics on which we wanted to contribute a novel perspective, we functioned as both editors and authors. Thoughtfully approached and deliberately completed, we anticipate that this book will contribute usefully to the continued growth and development of BN&NP and will equip its readers to apply its principles to the study of neurobehavioral disorders and to the care of the individuals and families they affect. We appreciate deeply the time and effort offered by the authors who contributed the chapters comprising this volume as well as their patience and support during the editorial phase of its development. We also are grateful for the invaluable advice and consistent support for this project offered by the editorial group at Cambridge University Press, including Pauline Graham, Alison C. Evans, Katie James, Joanna Chamberlin, and Richard Marley, whose generosity and flexibility ensured that we were provided the time needed to develop this work according to its own requirements. We are thankful for the insights, feedback, and encouragement offered by colleagues, students, friends, family, and advisors who provided guidance on the development of this volume, including: Laura B. Arciniegas, JD, Marsha S. Anderson, MD, Richard L. Gallimore, PhD, Kimberly L. Frey, MS, Hal S. Wortzel, MD, Peter Wagner, MD, Donald C. Rojas, PhD, Jody Newman, MA, Jonathan M. Silver, MD, Thomas W. McAllister, MD, Stuart C. Yudofsky, MD, and Steven L. Preface Dubovsky, MD. We also acknowledge the support and contributions of the many other colleagues who provided the inspiration and support for the approach to BN&NP we established at the University of Colorado School of Medicine, including: Norman Geschwind, MD, Michael P. Alexander, MD, Jeffrey L. Cummings, MD, C. Edward Coffey, MD, Daniel I. Kaufer, MD, Tiffany Chow, MD, Robin A. Hurley, MD, Randolph B. Schiffer, MD, John Hart, Jr., MD, John J. Campbell, III, MD, Robert A. Bornstein, PhD, Sandra Bornstein, OT, C. Munro Cullum, PhD, Louis R. Caplan, MD, Harold P. Adams, MD, James P. Kelly, MD, Bruce H. Price, MD, Neill R. Graff-Radford, MD, Bruce L. Miller, MD, Jeremy D. Schmahmann, MD, Kenneth M. Heilman, MD, Kirk R. Daffner, MD, Antonio R. Damasio, MD, Hanna Damasio, MD, Thomas P. Beresford, MD, Lawrence E. Adler, MD, Martin L. Reite, MD, Robert Freedman, MD, James H. Austin, MD, Stuart A. Schneck, MD, Donald H. Gilden, MD, Kenneth L. Tyler, MD, Steven P. Ringel, MD, Hans E. Neville, MD, Roy R. Wright, MD, Edward Lewin, MD, Victoria S. Pelak, MD, Mark C. Spitz, MD, John R. Corboy, MD, Maureen A. Leehey, MD, Richard L. Hughes, MD, Patrick J. Bosque, MD, Jonathan H. Woodcock, MD, Ann H. Craig, MD, Elizabeth Kozora, PhD, Josette G. Harris, PhD, Bruce F. Pennington, PhD, Lisa A. Brenner, PhD, Catharine H. Johnston-Brooks, Brian D. Hoyt, PhD, Michael R. Greher, PhD, Yechiel Kleen, MD, William A. Locy, EdD, Cindy Kreutz, MBA, Estela Bogaert-Martinez, PhD, Jason Nupp, PsyD, Gail Ramsberger, PhD, Lise Menn, PhD, G. Vernon Wood, PhD, Robert B. Goos, MD, Lawrence Rodriguez, RN, James D. Hooker, MD, Al O. Singleton, III, MD, John DeQuardo, MD, Keith LaGrenade, MD, Bruce D. Leonard, MD, Jack H. Simon, MD, David Rubinstein, MD, Jody Tanabe, MD, Bette K. Kleinschmidt-DeMasters, MD, Philip J. Boyer, MD, and Jonathan Filley, PhD. We are honored by the contributions of M.-Marsel Mesulam, MD, who reviewed the structure and content of this volume early in its development and generously offered its Foreword. Most importantly, we are thankful to our families for their support, encouragement, and tolerance of our efforts on this project and forbearance of its familial costs. We are especially indebted to T. Angelita Garcia, who served as the Managing Editor of this project at the University of Colorado School of Medicine. Ms. Garcia contributed to the review of every chapter included in this volume, constructed a master database containing its several thousand citations and ensured their uniform presentation, secured permissions for the use of materials reproduced from other works, assembled the final manuscript for submission to Cambridge University Press, and coordinated communication between the editors and publisher. Her efforts were essential to the successful completion of this work, and for her contributions we offer our most grateful acknowledgment and thanks. 1. Arciniegas DB, Kaufer DI, , Joint Advisory Committee on Subspecialty Certification of the American Neuropsychiatric Association, Society for Behavioral and Cognitive Neurology. Core curriculum for training in Behavioral Neurology & Neuropsychiatry. J Neuropsychiatry Clin Neurosci. 2006;18(1):6–13. 2. Silver JM. Behavioral Neurology & Neuropsychiatry is a subspecialty. J Neuropsychiatry Clin Neurosci. 2006;18(2):146–8. xv Chapter 1 Introduction Christopher M. Filley, C. Alan Anderson, and David B. Arciniegas The writing of a new textbook in medicine presents both an exciting opportunity and a daunting challenge. Whereas any contemporary medical field witnesses timely new developments that call for rapid dissemination, it is uncertain how much time busy clinicians and investigators can devote to reading an entire textbook, especially when ready access to a range of online resources is increasingly available. We have ventured forth nonetheless, with the goal of creating a novel synthesis of the strong intellectual and academic traditions of Behavioral Neurology & Neuropsychiatry (BN&NP). The unification of the historically separate but parallel subspecialties of behavioral neurology and neuropsychiatry is a relatively recent event [1, 2]. These subspecialties were joined through the work of the American Neuropsychiatric Association and the Society for Behavioral and Cognitive Neurology to create the BN&NP subspecialty under the auspices of the United Council for Neurologic Subspecialties (UCNS). The goals of this effort included advancing and enriching this area of clinical practice and scientific research, in which the brain is recognized as the organ of the mind, as well as facilitating the continued growth of this subspecialty through standardization and accreditation of fellowship training programs and certification of its practitioners. This development reflects a broader re-engagement between neurology and psychiatry more generally [3–5]. Traditional academic boundaries are being reassessed from all sides as medical progress continues. As we have observed and contributed to this process of gradual rapprochement [2], and because we apply an integrated model of BN&NP in our daily work as clinicians, educators, and scientists, the idea of producing this volume arose naturally. This book thus embodies a summary of our thoughts and practice in these fields – as well as those of our colleagues – as developed over more than two decades of clinical care, education, research, and reflection. As an overture to what follows, some preliminary information will help set the stage. Historical background Neurology and psychiatry Neurology and psychiatry are securely established medical specialties with well-demarcated areas of clinical and research expertise. Although it seems natural that their common interest in focusing on the brain would foster interdisciplinary ties, close collaboration between these fields and their practitioners are the exception rather than the rule. Many physicians – past and present – have promulgated various degrees of separation between the two fields and rigidly maintained that neurologists study the brain and psychiatrists study the mind. This split fosters a strict dichotomy that keeps apart the professions and professionals most concerned with the myriad and often disabling problems of human behavior. Some argue that neurology will remain separated from psychiatry because each does something unique [6, 7] – the former being “objective” and the latter “humanistic” – while critics respond by chiding the simplistic gap between “mindless neurology” and “brainless psychiatry” [3, 8]. These fields share common origins in Western philosophical and medical traditions [9]. Many Renaissance-era physicians were committed to the thesis that mental states are brain states, and that aberrations of mental functioning are the products of Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 1 Behavioral Neurology & Neuropsychiatry a disordered brain. Cullen (1710–1790) was the first among such physicians to include the mental disorders in his taxonomy of brain illnesses, and was the progenitor of the term “neuroses.” In his classification of disease, the neuroses included the comata, adynamiae, spasmi, and vesaniae, with this latter group consisting of many of the classic “psychiatric” illnesses (e.g., mania, depression, psychosis, and dementia). His work influenced Coombe (1797–1847) in his classification of brain diseases into two major categories, “organic” and “functional.” Coombe intended these terms to sort diseases of the brain into two categories based on the presence or absence of localizable abnormalities. It does not appear to have been his intention to establish a system in which some brain diseases are considered “real” brain problems and others are not considered brain problems at all. Griesinger (1817–1868) subsequently advanced the thesis that even normal mental processes are the direct result of brain activity alone, echoing Hippocrates’ view that mental illness has its origin in the brain [10]. Griesinger viewed psychiatry and neuropathology as a single field with one language and one set of operative laws, and advised physicians to “primarily and in every case of mental disease, recognize a morbid action of that organ [the brain]” [11]. In the following 50 years, a host of European physicians began in earnest to examine the brain with respect to mental processes. A common body of work by pioneering physician-scientists of the nineteenth and early twentieth centuries – among them Theodor Meynert, Jean-Martin Charcot, Sergei Korsakoff, John Hughlings Jackson, Henry Harlow, Eugen Bleuler, Emil Kraepelin, Arnold Pick, and Alois Alzheimer – whose efforts were neither guildspecific nor dominated by concerns regarding the primacy of one or another medical specialty. Their efforts steadily advanced knowledge of neuroanatomy, neurophysiology, and neuropathology as applied to behavioral phenomena in the quest to understand the mind as a function of the brain. This unity of purpose was so pervasive that these physicians were typically referred to as neuropsychiatrists, heralding more formal developments in this direction a century later. During this same period, however, neurology began to develop as an independent field of study, most notably after the formation of the National Hospital for the “Relief of Paralysis, Epilepsy, and Allied Diseases” in Britain in 1860. Concurrently, Charcot (1825–1893) and his students began concentrating on the “neuroses,” and pursuing a line of inquiry that turned the 2 interest of psychiatry at the beginning of the twentieth century toward introspection, reflection, and consideration of the person as a whole. Notably, as psychoanalysis became a more dominant force within psychiatry, this “person as a whole” became increasingly less whole with respect to a complete understanding of the neurology underlying neurotic conditions. As the twentieth century progressed, neurology and psychiatry became polarized with respect to the focus and content of their studies. Neurology was interested in localizable pathology, the “organic” problems, and psychiatry focused on the functioning of an individual’s psyche, internally and interpersonally. These “functional” problems, whose consideration was divorced of their neurological bases, became the province of psychiatry. Interestingly, this was not the initial objective of Sigmund Freud (1856–1939), the progenitor of psychoanalysis. A neurologist by training, Freud was committed to a form of substance materialism: “Research has afforded irrefutable proof that mental activity is bound up with the function of the brain as with no other organ. The discovery of the unequal importance of the different parts of the brain and their individual relations to particular parts of the body and to intellectual activities takes us a step further . . . ” [10]. Freud maintained that the science of his time could not establish clearly the relationship between the complex operations of mental processes and brain function. His theories therefore assumed a form of logical positivism in which he identified the concepts and mechanisms of mental operations from a purely psycho-philosophical perspective. Early in this endeavor, he offered cautionary notes on this approach: “Our mental topography has for the present nothing to do with anatomy . . . In this respect, then, our work is untrammeled and may proceed according to its own requirements. It will, moreover, be useful to remind ourselves that our hypotheses can in the first instance lay claim only to the value of illustrations” [10]. Freud (1895) [12] envisioned a future in which a scientific account of mental processes would be possible. However, de facto dualist perspectives on mind– body issues supplanted his early materialist positions on such matters and pervaded early and midtwentieth century psychiatric practice and popular culture. Indeed, when previously “functional” disorders like general paresis of the insane (a form of neurosyphilis) were discovered to have an “organic” basis, Chapter 1: Introduction they were quickly eschewed as proper subjects of psychiatric study and treatment and taken up by neurology. As a result, the early part of the twentieth century witnessed the progressive movement of a significant part of psychiatry away from its neuropsychiatric foundations, and facilitated the continued division of neurology and psychiatry into separate disciplines. As described by Hollender (1991) in American Board of Psychiatry and Neurology: The First Fifty Years [13], the unification of neurology and psychiatry under the American Board of Psychiatry and Neurology (ABPN) in 1934 had the potential to unify the fields. However, the manner in which the ABPN was created contributed substantially to the formal separation of psychiatry and neurology. In the early 1930s, a group of neuropsychiatrists within the Section on Nervous and Mental Disease of the American Medical Association (AMA) suggested that psychiatry and neurology be united under a common board of examiners for the purpose of establishing criteria and examinations for certification to practice in these medical specialties. Their explicit purposes were to recognize the common interests of these specialties in brain– behavior relationships, and to protect both the public and also the reputations of these fields by distinguishing qualified from unqualified practitioners. In order to develop a board that would be widely accepted by the practicing clinicians of that time, the AMA solicited the participation of representatives from the American Psychiatric Association (APA) and the American Neurological Association (ANA) in discussions regarding the development of a unified examining board. In recommending a unified board, the AMA made clear its position that the content and practice of psychiatry and neurology overlapped substantially, and that both fields would be best served by an examining board that acknowledged their similarity. Ongoing tensions between the fields with respect to public legitimacy and scientific dominance limited the ability of the participating psychiatrists and neurologists to work cooperatively on this task. Although the AMA representatives initiating these discussions were self-described neuropsychiatrists, the representatives from the APA and the ANA elected not to recognize neuropsychiatry as a field of practice. Instead, Hollender (1991) [13] notes that the APA and ANA representatives elected first to demarcate sharply the differences in training and certification between psychiatry and neurology and then argued over the order in which the two fields should be represented in the Board’s official title. Despite their posturing, the ABPN administered the same examination to candidates in both areas for the first decade of its operations. Over time, and as a consequence of training differences driven by the ABPN guidelines, the examination became increasingly focused on the candidate’s field of training. Nonetheless, 25% or more of the board examination content for each remains based on the other specialty’s material. This continues to acknowledge, albeit implicitly, that much of what constitutes neurology and psychiatry is scientifically inseparable and the practice of both specialties requires a transdisciplinary knowledge base and skill set. Nonetheless, the creation of ABPN left a legacy of an uneasy, if not occasionally hostile, alliance between psychiatrists and neurologists. Its creation also effected an apparent amnesia within these specialties for the historical and philosophical background that resulted in their regulation under a combined board. As noted earlier in this chapter, thought leaders in both fields occasionally call for reunification of psychiatry and neurology [3–5]. However, attempts to unite these fields are met with skepticism, at best, outside of a small number of academic and private institutions. Similarly, requests to the ABPN for the establishment of Added Qualifications in Neuropsychiatry have not thus far been successful. Behavioral Neurology & Neuropsychiatry The contemporary subspecialties of behavioral neurology and neuropsychiatry have taken separate but converging paths to their current positions. Behavioral neurology is widely held to have begun with the work of Norman Geschwind in the midtwentieth century [14], who established a fellowship program at the Boston Veterans Administration Hospital while rising to the position of James Jackson Putnam Professor of Neurology at Harvard Medical School. Geschwind reintroduced and expanded on observations of brain lesions and behavioral disturbances made by neurologists such as Paul Broca, Karl Wernicke, Hugo Liepmann, Hienrich Lissauer, and Jules Dejerine in the previous century, and wrote a seminal paper on disconnection syndromes in 1965 that inspired a generation of research on brain–behavior relationships [15, 16]. With this foundation, behavioral neurology firmly took 3 Behavioral Neurology & Neuropsychiatry hold; the lesion method of studying brain–behavior relationships proved highly productive [17] and the idea of distributed neural networks subserving cognition appeared as a major organizing principle in the field [18]. Neuropsychiatry, on the other hand, flourished first as a discipline in the late nineteenth century as discussed above, but then fell into relative obscurity during the mid-twentieth century. Psychoanalytic theory and practice dominated psychiatry for the first half of the twentieth century, especially in the USA where psychoanalysts fleeing Hitler’s Germany exerted much influence [19], and neurobiological correlates of behavior were relatively neglected. This situation began to change with the introduction of modern psychopharmacology in the 1950s that ushered in the field of biological psychiatry – a powerful stimulus for adopting a neurobiological model of mental function. In this setting, a neuropsychiatric approach to patient care began to reemerge and steadily gain momentum as physicians increasingly appreciated the neurologic bases of psychiatric disease and the psychiatric aspects of neurologic disease [19–22]. The organization of a professional association for neuropsychiatrists and the development of neuropsychiatry as a medical subspecialty derive from the efforts of many physicians, most notably Jeffrey L. Cummings, Randall B. Schiffer, and Stuart C. Yudofsky. Two other factors also fueled the development of both behavioral neurology and neuropsychiatry; the rapid growth of neuropsychology, arising in large part from the influence of Alexander R. Luria in mid-century Russia [23], and the spectacular advances of neuroimaging from the 1970s onward that increasingly enabled precise structural [24] and later functional imaging of the brain [25]. These fields contributed cognitive measures and neuroradiologic techniques for detailed study of brain–behavior relationships that substantially augmented existing methods of clinical–pathologic correlation. With the advent of the twenty-first century, clinical neuroscience is flourishing, leading to thoughtful commentary advocating the closing, or at least narrowing, of the “great divide” that has existed between neurology and psychiatry [3]. Philosophical antecedents to Behavioral Neurology & Neuropsychiatry The fluctuating relationship between the previously separate subspecialties of behavioral neurology 4 and neuropsychiatry derives principally from the challenges of investigating the human mind and its disorders in a medical context. At the root of this conundrum is the ancient philosophical question of the relation of mind and body, recast in the modern formulation of the mind–brain debate. What is the source of human consciousness – an ineffable, immaterial mind that exists apart from any physical structure, or the collection of nerve cells and chemicals known as the brain that accounts for all behavior? To many, it is inconceivable that the “gray and white gook” inside the skull could actually be conscious [26], and thus responsible for such cherished capacities as intelligence, creativity, and altruism. The towering influence of the seventeenth-century dualist philosopher Rene Descartes [27] continues today in our society – and even to some extent in medicine. The era in which psychoanalysis dominated psychiatric practice created an intellectual environment in medicine that was sympathetic to dualistic thinking despite Freud’s early career as a neurologist. While not avowedly dualistic in the philosophical sense, mainstream psychiatry for much of the twentieth century contrasted “functional” with “organic” disorders as a way of establishing a group of mental disorders in which brain structure and function were essentially irrelevant [3]. Psychiatry focused primarily on symptoms rather than signs, and as the metaphors of psychoanalytic theory captured public and professional imagination, the use of neurological examination and laboratory data in studying behavior diminished [3]. Simultaneously, neurology chose to care for those patients in whom structural brain disease could be detected, eschewing the unavoidable subjectivity of behavioral analysis in favor of the “hard” scientific data of the neurological examination, cerebrospinal fluid analysis, electroencephalography, and neuropathology [3]. As the influence of psychoanalysis began to recede in the mid-twentieth century, strong proponents of mind–brain unity questioned the authority of Descartes [28–30], and the dichotomy of mind versus brain began to lose ground in medical thinking. Neurology and psychiatry gradually became more inclined to share the view that the brain is central to human behavior, and by 1987 the organic–functional distinction was explicitly discouraged by the influential Diagnostic and Statistical Manual of Mental Disorders [31]. Nonetheless, vestiges of philosophical dualism persist in medicine, in part because the Chapter 1: Introduction study of behavior and the vexing emotional disorders commonly considered “psychiatric” are so complex. To cite a clinical example, the floridly abnormal behavior of many psychotic patients with normal conventional clinical neuroimaging and laboratory studies seems to some critics to undermine arguments asserting that this illness is neurobiologically based – how can such abnormal behavior derive from a person whose brain is structurally normal? Perhaps it is not a surprise that lingering dualism sometimes still impacts clinical thinking [32]. Modern neuroscience notwithstanding, the fact remains that disorders of behavior are the most challenging and among the most difficult to describe objectively. The unifying foundation of BN&NP, however, is the shared philosophical position that brain and behavior are inseparable [2]. The organic–functional dichotomy cannot be maintained because all thought, emotion, and behavior are brain-based. Although traditionally trained behavioral neurologists tend to focus on brain disorders in which structural pathology is in some way demonstrable, and neuropsychiatrists hailing from general psychiatry are comfortable conceptualizing mental disorders as stemming from neurochemical deficits, all agree that higher functions – normal or abnormal – originate as neural events that involve the macro- or microstructure of the brain in the process of subserving mental operations. Thus, whereas the assessment and treatment of a person with Broca’s aphasia from an observable left inferior frontal lobe infarct differs markedly from that of an acutely psychotic schizophrenic individual with normal conventional neuroimaging, the underlying principle in dealing with both patients is the same: both syndromes involve an alteration of brain–behavior relationships that requires a thorough understanding of how the brain operates at all levels of analysis. The triage of patients into neurologic versus psychiatric settings should depend only on the evaluation and services required – such as a neurologic intensive care unit for emergent stroke treatment, or a locked psychiatric unit for acute agitated psychosis – and not on archaic notions of whether a patient has “organic” or “functional” disease. Individual temperament and interest will naturally influence the kind of clinical setting in which a physician may prefer to work, but the common principle that all patients in these settings have disorders of the brain must be honored if patient care is to be optimal and intellectual progress facilitated. Indeed, we have described an integrated practice of BN&NP that fosters excellence in patient care, education, and research within a setting that explicitly avoids the arbitrary divisions that have often hampered collaboration between neurology and psychiatry [33]. The state of the field The prospect of behavioral neurology and neuropsychiatry drawing together finds considerable support in academia. Textbooks of behavioral neurology [34– 36] and neuropsychiatry [8, 37, 38] have proliferated in recent years. Annual scholarly meetings are held conjointly by the Society for Behavioral and Cognitive Neurology and the American Neuropsychiatric Association in order to disseminate new research findings and educate practitioners. Structural and functional neuroimaging, neuroanatomy, neuropsychology, neuropharmacology, neuropathology, clinical neurophysiology, and genetics are all receiving much-needed attention. Moreover, in a remarkable development, topics previously regarded as unapproachable for neuroscientists are being vigorously considered; accounts of the neural correlates of consciousness [39], ethics [40], and creativity [41], for example, are now regular reading for devotees of brain–behavior relationships. However, postgraduate training in neurology or psychiatry exerts a powerful socializing force, and fellowship experiences in the higher functions of the brain modify but do not undo these fundamental affiliations. The development of an integrated core curriculum for fellowship training [2] and the development of the UCNS fellowship accreditation processes may create a structure within which a professional identity and practice as a subspecialist in BN&NP becomes possible. This comprehensive curriculum is derived from the traditions of neurology, psychiatry, behavioral neurology, and neuropsychiatry, while drawing heavily from neuroanatomy, neurophysiology, neuroimaging, neuropsychology, neuropharmacology, and internal medicine. A range of supplementary topics is also included, including neurosurgery, neuropathology, neurorehabilitation, neurogenetics, sleep medicine, forensic psychiatry, epidemiology, and public policy. Fellowship training in this subspecialty requires the participation of faculty from both the psychiatry and the neurology departments at each institution, and requires that these faculties possess expertise in this area and the ability to provide clinical training in a transdisciplinary manner. A minimum of one year is 5 Behavioral Neurology & Neuropsychiatry required for fellowship training in BN&NP, and more time can be arranged as indicated by the individual fellow’s interests and the availability of program resources. Although many fellowship trainees may work in academic settings upon completing their training, community practice opportunities are emerging rapidly. Among these are: (1) the need for physicians prepared to care for patients with complex, multifactorial disorders of behavior that call upon the expertise of both neurology and psychiatry; (2) the aging of the population in industrialized countries that will render more people at risk for common late-life neurodegenerative disorders such as Alzheimer’s disease (AD); (3) the continuing epidemic of traumatic brain injury (TBI) in times of peace as well as in war; and (4) astonishing advances in diagnosis and treatment of many neuropsychiatric disorders that formerly had mysterious etiologies, limited therapeutic options, and relatively poor prognoses. Certification in BN&NP will enhance opportunities by signifying special competence in these disciplines. Future prospects While gazing ahead is always fraught with uncertainty, some speculations about where the future will lead are warranted as this combined subspecialty moves ahead. One prediction likely to be met is that the intellectual vigor of these fields will stimulate much continued discussion and steady development of concepts. A major goal of these two disciplines is the integration of structural and molecular paradigms in constructing a modern synthesis of brain–behavior relationships. As discussed above, those who think about structural brain lesions can learn from those whose emphasis is on abnormal neurotransmission, and surely the converse is true as well. How does the intricate neurochemistry of the brain map onto the familiar gyri, tracts, and nuclei to enrich our knowledge of distributed neural networks? Such a portrait will enable increasing sophistication of medical and surgical therapy based on manipulation of neuroanatomically localized neurotransmitter systems. A complete understanding of the brain as the organ of the mind requires the unification of knowledge from both traditions. This approach may plausibly lead to the reintegration of Freudian thinking into the mainstream of medicine. Freud himself harbored the belief 6 that the phenomena of psychoanalytic theory – the unconscious, the id, ego, and superego, repression, transference, dream analysis, and the like – would someday find correlates in the brain, and that a neurobiological model of the mind would develop [10]. Such a synthesis may become more feasible with modern investigative techniques [42]. Subspecialists in BN&NP do not see a need to vindicate Freudianism – indeed, difficulty establishing the neural basis of psychoanalytic theory and its clinical efficacy has been a major source of criticism – but the notion that complex psychological processes have a basis in brain function is fundamental. Freudian concepts may not readily be seen to correlate with brain structures as our understanding increases; however, the behaviors Freud observed in his patients – like any other – should result from the operations of neural systems. As Geschwind aptly wrote: “It must be realized that every behavior has an anatomy” [43]. The ultimate goal is to understand how the brain mediates behavior, whether investigators use a model of structural cortical damage affecting language, or examine altered neurotransmitter systems that influence personality. A topic sure to garner much attention is the continued application of functional imaging technology to understanding brain–behavior relationships. The lesion method has a time-honored tradition in behavioral neurology, and will continue to flourish, but what of functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and the like? Will these tools add significantly to the understanding of brain and behavior, or will they turn out to be no more than neo-phrenological instruments producing appealing but easily misinterpreted images? The impressive technique of fMRI has become the most promising of these modalities, about which thousands of research papers are now published every year, but fMRI remains limited by the extraordinary complexity of the brain’s functional organization [44]. SPECT scanning has the advantage of being readily available, but has proven disappointing when applied to clinical disorders [45]. At the same time, much excitement attends the advances of structural neuroimaging, which is now disclosing details of neuroanatomy and neuropathology as never before. Exploiting the remarkable success of MRI, investigators are pursuing more detailed volumetric quantitation of cerebral structures with voxelbased morphometry [46], and measurement of the Chapter 1: Introduction chemical composition of brain regions has become possible with magnetic resonance spectroscopy [47]. The study of white matter, long relegated to the background of cognitive neuroscience as the cerebral cortex has dominated thinking and research, will now be feasible with diffusion tensor imaging [48], which is generating elegant depictions of white matter tracts in health and disease. White matter disorders in general will stimulate wide-ranging investigation as the importance of white matter for human behavior is increasingly appreciated [49, 50]. All of these techniques create a context in which the study of the structure of distributed neural networks can be integrated with an understanding of their functional connectivity and role in neurobehavioral health and disease. Advances in genetics that are occurring at a rapid pace will also enhance understanding and clinical diagnosis of neurobehavioral disorders. Testing for autosomal dominant transmission in Huntington’s disease is a straightforward and well-known example of how genetic analysis can be applied clinically [51], and the list of genetic diseases in which such testing can be considered is quickly growing. Genetic testing for AD, while only exceptionally providing definitive results for patients and families, is gradually improving [52], and may be able to identify presymptomatic individuals in whom dementia can someday be prevented. While these developments are encouraging, the reality that genetic diseases are, presently, for the most part irreversible provides a strong impetus for further study of the pathogenesis of neurogenetic disorders. Nevertheless, treatment of patients with disorders of all types affecting behavior will surely come to be a major topic in the coming years. Many of the treatment options for patients with these disorders are based on scanty evidence, and convincing clinical trials are sorely needed. Randomized controlled trials for treatment of AD have been familiar to clinicians for decades, and serve as a model for large-scale investigation of treatments for a host of cognitive disorders. Traumatic brain injury occupies a major portion of the practice of many subspecialists in BN&NP and additional studies are needed to better define the best methods of neurorehabilitation. More study of neurosurgical interventions, such as for the treatment of neoplasms and hydrocephalus, will be helpful. More controversially, the oft-reviled area of psychosurgery may merit reconsideration as a treatment option for those with intractable and devastating disorders, particularly as modern surgical techniques permit more precise procedures and research advances allow accurate evaluation of safety and efficacy [53]. The assessment of the efficacy of psychotherapy – recognized three decades ago to be a neurobiological phenomenon in which the brain undergoes synaptic change as in any other kind of learning [54] – is now feasible by functional neuroimaging [55], and is being further pursued. Evolving new modalities such as embryonic stem cell transplantation [56], gene therapy [57], deep brain stimulation [58], and transcranial magnetic stimulation [59] hold forth much promise. In the foreseeable future, basic science advances may disclose strategies for enhancing synaptic plasticity [60], stimulating remyelination [61], and promoting neurogenesis [62] – even in the brains of older adults. Lastly, a host of issues with public policy implications spring directly from the advances of BN&NP. Some examples will prove illustrative. What are the sociopolitical implications of the study of aggression, violence, and war as neurally based behaviors [63]? Which individuals with brain disorders should be held accountable for criminal behavior and punished, and which should be exonerated and treated as patients? How is the question of free will to be addressed in light of modern technology? What are the implications for individuals who will be found to have genetic diseases for which no cure is available? Will new treatments for cognitive disorders involving the activation of endogenous or surgically implanted stem cells produce cures for dreaded diseases, or might they possibly result in grossly aberrant behavior from novel and unpredictable neuronal connections? What are the implications of detecting residual cerebral activity with fMRI in patients diagnosed with vegetative or minimally conscious state? While not presuming to answer such imposing questions, subspecialists in BN&NP are ideally suited to illuminate the issues and inform public discourse so that society can more rationally adopt meaningful solutions. Whither Behavioral Neurology & Neuropsychiatry? As this book goes to press, several clinical neuroscientific principles enjoy wide support: the constructs of mindless brain and brainless mind are outdated, the descriptors organic and functional are no longer meaningful, and human behavior is usefully conceptualized as no more, or less, than the product of brain activity. From these statements, hard won 7 Behavioral Neurology & Neuropsychiatry through much effort over many years, all physicians dealing with neurobehavioral or neuropsychiatric disorders can legitimately aspire to be both objective and humanistic. Behavior is a measurable neural phenomenon, but its understanding also requires an exceptional degree of interpersonal sensitivity and interpretive skill. Nowhere is the art of medicine more critical – objective data such as mental status test scores and neuroimaging findings must be adroitly combined with subjective assessment of the person suffering with the disorder. The notion that only those in neurology can lay claim to neuroscience, while only those in psychiatry can appreciate the whole person, is confining, inaccurate, and unproductive. But there remains the issue of what exactly is the subject matter of BN&NP. A reasonable listing of the major disorders currently regarded as falling within the scope of this subspecialty includes the focal neurobehavioral syndromes (e.g., aphasia, apathy, orbitofrontal syndrome); delirium, dementia (e.g., AD), and major primary psychiatric disorders; neurological conditions with prominent cognitive, emotional, and behavioral features such as movement disorders, stroke, epilepsy, multiple sclerosis, and TBI [2]. Others can be added to or subtracted from this list, and physicians will naturally gravitate toward those disorders for which their training and inclination render them most suitable. Many subspecialists in BN&NP with primary training in neurology assume the care of dementia or stroke with focal syndromes, for example, while those with primary training in psychiatry take on schizophrenia, depression, obsessive-compulsive disorder, and the psychiatric sequelae of neurological conditions. Traumatic brain injury appears to be a special case, as subspecialists in BN&NP are increasingly committed to this common problem [64, 65]. As knowledge of brain–behavior relationships grows, these areas can be expected to evolve concomitantly, and with them practice patterns as well. Despite the enthusiasm generated by the development of BN&NP as a subspecialty [1], uncertainty persists about the future organization and direction of the combined field. Will a single name for this discipline come to replace the combined moniker? If so, what should it be? Perhaps “cognitive neuroscience” would suffice, but does this adequately capture the disorders traditionally considered to be “emotional?” An alternative is “medical neuroscience,” which serves to distinguish it from the surgical neurosciences but its 8 referents may be too broad. Will neurology and psychiatry residences modify their curricula to reflect a converging interest in brain–behavior relationships? Will there be combined clinical services where “neurologic” and “psychiatric” patients with cognitive disorders are evaluated and treated as a single group with altered brain function? Many influential opinions will doubtlessly line up on all sides of such questions. Whether some overarching category comes to encompass all the work in these fields remains to be disclosed, and we do not presume to make a prediction. For now, we believe this evolving alliance stands as a productive approach to bridge the gaps between neurology and psychiatry, body and mind, physical and mental. Whatever one’s perspective on the status of the subspecialty, continuing advances in understanding neuroscience as applied to patient care, education, and research demand attention. Given the powerful recent findings of neuroscience, adopting this attitude in clinical medicine seems eminently appropriate. Our attitude has always been that getting the work done is far more important than debating what to call ourselves. About this book We have noted, as have many before us, that neurology and psychiatry suffer needlessly from arbitrary interdisciplinary barriers, maintained by the power of tradition, that often impede intellectual progress. While differences between these fields clearly exist and will not soon disappear, their commonalities promise to further understanding of brain–behavior relationships as never before. BN&NP represents the flagship subspecialty that aims to find and develop the intellectual common ground that beckons so strongly to students of human behavior. The mind and the brain are but two constructs describing the same entity, and medicine and society are best served by acknowledging this fundamental principle. This book reflects our view of the clinical neuroscience of behavior in the context of patients coping with a host of brain disorders, be they neurologic or psychiatric. Intended primarily for physicians and investigators entering the field or early in their careers, we hope the book can both inform and inspire its readers. Those farther along in their careers may also find information of value in these pages. We hope this book builds upon and extends the work of many previous volumes from which we have learned [8, 34–38] in a Chapter 1: Introduction comprehensive attempt to draw behavioral neurology and neuropsychiatry closer. We present this book in the spirit of promoting this effort for the good of our patients, our profession, and our world. The structure of the book follows the core curriculum for fellowship training discussed above [2]. Included are major sections on structural and functional neuroanatomy, assessment, and treatments, reflecting the primary topic areas for fellowship training [2]. The book is intended to convey principles of BN&NP rather than to present an exhaustive account of disease states. Accordingly, condition-specific chapters are not presented. Instead, disorders will be introduced when they serve to illustrate the principles under consideration. Readers may recognize points made in our previous work that have served to build a foundation for this text [66, 67], but this book offers something new: our combined approach to this intriguing area, including the expert contributions of many colleagues who have graciously devoted their time and effort to this project, that we hope will serve as a model for productive transdisciplinary collaboration. 12. Freud S, Strachey J, Freud A et al. The Standard Edition of the Complete Psychological Works of Sigmund Freud. London: Hogarth Press; 1953. References 17. Damasio AR. Behavioral neurology – research and practice. Semin Neurol. 1984;4(2):117–19. 1. Silver JM. Behavioral neurology and neuropsychiatry is a subspecialty. J Neuropsychiatry Clin Neurosci. 2006;18(2):146–8. 2. Arciniegas DB, Kaufer DI, Joint Advisory Committee on subspecialty Certification of the American Neuropsychiatric Association, Society for Behavioral and Cognitive Neurology. Core curriculum for training in Behavioral Neurology & Neuropsychiatry. J Neuropsychiatry Clin Neurosci. 2006;18(1):6–13. 3. Price BH, Adams RD, Coyle JT. Neurology and psychiatry: closing the great divide. Neurology 2000;54(1):8–14. 4. Martin JB. The integration of neurology, psychiatry, and neuroscience in the 21st century. Am J Psychiatry 2002;159(5):695–704. 5. Yudofsky SC, Hales RE. Neuropsychiatry and the future of psychiatry and neurology. Am J Psychiatry 2002;159(8):1261–4. 6. Sachdev PS. Whither neuropsychiatry? J Neuropsychiatry Clin Neurosci. 2005;17(2):140–4. 7. Pies R. Why psychiatry and neurology cannot simply merge. J Neuropsychiatry Clin Neurosci. 2005;17(3): 304–9. 8. Cummings JL, Mega MS. Neuropsychiatry and Behavioral Neuroscience. Oxford: Oxford University Press; 2003. 9. Arciniegas DB. Neuropsychiatry – an introduction and brief history. In Arciniegas DB, Beresford TP, editors. Neuropsychiatry: An Introductory Approach. Cambridge: Cambridge University Press; 2001, pp. 3–12. 10. Encyclopaedia Britannica Inc., University of Chicago, Hutchins RM. Great Books of the Western World. Founders’ edition. Chicago, IL: W. Benton; 1952. 11. Griesinger W. Mental Pathology and Therapeutics. 2nd edition. New York, NY: William Wood & Company; 1882. 13. Hollender MH. American Board of Psychiatry and Neurology: The First Fifty Years. Deerfield, IL: American Board of Psychiatry and Neurology; 1991. 14. Schachter SC, Devinsky O. Behavioral Neurology and the Legacy of Norman Geschwind. Philadelphia, PA: Lippincott-Raven; 1997. 15. Geschwind N. Disconnexion syndromes in animals and man. I. Brain 1965;88(2):237–94. 16. Geschwind N. Disconnexion syndromes in animals and man II. Brain 1965;88(3):585–644. 18. Mesulam M (editor). Behavioral neuroanatomy. Large-scale neural networks, association cortex, frontal systems, the limbic system and hemispheric specializations. In Principles of Behavioral and Cognitive Neurology. 2nd edition. Oxford: Oxford University Press; 2000, pp. 1–120. 19. Caine ED, Joynt RJ. Neuropsychiatry . . . again. Arch Neurol. 1986;43(4):325–7. 20. Yudofsky SC, Hales RE. The reemergence of neuropsychiatry: definition and direction. J Neuropsychiatry Clin Neurosci. 1989;1(1):1–6. 21. Trimble MR. Neuropsychiatry or behavioral neurology. Neuropsychiatry Neuropsychol Behav Neurol. 1993;6(1):60–4. 22. Benson DF. Neuropsychiatry and behavioral neurology: past, present, and future. J Neuropsychiatry Clin Neurosci. 1996;8(3):351–7. 23. Luria AR. Higher Cortical Functions in Man. 2nd edition. New York, NY: Basic Books; 1980. 24. Turner R. Magnetic resonance imaging of brain function. Ann Neurol. 1994;35(6):637–8. 25. Raichle ME. Visualizing the mind. Sci Am. 1994; 270(4):58–64. 26. Searle JR. Minds, Brains, and Science. Cambridge, MA: Harvard University Press; 1984. 9 Behavioral Neurology & Neuropsychiatry 27. Descartes R. Discourse on Method. 2nd (rev.) edition. New York, NY: Liberal Arts Press; 1956. 28. Damasio AR. Descartes’ error and the future of human life. Sci Am. 1994;271(4):144. 44. Logothetis NK. What we can do and what we cannot do with fMRI. Nature 2008;453(7197):869–78. 29. Churchland PS. Neurophilosophy: Toward a Unified Science of the Mind–Brain. Cambridge, MA: MIT Press; 1986. 45. Wortzel HS, Filley CM, Anderson CA, Oster T, Arciniegas DB. Forensic applications of cerebral single photon emission computed tomography in mild traumatic brain injury. J Am Acad Psychiatry Law 2008;36(3):310–22. 30. Dennett DC. Consciousness Explained. 1st edition. Boston, MA: Little, Brown and Co.; 1991. 31. American Psychiatric Association. Work Group to Revise DSM–III. Diagnostic and Statistical Manual of Mental Disorders, Third Edition Revised: DSM–III–R. Washington, DC: American Psychiatric Association; 1987. 32. Miresco MJ, Kirmayer LJ. The persistence of mind–brain dualism in psychiatric reasoning about clinical scenarios. Am J Psychiatry 2006;163(5): 913–18. 33. Filley CM, Arciniegas DB, Wood GV et al. Geriatric treatment center: a contemporary model for collaboration between psychiatry and neurology. J Neuropsychiatry Clin Neurosci. 2002;14(3):344–50. 34. Devinsky O. Behavioral Neurology: 100 Maxims. 1st edition. New York, NY: Mosby-Year Book; 1992. 35. Feinberg TE, Farah MJ. Behavioral Neurology and Neuropsychology. New York, NY: McGraw-Hill; 1997. 36. Rizzo M, Eslinger PJ. Principles and Practice of Behavioral Neurology and Neuropsychology. Philadelphia, PA: W.B. Saunders; 2004. 37. Yudofsky SC, Hales RE. The American Psychiatric Publishing Textbook of Neuropsychiatry and Clinical Neurosciences. 4th edition. Washington, DC: American Psychiatric Publishing; 2002. 38. Schiffer RB, Rao SM, Fogel BS. Neuropsychiatry. 2nd edition. Philadelphia, PA: Lippincott Williams & Wilkins; 2003. 39. Crick F. The Astonishing Hypothesis: The Scientific Search for the Soul. New York, NY: Scribner and Maxwell Macmillan International; 1994. 40. Gazzaniga MS. The Ethical Brain. New York, NY: Dana Press; 2005. 41. Heilman KM. Creativity and the Brain. New York, NY: Psychology Press; 2005. 42. Carhart-Harris RL, Mayberg HS, Malizia AL, Nutt D. Mourning and melancholia revisited: correspondences between principles of Freudian metapsychology and empirical findings in neuropsychiatry. Ann Gen Psychiatry 2008;7:9. 43. Geschwind N. The borderland of neurology and psychiatry: some common misconceptions. In Benson DF, Blumer D, editors. Psychiatric Aspects of 10 Neurological Disease. New York, NY: Grune & Stratton; 1975, pp. 1–8. 46. Ashburner J, Friston KJ. Why voxel-based morphometry should be used. Neuroimage 2001; 14(6):1238–43. 47. Ross B, Michaelis T. Clinical applications of magnetic resonance spectroscopy. Magn Reson Q. 1994;10(4): 191–247. 48. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci. 2008;34(1):51–61. 49. Filley CM. The Behavioral Neurology of White Matter. 2nd edition. New York, NY: Oxford University Press; 2012. 50. Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford: Oxford University Press; 2006. 51. Myers RH. Huntington’s disease genetics. NeuroRx. 2004;1(2):255–62. 52. Howard KL, Filley CM. Advances in genetic testing for Alzheimer’s disease. Rev Neurol Dis. 2009;6(1): 26–32. 53. Anderson CA, Arciniegas DB. Neurosurgical interventions for neuropsychiatric syndromes. Curr Psychiatry Rep. 2004;6(5):355–63. 54. Kandel ER. Psychotherapy and the single synapse. The impact of psychiatric thought on neurobiologic research. N Engl J Med. 1979;301(19): 1028–37. 55. Linden DE. How psychotherapy changes the brain – the contribution of functional neuroimaging. Mol Psychiatry 2006;11(6):528–38. 56. Khanna A, Shin S, Rao MS. Stem cells for the treatment of neurological disorders. CNS Neurol Disord Drug Targets 2008;7(1):98–109. 57. Okada T, Shimazaki K, Nomoto T et al. Adeno-associated viral vector-mediated gene therapy of ischemia-induced neuronal death. Methods Enzymol 2002;346:378–93. 58. Schiff ND, Fins JJ. Deep brain stimulation and cognition: moving from animal to patient. Curr Opin Neurol 2007;20(6):638–42. 59. Rossini PM, Rossi S. Transcranial magnetic stimulation: diagnostic, therapeutic, and research potential. Neurology 2007;68(7):484–8. Chapter 1: Introduction 60. Wieloch T, Nikolich K. Mechanisms of neural plasticity following brain injury. Curr Opin Neurobiol. 2006;16(3):258–64. 61. Franklin RJ, Kotter MR. The biology of CNS remyelination: the key to therapeutic advances. J Neurol. 2008;255(Suppl. 1):19–25. 62. Zhao C, Deng W, Gage FH. Mechanisms and functional implications of adult neurogenesis. Cell 2008;132(4):645–60. 63. Filley CM, Price BH, Nell V et al. Toward an understanding of violence: neurobehavioral aspects of unwarranted physical aggression: Aspen Neurobehavioral Conference consensus statement. Neuropsychiatry Neuropsychol Behav Neurol. 2001; 14(1):1–14. 64. Kelly JP, Nichols JS, Filley CM et al. Concussion in sports. Guidelines for the prevention of catastrophic outcome. J Am Med Assoc. 1991;266(20): 2867–9. 65. Arciniegas D, Adler L, Topkoff J et al. Attention and memory dysfunction after traumatic brain injury: cholinergic mechanisms, sensory gating, and a hypothesis for further investigation. Brain Inj. 1999;13(1):1–13. 66. Filley CM. Neurobehavioral Anatomy. 3rd edition. Boulder, CO: University Press of Colorado; 2011. 67. Arciniegas DB, Beresford TP. Neuropsychiatry: An Introductory Approach. Cambridge: Cambridge University Press; 2001. 11 Section I Structural and Functional Neuroanatomy Chapter Behavioral neuroanatomy 2 C. Alan Anderson, David B. Arciniegas, Deborah A. Hall, and Christopher M. Filley Understanding human behavior in health and disease begins with structural and functional neuroanatomy. The two major divisions of the human nervous system are the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS consists of the brain and the spinal cord. The PNS includes the cranial and spinal nerves, the distal branches of which form efferent and afferent connections carrying information between the CNS and the entire body. The autonomic nervous system includes components of both the CNS and the PNS, and serves to regulate many aspects of visceral function that provide homeostatic balance and the capacity to adjust to the ever-changing demands of both internal and environmental needs. There are approximately 100 billion neurons in the human brain, and these are supported by, and interact with, as many as ten times that number of glial cells [1]. Each neuron makes contact with thousands of other neurons as well as with a large number of glial cells. The vast majority of brain neurons are classified as interneurons, which are thought to participate in the processing of information rather than with sensory input or motor output. This enormous degree of connectivity is the basis of the computational power that produces human arousal, awareness, consciousness, and behavior. While our knowledge of the full complement of mechanisms by which the brain produces consciousness and other neuropsychiatric functions remains incomplete, it is clear that all aspects of human behavior are firmly grounded in brain function. From the meticulous dissections performed by generations of neuroanatomists through the information obtained through advanced functional imaging techniques, our knowledge and understanding of the structure of the brain as well as the functional relationship between its components has advanced substantially. In this chapter, we focus on those aspects of structural and functional neuroanatomy that are most relevant to Behavioral Neurology & Neuropsychiatry (BN&NP). We consider the general structure of the brain from the brainstem through the cerebral cortex (Figure 2.1), including a review of white matter anatomy, the cerebral vasculature, and the ventricular system. With this background, we then discuss the interactions of cortical, subcortical, and white matter regions, and cerebral lateralization. This approach leads us to the general concept of distributed neural networks, which will serve as a comprehensive organizing principle for understanding the neurobiology of cognition, emotion, and behavior. In the chapters that follow, in-depth consideration is given to several aspects of specific brain structures and functional systems. For those interested in a more detailed review of general and behavioral neuroanatomy, several excellent resources are available [2–5]. As a general principle, normal function at any level depends in part on normal function at lower levels. The CNS includes (in ascending order) the spinal cord, brainstem, cerebellum, hypothalamus, thalamus, and the paired cerebral hemispheres. The neuroanatomy relevant to BN&NP therefore begins with the brainstem. Brainstem and cerebellum The brainstem comprises the medulla oblongata (myelencephalon), pons and cerebellum (metencephalon), and midbrain (mesencephalon). Each of these areas and the neurobehaviorally salient structures they Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 12 Chapter 2: Behavioral neuroanatomy (A) (B) (C) (D) (E) Figure 2.1. General structure of the brain (A). Major areas considered in this chapter include the brainstem and cerebellum (B), diencephalon (C), limbic and paralimbic structures (D), basal ganglia (see Figure 2.5), and cerebral cortex (E). This figure is presented in color in the color plate section. contain are reviewed briefly in this section. The reticular formation (which is contributed to by several brainstem substructures) and the cranial nerves (some, but not all, of which are located within the brainstem) also are discussed in this section. Myelencephalon The medulla oblongata is the most caudal part of the brain, forming the junction of the brain with the spinal cord [6]. Anteriorly the medulla lies against the basilar portion of the occipital bone and posteriorly it abuts the ventral surface of the cerebellum. Descending pyramidal (motor) tracts and ascending spinothalamic tracts (pain and temperature sensation) decussate as they pass through the medulla. Cranial nerve (CN) nuclei located in the medulla include the inferior portions of CN V (trigeminal), and VIII (vestibulocochlear), IX (glossopharyngeal), X (vagus), XI (accessory), and XII (hypoglossal). The medulla also contains the nuclei for a variety of modulatory ascending and descending pathways including serotonergic, adrenocorticotropic, -endorphin, melanocyte stimulating hormone, and adrenergic fibers. These systems are involved in modulating ascending and descending tracts, and also provide input into the reticular activating system more rostrally. Metencephalon Pons The word pons means “bridge” in Latin, which is appropriate given the extensive motor, sensory, and other pathways between the cerebrum, the cerebellum, and the spinal cord that pass through it. The pons has extensive connections with the cerebellum, forming bundles of transversely oriented fibers that link pontine nuclei and the cerebellum via the middle cerebellar peduncles. The ventral surface of the pons abuts the occipital bone and its dorsal surface lies along the ventral surface of the cerebellum. The pons contains the nuclei for CN VI (abducens), VII (facial), and the superior portions of the nuclei for CN V (trigeminal), and VIII (vestibulocochlear). The pons also contains the locus ceruleus, a noradrenergic nucleus with diffuse projections throughout the cerebral hemispheres [7], and serotonergic nuclei (the pontine raphe nuclei) providing modulatory input into the reticular formation [6, 8]. 13 Section I: Structural and Functional Neuroanatomy 14 Cerebellum Mesencephalon The cerebellum also is a portion of the metencephalon. Together with the brainstem, the cerebellum occupies the posterior cranial fossa and is separated from the cerebrum rostrally by the tentorium cerebelli. For practical purposes, the cerebellum has three major functional subdivisions [3]. The first is the archicerebellum (flocculonodular lobe). It is the oldest part of the cerebellum phylogenetically, which includes the nodules and flocculi of the vermis, and connects extensively to the vestibular system. The second subdivision is the paleocerebellum, which includes portions of the anterior lobe, the lingula, the central lobule and portions of the lower vermis, the paraflocculus, and the cerebellar tonsils. This subdivision of the cerebellum receives its principal input from spinocerebellar pathways. The third subdivision is the neocerebellum, which includes the remainder of the vermis and the cerebellar hemispheres. It is the youngest subdivision phylogenetically and the largest. The neocerebellum receives its major input from the pons, and is involved in the maintenance of posture, motor coordination, and the cerebellar contributions to higher cognitive and emotional processing. Dense bidirectional cerebellar connections to the brainstem are carried by the paired peduncles to the medulla (inferior cerebellar peduncles or corpora restiformia), the pons (middle cerebellar peduncles or brachia pontis), and the midbrain (superior cerebellar peduncles or brachia conjunctiva). The surface of the cerebellum forms small parallel convolutions called folia, separated by fissures into various subdivisions. At a microscopic level, the cerebellar cortex is divided into three layers: the molecular layer, the Purkinje cell layer, and the granular layer. The Purkinje cells provide the output of the cortical layers to deeper cerebellar nuclei. Input into the cerebellum includes afferent fibers originating in the cerebral cortex, the brainstem (including the reticular formation, vestibular nuclei, and inferior olive), and the spinal cord [3]. The cerebellum possesses tremendous processing power and plays a role in the integration of motor and sensory information coordinating movement, posture, motor learning, memory, and other higher-order cognitive functions [9, 10]. The role of the cerebellum in cognitive and emotional function, as well as the neurobehavioral consequences of cerebellar injury or disease, is discussed in detail in Chapter 3. The mesencephalon is the midbrain, and comprises the tectum (corpora quadrigemina), tegmentum, the cerebral peduncles, multiple nuclei and fasciculi, and the ventricular mesocoelia (or iter, which refers to the passage between the third and fourth ventricles, or the aqueduct of Sylvius). Ventral to the midbrain lies the basilar portion of the occipital bone, and the dorsal surface of the midbrain forms an isthmus between the cerebrum and the cerebellum. Rostrally, the midbrain adjoins the pons and caudally it adjoins the diencephalon. CN nuclei located in the midbrain include cranial nerves III (oculomotor), IV (trochlear), and the most superior portions of cranial nerves V and VIII. The midbrain connections with the cerebrum are carried by the cerebral peduncles, which contain the afferent and efferent connections between the spinal cord, brainstem, and the neocortex. The most dorsal portion of the midbrain is the tectum, with its quadrigeminal plate formed by the paired inferior and superior colliculi. The colliculi are relay stations for auditory information (the inferior colliculi) and visual information (the superior colliculi). The superior colliculi receive inputs from fibers of the optic tract, the occipital cortex, the inferior colliculi, and the spinal cord; these nuclei play a role in sensory processing and integration, reflex responses to visual threat including eye closure, aversive movements, and defensive posturing [3]. The upper brainstem, including the mesencephalon and upper pons, also contain several of the major ascending modulatory neurotransmitter nuclei (see Figures 2.2 and 2.3). Within the midbrain are the substantia nigra and the ventral tegmental area. The substantia nigra provides much of the dopaminergic innervation of the basal ganglia and plays a critical role in the modulation of “extrapyramidal” motor function. In addition, afferent fibers from dopaminergic nuclei in the ventral tegmental area project to the limbic system through the mesolimbic pathway, and to the neocortex via the mesocortical pathway. The midbrain also provides serotonergic innervation to the cerebrum from the dorsal raphe nucleus. Like other brainstem nuclei, these collections of serotonergic neurons extend caudally into the pons and medulla. Serotonergic afferent fibers from these nuclei project diffusely to the cerebral cortex and modulate arousal through the reticular activating system. Chapter 2: Behavioral neuroanatomy Figure 2.2. Modulatory neurotransmitter nuclei. A major input to the relay and reticular nuclei of the thalamus originates from cholinergic (ACh) cell groups in the upper pons, the pedunculopontine (PPT) and laterodorsal tegmental nuclei (LDT). These inputs facilitate thalamocortical transmission. A second pathway activates the cerebral cortex to facilitate the processing of inputs from the thalamus; this arises from neurons in the monoaminergic cell groups, including the tuberomammillary nucleus (TMN) containing histamine (His), ventral periaqueductal gray (vPAG) dopamine-containing cell groups (DA), the dorsal and median raphe nuclei containing serotonin (5-HT), and the noradrenergic locus coeruleus (LC) containing noradrenaline (NA, also known as norepinephrine). This pathway also receives contributions from peptidergic neurons in the lateral hypothalamus (LH) containing orexin (ORX) or melanin-concentrating hormone (MCH), and from basal forebrain (BF) neurons that contain gamma-aminobutyric acid (GABA) or ACh. Reproduced from Saper CB, Scammell TE, Lu J. Hypothalamic regulation of sleep and circadian rhythms. Nature 2005;437(7063):1257–63, with permission from Nature Publishing Group. The serotonergic system provides largely inhibitory modulation to the activity of frontal–subcortical and limbic circuits [8]. The pedunculopontine tegmental cholinergic nuclei (Ch5 and Ch6, commonly abbreviated as Ch5–6) also are located predominantly in the mesencephalon [11, 12]. This group of cells provides cholinergic innervation to the thalamus, cerebellum, globus pallidus, subthalamic nucleus, substantia nigra (pars compacta); medullary reticular formation and spinal cord; and, to a lesser extent, the striatum (caudate nucleus and putamen). The periaqueductal gray (PAG) is the gray matter surrounding the cerebral aqueduct in the midbrain tegmentum and is a behaviorally relevant structure. Figure 2.3. Modulatory neurotransmitter nuclei. A schematic drawing to show the key projections of the ventrolateral preoptic nucleus (VLPO) to the main components of the ascending arousal system. It includes the monoaminergic cell groups such as the tuberomammillary nucleus (TMN), the A10 cell group, the raphe cell groups and the locus coeruleus (LC). It also innervates neurons in the lateral hypothalamus, including the perifornical (PeF) orexin (ORX) neurons, and interneurons in the cholinergic (ACh) cell groups, the pedunculopontine (PPT) and laterodorsal tegmental nuclei (LDT). Additional abbreviations: 5-HT – serotonin; GABA – gamma-aminobutyric acid; Gal – galanin; NA – noradrenaline; His – histamine. Reproduced from Saper CB, Scammell TE, Lu J. Hypothalamic regulation of sleep and circadian rhythms. Nature 2005;437(7063):1257–63, with permission from Nature Publishing Group. This collection of nuclei is connected to multiple cortical, limbic, diencephalic, and brainstem areas and also is connected extensively with the reticular formation. Functionally, the periaqueductal gray area is part of the limbic system, participating in the modulation of pain, arousal in the face of threat, emotional expression, sexual and feeding behaviors, and the regulation of metabolism. It may also have a role in modulating attention for salient internal and external stimuli related to survival, emotion, and memory [8]. Reticular formation The reticular formation is the collective term for a group of brainstem nuclei along with their projections that are spread throughout the brainstem from the medulla through the midbrain. Its name derives from its net-like organization (from the Latin reticulum for 15 Section I: Structural and Functional Neuroanatomy “little net”). The reticular formation extends rostrally from the brainstem into the intralaminar nuclei of the thalamus and certain aggregations of subthalamic cells (the zona incerta). These are discrete nuclei with specific interconnections involved in the modulation of arousal and associated autonomic functions [13, 14]. The nuclei of the reticular formation can be divided into three zones with distinct anatomy and function. First is the median and paramedian zone formed by the serotonergic raphe nuclei. Second is a medial zone that appears to integrate signals from ascending sensory and descending motor pathways. The third group is the lateral zone that includes cholinergic, noradrenergic, and adrenergic nuclei. Nuclei in the more rostral portion of all three zones contribute to the maintenance and modulation of arousal, wakefulness, and thus consciousness. Nuclei in the more caudal portion of all three zones support respiratory drive, and modulate reflex mechanisms involving the gastrointestinal, genitourinary, cardiopulmonary, and vestibular systems. The reticular formation’s role in arousal can be divided into an ascending reticular activating system (ARAS) and (providing homeostatic balance) an ascending reticular inhibiting system (ARIS). The ARAS is a collection of nuclei including the noradrenergic nuclei in the locus coeruleus in the pons, dopaminergic nuclei in the ventral tegmental area in the midbrain and Ch5–6 cholinergic nuclei, with widely distributed projections including the thalamus, subthalamic nuclei, limbic system, and widespread regions of the cerebral cortex. The ARAS integrates both bottom-up and top-down input to modulate levels of general and specific arousal and attention based on internal and external stimuli and circumstances [13]. We find it useful to use the construct of a corresponding ARIS as the opposing system to the ARAS, reducing arousal and modulating attention when appropriate [8]. This system is composed of the ascending serotonergic projections of the raphe nuclei providing an inhibitory balance to the activating influence of the cholinergic, noradrenergic, and dopaminergic systems. The system modulates wakefulness, promoting sleep. Thus, sleep is not simply the loss of activation from the ARAS, but rather an active process with input from these inhibitory fibers originating largely in the medullary portions of the reticular formation. Ascending serotonergic projections 16 III IV V. V V (Motor root) VI VII XII Nucleus ambiguus IX and X XI Cervical nerves Figure 2.4. Lateral view of the brainstem showing the relative positions of the nuclei of cranial nerves III to XII. Adapted from Gray’s Anatomy of the Human Body originally published in 1918. from the dorsal raphe and central superior nuclei in the midbrain influence behavior through their modulation of frontal-subcortical circuits. The complex interaction of these fundamental systems regulating arousal and awareness will be described further in later chapters. Cranial nerves The 12 cranial nerves (CNs) provide motor and sensory innervation to the head and neck. All but CN I and II connect to the brain via the brainstem (Figure 2.4). The CNs each exist in pairs, and their crossed and uncrossed connections with other structures in the CNS are important in understanding the anatomy of the brain [3]. The olfactory nerve (CN I) mediates the sense of smell. The collected axons of olfactory receptor cells in the roof of the nasal cavity form the olfactory nerve. These fibers pass through the cribriform plate of the ethmoid bone, and terminate in the olfactory bulb at the base of the frontal lobe. From there, the olfactory tract projects to the olfactory cortex in the medial temporal lobe. Chapter 2: Behavioral neuroanatomy The optic nerve (CN II) carries visual information from the eye to the rest of the brain. The optic nerve is actually a tract of the brain. More than any other sensory modality, the sense of vision is of central importance in human life. Light signals are initially processed in the eye, where photoreceptor cells in the retina transduce the patterns of light into electrochemical signals carried by the optic nerves. The optic nerve splits in the optic chiasm with the result that each hemisphere receives input from the contralateral visual field. Thus the right hemisphere receives input from the left visual field and vice versa. The bulk of the visual information generated by the retina goes to the lateral geniculate nucleus (LGN) of the thalamus. Efferent connections from the LGN form the optic radiations coursing through the temporal and parietal lobes carrying visual information to the primary visual cortex in the occipital lobes. Other areas of the brain receiving retinal input include the superior colliculus, suprachiasmatic nucleus, medial, lateral and dorsal terminal accessory optic nuclei, olivary nucleus, and the inferior pulvinar [15]. These projections play a role in visual targeting and visual processing, and may serve as alternate routes for visual information when the LGN pathway is injured. The oculomotor (CN III), trochlear (CN IV), and abducens (CN VI) nerves are considered together because of their role in conjugate eye movements. CN III also provides the afferent limb of the pupillary light reflex, and mediates eyelid opening. The trigeminal nerve (CN V) has both motor and sensory functions. The trigeminal nerve mediates somatic sensation from the face via its three divisions: ophthalmic (V1), maxillary (V2), and mandibular (V3). Afferent fibers from the three divisions form the trigeminal ganglion outside the brainstem, and then coalesce to enter the pons as a single nerve. Facial somatosensory information then projects to the ventral posterior medial nucleus of the thalamus and on to primary sensory cortex. The motor nuclei of CN V supply the muscles of mastication. The facial nerve (CN VII) originates from the facial nucleus in the pons and is primarily motor in its function, innervating the ipsilateral muscles of the face. Facial weakness related to CN VII dysfunction is frequently seen in clinical neurology. In addition to its motor component, CN VII also has one notable sensory function, carrying taste sensation from the anterior two-thirds of the tongue, via a branch named the chorda tympani, to the solitary tract extending from the pons into medulla. The vestibulocochlear nerve (CN VIII) has two sensory components, the vestibular and cochlear divisions. These mediate the vestibular (balance) system and the sense of audition (hearing), respectively. Mechanoreceptors located in the inner ear respond to physical stimulation with the cells that form the vestibular division responding to positional head movements, and those of the cochlear division responding to sound stimuli. CN VIII carries auditory and positional sensory input to the vestibular and cochlear nuclei in the pons. This information is extensively processed in the brainstem and cerebellum, with auditory input relayed on to the medial geniculate nucleus of the thalamus and then on to the primary auditory cortex in the temporal lobe (Heschl’s gyrus). The glossopharyngeal nerve (CN IX) mediates motor, sensory, and autonomic functions of the face. Its motor fibers innervate the muscles of the pharynx, and its sensory fibers mediate somatic sensation of the tongue, nasopharynx, and middle and external ear as well as taste from the posterior one-third of the tongue. Autonomic fibers carried by CN IX supply parasympathetic input to the parotid gland. The vagus nerve (CN X) is the most widely distributed of the CNs, carrying parasympathetic signals to thoracic and abdominal organs, as well as motor and sensory innervation to the larynx, pharynx, and external ear. The accessory nerve (CN XI) arises from the lower medulla and upper spinal cord supplying motor innervation to the ipsilateral sternocleidomastoid and trapezius muscles. The hypoglossal nerve (CN XII) originates in the medulla and provides ipsilateral motor innervation to the muscles of the tongue. Diencephalon The diencephalon includes the thalamus, metathalamus (medial and lateral geniculate nuclei), epithalamus (habenula, stria medullaris, and pineal body), and subthalamus (often referred to as the prethalamus by developmental neurobiologists, of which the major part is the subthalamic nucleus). Of these, we will consider briefly the thalamus, hypothalamus (and pituitary), and the epithalamus; the subthalamic nucleus is discussed in the context of frontal-subcortical circuits (see Chapter 5). 17 Section I: Structural and Functional Neuroanatomy Table 2.1. Thalamic nuclei, their afferent and efferent connections, and their major functions. Region Nucleus Afferent(s) Efferent(s) Function(s) Motor Ventroanterior Globus pallidus Frontal cortex Modulation of motor function Ventrolateral Cerebellum Frontal cortex Modulation, coordination, and learning of movements Ventral posterolateral Sensory tracts from body Parietal cortex Somatosensation Ventral posteromedial Sensory tracts from body Parietal cortex Facial sensation Solitary tract Cortical gustatory area, anterior insula Taste Lateral geniculate Optic tracts Occipital cortex Vision Medial geniculate Inferior colliculi Temporal cortex Hearing Medial dorsal Globus pallidus, amygdala, temporal cortex, frontal cortex Prefrontal cortices Executive function, memory, social cognition, emotion Lateral nuclear group (pulvinar) Frontal, parietal, temporal, and occipital cortices Frontal, parietal, temporal, and occipital cortices Coordination of intra- and cross-modal cortical information processing Limbic Anterior and laterodorsal Mammillary bodies Posterior cingulate, retrosplenial area, entorhinal-hippocampal complex Learning and memory Non-specific Midline Hypothalamus Amygdala, cingulate, hypothalamus Visceral function Intralaminar Reticular formation, precentral and premotor cortex Striatum, cortex Activation Reticular Thalamic nuclei, cortex Dorsal thalamic nuclei Sampling, gating, and focusing thalamocortical outputs Sensory Association Thalamus The thalamus relays and integrates ascending information from arousal networks in the brainstem and sensory pathways from the visual system, olfactory system, brainstem, and spinal cord with connections traversing between cortical and subcortical structures [8]. The thalamus is not a unitary structure but rather a collection of smaller nuclei that are topographically related to the parts of the brain to which they are connected (Table 2.1). The anterior thalamus has bidirectional connections to frontal cortical regions, the superior and posterior thalamus are interconnected with parietal and occipital cortex, the inferior tier of thalamic nuclei is connected to the orbitofrontal, insular, and temporal regions, and the ventral thalamus is connected with limbic structures. The various subgroups of nuclei of the thalamus are interconnected as well. As such, the thalamus plays an integral 18 role in arousal systems, sensory and motor processing, attention, language, visuospatial function, and memory [16, 17]. Hypothalamus The hypothalamus rests inferior to the thalamus and superior to the pituitary gland. It is composed of nuclei involved in the integration and the expression of higher cerebral functions (in particular the limbic system) through autonomic, endocrine, and emotional systems (Table 2.2). The hypothalamus monitors and modulates fluid electrolyte balance, body temperature, sexual function, circadian rhythms, satiety and appetitive behaviors, and arousal in the setting of environmental threat. Anterior regions of the hypothalamus innervate the parasympathetic autonomic nervous system with posterior regions affiliated with the sympathetic branch. Chapter 2: Behavioral neuroanatomy Table 2.2. Selected nuclei of the hypothalamus and their key functions. Region Zone Preoptic Anterior Medial Nucleus Function(s) Periventricular Thirst and hunger Thyroid releasing hormone production Somatostatin, leptin, and gastrin production Paraventricular Corticotropinreleasing hormone Oxytocin release Vasopressin release Thermoregulation Thyrotropin inhibition Vasopressin release Circadian rhythms Oxytocin release Vasopressin release Thirst and hunger Anterior Suprachiasmatic Lateral Supraoptic Lateral Tuberal Medial Lateral Mammillary Medial Lateral Ventromedial Satiety Neuroendocrine control Dorsomedial Blood pressure Heart rate Gastrointestinal stimulation Lateral Thirst and hunger Tuberomammillary Histamine release Mammillary Dorsal premammillary Posterior Memory Thirst and hunger Blood pressure control Pupillary dilation Temperature control Ventral Reproductive premammillary control Tuberomammillary Histamine release Lateral Thirst and hunger The hypothalamus governs endocrine functions through its innervation and vascular connections with the pituitary gland. The hypothalamus is closely linked to the limbic system, contributing to emotional function and matching the autonomic response to internal drive states and the response to environmental needs. A dramatic example of this is the change in autonomic function seen as part of the flight or fight response. Epithalamus This portion of the diencephalon comprises the pineal body, habenula, and stria medullaris. The pineal body is a midline structure located in the posterior portion of the diencephalon, rostral and dorsal to the superior colliculus and beneath the stria medullaris. It produces a variety of neurotransmitters (serotonin, melatonin, norepinephrine, luteinizing hormone, thyrotropinreleasing hormone, and somatostatin), is connected to the visual system and the suprachiasmatic nucleus of the hypothalamus, and plays a role in sleep–wake cycles and the maintenance and regulation of circadian rhythms [18]. The habenula (also referred to as the habenular nuclei or habenular complex) refers to a cell mass embedded in the posterior end of the stria medullaris, through which they receive their principal afferents from limbic structures (e.g., hypothalamus, septum [11]). The medial habenula includes, among other components, the Ch7 cholinergic nucleus [19]. It, along with the other medial and lateral habenular subnuclei, projects to the interpeduncular nucleus (located at the base of the midbrain between the cerebral peduncles) via the retroflex fasciculus (i.e., habenulointerpeduncular tract) [11]. The interpeduncular nucleus, in turn, projects to the serotonergic mesencephalic raphe nuclei, as well as the PAG. The habenula is involved in multiple neurobehaviorally important functions, including pain processing, stress responses, sleep regulation, appetitive and reproductive behaviors, and learning (including encoding of negative reward signals) [20, 21]. Basal ganglia Basal ganglia include several subcortical gray matter nuclei deep in the cerebral hemisphere (Figure 2.5). The structures included under the umbrella term basal ganglia vary, but typically include the caudate nucleus, the putamen, and globus pallidus. The caudate nucleus is contiguous with the putamen in its ventral portions, and is divided into three parts: head, body, and tail. The caudate head bulges into the anterior horn of the lateral ventricle, the body extends along the thalamus, and the tail ends in the temporal lobe. The putamen is the largest nucleus of the basal ganglia; its dorsal and lateral portions surround the medial elements of the basal ganglia (hence the name putamen, meaning shell in Latin). The globus pallidus, also called the pallidum, lies medial to the putamen and is divided into external (lateral) and internal (medial) components. The caudate and putamen comprise the striatum. The striatum also may be divided into dorsal and ventral components; the dorsal striatum (neostriatum) 19 Section I: Structural and Functional Neuroanatomy decision-making. These networks have the same general anatomy as the motor circuit with reciprocal connections between specific regions of cortex, specific thalamic nuclei, and the basal ganglia. These networks are discussed in more detail in Chapter 5. The close relationship between these networks serving motivation, attention, cognition, visuospatial function, and voluntary movement help us understand the frequent existence of cognitive problems, visuospatial disturbances, motivational disturbances, and movement disorders across a variety of illnesses including Parkinson’s disease, Huntington’s disease, Wilson’s disease, and schizophrenia. Figure 2.5. Subcortical structures (coronal view). Labeled areas are: A – caudate nucleus; B – thalamus; C – putamen; D – globus pallidus; E – hypothalamus; F – mammillary body. refers to the combination of the caudate and putamen, and the ventral striatum (limbic striatum) consists of the nucleus accumbens and olfactory tubercle. The putamen and the globus pallidus (which is anatomically a component of the prethalamus) comprise the lenticular nucleus. The basal ganglia are functionally connected to several other brain structures, including the substantia nigra (pars compacta and reticulata), subthalamic nucleus, thalamus, raphe nuclei, and cerebral cortex (motor and premotor). The basal ganglia “bias” the automatic manner in which sensory inputs are interpreted and movements selected. These nuclei serve to integrate and modulate cerebral cortical control with sensory feedback for the generation of voluntary movement. The basal ganglia serve as a component of parallel loops, in combination with the cortex and the thalamus via connections mediated through internal and external capsules forming the extrapyramidal motor system [22]. The system is distinguished from the pyramidal motor system that includes the corticobulbar and corticospinal tracts. The extrapyramidal system links the globus pallidus to the ventroanterior and ventrolateral thalamic nuclei with connections to motor cortex which then connects back to the globus pallidus completing the loop. The basal ganglia are thought to modulate the initiation, cessation, and timing of voluntary movement. More recently recognized is the basal ganglia’s role in frontal-subcortical circuits involved in attention and visuospatial function, executive function, motivation, and the modulation of social behavior and 20 Limbic system The French neurologist Paul Broca initially described the limbic system in 1878 as a collection of structures at the junction of the cerebral hemispheres and the diencephalon (Figure 2.6). The term limbic, derived from the Latin term limbus (“edge,” “border,” ‘band,” or “girdle”), reflects both the location and orientation of the structures as well as the extensive connections between the limbic system and the thalamus, hypothalamus, and cortex. In 1937, James Papez wrote a paper hypothesizing that a network of cerebral structures that included the parahippocampal gyrus, hippocampus, fornix, mammillary bodies, mammillothalamic tract, anterior nucleus of the thalamus and the cingulate gyrus formed the neuroanatomic substrate for emotion [23] (described subsequently as the Papez circuit). Although the neuroanatomy of emotion is considerably more extensive than suggested by this early formulation (see Chapter 18), the structures in this circuit are intimately involved in the processing of episodic memory (memory with spatial and temporal context such as specific events), survival functions (eating, fighting, fear, sexual desire/mating, grooming, nurturing), and the complex interplay between these cerebral functions. As currently conceived, the fundamental components of the limbic system include the cingulate and orbitofrontal gyri, hippocampus, parahippocampal gyrus, hypothalamus, anterior and dorsomedial nuclei of the thalamus, amygdala, medial temporal cortex, and the periaqueductal gray area (the limbic midbrain) [1, 24]. Several major pathways (including the fornix, medial forebrain bundle, and the stria terminalis) form reciprocal connections to other areas Chapter 2: Behavioral neuroanatomy Corpus callosum Cingulate gyrus Orbital and medial prefrontal cortex Cut edge of midbrain Parahippocampal gyrus Temporal lobe Mammillothalamic tract Figure 2.6. Limbic and paralimbic areas (green shading) viewed parasagittally. The top panel depicts these areas as if viewed through the left hemisphere. The bottom panel illustrates these areas in greater detail. Reprinted from Purves D, Augustine GJ, Fitzpatrick D et al. (editors), Neuroscience, 2nd edition; 2001, with permission from Sinauer Associates. This figure is presented in color in the color plate section. Anterior nucleus of the thalamus Fornix Medial dorsal nucleus of the thalamus Anterior commissure Ventral basal ganglia Hypothalamus Optic chiasm Amygdala Mammillary body Hippocampus of the brain including the frontal-subcortical circuits, tightly linking these areas as well as the reticular activating system described above. The limbic system plays a central role in assessing the importance of incoming sensory information with high emotional and survival value, evaluating the current situation with reference to past experience, decision-making, and directing the behavioral response. The limbic system thus monitors and regulates internal emotional states, and integrates that information with the response to the external environment [24]. The connections to the ARAS modulate the overall level of arousal to meet the needs of the behavioral response [13]. The cingulate gyrus, septal nuclei, and other frontotemporal areas contribute to the experience of positive emotions while the amygdala, in connection with other frontotemporal areas, plays a prominent role in the experience of negative emotions. The external manifestations of emotion involve both hypothalamic modulation of autonomic (e.g., visceral) function through sympathetic and parasympathetic pathways (via the hypothalamus) and the corresponding automatic motor responses (via the basal ganglia). The amygdala, a collection of 21 Section I: Structural and Functional Neuroanatomy nuclei located in the anterior temporal lobe adjacent to the hippocampus, is centrally involved in emotional learning, and in processing sensory input so as to link emotional valence with new information. As an adaptive phenomenon, it makes teleological sense that the limbic system evolved to integrate emotion and new learning (episodic memory) where emotional associations with high survival value are best remembered so they may be repeated (or avoided). It also makes sense that the type of memory mediated by limbic structures should include episodic memory, with spatial and temporal components evoking memories of prior similar situations in response to current sensory input [25]. Just as our understanding of the role of the amygdala in emotion has advanced, so has our appreciation of the role of the hippocampus in memory. The hippocampus is a curved structure in the medial temporal lobe, comprised of three-layered archicortex divided into the dentate gyrus, hippocampus proper, and the subiculum. The hippocampus has an integral role in the learning of facts and events (declarative memory). The acquisition of new memories also includes other structures, tightly connected to the hippocampus, including the basal forebrain and the dorsal medial nucleus of the thalamus [26, 27]. A long history of clinical experience demonstrates that bilateral injury to any of these structures produces severe impairment of recent declarative memory. In summary, the limbic system is integrally involved with episodic memory and emotion. Key structures including the amygdala and hippocampus process sensory input, assign an emotional valance, and initiate the process encoding new events into memory. This is an active process with the “emotional” elements of the circuit (e.g., extended amygdala and cingulate areas) modulating the focus of attention and the priority with which memories are created. A potent emotional valence (either positive or negative) attached to a candidate input for memory serves as strong internal reinforcement (i.e., has high survival value) for learning and the eventual formation of permanent memories; conversely, input with limited emotional impact (i.e., low survival value) is less likely to be encoded and retained for later recall. Cerebral cortex (telencephalon) The cerebral cortex varies from 1.5 to 4.5 mm in depth and consists of a thin sheet of neurons and supporting 22 Figure 2.7. Drawings of cortical lamination by Santiago Ramon y Cajal. Each shows a vertical cross-section of the cortex, the surface of which is at the top. Left: Nissl-stained visual cortex of a human adult. Middle: Nissl-stained motor cortex of a human adult. Right: Golgi-stained cortex of a 1.5-month-old infant. The Nissl stain shows the cell bodies of neurons; the Golgi stain shows the dendrites and axons of a random subset of neurons. glial cells that form the outermost layer of the brain. It has been estimated that there are 14 billion neurons in the cerebral cortex with as many as 300 trillion synapses [18]. An appreciation of the structure and function of the cerebral cortex is key to understanding brain–behavior relationships. More than 90% of the cerebral cortex is classified as neocortex, recognized for its recent arrival in the course of brain evolution. Microscopically, the neocortex is formed as a horizontally laminated structure with six layers: the outermost molecular layer, the external granular cell layer, the external pyramidal cell layer, the internal granular cell layer, the internal pyramidal cell layer, and deepest, the multiform layer (Figure 2.7). Functionally, columns of cells arranged perpendicular to the surface of the cortex respond as a unit, both receiving and processing incoming stimuli and then generating efferent signals. Traditionally, the neocortex has been divided into the frontal, temporal, parietal, and occipital lobes based on the gross anatomy of the cortical surface Chapter 2: Behavioral neuroanatomy Table 2.3. Traditional brain–behavior relationships. Frontal Temporal Parietal Occipital Motor planning Audition Somatosensation Vision Voluntary movement Auditory association Somatosensory association Visual perception Language (fluency) Language (comprehension) Cross-modal sensory association Motor prosody Sensory prosody Visuospatial function Motivation Visual recognition Praxicon Working memory Declarative memory Derivative language functions (reading, writing, calculation) Executive function Emotional generation Comportment Olfaction Figure 2.8. Lobar divisions of the cerebral hemispheres (left lateral view). This figure is presented in color in the color plate section. (Figure 2.8); these areas are associated traditionally with specific neurobehavioral functions (Table 2.3). The frontal lobe is the most rostral portion of the brain, with its boundaries defined posteriorly by the Rolandic fissure and inferiorly by the Sylvian fissure. The temporal lobe is demarcated superiorly by the Sylvian fissure with its posterior boundary defined by the junction of two lines: the first a line from the parieto-occipital sulcus to the pre-occipital notch and the second a continuation extending posteriorly from the Sylvian fissure. The parietal lobe has the Rolandic fissure as its anterior boundary, and its inferior and posterior extent are determined by the lines that demarcate the temporal and occipital lobes. The occipital lobe is located posterior to the temporal and parietal lobes. The insula, a small neocortical region of neurobehavioral relevance, lies deep in the Sylvian fissure beneath the overlying cortex of the frontal, temporal, and parietal lobes [18]. While the surface of the brain is commonly divided into the frontal, parietal, temporal, and occipital lobes, a more detailed and functionally relevant mapping of the cerebral cortex comes from Korbinian Brodmann, a German neurologist and neuroanatomist who based his cortical maps on the cytoarchitectonic arrangement of neurons established with meticulous dissections using the Nissl stain [28]. These histologically based areas described by Brodmann remain useful (and in use) today for neurobehavioral localization (Figure 2.9). Another way of classifying the functional areas of the neocortex is to divide it into primary and association (secondary, tertiary, or quaternary) cortices based on the level of information processing (Figure 2.10). The primary motor cortex is located in the precentral gyrus of the frontal lobe. Secondary motor association cortex (supplementary motor cortex) generates the impulses that are then developed into motor output by the primary motor cortex. Primary motor cortex generates afferent signals that directly control voluntary movement. The majority of ascending sensory tracts project to primary sensory cortex. The assorted primary cortical areas project to adjacent secondary association cortices where sensory input (e.g., visual, auditory, tactile, olfactory, gustatory) is compared with previously experienced (encoded) sensory inputs. This processed information from the sensory association cortex is then further communicated to tertiary and quaternary association areas for further elaboration, identification, comparison, and integration into cognitive, emotional, and behavioral networks. Primary sensory processing is subserved by the postcentral gyrus (tactile and proprioceptive), the medial and superior temporal lobe (olfactory/gustatory and auditory, respectively), and the occipital lobe (visual). These sensory areas are unimodal (serving only one sensory type), and project to unimodal secondary association areas associated with and adjacent to them. These unimodal association 23 Section I: Structural and Functional Neuroanatomy Figure 2.9. Brodmann’s areas in the human brain. Reprinted with permission from Mark Dubin, PhD, Department of Molecular, Cellular & Developmental Biology, University of Colorado at Boulder (http://spot.colorado.edu/∼dubin/talks/brodmann/brodmann.html). This figure is presented in color in the color plate section. areas project to higher-order areas for cross-modal processing and integration of sensory information into memory, cognitive, and emotional networks. Two of the higher-order association areas are integral to the function of the human brain, and therefore warrant additional description. First, the inferior parietal lobule (including the supramarginal and angular gyri) receives input from all of the secondary association areas, and is classified as a cross-modal (heteromodal) or tertiary association cortex. This area of cortex serves to integrate sensory information across modalities, giving humans the ability to associate sights with sounds, sight with touch, smell with sight, and so on. This tertiary association cortex has reciprocal connections with the reticular formation, the limbic system, and the frontal lobes, which both modulate and integrate arousal, attention, and emotion with sen- 24 sory experiences, based in large part on their relative emotional valence and/or survival value. Second, the frontal lobes serve as quaternary association areas integrating extensively processed information from four areas, the tertiary sensory association cortices (heteromodal cortex), the secondary association cortices, and limbic-subcortical and reticular areas, for the highest level of synthesis, elaboration, and regulation of emotional, cognitive, and behavioral/motor processes. The frontal lobes are the most phylogenetically recent addition to the brain, and the slowest to mature during human development. Their connections with the rest of the brain are dense and richly reciprocal. Input to the frontal lobes is segregated, with sensory information largely projecting to the lateral convexities of the prefrontal cortices, and limbic information projecting Chapter 2: Behavioral neuroanatomy 2º M 1º M 1º S 2º S 2º V 3º 4º 1º V 1º A 2º A 2º V heteromodal cortices with efferents back to tertiary and secondary association cortices [24]. This system modulates directed attention to sensory and emotional information with high survival value. These reciprocal cortico-cortical circuits control and determine where internal and external attentional systems are directed, and determine which mental processes will be continued, and when and how any changes in mental direction will occur. White matter Motor Output Ascending Input Figure 2.10. Schematic diagram of the information flow between the primary, secondary, tertiary, and quaternary cortical areas. The ascending input and motor outputs are simplified in order to highlight the cortical elements through which representational information processing networks are developed. Abbreviations: M – motor; S – somatosensory; A – auditory; V – visual; 1◦ – primary cortical area; 2◦ – secondary association cortex; 3◦ – heteromodal cortex; and 4◦ – prefrontal cortex. to the basilar (orbitomedial) surface of the frontal lobes. Subcortical structures (striatum, globus pallidus, subthalamic nucleus, and thalamus) form five major circuits with various frontal gyri, including primary motor cortex, frontal eye fields, dorsolateral prefrontal cortex (made up of the middle and superior frontal gyri), orbitofrontal cortices (gyrus rectus and medial orbital gyrus medially and lateral orbital gyrus and medial inferior frontal gyrus laterally), and the anterior cingulate gyrus [29]. The five major frontalsubcortical circuits subserve key functions including motor (voluntary motor function), frontal eye fields (eye movements), dorsolateral prefrontal (executive function), lateral orbitofrontal (“social cognition”), and anterior cingulate (motivation and emotional experience) and are discussed in detail in Chapter 5. Reticular formation afferents project diffusely throughout the frontal lobes, modulating arousal and attention in response to sensory and limbic inputs. Output from the frontal lobes is essentially reciprocal to these same structures, as well as to frontal motor areas for the final step of generating a motor response. The dorsolateral, orbitofrontal, and anterior cingulate areas integrate cognitive, emotional, and motivational information with somatosensory information received from the secondary sensory association and While much of the focus of neuroanatomy is on gray matter and the relationships between and within the cerebral cortex and subcortical structures, we should emphasize that cerebral white matter occupies nearly one half the total volume of the brain, serving to link cortical and other gray matter regions [30]. The white matter is formed by collections of CNS axons ensheathed with myelin that are most commonly called tracts, but that may also be termed fasciculi, bundles, lemnisci, funiculi, and peduncles based on their location and the structures they connect. A detailed discussion of the structural and functional anatomy of white matter is presented in Chapter 4. The myelin sheath surrounding axons dramatically increases conduction velocity allowing for the rapid transfer of information along white matter tracts that is necessary for efficient communication in sensory and motor systems, as well as the integration of higher functions mediated by other cortical and subcortical networks subserving cognition, emotion, and attention. These tracts connect widely dispersed gray matter regions, linking cortical and subcortical areas to form unified neural networks. These networks in turn subserve the many unique functions of the brain, from basic sensation and motor function to cognition and emotion. In particular, the commissural and association fibers play a major role in the mediation of higher cerebral functions. Commissural fibers are white matter tracts that link the left and right cerebral hemispheres via the cerebral commissures. The corpus callosum, the largest of these commissures, is a massive tract that connects all four lobes of the brain with the homologous regions in the contralateral hemisphere. The anterior and hippocampal commissures are much smaller commissural fiber systems serving a similar function on a much smaller scale. 25 Section I: Structural and Functional Neuroanatomy Association tracts link gray matter regions within each hemisphere. Neuroanatomists have distinguished two types of association fibers: short (arcuate or U fibers) and long association fibers. The short association fibers connect adjacent cortical gyri throughout the cerebrum in contrast to the long association fiber systems that are longer and link cerebral lobes within the ipsilateral cerebral hemisphere. Examples of the long association fibers include the superior occipitofrontal fasciculus, the inferior occipitofrontal fasciculus, the arcuate fasciculus, the uncinate fasciculus, and the cingulum. An interesting neuroanatomic feature of these tracts, and a measure of the central role of the frontal lobes, is that they all have one terminus in a frontal lobe, while the other terminus is variably in more posterior regions. Given their relevance to BN&NP, two other white matter tracts are particularly relevant to neuropsychiatry and merit special mention: the fornix and the medial forebrain bundle. The fornix is a prominent arched tract that connects the hippocampus with the mammillary bodies as part of the Papez circuit, playing a key role in attention and memory formation. The medial forebrain bundle joins the hypothalamus with both caudal and rostral brain regions and participates in the hypothalamic control of the autonomic nervous system. These examples emphasize the essential role of white matter in the operations of all distributed neural networks [30]. Vascular supply Normal brain function requires a steady delivery of well-oxygenated blood. Brain ischemia or anoxia causes neurologic symptoms within seconds, and irreversible neuronal and glial damage and ultimately cell death occurs if the interruption lasts more than a few minutes. The clinical effects of hypoxic-ischemic injury comprise an important area of BN&NP [31] and the effects of focal ischemic injury inform much of what we know about brain–behavior relationships [3]. A working knowledge of the complex system of arteries and veins supplying the brain is necessary for understanding the neurobehavioral effects of many neurologic disorders, including one of the most common, ischemic stroke. At the microscopic level, numerous cerebral capillaries serve as the bridging vessels between the arterial and venous systems. These capillaries consist of tightly packed endothelial cells mediating the 26 exchange of oxygen, glucose and other critical nutrients and removing the byproducts of metabolism. These capillaries regulate the interchange between the CNS and the blood supply, forming a major part of the blood–brain barrier and protecting the brain from the entry of pathogens and toxins from the bloodstream. Four large arteries, sometimes called great vessels of the neck, provide the blood supply to the brain (Figures 2.11 and 2.12). The right and left common carotid arteries usually arise from the right subclavian artery and the ascending aorta, respectively, and midway through their course through the neck they bifurcate into the external carotid artery supplying the face and other extracranial structures, and the internal carotid artery (ICA) irrigating a substantial portion of the brain. The paired vertebral arteries, typically somewhat smaller than the common carotids, usually arise from the subclavian arteries and ascend in parallel to a level just below the pons, where they merge to form the single basilar artery. The basilar artery links with the paired internal carotid arteries to form the Circle of Willis, a vascular loop at the base of the brain from which arise all the arteries supplying the cerebrum. The Circle of Willis is formed by the paired posterior cerebral arteries (PCAs) that bifurcate from the top of the basilar artery and proceed on to supply posterior and basal cerebral regions, the paired posterior communicating arteries (PCoAs) that connect the PCAs with the ICAs, the paired anterior cerebral arteries (ACAs) that arise from the ICAs and go on to irrigate anterior and dorsal regions of the cerebrum, and a single anterior communicating artery (ACoA) that joins the two ACAs. The largest vessels arising from the Circle of Willis are the two middle cerebral arteries (MCAs), each ascending to supply a substantial portion of the ipsilateral cerebral hemisphere. Whereas vascular disease of many kinds can affect any of the cerebral vasculature and dramatically disrupt neurologic function, occlusions of the MCA, ACA, and the PCA are most important in terms of neurobehavioral function because of the key cortical and subcortical structures they supply. It is important to note the variability of the vascular anatomy and region of distribution of these vessels in normal individuals. There are a variety of normal anatomical variants of the origin of the great vessels from the aorta, the anatomy of the Circle of Willis, and the specific territory and volume of brain irrigated by any given vessel [32]. Chapter 2: Behavioral neuroanatomy A richly anastomosed system of cerebral veins provides the venous drainage of the cranium [32]. These venous systems are conventionally divided into superficial and deep groups, both of which drain into a network of dural sinuses. Superficial veins near the brain surface typically drain into the superior sagittal sinus, a long, tubular structure that runs along the inner table of the skull in the interhemispheric fissure at the top of the brain. Deep veins draining subcortical structures including the basal ganglia and thalamus empty into the straight sinus that courses superior to the cerebellum. The major deep and superficial dural venous sinuses coalesce at the confluence of sinuses, and then empty into the bilateral transverse sinuses and on to the internal jugular veins returning blood to the heart via the superior vena cava. Vascular disorders involving the cerebral venous system are less common than those of the arterial system, but the neurological and neuropsychiatric outcomes can be equally catastrophic. Ventricular system Figure 2.11. Orthogonal frontal projection of the cerebral and cerebellar arteries in situ, together with some bony landmarks and the lateral ventricles. Labeled structures include: 1 – Calvaria (inner border). 2 – Medial occipital artery, parieto-occipital branch. 3 – Trunk of the corpus callosum. 4 – Lateral ventricle. 5 – Insula. 6 – Medial occipital artery. 7 – Superior cerebellar artery, medial branch. 8 – Lateral occipital artery. 9 – Free margin of the lesser wing of the sphenoid bone. 10 – Middle meningeal artery, intraosseous part (in-constant). 11 – Middle meningeal artery, frontal branch. 12 – Middle meningeal artery, parietal branch. 13 – Superior margin of petrous part of the temporal bone. 14 – Superior cerebellar artery, lateral branch. 15 – Posterior cerebral artery. 16 – Superior cerebellar artery. 17 – Basilar artery. 18 – Anterior inferior cerebellar artery. 19 – Posterior inferior cerebellar artery, medial branch. 20 – Posterior inferior cerebellar artery, lateral branch. 21 – Posterior inferior cerebellar artery. 22 – Vertebral artery, intracranial part. 23 – Maxillary artery, pterygoid part. 24 – Middle meningeal artery. 25 – Superficial temporal artery. 26 – Maxillary artery, manidibular part. 27 – Vertebral artery, atlantal part. 28 – External carotid artery. 29 – Facial artery. 30 – Vertebral artery, cervical part. 31 – Paracentral artery. 32 – Pericallosal artery. 33 – Callosomarginal artery. 34 – Middle cerebral artery, terminal part. 35 – Middle cerebral artery, insular part. 36 – Anterior cerebral artery, post-communicating part. 37 – Anterior communicating artery. 38 – Anterior cerebral artery, pre-communicating part. 39 – Middle cerebral artery, sphenoid part. 40 – Internal carotid artery, cavernous part. 41 – Internal carotid artery, petrous part. 42 – Internal carotid artery, cervical part. 43 – Common carotid artery. Reprinted from Nieuwenhuys R, Voogd J, Huijzen CV. The Human Central Nervous System. 4th edition. New York, NY: Springer; 2008, with permission from Springer Science+Business Media. This figure is presented in color in the color plate section. Four internal cavities called ventricles sit in the center of the brain. These cavities are filled with cerebrospinal fluid (CSF), a colorless liquid produced by the choroid plexuses within the ventricles and that surrounds and bathes the entire CNS (Figure 2.13). The ventricular system together with the production and reabsorption of the CSF are important in governing the intracranial pressure and fluid dynamics of the brain. The CSF plays a supportive role in normal CNS function, and abnormalities of the CSF may reflect the presence of infection, malignancies, inflammatory processes, and many other neurologic disorders. The two largest ventricles are the lateral ventricles, one in each cerebral hemisphere, and deep within the frontal, temporal, parietal, and occipital lobes. Both lateral ventricles communicate with a single third ventricle, a narrow cavity located on the midline between the right and left thalami, via an opening in each lateral ventricle called the foramen of Monro. The tent-shaped fourth ventricle lies on the dorsum of the brainstem and is connected to the third ventricle by a small conduit through the midbrain known as the cerebral aqueduct. Finally, the fourth ventricle empties into the cisterna magna through three apertures, the midline foramen of Magendie and the two lateral foramina of Luschka. From there the CSF then circulates through the spinal canal, irrigating and supporting the spinal cord to its 27 Section I: Structural and Functional Neuroanatomy Figure 2.12. Orthogonal lateral projection of the cerebral and cerebellar arteries, together with external and bony landmarks. Some neural structures are illustrated in outline; in the center, two lines tangential to the anterior and posterior commisures (AC and PC, respectively) are seen: the one passing above the AC and beneath the PC is part of the bicommissural line of Talairach (BC); the other tangent is part of the upper horizontal line of Krönlein (CH). Additional abbreviations include: CM – canthus-meatus line; FH – horizontal line of Frankfurt; GI – glabella-inion line; VCA – vertical tangent to anterior commissure; and VCP – vertical tangent to posterior commissure. Labeled structures include: 1 – Central sulcus. 2 – Pericallosal artery. 3 – Callosomarginal artery. 4 – Corpus callosum. 5 – Outline of ventricles. 6 – Outline of insula. 7 – Anterior cerebral artery. 8 – Middle cerebral artery, frontal trunk. 9 – Anterior commissure. 10 – Middle cerebral artery, parietal trunk. 11 – Middle cerebral artery, temporal trunk. 12 – Posterior commissure. 13 – Medial occipital artery. 14 – Lateral occipital artery. 15 – Superior cerebellar artery, medial branch. 16 – Superior cerebellar artery, lateral branch. 17 – Superior cerebellar artery. 18 – Posterior cerebral artery. 19 – Posterior communicating artery. 20 – Internal carotid artery, cerebral part. 21 – Internal carotid artery, cavernous part. 22 – Siphon point. 23 – Middle cerebral artery, sphenoid part. 24– Ektocanthion (Canthus externus). 25 – Glabella. 26 – Orbital (on infraorbital margin). 27 – Internal carotid artery, petrous part. 28 – Basilar artery. 29 – Superior margin of petrous part of the temporal bone. 30 – Anterior inferior cerebellar artery. 31 – Porion (on supramental margin). 32 – Fourth ventricle. 33– Posterior inferior cerebellar artery, medial branch. 34 – Posterior inferior cerebellar artery, lateral branch. 35 – Posterior inferior cerebellar artery. 36 – Vertebral artery, intracranial part. 37 – Vertebral artery, atlantal part. 38 – Internal carotid artery, cervical part. 39 – Maxillary artery. 40 – Middle meningeal artery. 41 – External carotid artery. 42 – Vertebral artery, cervical part. 43 – Common carotid artery. 44 – Spinal cord. 45 – Inion (external occipital protuberance). Reprinted from Nieuwenhuys R, Voogd J, Huijzen CV. The Human Central Nervous System. 4th edition. New York, NY: Springer; 2008, with permission from Springer Science+Business Media. This figure is presented in color in the color plate section. lower end and then recirculating rostrally to the convexities of the brain, where it is eventually absorbed into the cerebral venous sinuses through structures called the arachnoid villi. The choroid plexus in all four ventricles steadily produces CSF at a rate of about 450 ml per day. Thus the total CSF volume turns over about three times daily. The total volume of CSF in and around the CNS 28 is approximately 140 ml, whereas the volume of CSF contained within the ventricles at any given time is a small fraction of this, about 25 ml. The neuroanatomic importance of the CSF is twofold: physically, it serves a supportive role in providing buoyancy that cushions the brain from the rigid bony protuberances of the inner aspect of the skull, and functionally, the CSF takes part in regulating the chemical environment of Chapter 2: Behavioral neuroanatomy Figure 2.13. The ventricular system, including the lateral ventricles (dark blue, rostral), third ventricle (purple), cerebral aqueduct (green), fourth ventricle (light blue, caudal), and choroid plexus (red). Adapted from 3D Brain from G2C Online (www.g2conline.org), produced by the Dolan DNA Learning Center, Cold Spring Harbor Laboratory. This figure is presented in color in the color plate section. brain neurons. Directly related to these roles, the ventricular system and the CSF have many important clinical implications. Enlargement of the ventricular system, known as hydrocephalus, results from an excess of CSF production, disturbed circulation, or impaired absorption, and can have major neurologic consequences. Analysis of the constituents of the CSF after lumbar puncture is crucial for diagnosis of many neurologic disorders, such as meningitis and encephalitis. Conclusion Our understanding of the neuroanatomy of cognition, emotion, behavior, and sensorimotor function is an amalgam of traditional neuroanatomic dissection, the information gleaned from the clinical study of neurologic patients, and the methods of modern neuroscience, all of which are expanding our insights into brain–behavior relationships as never before. The localization of higher function in the brain is a central goal of neuroscience, and with our expanding knowledge of the relationships between brain structure and function, the neurobiological basis of human behavior becomes ever more clear. How the brain generates cognitive and especially emotional function has long been the subject of vigorous debate. Accepting the premise that all behavioral function originates in brain activity was the first step, but the understanding of the specific localization and nature of higher functions within the brain requires more work. Much of our knowledge of the relation between specific regions and structures in the brain and specific cognitive functions was obtained through careful clinical description combined with detailed post-mortem study. The advent of modern neuroimaging techniques has expanded and refined our understanding. However, even with this large body of knowledge, uncertainty remains about how any given brain region generates a specific cognitive function. Reduced to its simplest formulations, theories about the cerebral representation of higher mental function fall into two camps: localization and equipotentiality. The foundation of the localizationist’s conception of brain function originated in the time-honored practice of neurologists and neuropsychiatrists that emphasizes a detailed understanding of neuroanatomy and the localization of functions within it. The lesion method, the study of focal brain injury and the resultant disturbance of function, produced a relatively secure, and clinically useful map of brain–behavior relationships. While the lesion method has proved highly effective in demonstrating the anatomy of elemental neurologic deficits such as CN deficits and hemiparesis, it has been less helpful in identifying the sites, or explaining the underlying mechanism of higher functions. While the description of the effects of frontal, temporal, and parietal lesions has advanced our general understanding of the anatomic underpinnings of language, visual processing, and memory, it is increasingly clear that there is no simple correspondence between a given gyrus and a discrete cognitive domain. At the other end of the spectrum were the equipotential theorists, who contended that any specific localization of higher functions in the brain is impossible, and that all cerebral cortical areas are capable of supporting the operations of higher functions [1]. The cortex was considered to be essentially undifferentiated with respect to cognitive function and the effect of any lesion, anywhere in the cerebral hemispheres, would impair neurobehavioral function in proportion to the amount of tissue damaged. This notion, originally supported by experimental studies in higher primates, was then countered by evidence from a century of clinical and decades of neuroimaging studies that confirmed the specialization of cerebral areas with regard to higher functions. Thus, like strict 29 Section I: Structural and Functional Neuroanatomy localization, pure equipotentiality is insupportable in light of current knowledge. Rather than either of these extremes, the current formulation of brain function is based on the idea of distributed neural networks [24]. The central concept of neural networks is that integrated ensembles of interconnected cerebral structures subserve specific neurobehavioral domains. Thus there is no exclusive relationship between any single brain structure and a corresponding mental function, but rather, a neuroanatomically linked network operating as a functional unit subserves a given function. Examples of these distributed networks include the left perisylvian language zone and medial temporal, frontal, and thalamic contributions to memory formation, storage, and retrieval. Higher-order brain function is predicated on the structures and function of lower and more fundamental regions of the brain. Thus, the reticular activating system in the brainstem supports and modulates higher functions through its modulation of the diencephalon, basal ganglia, and cerebral cortex. The interaction of these structures as components of distributed neural networks provides the fundamental organization of cognition and emotion. Throughout the following chapters, the concept of distributed neural networks will serve as a foundation for brain–behavior relations and their clinical relevance. 9. Schmahmann JD. Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J Neuropsychiatry Clin Neurosci. 2004;16(3):367–78. 10. Schmahmann JD, Caplan D. Cognition, emotion and the cerebellum. Brain 2006;129(Pt 2):290–2. 11. Nieuwenhuys R, Voogd J, Huijzen CV. The Human Central Nervous System. 4th edition. New York, NY: Springer; 2008. 12. Mesulam M. Structure and function of cholinergic pathways in the cerebral cortex, limbic system, basal ganglia, and thalamus of the human brain. In Bloom FE, Kupfer DJ, American College of Neuropsychopharmacology, editors. Psychopharmacology: The Fourth Generation of Progress. New York, NY: Raven Press; 1995, pp. 135–146. 13. Pfaff D. Brain Arousal and Information Theory. Cambridge, MA: Harvard University Press; 2006. 14. Posner JB, Plum F. Plum and Posner’s Diagnosis of Stupor and Coma. 4th edition. Oxford: Oxford University Press; 2007. 15. Stoerig P, Cowey A. Blindsight in man and monkey. Brain 1997;120 (Pt 3):535–59. 16. Taber KH, Wen C, Khan A, Hurley RA. The limbic thalamus. J Neuropsychiatry Clin Neurosci 2004;16(2): 127–32. 1. Filley CM. Neurobehavioral Anatomy. 3rd edition. Boulder, CO: University Press of Colorado; 2011. 17. Benarroch EE. The midline and intralaminar thalamic nuclei: anatomic and functional specificity and implications in neurologic disease. Neurology 2008; 71(12):944–9. 2. Nauta WJH, Feirtag M. Fundamental Neuroanatomy. New York, NY: Freeman; 1986. 18. Carpenter MB. Core Text of Neuroanatomy. 3rd edition. Baltimore, MD: Williams & Wilkins; 1985. 3. Duus P. Topical Diagnosis in Neurology: Anatomy, Physiology, Signs, Symptoms. 2nd rev. edition. Stuttgart: Thieme Medical Publishers; 1989. 19. Herkenham M, Nauta WJ. Efferent connections of the habenular nuclei in the rat. J Comp Neurol. 1979; 187(1):19–47. 4. Filley CM. Neurobehavioral Anatomy. 2nd edition. Boulder, CO: University Press of Colorado; 2000. 20. Matsumoto M, Hikosaka O. Lateral habenula as a source of negative reward signals in dopamine neurons. Nature 2007;447(7148):1111–15. References 5. Snell RS. Clinical Neuroanatomy. 7th edition. Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2010. 6. Hurley RA, Flashman LA, Chow TW, Taber KH. The brainstem: anatomy, assessment, and clinical syndromes. J Neuropsychiatry Clin Neurosci. 2010; 22(1):iv, 1–7. 7. Sara SJ. The locus coeruleus and noradrenergic modulation of cognition. Nat Rev Neurosci. 2009;10(3):211–23. 30 8. Arciniegas DB, Beresford TP. Neuropsychiatry: An Introductory Approach. Cambridge: Cambridge University Press; 2001. 21. Andres KH, von During M, Veh RW. Subnuclear organization of the rat habenular complexes. J Comp Neurol. 1999;407(1):130–50. 22. Tisch S, Silberstein P, Limousin-Dowsey P, Jahanshahi M. The basal ganglia: anatomy, physiology, and pharmacology. Psychiatr Clin North Am. 2004;27(4): 757–99. 23. Papez JW. A proposed mechanism of emotion. Arch Neurol Psychiatry 1937;38(4):725–43. Chapter 2: Behavioral neuroanatomy 24. Mesulam M (editor). Behavioral neuroanatomy. Large-scale neural networks, association cortex, frontal systems, the limbic system and hemispheric specializations. In Principles of Behavioral and Cognitive Neurology. 2nd edition. Oxford: Oxford University Press; 2000, pp. 1–120. 28. Brodmann K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues, Barth, Leipzig (1909). Translated by Garey LJ as Localisation in the Cerebral Cortex. London: Smith-Gordon; 1994. New edition: London: Imperial College Press; 1999. 25. Phelps EA, LeDoux JE. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 2005;48(2):175–87. 29. Cummings JL. Frontal-subcortical circuits and human behavior. Arch Neurol. 1993;50(8):873–80. 26. Squire LR. Memory systems of the brain: a brief history and current perspective. Neurobiol Learn Mem. 2004;82(3):171–7. 27. Squire LR, Knowlton B, Musen G. The structure and organization of memory. Annu Rev Psychol. 1993; 44:453–95. 30. Filley CM. The Behavioral Neurology of White Matter. Oxford: Oxford University Press; 2001. 31. Anderson CA, Arciniegas DB. Cognitive sequelae of hypoxic-ischemic brain injury: a review. NeuroRehabilitation 2010;26(1):47–63. 32. Morris P. Practical Neuroangiography. Baltimore, MD: Williams & Wilkins; 1997. 31 Section I Structural and Functional Neuroanatomy Chapter Cerebellum 3 Jeremy D. Schmahmann The inclusion of a chapter on the cerebellum in a textbook devoted to Behavioral Neurology & Neuropsychiatry (BN&NP) reflects a paradigm shift in understanding the role of the cerebellum and the neural substrates of cognition. The cerebellum has long been recognized as important for motor control. Clinical evidence reveals, however, that some cerebellar lesions do not produce motor incapacity, and that cerebellar lesions can impair a number of higher-order functions. The focus of this chapter is to present an overview and practical approach to conceptualizing these manifestations of cerebellar lesions, and to outline the principles that govern the cerebellar contribution to cognition and emotion as well as to sensorimotor function. The cerebellum and cognition – the underlying neurobiology Distributed neural circuits Understanding how the cerebellum contributes to cognition is key to developing a clinical approach to evaluation and management. Central to this concept is the notion of distributed neural circuits, the idea that all behaviors are subserved by neural systems comprising anatomic regions, or nodes, each displaying unique architectural properties, distributed geographically throughout the nervous system, and linked anatomically and functionally in a precise and unique manner [1]. These nodes are not exclusive to the cerebral cortex, but include subcortical regions – striatum, thalamus, and cerebellum. The clinical manifestations of cerebellar lesions may thus be viewed as disconnection syndromes – focal disruptions of distributed cortical and subcortical neural circuits that are the basis of all neurological function [2–4]. Essential cerebellar anatomy Situated in the posterior fossa, the cerebellum is comprised of two hemispheres joined across the midline, or vermis. It contains cerebellar cortex, white matter, and deep nuclei (Figure 3.1). Cerebellar fissures (equivalent to cerebral sulci) separate the cerebellar folia (equivalent to cerebral gyri) to form ten lobules identified by the roman numerals I through X, grouped into three lobes. Lobules I through V constitute the anterior lobe, lobules VI through IX the posterior lobe, and lobule X is the flocculonodular lobe [5–7]. The histology of the deeply folded cerebellar cortex is essentially uniform throughout [8]. It has three layers: the Purkinje cell monolayer situated between the granule cell layer beneath and the molecular layer above. Purkinje cells (PCs) provide the sole axonal efferent from the cortex to the deep nuclei. The flattened arbor of the PC dendritic tree extends up into the molecular layer. Granule cells receive mossy fiber afferents from the spinal cord and brainstem including basis pontis, and send their axons into the molecular layer where they divide to form parallel fibers running along the long axis of the folium, synapsing with the distal dendrites of hundreds of PCs. Climbing fiber (CF) axons from the neurons of the inferior olivary nucleus in the medulla entwine themselves around the proximal dendrites of PCs in a one-CF-to-one-PC relationship, terminating in the cerebellar cortex in discretely organized parasagittal zones. Cortical interneurones include stellate and basket cells in the molecular layer that inhibit the PCs, and Golgi cells in the granule cell layer that inhibit the granule cells. Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 32 Chapter 3: Cerebellum (A) (B) (C) (F) (D) (G) (E) (H) Figure 3.1. A, Posterior, and B, right lateral surface reconstructions of the human cerebellum derived from MRI images. The named fissures are demarcated in color, and the fissures and lobules are identified. C, Surface reconstruction of the cerebellum seen from the oblique posterior view, with lobules demarcated. Parasagittal images of human cerebellum on MRI 2 mm lateral to midline in D, and 18 mm lateral to midline in E. Fissures are color coded according to the convention used in A and B, and the lobules are designated. F, Superior (SCP), middle (MCP), and inferior (ICP) cerebellar peduncles in human identified with diffusion spectrum imaging, overlaid on diffusion-weighted image of cerebellum and brainstem. G, Cryosection image of post-mortem human cerebellum in the coronal plane 52 mm behind the anterior commissure – posterior commissure (AC-PC), with deep cerebellar nuclei identified: D – dentate nucleus, E – emboliform nucleus, F – fastigial nucleus, G – globose nucleus. H, Diagram of a single cerebellar folium is shown sectioned in its longitudinal axis (diagram right) and transversely (left) to depict the histology of the cerebellar cortex. Purkinje cells are red; superficial and deep stellate, basket, and Golgi cells are black; granule cells and ascending axons and parallel fibers are yellow; mossy and climbing fibers are blue. Also shown are the glomeruli with mossy fiber rosettes, claw-like dendrites of granule cells, and Golgi axons. (A, B, D, E, G reproduced from Schmahmann JD, Doyon J, Toga A, Evans A, Petrides M. MRI Atlas of the Human Cerebellum. San Diego, CA: Academic Press; Copyright Elsevier, 2000, with permission from Elsevier; C from Makris N, Schlerf JE, Hodge SM et al. MRI-based surface-assisted parcellation of human cerebellar cortex: an anatomically specified method with estimate of reliability. Neuroimage 2005;25(4):1146–60, with permission from Academic Press; F reproduced from Granziera C, Schmahmann JD, Hadjikhani N et al. Diffusion spectrum imaging shows the structural basis of functional cerebellar circuits in the human cerebellum in vivo. PLoS One 2009;4(4):e5101, with permission; H reproduced from Williams PL, Bannister LH, Berry MM et al. (editors). Gray’s Anatomy. 38th edition. New York, NY: Churchill Livingstone; 1995; Copyright Elsevier, 1995, with permission from Elsevier. Redrawn from Eccles JC, Ito M, Szentágothai J. The Cerebellum as a Neuronal Machine. Berlin: Springer-Verlag; Copyright Elsevier, 1967. This figure is presented in color in the color plate section. 33 Section I: Structural and Functional Neuroanatomy The cerebellar nuclei are, from medial to lateral, the fastigial, globose, emboliform, and dentate nuclei; these are supplemented by the lateral vestibular nucleus in the medulla that has direct connections with cerebellar lobule X cortex. Three peduncles link the cerebellum to the neuraxis. The inferior cerebellar peduncle conveys afferents from the spinal cord (spinocerebellar tracts) and the climbing fiber inputs from the inferior olivary nucleus, as well as cerebellar efferents to the inferior olive and to vestibular nuclei. The middle cerebellar peduncle is the conduit of mossy fiber afferents from the pontine nuclei in the massively expanded ventral part of the pons. The superior cerebellar peduncle carries efferents from the cerebellar nuclei to the cerebral hemispheres, as well as some spinocerebellar afferents to cerebellum. Three pairs of blood vessels derived from the posterior circulation (vertebrobasilar system) irrigate the cerebellum. These are relevant in understanding the consequences of stroke. The posterior inferior cerebellar arteries (PICAs) irrigate the medial and lateral aspects of the posterior lobe, the ventral aspect of the cerebellar nuclei, and the medulla. The anterior inferior cerebellar arteries irrigate the anterior aspects of the posterior lobe, part of the anterior lobe, the middle cerebellar peduncle and the inferior aspect of the lateral pons. The superior cerebellar arteries supply the anterior lobe and rostral part of the posterior lobe, the superior aspect of the deep nuclei, the superior cerebellar peduncle, and the rostral part of the lateral pons. Cerebrocerebellar connections The anatomical connections of the cerebellum with spinal cord, brainstem and cerebral hemispheres are discretely organized in parallel anatomic subsystems (or loops) that provide the structural basis for the role of the cerebellum in movement, cognition, and emotion. Its connections with the cerebral hemispheres are via a two-stage feedforward projection from cerebral cortex through the nuclei of the basis pontis and a two-stage feedback system from the cerebellar nuclei through thalamus to cerebral cortex. Sensorimotor information reaches the cerebellum from the spinal cord via the spinocerebellar tracts, the spinal-recipient parts of the inferior olivary complex, and the motor recipient nuclei in the basis pontis [9–13]. Sensorimotor projections to cerebellar cortex terminate in the anterior lobe (lobules I–V), adjacent 34 parts of lobule VI, and lobule VIII. The sensorimotorrelated corticonuclear complexes engage the interpositus nucleus (globose and emboliform in the human) and the dorsal part of the dentate nucleus [14, 15] and project back to motor-related areas of the cerebral cortex [16]. The cerebrocerebellar link is predominantly contralateral, so that the right cerebellum communicates mostly with the left cerebral hemisphere, and vice versa. Cerebellar control of movement is overwhelmingly ipsilateral; i.e., the right cerebellum coordinates movements of the right side of the body. Higher-order information gains access to the cerebellum via the corticopontine pathway, transmitted from the pons through the middle cerebellar peduncle to the cerebellum. These inputs are derived from association areas in the prefrontal, posterior parietal, superior temporal, and dorsal parastriate cortices; and from paralimbic areas in the posterior parahippocampus, limbic regions of the cingulate gyrus and the anterior insular cortex [12, 17, 18]. The cerebellar cortical destinations of these afferents are predominantly in lobules VI and VII (Crus I and II being hemispheric extensions of lobule VIIA; and lobule VIIB). The more recently evolved ventral parts of the dentate nucleus [19] convey information from these cerebellar regions via thalamus back to the cerebral cortical association areas, thus closing the loop [17, 20]. Limbic and paralimbic cortices are interconnected with the cerebellar vermis and fastigial nucleus [21]. Cerebellar functional topography Physiological studies in cat [22] and human [23, 24] reveal cerebellar somatotopy confined to the anterior lobe, parts of lobule VI, and lobule VIII. Resting-state functional connectivity magnetic resonance imaging (fcMRI) in humans shows that activity in the anterior lobe, lobules VI and VIII, correlates with sensorimotor regions of the cerebral cortex, whereas activity in the posterior lobe (mostly Crus I and II of lobule VII) correlates with prefrontal, parietal, and temporal association areas and the cingulate gyrus [25]. Functional MRI (fMRI) studies indicate that whereas the cerebellar anterior lobe, adjacent parts of lobule VI, and lobule VIII are activated in sensorimotor tasks, lobules VI and VII in the posterior lobe are active during language, spatial, and executive function tasks, and affective processing engages the posterior lobes including the vermis [26, 27] (Figures 3.2 and 3.3). Chapter 3: Cerebellum (A) (D) (B) (C) Figure 3.2. Activation Likelihood Estimation (ALE) activation maps for the domains of A, spatial cognition, B, motor tapping with the right hand, and C, language tasks drawn from a meta-analysis of functional imaging studies [26]. The right cerebellum is depicted on the right. The results are overlaid upon an image of the cerebellum in the coronal plane at y = −70 from the MRI Atlas of the Human Cerebellum [7], and the cerebellar fissures and lobules at this level are identified in D. Reproduced from Schmahmann JD, Doyon J, Toga A, Evans A, Petrides M. MRI Atlas of the Human Cerebellum. San Diego, CA: Academic Press; Copyright Elsevier, 2000, with permission of Elsevier; and with permission of Academic Press from Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 2009;44(2):489–501. This figure is presented in color in the color plate section. The recognition that sensorimotor control is topographically separate and distinct from cognitive and emotional regulation in the cerebellum represents a major departure from earlier conventional wisdom [28]. It now appears that the anterior lobe and parts of medial lobule VI, together with lobule VIII of the posterior lobe and the globose and emboliform nuclei (or, more accurately the interpositus nucleus in the experimental animal) constitute the sensorimotor cerebellum. Lobule VII (that includes crus I and crus II of lobule VIIA, and lobule VIIB), parts of lobule VI, and the ventral part of the dentate nucleus, constitute the anatomical substrate of the cognitive cerebellum. The limbic cerebellum appears to have an anatomical signature in the fastigial nucleus and the cerebellar vermis, particularly the posterior vermis. Little is known of the possible cognitive role of lobule IX, although early fcMRI data provide some insights into its potential incorporation into the default mode network [29]. [As discussed in Chapter 5, Frontal-subcortical circuits] Lobule X is an essential node in the vestibular system. Clinical manifestations of cerebellar lesions The cerebellar motor syndrome Lesions of the cerebellum have traditionally been regarded as producing motor impairments only. The cerebellar motor syndrome is characterized by widebased and unsteady, or ataxic, gait; incoordination, or dysmetria, of the arms and legs; articulation impairment, or dysarthria; and eye movement abnormalities that disturb vision, among other problems [30, 31]. These deficits are present in patients with neurodegenerative ataxias involving the entire cerebellum, but in stroke patients they arise following lesions principally in the anterior lobe [32, 33]. Oculomotor abnormalities and vestibular symptoms (vertigo, nausea, emesis) result from lesions of lobules IX and X, but lesions in the hemispheres that avoid the anterior lobe do not result in the cerebellar motor syndrome. Indeed, when these patients are examined a few days after stroke 35 Section I: Structural and Functional Neuroanatomy Figure 3.3. Representative rostral (y = −44) to caudal (y = −76) coronal sections through a human cerebellum showing activation patterns in a functional magnetic resonance imaging experiment in a single subject [28]. Tasks investigated sensorimotor function (finger tapping, red), language (verb generation, blue), spatial cognition (mental rotation, green), working memory (n-back task, purple), and emotional processing (viewing images from the International Affective Picture System, yellow). Lobules V, VI, Crus I (Cr I), Crus II (Cr II), VIIB and VIII are labeled. The right and left cerebellar hemispheres are as indicated. Reproduced with permission of Masson Spa from Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 2010;48(7):831–844. This figure is presented in color in the color plate section. when the vestibular symptoms have subsided, it can be difficult to detect any sign of a motor disorder [33]. The cerebellar cognitive affective syndrome The importance of the cerebellum for non-motor function was highlighted by the description of the cerebellar cognitive affective syndrome (CCAS) [34]. The CCAS results from lesions of the posterior lobe, characterized by clinically relevant deficits in executive function, visual spatial performance, linguistic processing, and dysregulation of affect (Table 3.1). In the original report of 20 patients, 18 demonstrated problems with executive functions, including poor working memory (in 11 of 16 tested), motor or ideational set shifting (in 16 of 19), and perseveration of actions or drawings (in 16 of 20). Verbal fluency was impaired in 18 patients, presenting as telegraphic speech, occasionally so limited as to resemble mutism. Decreased verbal fluency was unrelated to dysarthria. Visuospatial disintegration was found in 19 patients, 36 who were disorganized in their sequential approach to drawing, and the conceptualization of the figures was disorganized. Four patients demonstrated simultanagnosia. Naming was impaired in 13 patients, generally being spared in those with smaller lesions. Six with bilateral acute disease had agrammatic speech, and elements of abnormal syntactic structure were noted in others. Prosody was abnormal in eight patients, with tone of voice characterized by a high-pitched, whining, childish, and hypophonic quality. Mental arithmetic was deficient in 14 patients. Verbal learning and recall were mildly abnormal in 11, and visual learning and recall were impaired in four (of 13 patients tested). Ideational apraxia was evident in two individuals. Difficulty modulating behavior and personality style was a prominent feature of the bedside mental state examination in 15 patients, particularly those with large or bilateral PICA territory infarcts, and in one with surgical excision of the vermis and paravermian structures. Flattening of affect or disinhibition were manifested as overfamiliarity, flamboyant and Chapter 3: Cerebellum Table 3.1. Deficits that characterize the cerebellar cognitive affective syndrome (CCAS). The net effect of these disturbances in cognitive functioning is a general lowering of overall intellectual function. Neurobehavioral domain Deficits characterizing CCAS Executive function Spatial cognition Deficient planning, motor or ideational set-shifting, abstract reasoning, working memory and multi-tasking; decreased verbal fluency, sometimes to the point of telegraphic speech or mutism; perseverative ideation in thought and/or action Visuospatial disintegration with impaired attempts to draw or copy a diagram; disorganized conceptualization of figures; impaired visual-spatial memory; in some cases, simultanagnosia may be present Language Anomia, agrammatic speech, abnormal syntactic structure, abnormal prosody Personality Aberrant modulation of behavior and personality with posterior lobe lesions that involve midline structures; flattening or blunting of affect alternates or coexists with disinhibited behaviors such as overfamiliarity, flamboyant and impulsive actions, and humorous but inappropriate and flippant comments; regressive, childlike behaviors and obsessive-compulsive traits can be observed impulsive actions, and humorous but inappropriate and flippant comments. Behavior was regressive and childlike, and obsessive-compulsive traits were occasionally observed. Autonomic changes were a central feature in a patient with stroke involving the fastigial nucleus and paravermian cortex. This manifested as spells of hiccupping and coughing, which precipitated bradycardia and syncope. Neuropsychological testing confirmed the observations from the bedside evaluation (Table 3.2). Deficits were more pronounced and generalized in patients with large, bilateral, or pancerebellar disorders, and particularly in those with acute onset cerebellar disease. Lesions of the posterior lobe were particularly important in the generation of the CCAS; the vermis was consistently involved in patients with pronounced affective presentations; and the anterior lobe seemed to be less involved in the generation of these cognitive and behavioral deficits. Patients with stroke improved over time, although executive function remained abnormal. Table 3.2. Neuropsychological findings in the cerebellar cognitive affective syndrome reported by Schmahmann and Sherman, 1998 [34]. These findings are grouped according to major functional category. Abbreviations: WAIS: Wechsler Adult Intelligence Scale; WAIS–R: Wechsler Adult Intelligence Scale–Revised; WMS–R: Wechsler Memory Scale–Revised; n = number of patients who received each test. ∗∗∗ P ⬍ 0.001; ∗∗ P ⬍ 0.01; and ∗ P ⬍ 0.05. Reprinted from Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain 1998;121(Pt 4):561–79, by permission of Oxford University Press. Test Z-score Mean SD Intellectual functioning WAIS – Full-scale IQ WAIS–R Verbal IQ WAIS–R Performance IQ −1.0 −0.93 −1.3 0.123 0.0002∗∗∗ 13 0.145 ⬍0.0001∗∗∗ 15 ∗∗∗ 0.127 0.0006 13 Executive functioning Word Association Animal Naming Trails A Trails B Wisconsin Card Sorting Test −2.7 −1.5 −1.2 −0.89 −0.83 1.8 0.77 1.3 0.76 1.7 ⬍0.0001∗∗∗ 0.0002∗∗∗ 0.0067∗∗ 0.0030∗∗ 0.2205 16 10 12 11 8 Reasoning and abstraction Similarities Comprehension Picture Completion Arithmetic Picture Arrangement −0.42 −0.79 −0.77 −0.86 −1.4 0.99 0.67 0.98 1.1 0.74 0.1141 0.0120∗ 0.0150∗ 0.0112∗ ⬍0.0001∗∗∗ 16 8 13 13 14 Visuospatial/visual construction Rev Complex Figure: Copy WAIS–R Block Design WAIS–R Object Assembly Hooper Visual Orientation −5.9 −1.2 −0.81 −0.42 3.2 0.90 0.84 0.89 0.0002∗∗∗ 0.0006∗∗∗ 0.0431∗ 0.3038 13 12 7 6 −1.4 −0.40 1.4 1.4 0.0047∗∗ 0.6097 13 4 −0.13 −0.51 1.3 0.93 0.7448 0.0501 10 15 Attention and orientation Digit Span – forward Digit Span – backward Tapping Span – forward Tapping Span – backward Digit Symbol Stroop −0.51 −0.61 −0.78 −0.85 −1.3 0.07 1.3 1.2 1.0 0.84 0.67 0.95 0.1501 0.0644 0.0844 0.0571 0.0004∗∗∗ 0.8769 15 15 7 7 9 4 Memory WMS–R Logical Memory I Logical Memory II Visual Reproduction I Visual Reproduction II Rey Complex Figure: Memory −0.40 −0.42 −1.1 −1.4 −1.7 1.1 0.89 1.1 0.84 0.76 0.1756 0.1046 0.0038∗∗ 0.0001∗∗∗ 0.0012∗∗ 14 14 12 12 7 Language Boston Naming Test Peabody Picture Vocab. PPVT-R WAIS–R Vocabulary WAIS–R Information P-value n The principal features and clinical relevance of the CCAS have been emphasized and elaborated upon in subsequent clinical reports (cerebellar stroke [35–39]; mass lesions [40]; and superficial siderosis [41, 42]). 37 Section I: Structural and Functional Neuroanatomy Cerebellar stroke patients have been reported to show deficits in executive function as revealed by poor performance on phonemic and alternate categorical fluency, naming with and without interference, and a paced auditory serial addition task; and visual spatial deficits with low scores on the Wechsler Adult Intelligence Scale–Revised (WAIS–R) Block Design subtest [43]. These deficits are clinically relevant; young adults (ages 18–44) with cerebellar strokes producing impaired working memory, visuospatial skills, and cognitive flexibility have delayed return to the work force because of cognitive limitations, not motor incapacity [44]. Our understanding of the manifestations of the CCAS continues to evolve, as exemplified by cerebellar patients who report problems with multi-tasking, organizing their thoughts, sustaining their level of concentration and energy, and being somewhat forgetful. One with post-infectious cerebellitis developed impaired judgment, diminished insight, and inability to predict consequences of his actions. An artist with a PICA territory infarct reported loss of creativity that persisted for almost a year, no longer able to experience the flow of visual images that premorbidly characterized his artistic process. Another suffered bilateral cerebellar strokes – one in the left anterior lobe that resulted in left-sided dysmetria, and the other in the right posterior lobe and vermis. His chief complaints were post-stroke executive impairments, irritability, poor impulse control, and depression. The CCAS occurs in children as well. Children who had undergone resection of cerebellar tumors but who did not receive radiation therapy or methotrexate achieved poor scores on tests of expressive language, word finding, and digit span, visual-spatial functions and verbal memory, and they showed perseveration and difficulties establishing set [45]. More than half of these children with vermis involvement experienced the posterior fossa syndrome (PFS) [46, 47], which was described initially as mutism without behavioral impairment [48, 49]; pseudobulbar palsy [50]; cerebellar speech syndrome [51]; and mutism and subsequent dysarthria [52]. In our series [45], this phenomenon developed after a 1–2-day latent period of normal behavior following surgery, and was characterized by the development of mutism followed by recovery over months with dysarthria, and buccal and lingual apraxia. Children also exhibited behavioral changes including regressive personality, apathy, and poverty 38 of spontaneous movement. Emotional lability was marked, with rapid fluctuation of emotional expression gravitating between irritability with inconsolable crying and agitation, to giggling and easy distractibility. The PFS generally follows a surgical approach to tumor resection through the cerebellar vermis, occurring in about 15–50% of patients according to different studies. The relationship between PFS and CCAS in the post-operative population has been specifically considered [53], although it should be noted that PFS, the hallmark feature of which is post-operative mutism following a delay, is not a prerequisite for the development of the intellectual and emotional deficits that comprise the CCAS, either acutely (as in stroke or tumor patients) or more chronically as occurs in destructive or degenerative lesions of the cerebellum. Subsequent analyses of children following cerebellar tumor resection reveal auditory sequential memory and language deficits following right-sided tumors, and deficient spatial and visual sequential memory with left hemisphere tumors [54]. Impaired executive functions have been reported, including impaired planning, sequencing, mental flexibility and hypothesis generation and testing, visual-spatial function, expressive language, and verbal memory [55–60]. Behavioral changes have been noted, with apparent increased thoughtfulness, anxiety and aggression [61], hyperspontaneous, disinhibited behavior, and hypospontaneous, flattened affect [62]. Vermal lesions in particular lead to post-surgical mutism and agrammatism, and behavioral disturbances include irritability, decreased ability to tolerate the company of others, and autistic features such as avoidance of physical and eye contact, complex repetitive and rhythmic rocking movements, stereotyped linguistic utterances, and lack of empathy [63]. Attention deficit disorder, addiction, anorexia, uncontrolled temper tantrums and phobias have also been described in the post-tumor resection pediatric population [64], and the posttumor CCAS deficits in children may be persistent [65, 66]. The cerebellar cognitive affective syndrome – the emotional deficits Emotional dysregulation can be a striking aspect of the CCAS [34, 45]. Behavioral aberrations in children following cerebellar tumor resection include disinhibition, impulsivity and irritability [67], dysphoria, Chapter 3: Cerebellum inattention and irritability [57], anxiety and aggression [61], and stereotypies and aberrant interpersonal relations that meet criteria for the diagnosis of autism [63]. The immune-mediated syndrome of opsoclonus– myoclonus–ataxia that occurs in children [68] and adults [69] produces a psychiatric constellation of mood changes and inconsolable irritability with lability, aggression, and night terrors. Dysphoric mood, disinhibition and poor affect regulation, disruptive behaviors and temper tantrums occur together with the cognitive and language impairments typically seen in the CCAS. Further, these behavioral changes have a predilection for lesions involving the vermis and paravermian regions, findings that are consistent with earlier clinical [70] and electrophysiological studies in experimental animals [71] and patients [72] that led to the first indication of the cerebellum as an “emotional pacemaker” [73]. Pathological laughing and crying (PLC), a manifestation of disordered voluntary control of emotional expression, occurs following lesions of the pontocerebellar circuit [74, 75] including post-infectious cerebellitis [76] and stroke in the basis pontis [13, 77], and it is described in patients with the cerebellar form of multiple system atrophy (MSA) [78]. These findings provide support for the notion that the cerebellum is engaged in the voluntary control of emotional expression. PLC may occur in the absence of a mood disorder, but some MSA patients also report true depression, suggesting that the cerebellum may influence the feeling state as well as the affective display. In our continuing observations of adults and children with cerebellar lesions we have noted altered regulation of mood and personality, psychotic thinking, and behaviors that meet criteria for diagnoses of attention-deficit hyperactivity disorder, obsessivecompulsive disorder, depression, bipolar disorder, disorders on the autism spectrum, anxiety, panic, and often, “atypical psychosis.” Other features include a lack of initiation, apathy, and irritability. We have conceptualized these behaviors as either excessive or reduced responses to the external or internal environment. The exaggerated, positive, released, or hypermetric responses may be regarded as analogous to the overshoot in the motor domain resulting from cerebellar lesions (akin to “cognitive overshoot” [79]). The diminished, negative, restricted or hypometric responses may be likened to hypotonia [80], or to hypometric movements (undershoot) in the motor Table 3.3. Neuropsychiatric manifestations in patients with cerebellar disorders, arranged according to major neurobehavioral domains, each with positive and negative symptoms. Adapted from Schmahmann JD, Weilburg JB, Sherman JC. The neuropsychiatry of the cerebellum – insights from the clinic. Cerebellum 2007;6(3):254–67 and reproduced with permission from Taylor & Francis Ltd. Positive (exaggerated) symptoms Negative (diminished) symptoms Attentional control Inattentiveness Distractibility Hyperactivity Compulsive and ritualistic behaviors Ruminativeness Perseveration Difficulty shifting focus of attention Obsessional thoughts Emotional control Impulsiveness Disinhibition Lability Unpredictability Incongruous feelings Pathological laughing and crying Anxiety Panic Agitation Anergy Anhedonia Dysphoria Sadness Depression Hopelessness Autism Stereotypical behaviors Avoidant behaviors spectrum Self-stimulation Tactile defensiveness behaviors Easy sensory overload Psychosis Illogical thought spectrum Paranoia Hallucinations Lack of empathy Muted affect Emotional blunting Apathy Social skill set Passivity Immaturity Childishness Difficulty with social cues and interactions Unawareness of social boundaries Overly gullible and trusting Anger Aggression Irritability Overly territorial Oppositional behavior system following cerebellar lesions. Further, we conceptualize these manifestations as falling into five neuropsychiatric domains – attentional control, emotional control, social skill set, autism spectrum disorders, and psychosis spectrum disorders [66] (Table 3.3). Certain manifestations, such as the social skill set negative symptoms, are reminiscent of observations regarding the cerebellar role in theory of mind studies [81, 82], providing a clinical underpinning to this observation from experimental psychology. Some patients with acquired cerebellar lesions develop panic disorder, perhaps reflecting overshoot of anticipatory fear [83] resulting from cerebellar-induced emotional dyscontrol. 39 Section I: Structural and Functional Neuroanatomy Effect of cerebellar lesions on development The developing nervous system appears to require an intact cerebellum for normal intellectual and social/emotional function as well as for motor skill. The cerebellum has a protracted developmental trajectory: it reaches its peak volume later than the cerebrum and more phylogenetically recent cerebellar regions (engaged in cognitive processing) mature particularly late [84]. This renders the cerebellum vulnerable to environmental influences, and is clinically relevant in infants born prematurely, as the rapid rate of growth of the cerebellum during late gestation is impeded by the premature birth [85]. Thus, adolescents who had been born very pre-term (⬍33 weeks’ gestation) have reduced cerebellar volumes and testing reveals deficits in executive, visual-spatial, and language skills including impaired reading [86]. Malformations, agenesis, and hypoplasia of the cerebellum are associated with a range of motor, linguistic, intellectual, and emotional manifestations [61, 87–89]. Children with cerebellar hypoplasia and nonprogressive cerebellar ataxia struggle with cognitive and emotional deficits conforming to the description of the CCAS [90]. Indeed, autistic features and speech delay, together with ataxia, hypotonia, and ocular signs correctly predict 86% of children with cerebellar hypoplasia [91]. Early damage to the cerebellar vermis is particularly relevant for the later emergence of neuropsychiatric phenomena. Vermal regions are among the brain areas showing structural differences in autism spectrum disorders [92–94]; pre-term infants with cerebellar hemorrhage in the vermis have pervasive developmental disorder and diagnostic scores on autism screening questionnaires [95]; the vermis has been implicated in psychoneurotic symptoms following early childhood trauma, and in addictive behaviors that underlie substance abuse [96, 97]; and morphometric studies reveal reduced size of the posterior vermis (lobules VIII through X) in attention-deficit hyperactivity disorder [98–100]. The recognition that cerebellum appears to have a trophic influence on the development of interconnected brain systems subserving higher-order function is of great interest and also has practical implications for diagnosis and management. Even at the level of gross morphology, early damage to cerebellar hemispheres results in volume loss in the contralateral cerebral hemisphere [101, 102], a phenomenon 40 that harks back to the early descriptions by Gudden and von Monakow of sustaining projections [103], and which appears to be clinically significant. The cerebellar component of primary psychiatric disease Reports in the 1800s noted deviant and aberrant behaviors in individuals with cerebellar anomalies. Later clinical observers [70, 104] noted a relationship between the cerebellum and personality, aggression, and emotion, suggesting a casual link between cerebellar structural abnormalities and psychosis. The connections of the cerebellum with brain circuits implicated in psychiatric illness, and notably in schizophrenia, are particularly important in this regard [21, 105–109]. There is now a sizeable literature examining structural and functional imaging observations in cerebellum in early infantile autism, schizophrenia, depression, bipolar disorder, panic and obsessivecompulsive disorder (for more recent reviews, see [110–113]). These observations build upon early reports suggesting that the vermis and fastigial nucleus in humans and monkeys play a role in emotion, aggression, and psychosis [73, 104, 114–116]. The definition of the five domains of neuropsychiatric impairments arising from lesions of the cerebellum – attentional control, emotional control, social skill set, autism spectrum disorders, and psychosis spectrum disorders, provides a clinical framework within which to consider the role of the cerebellum in psychiatric illness. The implications of a cerebellar role in the pathophysiology of mental illness are far-reaching, and include new approaches to treatment. Cognition in ataxic disorders To develop an understanding of the cerebellar role in non-motor function, it has been necessary to study patients with lesions confined to the cerebellum. Disorders such as the hereditary spinocerebellar ataxias (SCAs) are noteworthy for at least two opposing reasons. Whereas the neuropathology in some like SCA 5, SCA 6, and SCA 8 involves cerebellum exclusively or overwhelmingly, in many others like SCA 1, SCA 2, SCA 3, and SCA 17 the brainstem, basal ganglia, and cerebral cortex are also affected. In these cases it is prudent to be cautious about ascribing to cerebellum functional impairments, including dementia, which may arise in these patients from lesions of cerebral cortex, Chapter 3: Cerebellum cerebral white matter tracts, or other subcortical areas including striatum and thalamus. Patients with SCA 17, for example, may present with cognitive decline and psychiatric manifestations resembling the phenotype of Huntington’s disease [117]. From the perspective of clinical diagnosis and management, however, it is important to recognize that patients with the inherited ataxias may have cognitive and/or neuropsychiatric impairments, and to treat these clinical symptoms no matter the precise location of the pathology. Mechanisms of the cerebellar contribution to cognition and emotion Earlier investigators regarded the cerebellum as the “great modulator of neurologic function” [73, 114]. The fundamental mechanism by which the cerebellum modulates movement, intellect, and emotion remains a matter of debate [118–121]. Our dysmetria of thought theory [4, 17, 34, 108, 109] is based upon two complementary facts of anatomical organization. First, cerebellar histology is essentially uniform throughout. This enables a computation we have termed the Universal Cerebellar Transform (UCT), allowing the cerebellum to modulate behavior, acting as an oscillation dampener maintaining function in equilibrium around a homeostatic baseline and smoothing out performance, modifying it according to context. It does so automatically, and without conscious awareness. Second, the specificity of the anatomical connections between cerebellum and the spinal cord and brainstem, and the feedforward (cortico-ponto-cerebellar) and feedback (cerebellar-thalamic-cortical) connections with sensorimotor as well as association areas of the cerebral hemispheres facilitates functional topography within the cerebellum. Thus, in the same way that the cerebellum regulates the rate, force, rhythm, and accuracy in the motor domain, so does it regulate the speed, capacity, consistency, and appropriateness of cognitive and emotional processes. If the UCT is the essential functional contribution that the cerebellum makes to the distributed neural system, then by corollary, there should be a Universal Cerebellar Impairment – namely, dysmetria (the Greek term originally used to designate motor incoordination). When the motor cerebellum is damaged the dysmetria manifests as ataxia of extremity movements, eye movements, speech and equilibrium. When the lesion is in the cognitive cerebellum, the result is dysmetria of thought (or, more recently, cognitive dysmetria [122]), which manifests as the various components of the CCAS. When the limbic (midline) cerebellum is damaged, the dysmetria of emotional control manifests predominantly as neuropsychiatric impairments. Studies employing the contemporary methods of cognitive neuroscience are under way in a number of centers to attempt to define whether the UCT indeed exists, and the precise nature of the computation that defines the transform. Implications for therapy Appreciation of the cerebellar contribution to higher function has relevance for understanding the neural bases of intellect and emotion and the role of cerebellum in neuropsychiatric diseases. Knowledge of the characteristic features of the CCAS provides an opportunity for counseling and education, thus fulfilling the patient’s “need-to-know-imperative.” It facilitates therapeutic intervention by cognitive and behavioral therapy for previously unrecognized emotional disturbances in the cerebellar patient population. It opens the way to the use of available pharmacologic agents that treat the presenting neurobehavioral and neuropsychiatric symptoms. There is already considerable anecdotal experience from our Ataxia Unit and elsewhere that treatment of the neuropsychiatric symptoms resulting from cerebellar lesions can be effective, regardless of which node in the distributed circuit is disturbed. Prospective studies of the pharmacologic treatment of the CCAS have not yet been performed. The role of cerebellar–vestibular interactions in dyslexia has been proposed previously [123–125], and we have postulated that cross-model therapies may be useful in disorders of higher function [126]. Some have claimed success in the management of attention deficit disorder and dyslexia by maneuvers that emphasize cerebellar motor control [127] but this approach is controversial and requires further empirical study. Earlier investigators [73, 128] reported that electrical stimulation of the cerebellum resulted in successful treatment of behavioral disorders including aggression and psychosis. We have suggested that applying repetitive transcranial magnetic stimulation (TMS) to the limbic cerebellum in the vermis may improve psychiatric disorders such as schizophrenia by upregulating cerebellar modulation of cerebrocerebellar circuits engaged in cognition and emotion [66, 112]. Our preliminary results using intermittent theta burst stimulation in eight patients with refractory 41 Section I: Structural and Functional Neuroanatomy schizophrenia provide support for this contention, resulting in improvements in mood as well as working memory, attention, and visual-spatial ability [129]. Conclusion The recognition that the cerebellum is a key element in the neural substrates of cognition is a relatively recent development, and there are many outstanding questions still to be answered. Among the most interesting is why some patients with cerebellar disorders appear cognitively intact despite a prominent cerebellar motor syndrome. A ready explanation may be that in these patients with focal lesions the non-motor cerebellum is spared. In patients with cerebellar neurodegenerative disease, however, widespread degeneration throughout the cerebellum and a prominent cerebellar motor syndrome may be accompanied by only subtle cognitive impairments. It remains to be shown whether this is a result of such factors as time course and age of onset of the illness, and variations in the neuronal elements of the cerebrocerebellar circuit involved, or whether this is simply a reflection of insufficiently sensitive testing in these cases. We have shown, for example, that language deficits do occur in nondemented patients with degeneration confined to the cerebellum [130]. These and other aspects of the relationship between cerebellum and higher function need further investigation. Acknowledgments Supported in part by R01 MH67980, the Birmingham Foundation, and the MINDLink Foundation. Valuable assistance was provided by Jason MacMore. References 1. Mesulam MM. A cortical network for directed attention and unilateral neglect. Ann Neurol. 1981; 10(4):309–25. 2. Geschwind N. Disconnexion syndromes in animals and man. II. Brain. 1965;88(3):585–644. 3. Geschwind N. Disconnexion syndromes in animals and man. I. Brain. 1965;88(2):237–94. 4. Schmahmann JD, Pandya DN. Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems. Cortex 2008;44(8): 1037–66. 5. Larsell O, Jansen J. The Comparative Anatomy and Histology of the Cerebellum. The Human Cerebellum, 42 Cerebellar Connections, and Cerebellar Cortex. Minneapolis, MN: The University of Minnesota Press; 1972. 6. Schmahmann JD, Doyon J, McDonald D et al. Three-dimensional MRI atlas of the human cerebellum in proportional stereotaxic space. Neuroimage 1999; 10(3 Pt 1):233–60. 7. Schmahmann JD, Doyon J, Toga A, Evans A, Petrides M. MRI Atlas of the Human Cerebellum. San Diego, CA: Academic Press; 2000. 8. Eccles JC, Ito M, Szentágothai J. The Cerebellum as a Neuronal Machine. Berlin: Springer-Verlag; 1967. 9. Oscarsson O. Functional organization of the spinoand cuneocerebellar tracts. Physiol Rev. 1965;45: 495–522. 10. Brodal A, Walberg F. The olivocerebellar projection in the cat studied with the method of retrograde axonal transport of horseradish peroxidase. IV. The projection to the anterior lobe. J Comp Neurol. 1977;172(1): 85–108. 11. Brodal P. The corticopontine projection in the rhesus monkey. Origin and principles of organization. Brain 1978;101(2):251–83. 12. Schmahmann JD, Pandya DN. The cerebrocerebellar system. Int Rev Neurobiol. 1997;41:31–60. 13. Schmahmann JD, Ko R, MacMore J. The human basis pontis: motor syndromes and topographic organization. Brain 2004;127(Pt 6):1269–91. 14. Jansen J, Brodal A. Experimental studies on the intrinsic fibers of the cerebellum. II. The cortico-nuclear projection. J Comp Neurol. 1940;73: 267–321. 15. Haines DE. HRP study of cerebellar corticonuclearnucleocortical topography of the dorsal culminate lobule – lobule V – in a prosimian primate (Galago): with comments on nucleocortical cell types. J Comp Neurol. 1989;282(2):274–92. 16. Middleton FA, Strick PL. Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science 1994;266(5184): 458–61. 17. Schmahmann JD. From movement to thought: anatomic substrates of the cerebellar contribution to cognitive processing. Human Brain Mapp. 1996;4: 174–98. 18. Schmahmann JD, Pandya DN. Anatomic organization of the basilar pontine projections from prefrontal cortices in rhesus monkey. J Neurosci. 1997;17(1): 438–58. 19. Leiner HC, Leiner AL, Dow RS. Does the cerebellum contribute to mental skills? Behav Neurosci. 1986; 100(4):443–54. Chapter 3: Cerebellum 20. Kelly RM, Strick PL. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci. 2003;23(23):8432–44. 36. Exner C, Weniger G, Irle E. Cerebellar lesions in the PICA but not SCA territory impair cognition. Neurology 2004;63(11):2132–5. 21. Schmahmann JD. The cerebrocerebellar system: anatomic substrates of the cerebellar contribution to cognition and emotion. Int Rev Psychiatry. 2001; 13:247–60. 37. Yang Y, Kim JE, Lee JS, Kim S. Akinetic mutism and cognitive-affective syndrome caused by unilateral PICA infarction. J Clin Neurol. 2007;3(4):192–6. 22. Snider RS, Stowell A. Receiving areas of the tactile, auditory, and visual systems in the cerebellum. J Neurophysiol. 1944;7:331–57. 23. Bushara KO, Wheat JM, Khan A et al. Multiple tactile maps in the human cerebellum. Neuroreport 2001; 12(11):2483–6. 24. Grodd W, Hulsmann E, Lotze M, Wildgruber D, Erb M. Sensorimotor mapping of the human cerebellum: fMRI evidence of somatotopic organization. Hum Brain Mapp. 2001;13(2):55–73. 25. Krienen FM, Buckner RL. Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex. 2009;19(10):2485–97. 26. Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 2009;44(2): 489–501. 27. Stoodley CJ, Valera EM, Schmahmann JD. An fMRI case study of functional topography in the human cerebellum. Behav Neurol. 2010; 23(1): 65–79. 28. Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 2010;46(7):831–844. 29. Habas C, Kamdar N, Nguyen D et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci. 2009;29(26):8586–94. 30. Babinski J. De l’asynergie cérébelleuse. Revue Neurologique 1899;7:806–16. 31. Holmes G. The cerebellum of man (Hughlings Jackson memorial lecture). Brain 1939;62:1–30. 32. Schoch B, Dimitrova A, Gizewski ER, Timmann D. Functional localization in the human cerebellum based on voxelwise statistical analysis: a study of 90 patients. Neuroimage 2006;30(1):36–51. 33. Schmahmann JD, MacMore J, Vangel M. Cerebellar stroke without motor deficit: clinical evidence for motor and non-motor domains within the human cerebellum. Neuroscience 2009;162(3):852–61. 34. Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain 1998;121(Pt 4): 561–79. 35. Paulus KS, Magnano I, Conti M et al. Pure post-stroke cerebellar cognitive affective syndrome: a case report. Neurol Sci. 2004;25(4):220–4. 38. Baillieux H, De Smet HJ, Dobbeleir A et al. Cognitive and affective disturbances following focal cerebellar damage in adults: A neuropsychological and SPECT study. Cortex 2010;46(7):869–79. 39. Marien P, Baillieux H, De Smet HJ et al. Cognitive, linguistic and affective disturbances following a right superior cerebellar artery infarction: a case study. Cortex 2009;45(4):527–36. 40. Akil H, Statham PF, Gotz M, Bramley P, Whittle IR. Adult cerebellar mutism and cognitive-affective syndrome caused by cystic hemangioblastoma. Acta Neurochir (Wien). 2006;148(5):597–8. 41. van Harskamp NJ, Rudge P, Cipolotti L. Cognitive and social impairments in patients with superficial siderosis. Brain 2005;128(Pt 5):1082–92. 42. Uttner I, Tumani H, Arnim C, Brettschneider J. Cognitive impairment in superficial siderosis of the central nervous system: a case report. Cerebellum 2009;8(1):61–3. 43. Neau JP, Arroyo-Anllo E, Bonnaud V, Ingrand P, Gil R. Neuropsychological disturbances in cerebellar infarcts. Acta Neurol Scand. 2000;102(6):363–70. 44. Malm J, Kristensen B, Karlsson T et al. Cognitive impairment in young adults with infratentorial infarcts. Neurology 1998;51(2):433–40. 45. Levisohn L, Cronin-Golomb A, Schmahmann JD. Neuropsychological consequences of cerebellar tumour resection in children: cerebellar cognitive affective syndrome in a paediatric population. Brain 2000;123(Pt 5):1041–50. 46. Pollack IF, Polinko P, Albright AL, Towbin R, Fitz C. Mutism and pseudobulbar symptoms after resection of posterior fossa tumors in children: incidence and pathophysiology. Neurosurgery 1995;37(5):885–93. 47. Sadeh M, Cohen I. Transient loss of speech after removal of posterior fossa tumors – one aspect of a larger neuropsychological entity: the cerebellar cognitive affective syndrome. Pediatr Hematol Oncol. 2001;18(7):423–6. 48. Rekate HL, Grubb RL, Aram DM, Hahn JF, Ratcheson RA. Muteness of cerebellar origin. Arch Neurol. 1985;42(7):697–8. 49. Catsman-Berrevoets CE, van Dongen HR, Zwetsloot CP. Transient loss of speech followed by dysarthria after removal of posterior fossa tumour. Dev Med Child Neurol. 1992;34(12):1102–9. 43 Section I: Structural and Functional Neuroanatomy 50. Wisoff JH, Epstein FJ. Pseudobulbar palsy after posterior fossa operation in children. Neurosurgery 1984;15(5):707–9. 51. Kingma A, Mooij JJ, Metzemaekers JD, Leeuw JA. Transient mutism and speech disorders after posterior fossa surgery in children with brain tumours. Acta Neurochir. (Wien) 1994;131(1–2):74–9. 52. van Dongen HR, Catsman-Berrevoets CE, van Mourik M. The syndrome of ‘cerebellar’ mutism and subsequent dysarthria. Neurology 1994;44(11): 2040–6. 53. Wells EM, Walsh KS, Khademian ZP, Keating RF, Packer RJ. The cerebellar mutism syndrome and its relation to cerebellar cognitive function and the cerebellar cognitive affective disorder. Dev Disabil Res Rev. 2008;14(3):221–8. 54. Riva D, Giorgi C. The neurodevelopmental price of survival in children with malignant brain tumours. Childs Nerv Syst. 2000;16(10–11):751–4. 55. Karatekin C, Lazareff JA, Asarnow RF. Relevance of the cerebellar hemispheres for executive functions. Pediatr Neurol. 2000;22(2):106–12. 64. Steinlin M, Imfeld S, Zulauf P et al. Neuropsychological long-term sequelae after posterior fossa tumour resection during childhood. Brain 2003;126(Pt 9):1998–2008. 65. Baillieux H, De Smet HJ, Lesage G et al. Neurobehavioral alterations in an adolescent following posterior fossa tumor resection. Cerebellum 2006;5(4): 289–95. 66. Schmahmann JD, Weilburg JB, Sherman JC. The neuropsychiatry of the cerebellum – insights from the clinic. Cerebellum 2007;6(3):254–67. 67. Maryniak A, Roszkowski M. Cognitive and affective disturbances in children after surgical treatment of cerebellar tumors. Neurol Neurochir Pol. 2005;39(3): 202–6. 68. Turkel SB, Brumm VL, Mitchell WG, Tavare CJ. Mood and behavioral dysfunction with opsoclonusmyoclonus ataxia. J Neuropsychiatry Clin Neurosci. 2006;18(2):239–41. 56. Grill J, Viguier D, Kieffer V et al. Critical risk factors for intellectual impairment in children with posterior fossa tumors: the role of cerebellar damage. J Neurosurg. 2004;101(2 Suppl):152–8. 69. Ohara S, Iijima N, Hayashida K, Oide T, Katai S. Autopsy case of opsoclonus-myoclonus-ataxia and cerebellar cognitive affective syndrome associated with small cell carcinoma of the lung. Mov Disord. 2007; 22(9):1320–4. 57. Turkel SB, Shu Chen L, Nelson MD et al. Case series: acute mood symptoms associated with posterior fossa lesions in children. J Neuropsychiatry Clin Neurosci. 2004;16(4):443–5. 70. Heath RG, Franklin DE, Shraberg D. Gross pathology of the cerebellum in patients diagnosed and treated as functional psychiatric disorders. J Nerv Ment Dis. 1979;167(10):585–92. 58. Berger A, Sadeh M, Tzur G et al. Task switching after cerebellar damage. Neuropsychology 2005;19(3): 362–70. 71. Heath RG, Harper JW. Ascending projections of the cerebellar fastigial nucleus to the hippocampus, amygdala, and other temporal lobe sites: evoked potential and histological studies in monkeys and cats. Exp Neurol. 1974;45(2):268–87. 59. Ronning C, Sundet K, Due-Tonnessen B, Lundar T, Helseth E. Persistent cognitive dysfunction secondary to cerebellar injury in patients treated for posterior fossa tumors in childhood. Pediatr Neurosurg. 2005;41(1):15–21. 60. Vaquero E, Gomez CM, Quintero EA, Gonzalez-Rosa JJ, Marquez J. Differential prefrontal-like deficit in children after cerebellar astrocytoma and medulloblastoma tumor. Behav Brain Funct. 2008;4:18. 61. Richter S, Dimitrova A, Maschke M et al. Degree of cerebellar ataxia correlates with three-dimensional MRI-based cerebellar volume in pure cerebellar degeneration. Eur Neurol. 2005;54(1):23–7. 62. Aarsen FK, Van Dongen HR, Paquier PF, Van Mourik M, Catsman-Berrevoets CE. Long-term sequelae in children after cerebellar astrocytoma surgery. Neurology 2004;62(8):1311–16. 63. Riva D, Giorgi C. The cerebellum contributes to higher functions during development: evidence from a series 44 of children surgically treated for posterior fossa tumours. Brain 2000;123(Pt 5):1051–61. 72. Nashold BS, Jr., Slaughter DG. Effects of stimulating or destroying the deep cerebellar regions in man. J Neurosurg. 1969;31(2):172–86. 73. Heath RG. Modulation of emotion with a brain pacemaker. Treatment for intractable psychiatric illness. J Nerv Ment Dis. 1977;165(5):300–17. 74. Gondim FA, Parks BJ, Cruz-Flores S. “Fou rire prodromique” as the presentation of pontine ischaemia secondary to vertebrobasilar stenosis. J Neurol Neurosurg Psychiatry 2001;71(6):802–4. 75. Parvizi J, Anderson SW, Martin CO, Damasio H, Damasio AR. Pathological laughter and crying: a link to the cerebellum. Brain 2001;124(Pt 9): 1708–19. 76. Dimova PS, Bojinova VS, Milanov IG. Transient mutism and pathologic laughter in the course of cerebellitis. Pediatr Neurol. 2009;41(1):49–52. Chapter 3: Cerebellum 77. Tei H, Sakamoto Y. Pontine infarction due to basilar artery stenosis presenting as pathological laughter. Neuroradiology 1997;39(3):190–1. 78. Parvizi J, Joseph J, Press DZ, Schmahmann JD. Pathological laughter and crying in patients with multiple system atrophy-cerebellar type. Mov Disord. 2007;22(6):798–803. 79. Schmahmann JD. Dysmetria of thought. Clinical consequences of cerebellar dysfunction on cognition and affect. Trends Cogn Sci. 1998;2:362–70. 80. Holmes G. The symptoms of acute cerebellar injuries due to gunshot wounds. Brain 1917;40: 461–535. 81. Brunet E, Sarfati Y, Hardy-Bayle MC, Decety J. A PET investigation of the attribution of intentions with a nonverbal task. Neuroimage 2000;11(2):157–66. 82. Calarge C, Andreasen NC, O’Leary DS. Visualizing how one brain understands another: a PET study of theory of mind. Am J Psychiatry 2003;160(11): 1954–64. 92. Bauman ML, Kemper TL. Neuroanatomic observations of the brain in autism: a review and future directions. Int J Dev Neurosci. 2005;23(2–3): 183–7. 93. Courchesne E, Yeung-Courchesne R, Press GA, Hesselink JR, Jernigan TL. Hypoplasia of cerebellar vermal lobules VI and VII in autism. N Engl J Med. 1988;318(21):1349–54. 94. Penn HE. Neurobiological correlates of autism: a review of recent research. Child Neuropsychol. 2006;12(1):57–79. 95. Limperopoulos C, Bassan H, Gauvreau K et al. Does cerebellar injury in premature infants contribute to the high prevalence of long-term cognitive, learning, and behavioral disability in survivors? Pediatrics 2007;120(3):584–93. 96. Anderson CM, Teicher MH, Polcari A, Renshaw PF. Abnormal T2 relaxation time in the cerebellar vermis of adults sexually abused in childhood: potential role of the vermis in stress-enhanced risk for drug abuse. Psychoneuroendocrinology 2002;27(1–2):231–44. 83. Weilburg JB, Bear DM, Sachs G. Three patients with concomitant panic attacks and seizure disorder: possible clues to the neurology of anxiety. Am J Psychiatry 1987;144(8):1053–6. 97. Anderson CM, Maas LC, Frederick B et al. Cerebellar vermis involvement in cocaine-related behaviors. Neuropsychopharmacology 2006;31(6):1318–26. 84. Tiemeier H, Lenroot RK, Greenstein DK et al. Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study. Neuroimage 2010;49(1):63–70. 98. Berquin PC, Giedd JN, Jacobsen LK et al. Cerebellum in attention-deficit hyperactivity disorder: a morphometric MRI study. Neurology 1998;50(4): 1087–93. 85. Limperopoulos C, Soul JS, Gauvreau K et al. Late gestation cerebellar growth is rapid and impeded by premature birth. Pediatrics 2005;115(3):688–95. 99. Mostofsky SH, Reiss AL, Lockhart P, Denckla MB. Evaluation of cerebellar size in attention-deficit hyperactivity disorder. J Child Neurol. 1998;13(9): 434–9. 86. Allin M, Matsumoto H, Santhouse AM et al. Cognitive and motor function and the size of the cerebellum in adolescents born very pre-term. Brain 2001;124(Pt 1): 60–6. 87. Chheda M, Sherman J, Schmahmann JD. Neurologic, psychiatric and cognitive manifestations in cerebellar agenesis. Neurology 2002;58(Suppl. 3):356. 88. Gross-Tsur V, Ben-Bashat D, Shalev RS, Levav M, Sira LB. Evidence of a developmental cerebellocerebral disorder. Neuropsychologia 2006;44(12): 2569–72. 89. Tavano A, Grasso R, Gagliardi C et al. Disorders of cognitive and affective development in cerebellar malformations. Brain 2007;130(Pt 10):2646–60. 90. Steinlin M, Zangger B, Boltshauser E. Non-progressive congenital ataxia with or without cerebellar hypoplasia: a review of 34 subjects. Dev Med Child Neurol. 1998;40(3):148–54. 91. Wassmer E, Davies P, Whitehouse WP, Green SH. Clinical spectrum associated with cerebellar hypoplasia. Pediatr Neurol. 2003;28(5):347–51. 100. Castellanos FX, Giedd JN, Berquin PC et al. Quantitative brain magnetic resonance imaging in girls with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 2001;58(3):289–95. 101. Dow RS, Moruzzi G. The Physiology and Pathology of the Cerebellum. Minneapolis, MN: University of Minnesota Press; 1958. 102. Limperopoulos C, Soul JS, Haidar H et al. Impaired trophic interactions between the cerebellum and the cerebrum among preterm infants. Pediatrics 2005;116(4):844–50. 103. Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford: Oxford University Press; 2006. 104. Cooper IS, Riklan M, Amin I, Cullinan T. A long-term follow-up study of cerebellar stimulation for the control of epilepsy. In Cooper IS, editor. Cerebellar Stimulation in Man. New York, NY: Raven Press; 1978, pp. 19–38. 105. Watson PJ. Nonmotor functions of the cerebellum. Psychol Bull. 1978;85(5):944–67. 45 Section I: Structural and Functional Neuroanatomy 106. Frick RB. The ego and the vestibulocerebellar system: some theoretical perspectives. Psychoanal Q. 1982; 51(1):93–122. 118. Ivry R. Cerebellar timing systems. In Schmahmann JD, editor. Cerebellum and Cognition. San Diego, CA: Academic Press; 1997, pp. 555–73. 107. Snider SR. Cerebellar pathology in schizophrenia – cause or consequence? Neurosci Biobehav Rev. 1982; 6(1):47–53. 119. Bower JM. Control of sensory data acquisition. In Schmahmann JD, editor. Cerebellum and Cognition. San Diego, CA: Academic Press; 1997, pp. 489–513. 108. Schmahmann J. An emerging concept. The cerebellar contribution to higher function. Arch Neurol. 1991; 48(11):1178–87. 120. Molinari M, Chiricozzi FR, Clausi S et al. Cerebellum and detection of sequences, from perception to cognition. Cerebellum 2008;7(4):611–15. 109. Schmahmann JD. The role of the cerebellum in affect and psychosis. J Neurolinguistics 2000;13: 189–214. 121. Ito M. Cerebellar microcomplexes. In Schmahmann JD, editor. Cerebellum and Cognition. San Diego, CA: Academic Press; 1997, pp. 475–89. 110. Konarski JZ, McIntyre RS, Grupp LA, Kennedy SH. Is the cerebellum relevant in the circuitry of neuropsychiatric disorders? J Psychiatry Neurosci. 2005;30(3):178–86. 122. Andreasen NC, Paradiso S, O’Leary DS. “Cognitive dysmetria” as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? Schizophr Bull. 1998;24(2):203–18. 111. Picard H, Amado I, Mouchet-Mages S, Olie JP, Krebs MO. The role of the cerebellum in schizophrenia: an update of clinical, cognitive, and functional evidences. Schizophr Bull. 2008;34(1):155–72. 123. Frank J, Levinson H. Dysmetric dyslexia and dyspraxia. Hypothesis and study. J Am Acad Child Psychiatry. 1973;12(4):690–701. 112. Hoppenbrouwers SS, Schutter DJ, Fitzgerald PB, Chen R, Daskalakis ZJ. The role of the cerebellum in the pathophysiology and treatment of neuropsychiatric disorders: a review. Brain Res Rev. 2008;59(1): 185–200. 113. Wolf U, Rapoport MJ, Schweizer TA. Evaluating the affective component of the cerebellar cognitive affective syndrome. J Neuropsychiatry Clin Neurosci. 2009;21(3):245–53. 114. Snider RS. Recent contributions to the anatomy and physiology of the cerebellum. Arch Neurol Psychiat. 1950;64(2):196–219. 115. Reis DJ, Doba N, Nathan MA. Predatory attack, grooming, and consummatory behaviors evoked by electrical stimulation of cat cerebellar nuclei. Science 1973;182(114):845–7. 116. Berman AF, Berman D, Prescott JW. The effect of cerebellar lesions on emotional behavior in the rhesus monkey. In Cooper IS, Riklan M, Snider RS, editors. The Cerebellum, Epilepsy and Behavior. New York, NY: Plenum Press; 1974, pp. 277–84. 117. Mariotti C, Alpini D, Fancellu R et al. Spinocerebellar ataxia type 17 (SCA17): oculomotor phenotype and clinical characterization of 15 Italian patients. J Neurol. 2007;254(11):1538–46. 46 124. Levinson HN. The cerebellar-vestibular basis of learning disabilities in children, adolescents and adults: hypothesis and study. Percept Mot Skills 1988; 67(3):983–1006. 125. Nicolson R, Fawcett AJ, Dean P. Dyslexia, development and the cerebellum. Trends Neurosci. 2001;24(9): 515–16. 126. Schmahmann JD. Therapeutic and research implications. In Schmahmann JD, editor. Cerebellum and Cognition. San Diego, CA: Academic Press; 1997, pp. 637–47. 127. Reynolds D, Nicolson RI, Hambly H. Evaluation of an exercise-based treatment for children with reading difficulties. Dyslexia 2003;9(1):48–71; discussion 46–7. 128. Riklan M, Marisak I, Cooper IS. Psychological studies of chronic cerebellar stimulation in man. In Cooper IS, Riklan M, Snider RS, editors. The Cerebellum, Epilepsy and Behavior. New York, NY: Plenum Press; 1974, pp. 285–342. 129. Demirtas-Tatlidede A, Freitas C, Cromer J et al. Safety and proof of principle study of cerebellar vermal theta burst stimulation in refractory schizophrenia. Schizophr Res. 2010;124:91–100. 130. Stoodley CJ, Schmahmann JD. The cerebellum and language: evidence from patients with cerebellar degeneration. Brain Lang. 2009;110(3):149–53. Section I Structural and Functional Neuroanatomy Chapter White matter 4 Christopher M. Filley The white matter of the brain has recently assumed a prominent role in the study of brain–behavior relationships. Although recognized by neuroanatomists for centuries, and long understood by clinicians as crucial for elemental neurologic function, white matter has come to be appreciated as a major contributor to higher function by virtue of its key position within the distributed neural networks that subserve all aspects of cognition, emotion, and behavior [1–3]. Clinical and basic neuroscience advances have offered new insights into the structure and function of myelinated systems that promise to enhance our understanding of the normal brain and the many disorders that may disturb its operations and produce neurobehavioral dysfunction. This chapter presents a summary of current knowledge in this field and its implications for the future of Behavioral Neurology & Neuropsychiatry (BN&NP). Historical background White matter was first depicted as a neuroanatomic structure by the great anatomist Andreas Vesalius (1514–1564) in 1543 [1]. In his monumental work De Humani Corporis Fabrica, a treatise that still stands as a landmark in the history of science, Vesalius clearly demarcated the white matter from the gray in drawings of the brain made from cadavers. In the spirit of the Renaissance – the first era in which dissection of the human body was fully explored – the drawings of this book display impressive artistic as well as scientific merit, reflecting the influence of the great painter Titian on Vesalius and his co-workers [1]. Progress on the function of white matter was slower to come, but in the seventeenth century Thomas Willis (1621–1675) proposed that white matter areas including the corpus callosum elaborated sensory signals into perceptions destined to be stored in the cerebral cortex [1]. Anticipating modern systems neuroscience, Franz Joseph Gall (1758–1828), who was a highly competent neuroanatomist despite his unfortunate forays into phrenology, established in the early 1800s that white matter was comprised of fascicles that connected cortical gray matter areas devoted to mental activity [1]. Later in the nineteenth century, the neurologist Jean-Martin Charcot (1825–1893) gained well-deserved fame for advancing the understanding of white matter and its functions through the study of multiple sclerosis (MS) and similar diseases [1]. In the mid-twentieth century, Norman Geschwind (1926–1984) emphasized the importance of white matter tracts in behavioral neurology in a classic treatise on disconnection syndromes that inspired a generation of clinicians and investigators [4, 5]. Geschwind’s Harvard Medical School associate M-Marsel Mesulam then advanced the influential notion of distributed neural networks by which cognitive operations are organized, leading to the concept that higher functions are subserved by gray and white matter structures integrated within widespread regions of the brain [6]. Today, as the twenty-first century begins, the understanding of white matter structure and function is rapidly advancing as increasingly sophisticated methods of study build on the steady accomplishments of the past [3, 7]. Neuroanatomy The anatomy of white matter is fundamental to understanding its role in brain–behavior relationships, and the bridging of structure and function is the principal goal of current investigations. Despite the bewildering array of fiber tracts coursing throughout the Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 47 Section I: Structural and Functional Neuroanatomy brain, progress is being made in sorting out the location and connectivity of these myelinated systems, as well as their functional affiliations. White matter makes up nearly one-half the brain volume [1], and it has been estimated that 135,000 km of myelinated fibers can be found interconnecting the roughly 100 billion neurons of the brain whose cell bodies reside in cortical and subcortical gray matter [8]. Organized in tracts, fasciculi, funiculi, lemnisci, peduncles, and bundles, white matter travels within and between the cerebral hemispheres and links these regions with the brainstem, cerebellum, and spinal cord [9]. Convenient distinctions between the projection, commissural, and association tracts are familiar from neuroanatomy textbooks, and it is recognized that the latter two categories are most important for the mediation of higher functions [9]. However, with few exceptions, the role of individual tracts is not well characterized in BN&NP, and even the identification of most tracts is impossible in routine clinical practice. Radiologists as well as clinicians tend to consider the cerebral white matter as an undifferentiated whole because the homogeneity of white matter on clinical imaging prohibits visualization of all but the largest tracts. Descriptive terms such the centrum semiovale and the corona radiata are used for clinical purposes, but reflect limited understanding of the tracts lying within the amorphous areas implied by these terms. The microstructure of brain white matter plays a complementary, and equally important, role in the organization of cognition and emotion. The defining neuroanatomical feature is myelin, the fatty insulation that invests most axons in the brain and dramatically increases neuronal conduction velocity [10]. Myelin, a complex mixture of about 70% lipid and 30% protein, encircles axons in a circumferential manner after being laid down by oligodendrocytes, glial cells in the brain responsible for myelination [11]. The white hue of the brain sectioned at autopsy in fact derives from myelin. At the neuronal level, myelin forms a concentric sheath along the length of the axon, leaving bare small unmyelinated segments called nodes of Ranvier [10]. These nodes permit saltatory conduction, by which the action potential “jumps” from one node to the next as it rapidly traverses the length of the axon to the terminal dendrites and synapses [10]. Traditional teaching on the anatomy of white matter systems relevant to brain–behavior relationships is founded on the knowledge of tracts established with gross brain dissection, myelin-staining, and 48 degeneration techniques developed in the centuries after the seminal observations of Vesalius [12]. A standard current formulation is that of Nolte [9], in which tracts within the association, commissural, and projection systems are distinguished (Table 4.1). Best understood is the corpus callosum, by far the largest single tract in the brain and easily recognized as the primary interhemispheric connection. The association tracts are divided into the short association fibers, also known as uncinate or U fibers, linking adjacent cortical gyri, and the long association tracts that connect intrahemispheric regions: the superior occipitofrontal fasciculus, the inferior occipitofrontal fasciculus, the arcuate fasciculus (superior longitudinal fasciculus), the cingulum bundle, and the uncinate fasciculus [9]. While this classification can still be productively used, advances in neuroimaging and experimental neuroanatomy are revising our understanding of the complexity of white matter systems. The burgeoning field of tractography with the use of diffusion tensor imaging (DTI) [13] is moving swiftly to clarify and expand knowledge of white matter tracts, and recent investigations of the rhesus monkey brain using autoradiography [12, 14] have introduced markedly different conceptualizations of previously accepted classifications. Table 4.1 lists three classifications of cerebral white matter systems based on the knowledge that is rapidly accumulating from these advances. Much uncertainty remains, as different tracts are apparent from the various methods employed, and the correspondence between monkey and human white matter systems is not fully established. Based on detailed studies of the rhesus monkey brain, for example, Schmahmann and colleagues have raised the possibility that the arcuate fasciculus may not in fact be related to language, potentially overturning neurologic teaching of the past century [12, 14]. As the field progresses, it can be anticipated that controversies such as this will be addressed and increasing consensus gained on the anatomy of white matter tracts by the combined application of complementary techniques. In parallel with this process, the functional affiliations of these tracts are likely to become more evident as well. Neurophysiology The major function of white matter can be conceived as the transfer of information within the nervous system, in contrast to the processing of information that Chapter 4: White matter Table 4.1. Evolving formulations of cerebral white matter pathways. The white matter pathways identified by anatomical examination of the human brain (gross dissection, myelin staining of human brain, lesion degeneration) as described in [9] are listed in the column on the left; those identified with diffusion tensor imaging of human brain, as described by [13], are listed in the center column; and those identified with autoradiography and diffusion spectrum imaging of rhesus monkey brain [12, 14] are listed in the column on the right. These pathways are organized in the table as association, commissural, and projection types. Anatomical examination of human brain Diffusion tensor imaging of human brain Autoradiography and diffusion spectrum imaging of rhesus monkey brain Short association (U) fibers Short association (U) fibers Local (U) fiber system Neighborhood fiber system Arcuate fasciculus Superior longitudinal (arcuate) fasciculus Arcuate fasciculus Superior longitudinal fasciculus 1 Superior longitudinal fasciculus 2 Superior longitudinal fasciculus 3 Middle longitudinal fasciculus Extreme capsule Association pathways Superior occipitofrontal fasciculus Superior fronto-occipital (subcallosal) fasciculus Fronto-occipital fasciculus Inferior occipitofrontal fasciculus Inferior fronto-occipital fasciculus Inferior longitudinal fasciculus Inferior longitudinal fasciculus Uncinate fasciculus Cingulum Uncinate fasciculus Cingulum Uncinate fasciculus Cingulum bundle Striatal pathways External capsule Subcallosal fasciculus of Muratoff (Muratoff bundle) Corpus callosum Corpus callosum Corpus callosum Anterior commissure Anterior commissure Anterior commissure Hippocampal commissure Hippocampal commissure Habenular commissure Posterior commissure Tectal commissure Hippocampal commissure Internal capsule Internal capsule Internal capsule Optic radiation Optic radiation Optic radiation (within the sagittal stratum) Thalamocortical radiation Acoustic radiation Fornix Thalamic peduncles Commissural pathways Projection pathways is the responsibility of gray matter [15]. The myelination of white matter enables the rapid transfer of information within distributed neuronal networks that subserve all higher functions of the brain [1–3, 15]. By virtue of the myelin sheath and critically placed nodes of Ranvier permitting saltatory conduction, myelinated fibers conduct impulses up to 100 times faster that unmyelinated fibers, vastly increasing the speed of information transfer. It is not surprising, therefore, that one of the most salient, albeit non-specific, neurobehavioral features of white matter disorders is cognitive slowing [1, 2, 15]. Myelination also enhances the timing and synchronization of neuronal arrays that are thus enabled to engage in highly integrative operations such as executive function, sustained attention, and working memory. Myelinated systems confer efficiency to the operations of neural networks so that the cognitive processing within cortical and subcortical gray matter structures can be optimized. In clinical practice, neurophysiological assessment using short latency evoked potentials has long been used for quantitating the integrity of primary sensory tracts, but the more challenging study of higher function has been limited to the research arena, where longer latency potentials such as the P50 and P300 have been employed to investigate cognitive systems [1]. 49 Section I: Structural and Functional Neuroanatomy White matter makes another unique contribution to function of large-scale distributed neural networks. The faster electrical conduction enabled by myelinated systems appears to have evolutionary importance. Myelin, a recent development in phylogeny, is almost exclusively confined to the nervous systems of vertebrate species [10]. Recent findings indicate a selective increase in prefrontal white matter volume in humans compared with non-human primates, whereas gray matter volume is not significantly different [16]. These observations suggest that the myelin within white matter has a special role not only in enhancing neuronal conduction velocity within the brain as a whole, but specifically within frontal networks subserving the most highly evolved human behaviors. Development and aging While the expansion and then reduction of cortical gray matter has long been assumed as crucial in normal cognitive development and aging, there is increasing recent evidence for a role of white matter in these processes. Since the early twentieth century, white matter systems in the forebrain have been known to continue developing into adolescence, with myelination generally regarded as an index of cerebral maturation [1]. More recent data suggest that, whereas gray matter development peaks at age four, myelination continues well into midlife [17] and even the sixth decade [18]. The implications of this protracted developmental course are notable, as it may be that cerebral myelination plays an essential role in normal cognitive and behavioral maturation, and conversely, white matter disorders during the multi-decade process of development may exert a profound effect on the transition to adulthood [1]. In the later decades of life, conversely, considerable support exists for a decline of white matter integrity [1]. The contribution of cortical gray matter loss to cognitive aging may have been overestimated in the past, as estimates of cortical neuronal loss were likely excessive, and in fact loss of white matter volume may actually be more significant [1, 17, 19]. Thus, whereas the dementia of Alzheimer’s disease (AD) features cortical volume loss in medial temporal and other areas, normal aging is characterized by prominent loss of white matter volume, especially in anterior cerebral regions [20, 21]. These structural differences have clinical correlates that implicate the oft-noted differences between the “cortical” cognitive features of AD and the 50 contrasting cognitive changes of normal aging: in AD, the familiar syndromes of amnesia, aphasia, apraxia, and agnosia result from widespread cortical involvement, whereas in the latter, slowed cognition, impaired vigilance, and executive dysfunction may represent the effects of progressive white matter decline most evident in the frontal lobes [1, 20–22]. Neuroimaging The development of magnetic resonance imaging (MRI) in the early 1980s has proven to be a pivotal event in the understanding of white matter and its impact on higher brain function [1]. As a clinical tool, MRI offered a major advance over computed tomography; not only did MRI improve the visualization of white matter in known myelin diseases such as MS, it enabled the identification of previously unrecognized white matter disorders such as the syndrome of dementia, ataxia, and other neurologic deficits in longterm solvent abusers who develop toluene leukoencephalopathy (TL) [1, 2]. Perhaps the most familiar discovery made possible by MRI was the unexpectedly high prevalence of cerebral white matter hyperintensities (Figure 4.1), seen to some degree in approximately 95% of community-dwelling older people [23]. The origin and consequences of these abnormalities remain incompletely understood, but many believe they represent ischemic white matter damage, and that Binswanger’s disease (BD) or subcortical ischemic vascular dementia (SIVD) can result if the lesion burden becomes sufficiently severe [1]. White matter hyperintensities have thus stimulated much interest in the possibility that millions of people have subtle brain lesions that may potentially be addressed by medical intervention before any clinical manifestations appear. As MRI grew more sophisticated, additional techniques were introduced to improve still further the neuroimaging of white matter tracts. One such technique is magnetic resonance spectroscopy (MRS), which enables measurement of the chemical composition of white matter to assess the integrity of axons and myelin [1]. MRS has the potential to be “noninvasive biopsy” of white matter that may significantly improve diagnosis since it can detect changes in the so-called “normal-appearing white matter” [1]. Another method attracting much attention is DTI, which allows the characterization of individual tracts – tractography – with unprecedented detail (Figure 4.2). Advances in understanding the connectivity of white Chapter 4: White matter Figure 4.2. Diffusion tensor image showing the arcuate fasciculus. Note the additional fascicle extending to Geschwind’s territory in the inferior parietal cortex, not recognized by traditional neuroanatomic investigation. Reproduced from Catani M, Jones DK, Ffytche DH. Perisylvian language networks of the human brain. Ann Neurol. 2005;57(1):8–16, with permission from John Wiley & Sons, Inc. This figure is presented in color in the color plate section. Figure 4.1. Brain MRI scan (FLAIR sequence) showing periventricular white matter hyperintensities in an older man with hypertension and cognitive impairment. matter appear most promising with DTI, which brings a new level of precision to the study of disconnection syndromes in a host of clinical disorders [24]. Combined with functional neuroimaging techniques such as positron emission tomography (PET) and functional MRI (fMRI), DTI promises to help define the distributed neural networks subserving higher function by identifying the white matter components of these networks that interact with cortical and subcortical gray matter structures [15]. Disorders of white matter Well over 100 white matter disorders of the brain have been described by neurologists, neuropathologists, and most recently, neuroradiologists [1]. Many of these disorders are classic neurologic diseases such as MS, but some, such as TL, have only become apparent with the routine application of MRI to clinical populations. Remarkably, despite the traditional emphasis on non-cognitive aspects of white matter disorders such as impaired vision, weakness, spasticity, ataxia, gait disorder, and sensory loss, close reading of clinical reports describing patients with cerebral white matter disorders reveals evidence of cognitive or emotional dysfunction in every one of these afflictions [1]. Ten categories of white matter disorder can be considered: genetic, demyelinative, infectious, inflammatory, toxic, metabolic, vascular, traumatic, neoplastic, and hydrocephalic [1]. Table 4.2 provides a listing of entities within these categories; additional entries are likely as knowledge accumulates. A brief consideration of representative disorders will serve to highlight the diversity of white matter pathology, and standard neurology textbooks can be consulted for more information. Metachromatic leukodystrophy (MLD), an autosomal recessive leukodystrophy that typically causes a devastating and rapidly fatal illness in infants or, in older children and adults, psychosis or dementia in association with extensive cerebral dysmyelination, is the prototype genetic white matter disorder [25]. MS is the most familiar demyelinative disorder, the neurobehavioral effects of which have been known since the time of Charcot but are now better appreciated as correlating with cerebral plaque burden and associated cerebral atrophy [26]. Among the infectious diseases, the acquired immumodeficiency syndrome dementia complex involves prominent leukoencephalopathy as well as subcortical gray matter involvement [27]. Systemic lupus erythematosus illustrates the growing recognition of the impact of inflammatory white matter disease on cognition [28]. TL is the best example of white matter neurotoxicity producing impaired 51 Section I: Structural and Functional Neuroanatomy Table 4.2. Cerebral white matter disorders. 52 Genetic Leukodystrophies (e.g., metachromatic leukodystrophy) Aminoacidurias (e.g., phenylketonuria) Phakomatoses (e.g., neurofibromatosis) Mucopolysaccharidoses Muscular dystrophy Callosal agenesis Demyelinative Multiple sclerosis Acute disseminated encephalomyelitis Acute hemorrhagic encephalomyelitis Schilder’s disease Marburg’s disease Balo’s concentric sclerosis Infectious HIV-associated dementia Progressive multifocal leukoencephalopathy Subacute sclerosing panencephalitis Progressive rubella panencephalitis Varicella zoster encephalitis Cytomegalovirus encephalitis Lyme encephalopathy Inflammatory Systemic lupus erythematosus Behcet’s disease Sjögren’s syndrome Wegener’s granulomatosis Temporal arteritis Polyarteritis nodosa Scleroderma Isolated angiitis of the central nervous system Sarcoidosis Toxic Cranial irradiation Therapeutic drugs Drugs of abuse (e.g., toluene) Environmental toxins (e.g., carbon monoxide) Metabolic Vitamin B12 (cobalamin) deficiency Folate deficiency Central and/or extra-pontine myelinolysis Hypoxia Hypertensive encephalopathy Eclampsia High altitude cerebral edema Vascular Binswanger’s disease CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) Leukoaraiosis Cerebral amyloid angiopathy White matter disease of prematurity Migraine Traumatic Traumatic brain injury Shaken baby syndrome Corpus callosotomy Neoplastic Gliomatosis cerebri Diffusely infiltrative gliomas Lymphomatosis cerebri Focal white matter tumors Hydrocephalic Early hydrocephalus Normal pressure hydrocephalus cognition [29], and one of the most convincing examples of white matter dementia (see below). Among the metabolic disorders, vitamin B12 (cobalamin) deficiency has been documented to produce reversible dementia related to white matter involvement [30]. White matter ischemia and infarcts lead to BD, the form of SIVD in which cerebral white matter damage gradually erodes cognitive function [31]; the vascular disease cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is similar neuropathologically and clinically [32]. Among the many neuropathological insults of traumatic brain injury (TBI), diffuse axonal injury (DAI) within the white matter is arguably the most common and devastating [33]. Neoplastic white matter results from the propensity of gliomas to originate and spread in myelinated regions of the brain, and gliomatosis cerebri vividly illustrates these features [34]. Finally, hydrocephalus, either in children or in adults, exerts its most prominent effects on white matter; in older adults, dementia as a result of normal pressure hydrocephalus (NPH) is a common sequel [35]. Many other examples can be found, including diseases, injuries, and intoxications occurring at all ages and presenting in any medical or surgical setting [1]. Neurobehavioral syndromes While the diversity of white matter disorders is surely impressive, common clinical patterns of cognitive and emotional dysfunction can nevertheless be discerned that cut across all etiological and neuropathological categories. From a comprehensive consideration of these unifying phenomena, it is possible to establish a coherent classification of neurobehavioral syndromes – a behavioral neurology of white matter [1–3]. Three major groups of syndromes emerge that capture the variety of cognitive and emotional impairments related to cerebral white matter disorders: focal neurobehavioral syndromes, white matter dementia, and neuropsychiatric syndromes [1–3]. Focal syndromes Focal neurobehavioral syndromes are well known to subspecialists in BN&NP from the classic literature beginning in late nineteenth century Europe that produced the first modern descriptions of aphasia, apraxia, agnosia, and related syndromes [1]. In 1965, Geschwind reintroduced these syndromes in the context of cerebral disconnection and revitalized Chapter 4: White matter Table 4.3. Focal neurobehavioral syndromes. Table 4.4. The profile of white matter dementia. Amnesia Cognitive slowing Aphasia Broca’s Wernicke’s Conduction Global Transcortical motor Transcortical sensory Anomic Mixed transcortical Executive dysfunction Apraxia Ideomotor Callosal Normal extrapyramidal function Alexia Pure alexia Alexia with agraphia Developmental dyslexia Gerstmann’s syndrome Agnosia Visual Auditory Neglect Visuospatial dysfunction Akinetic mutism Executive dysfunction Callosal disconnection behavioral neurology after decades of relative neglect [4, 5]. In some cases, these disconnections involve lesions in gray matter regions, but most imply white matter pathology, and examples of stroke, neoplasia, and demyelinative plaques interrupting neural networks to produce focal syndromes constitute a growing literature (Table 4.3). Today these syndromes remain foundational to behavioral neurology and continue to attract interest with the evolving neuroimaging that is further clarifying the underlying neuroanatomy of cognition [13]. White matter dementia White matter dementia is a term introduced in 1988 to call attention to the cognitive loss that can occur in patients with white matter disorders [36]. Examples such as MS [37] and TL [38] leave no doubt that cognitive impairment, often reaching the level of dementia, can occur with widespread white matter involvement. In individuals with early white matter pathology, symptoms are relatively mild and nonspecific – forgetfulness, lassitude, personality changes, Sustained attention (vigilance) deficit Memory retrieval impairment Visuospatial dysfunction Psychiatric disturbance Normal language Normal procedural memory and the like – and at the most severe end of the spectrum, the profound disturbances of the persistent vegetative and minimally conscious states can result from white matter destruction. Most patients fall between these extremes, where a need for defining the neurobehavioral profile of white matter dementia has been apparent. Whereas caution is advisable when attempting to compile a cognitive profile of white matter dementia, there are striking cognitive similarities among the disorders that may cause white matter dementia [1], and evidence suggests that white matter dementia can be differentiated phenomenologically from both cortical and subcortical dementia. Table 4.4 summarizes neurobehavioral features that emerge from preliminary compilation of the deficits encountered in white matter dementia, irrespective of the specific neuropathology involved [1, 15, 39, 40]. This profile, which should be regarded as a common but not invariant description, is most useful in the early stages of white matter dementia; clinical differentiation of various dementias becomes more difficult as neuropathology of any type advances and erases subtle distinctions based on relative involvement or sparing of critical cerebral areas. Table 4.4 is based on an initial consideration of subcortical (more recently frontal-subcortical) dementia, and like subcortical dementia, white matter dementia involves cognitive slowing, executive dysfunction, sustained attention (vigilance) deficits, memory retrieval impairment, visuospatial dysfunction, and psychiatric disturbance while memory encoding and language are spared [1, 15, 39]. Unlike subcortical dementia, however, white matter dementia features normal extrapyramidal function and, importantly, procedural memory [1, 15, 40]. Both these dementia syndromes are to be distinguished from cortical dementia, in which amnesia (memory encoding deficit), aphasia, apraxia, and 53 Section I: Structural and Functional Neuroanatomy agnosia are typical [1, 15, 39, 40]. More work is needed on refining these distinctions, but they point the way toward better recognition of the neurobehavioral deficits of patients whose white matter disorders might be identified and treated before dementia worsens. Milder forms of this syndrome may even serve to call attention to unrecognized cerebral white matter disease that has not yet caused dementia, as can be seen in SLE patients with cognitive impairment but normal conventional MRI who have MRS abnormalities consistent with early myelinopathy [28]. Indeed, one of the most useful aspects of pursuing such diagnostic distinctions is the opportunity to identify cognitive loss from white matter disorders when the potential for reversibility is at its highest. Further support for the concept of white matter dementia comes from both clinical and experimental laboratory studies. Mendez and colleagues reviewed 28 patients with white matter disorders causing dementia and found a neuropsychological profile similar to that depicted in Table 4.4 [41]. Studies of braininjured monkeys demonstrated DAI identical to that seen in humans after TBI, and the duration of coma and neurologic outcome were directly proportional to the degree of DAI [42]. Using a vascular disease model, Shibata and colleagues found that mice with bilateral carotid stenosis for 30 days had white matter ischemia sparing the hippocampus and cortices, and developed selective impairment in working memory [43]. The concept of white matter dysfunction producing a unique dementia syndrome has merit as a clinical model and a framework for brain–behavior research. However, overlap of white and gray matter pathology is often observed neuropathologically and complicates analysis. A particularly illustrative example of this complexity can be seen with AD, in which white matter is implicated in several ways. While indisputably a cortical neurodegenerative disorder, the neuropathology of AD often includes white matter changes related to three phenomena – cerebral ischemia [31], cerebral amyloid angiopathy [44], and Wallerian degeneration [45]. An intriguing hypothesis has also been advanced proposing that oligodendrocyte injury and myelin breakdown are core pathogenetic events in AD [46]. While certain disorders can be usefully conceptualized as white matter disorders – TL is an excellent example – in many others a commingling of gray and white matter pathology cannot be avoided and poses a major challenge for the study of higher function and its dissolution. Such complexity is in fact typical of 54 Table 4.5. Neuropsychiatric syndromes associated with white matter abnormalities. Psychiatric syndromes in white Psychiatric disorders with matter disorders white matter abnormalities Depression Schizophrenia Mania Depression Psychosis Bipolar disorder Pathologic affect Attention-deficit hyperactivity disorder Euphoria Autism Fatigue Aggression brain function, and while humbling, should encourage rather than deter a comprehensive effort to further explicate brain–behavior relationships. Neuropsychiatric syndromes Neuropsychiatric syndromes associated with white matter disorders prove to be the most difficult to describe because many uncertainties linger with regard to correlating psychiatric symptoms with any neuropathology, whether in white or gray matter. However, the frequent complaints of personality change, depression, inattention, fatigue, and similar non-specific symptoms heard in clinical settings often herald the onset of white matter disorders that will later produce more disabling deficits [1, 47]. From a nosologic perspective, it is useful to consider (1) the psychiatric syndromes that arise in patients with known white matter disorders, and (2) the evidence for white matter involvement in known psychiatric disorders [47]. To summarize a large and diverse literature, the white matter disorders have been noted to produce a variety of syndromes with prominent psychiatric features, whereas many established psychiatric disorders have been theorized to have a basis in white matter pathology [1, 25, 47]. Table 4.5 presents a sampling of entities within each of these categories, and many others are likely to be added to these lists. A recurring theme is that neuropsychiatric dysfunction in white matter disorders may be produced by microstructural injury that is more subtle than the neurologic lesions leading to focal neurobehavioral syndromes and dementia [1, 25, 47, 48]. By implication, neuropsychiatric dysfunction may reflect primary circuitry disturbances and “higherorder” disconnection syndromes [4, 5, 47, 48]. However, it must be acknowledged that these notions are Chapter 4: White matter largely speculative, and much work will be necessary to establish the precise contribution of white matter dysfunction or damage in the pathogenesis of many neuropsychiatric syndromes. At the same time, recognition of the possibility that study of the white matter may offer clues to understanding baffling diseases such as schizophrenia may be central to major advances that lie ahead. Treatment and prognosis The treatment of cerebral white matter disorders depends on the specific problem disclosed by the diagnostic search. Therapeutic options are well covered in standard textbooks of neurology, and may be (1) preventive, as in the case of reduction of cerebrovascular risk, prevention of TBI, or avoidance of leukotoxins; (2) pharmacologic, as with MS, various infectious and inflammatory diseases, metabolic disorders, or stroke; or (3) neurosurgical, as in neoplasia, NPH, and some cases of cerebrovascular and genetic disease. The range of medical and surgical interventions varies greatly, reflecting the diverse neuropathology of these disorders, but the goal from the perspective of BN&NP is the alleviation of suffering related to the often disabling cognitive and emotional effects of these disorders. In most cases, complete recovery cannot be expected, although with such a range of neuropathology, this generalization has exceptions. Symptomatic treatment, however, can often be provided with salutary effect. Non-pharmacologic approaches may be invaluable, offering patients much-needed explanation of their illness, counseling about future prospects, and specific psychotherapy as indicated. Antidepressant, antipsychotic, mood-stabilizing, anxiolytic, cholinesterase-inhibiting, stimulant, corticosteroid, immunomodulatory, antiepileptic, and anti-fatigue medications may all have a role in these patients. Caution is in order, however, since many patients are particularly vulnerable to adverse effects of treatment because of advanced age and concurrent medical problems. It is also well to keep in mind that few adequately controlled studies of the efficacy or safety of these drugs are available to guide treatment in this context. The prognosis of white matter disorders is as varied as the etiologies of these conditions. In some patients, a cure is possible, as in B12 deficiency that is recognized early and treated promptly. In many others, such as those with MS, lingering symptoms will typically be expected. In still others, including many with TBI, lifelong disability is inevitable. As a general rule, however, the prognosis for white matter disorders is more favorable than that of gray matter disorders such as AD, the best-recognized dementia that serves as a useful contrast to the white matter disorders. As the inexorable neuronal loss of AD does not necessarily occur in many white matter disorders, they are often preventable, partially reversible, or even curable. In addition, many white matter disorders only involve myelin loss or injury while sparing the axon – this phenomenon leaves intact the cellular apparatus supporting remyelination and preserves some potential for spontaneous recovery [1]. When the axons are damaged, the prognosis clearly worsens [49], but it has long been known in MS, for example, that remyelination can occur [1]. Even the renewal of axons has been proposed as a possible mechanism of recovery. Intriguing recent DTI data in post-TBI minimally conscious patients have suggested axonal regrowth in white matter that correlates with clinical improvement [50]. Another exciting possibility is the application of evolving stem cell techniques that propose the use of oligodendrocyte progenitor cells as precursors to remyelination [51]. Finally, the concept of plasticity has recently come to be applied to the white matter. The concept of activity-dependent myelination has been shown in several animal models, and it is likely that electrical activity within axons enhances the number of both oligodendrocytes and myelinated axons [52]. In humans, data consistent with this idea have been presented in piano players who developed increased white matter organization in the pyramidal tract that was proportional to the number of hours spent practicing [53]. The notion that “experience changes white matter” has far-reaching implications both for the development and maintenance of normal white matter structures and for the rehabilitation of patients with white matter disorders [52]. Conclusion White matter has become a crucial component of the study of higher brain function. The disorders of white matter that present to the subspecialist in BN&NP are common, often diagnostically difficult, and still more therapeutically challenging. These conditions are often unrecognized or underappreciated, leaving many patients undiagnosed and untreated. White matter 55 Section I: Structural and Functional Neuroanatomy dementia merits particular attention as a frequent and problematic syndrome for large numbers of patients at all ages. Treatment of white matter disorders and their neurobehavioral syndromes is not well understood, but prognosis may be favorable in many cases, especially when axons are spared and myelin can be restored. Despite these intriguing possibilities, the understanding of the behavioral neurology of white matter is in its infancy, reflecting a long-standing tradition of studying cortical and subcortical gray matter as the most prominent neurobehavioral substrate. From a theoretical perspective, white matter and its disorders offer an extraordinary opportunity to expand our appreciation of brain–behavior relationships by combining the lesion method of behavioral neurology with increasingly impressive neuroanatomical techniques, neuroimaging methods, and neuropathological examination to establish the neuroanatomical basis of white matter syndromes. The study of white matter is a natural outgrowth of prior investigations highlighting the importance of brain connectivity, and promises to fill an enormous gap in our understanding of the brain and its many complex operations. Allied with the study of microconnectivity in the cerebral cortex and subcortical gray matter, a focus on the macroconnectivity provided by white matter tracts promises to expand our understanding of the distributed neural networks that subserve all aspects of cognition, emotion, and behavior. The modern appreciation of white matter as fundamental to BN&NP began with Geschwind’s ideas on disconnection in 1965 [4, 5], but uncertainty persisted then about the role of myelinated tracts in higher function, and lingers to some extent today [54]. However, white matter is steadily receiving increased neuroscientific attention. In 2009, for example, the Human Connectome Project was launched by the US National Institutes of Health to compile functional (i.e., fMRI) and structural (i.e., DTI) imaging from hundreds of participants into a circuitry map of the human brain [55, 56]. The intent is to develop a large, publically accessible database derived from normal and abnormal brains, with the goals of improving knowledge of brain–behavior relationships and exploring network causes of brain disorders such as AD, schizophrenia, and autism. This project offers more evidence that the time has come to modify the familiar but overly narrow “corticocentric” approach to higher function [57] by focusing on the connectome as much as the synaptome [58, 59]. 56 References 1. Filley CM. The Behavioral Neurology of White Matter. 2nd edition. New York, NY: Oxford University Press; 2012. 2. Filley CM. The behavioral neurology of cerebral white matter. Neurology 1998;50(6):1535–40. 3. Schmahmann JD, Smith EE, Eichler FS, Filley CM. Cerebral white matter: neuroanatomy, clinical neurology, and neurobehavioral correlates. Ann N Y Acad Sci. 2000;1142:266–309. 4. Geschwind N. Disconnexion syndromes in animals and man. I. Brain. 1965;88(2):237–94. 5. Geschwind N. Disconnexion syndromes in animals and man. II. Brain. 1965;88(3):585–644. 6. Mesulam MM. Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann Neurol. 1990;28(5):597–613. 7. Schmahmann JD, Pandya DN. Cerebral white matter – historical evolution of facts and notions concerning the organization of the fiber pathways of the brain. J Hist Neurosci. 2007;16(3):237–67. 8. Saver JL. Time is brain-quantified. Stroke 2006;37(1): 263–6. 9. Nolte J. The Human Brain: An Introduction to its Functional Anatomy. 6th edition. Philadelphia, PA: Mosby/Elsevier; 2009. 10. Baumann N, Pham-Dinh D. Biology of oligodendrocyte and myelin in the mammalian central nervous system. Physiol Rev. 2001;81(2):871–927. 11. Benarroch EE. Oligodendrocytes: susceptibility to injury and involvement in neurologic disease. Neurology 2009;72(20):1779–85. 12. Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford: Oxford University Press; 2006. 13. Aralasmak A, Ulmer JL, Kocak M et al. Association, commissural, and projection pathways and their functional deficit reported in literature. J Comput Assist Tomogr. 2006;30(5):695–715. 14. Schmahmann JD, Pandya DN, Wang R et al. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 2007;130(Pt 3):630–53. 15. Filley CM. White matter: organization and functional relevance. Neuropsychol Rev. 2010;20(2):158–73. 16. Schoenemann PT, Sheehan MJ, Glotzer LD. Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nat Neurosci. 2005; 8(2):242–52. 17. Wozniak JR, Lim KO. Advances in white matter imaging: a review of in vivo magnetic resonance methodologies and their applicability to the study of Chapter 4: White matter development and aging. Neurosci Biobehav Rev. 2006;30(6):762–74. 18. Benes FM, Turtle M, Khan Y, Farol P. Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Arch Gen Psychiatry 1994;51(6):477–84. 19. Peters A. The effects of normal aging on myelin and nerve fibers: a review. J Neurocytol. 2002;31(8–9): 581–93. 20. Head D, Buckner RL, Shimony JS et al. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex. 2004;14(4):410–23. 21. Head D, Snyder AZ, Girton LE, Morris JC, Buckner RL. Frontal-hippocampal double dissociation between normal aging and Alzheimer’s disease. Cereb Cortex 2005;15(6):732–9. 22. Filley CM, Cullum CM. Attention and vigilance functions in normal aging. Appl Neuropsychol. 1994; 1(1–2):29–32. 23. Launer LJ. Epidemiology of white matter lesions. Top Magn Reson Imaging 2004;15(6):365–7. 24. Catani M. Diffusion tensor magnetic resonance imaging tractography in cognitive disorders. Curr Opin Neurol. 2006;19(6):599–606. 25. Filley CM, Gross KF. Psychosis with cerebral white matter disease. Neuropsychiatry Neuropsychol Behav Neurol. 1992;5:119–125. 26. Ghaffar O, Feinstein A. The neuropsychiatry of multiple sclerosis: a review of recent developments. Curr Opin Psychiatry 2007;20(3):278–85. 27. Boisse L, Gill MJ, Power C. HIV infection of the central nervous system: clinical features and neuropathogenesis. Neurol Clin. 2008;26(3): 799–819. 28. Filley CM, Kozora E, Brown MS et al. White matter microstructure and cognition in non-neuropsychiatric systemic lupus erythematosus. Cogn Behav Neurol. 2009;22(1):38–44. 29. Filley CM, Kleinschmidt-DeMasters BK. Toxic leukoencephalopathy. N Engl J Med. 2001;345(6): 425–32. 30. Stojsavljevic N, Levic Z, Drulovic J, Dragutinovic G. A 44-month clinical-brain MRI follow-up in a patient with B12 deficiency. Neurology 1997;49(3):878–81. 31. Roman GC, Erkinjuntti T, Wallin A, Pantoni L, Chui HC. Subcortical ischaemic vascular dementia. Lancet Neurol. 2002;1(7):426–36. 32. Harris JG, Filley CM. CADASIL: neuropsychological findings in three generations of an affected family. J Int Neuropsychol Soc. 2001;7(6):768–74. 33. Alexander MP. Mild traumatic brain injury: pathophysiology, natural history, and clinical management. Neurology 1995;45(7):1253–60. 34. Filley CM, Kleinschmidt-DeMasters BK, Lillehei KO, Damek DM, Harris JG. Gliomatosis cerebri: neurobehavioral and neuropathological observations. Cogn Behav Neurol. 2003;16(3):149–59. 35. Del Bigio MR, Wilson MJ, Enno T. Chronic hydrocephalus in rats and humans: white matter loss and behavior changes. Ann Neurol. 2003;53(3): 337–46. 36. Filley CM, Franklin GM, Heaton RK, Rosenberg NL. White matter dementia: clinical disorders and implications. Cogn Behav Neurol. 1988;1(4):239–54. 37. Franklin GM, Nelson LM, Filley CM, Heaton RK. Cognitive loss in multiple sclerosis. Case reports and review of the literature. Arch Neurol. 1989;46(2):162–7. 38. Filley CM, Heaton RK, Rosenberg NL. White matter dementia in chronic toluene abuse. Neurology 1990;40(3 Pt 1):532–4. 39. Filley CM, Heaton RK, Nelson LM, Burks JS, Franklin GM. A comparison of dementia in Alzheimer’s disease and multiple sclerosis. Arch Neurol. 1989;46(2):157–61. 40. Lafosse JM, Corboy JR, Leehey MA, Seeberger LC, Filley CM. MS vs. HD: can white matter and subcortical gray matter pathology be distinguished neuropsychologically? J Clin Exp Neuropsychol. 2007; 29(2):142–54. 41. Mendez MF, Perryman KM, Bronstein YL. White matter dementias: neurobehavioral aspects and etiology. J Neuropsychiatry Clin Neurosci. 2000;12(1): 133. 42. Gennarelli TA, Thibault LE, Adams JH et al. Diffuse axonal injury and traumatic coma in the primate. Ann Neurol. 1982;12(6):564–74. 43. Shibata M, Yamasaki N, Miyakawa T et al. Selective impairment of working memory in a mouse model of chronic cerebral hypoperfusion. Stroke 2007;38(10): 2826–32. 44. Jellinger KA. Alzheimer disease and cerebrovascular pathology: an update. J Neural Transm. 2002;109(5–6): 813–36. 45. Bozzali M, Falini A, Franceschi M et al. White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry 2002;72(6):742–6. 46. Bartzokis G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging 2004;25(1):5–18; author reply 49–62. 47. Filley CM. White matter: beyond focal disconnection. Neurol Clin. 2011;29(1):81–97. 57 Section I: Structural and Functional Neuroanatomy 48. Kumar A, Cook IA. White matter injury, neural connectivity and the pathophysiology of psychiatric disorders. Dev Neurosci. 2002;24(4): 255–61. 49. Medana IM, Esiri MM. Axonal damage: a key predictor of outcome in human CNS diseases. Brain 2003;126(Pt 3):515–30. 50. Voss HU, Uluc AM, Dyke JP et al. Possible axonal regrowth in late recovery from the minimally conscious state. J Clin Invest. 2006;116(7): 2005–11. 51. Franklin RJ, Ffrench-Constant C. Remyelination in the CNS: from biology to therapy. Nat Rev Neurosci. 2008;9(11):839–55. 52. Fields RD. White matter in learning, cognition and psychiatric disorders. Trends Neurosci. 2008;31(7): 361–70. 58 53. Bengtsson SL, Nagy Z, Skare S et al. Extensive piano practicing has regionally specific effects on white matter development. Nat Neurosci. 2005;8(9):1148–50. 54. Miller BL. A commentary on “disconnexion syndromes in animals and man”. Neuropsychol Rev. 2010;20(2):126–7. 55. Williams R. The human connectome: just another ‘ome’? Lancet Neurol. 2010;9(3):238–9. 56. Kennedy DN. Making connections in the connectome era. Neuroinformatics 2010;8(2):61–2. 57. Parvizi J. Corticocentric myopia: old bias in new cognitive sciences. Trends Cogn Sci. 2009;13(8):354–9. 58. Biswal BB, Mennes M, Zuo XN et al. Toward discovery science of human brain function. Proc Natl Acad Sci USA 2010;107(10):4734–9. 59. DeFelipe J. From the connectome to the synaptome: an epic love story. Science 2010;330(6008):1198–201. Section I Structural and Functional Neuroanatomy Chapter Frontal-subcortical circuits 5 David G. Lichter A series of parallel segregated frontal-subcortical circuits (FSCs) connect specific regions of the frontal cortex (including paralimbic frontal areas) to the striatum, the globus pallidus (GP) and substantia nigra (SN), and the thalamus [1–3]. Together, these constitute an important effector mechanism that allows the individual to interact adaptively with the environment. This chapter reviews the neuroanatomy and neurochemistry of FSCs, and outlines the signature syndromes of FSC circuit dysfunction. The similarity of cognitive and behavioral changes that accompany cortical and subcortical lesions is discussed within this framework. Select neuropsychiatric disorders that appear linked to FSC dysfunction are then presented. Finally, we explore the manner in which these insights may better inform clinical management of patients with relevant neuropsychological, behavioral, and neuropsychiatric disorders. Frontostriatal systems The frontal lobe may be viewed as comprising two distinct anatomical/functional systems, which reflect its dual developmental origin [4]. The sequential processing of sensory, spatially related, and motivational information is mediated by a dorsal system, which comprises dorsolateral and medial portions of the frontal lobes, interconnected with the posterior parietal lobe and cingulate gyrus. Emotional tone is mediated by a second, ventral system, which involves the orbital surface of the frontal lobes. The architectonic organization of the prefrontal cortex [5, 6] is reflected in the pattern of prefrontostriatal projections [7]. Thus, the dorsal architectonic trend, which originates in the rostral cingulate gyrus and culminates in the dorsal portion of the frontal eye field, maps onto the dorsal caudate nucleus. In contrast, the ventral architectonic trend, which originates in the ventral orbital region and extends to the ventral portion of the frontal eye field, maps onto the ventro-medial portion of the caudate and adjacent portion of nucleus accumbens (NAc). Closely connected cortical areas send converging projections into the striatum [8, 9]. Early experimental observations supported a role for discrete dorsal and ventral frontostriatal systems in cognition and behavior. Thus, lesions or electrical stimulation of the dorsolateral prefrontal (DLPF) cortex or of the anterodorsal head of the caudate nucleus, to which this region projects, produce deficits in delayed response and delayed alternation tasks [10, 11]. In contrast, lesions or electrical stimulation of either the orbitofrontal (OF) cortex or of the ventrolateral head of the caudate result in deficits in object alternation or response inhibition paradigms [12]. Disruption to discrete cognitive processes following striatal injury can be interpreted as the “downstream” interruption of anatomically congruent outflow from the frontal cortex [12–14]. Frontal-subcortical circuits: shared anatomy Basic circuit structure The following principal circuits may now be recognized: a motor circuit that originates in the supplementary motor area (SMA), an oculomotor circuit originating in the frontal eye fields, and three primary circuits/circuit networks mediating cognitive, behavioral, and affective functions [1–3]. Of these, the DLPF Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 59 Section I: Structural and Functional Neuroanatomy Frontal cortex Figure 5.1. The general structure shared by all frontal-subcortical circuits (direct connections). Striatum Globus pallidus/ substantia nigra Thalamus circuit mediates “executive functions,” i.e., the organization of information to facilitate a response. The lateral OF (LOF) circuit is involved with integration of limbic and emotional information into goal-directed and contextually appropriate behavioral responses. Finally, the rostromedial limbic network consists of the medial OF (MOF) circuit, which facilitates integration of information pertaining to emotions, and the linked anterior cingulate (AC) circuitry. The AC network is involved primarily in motivational mechanisms but also, through the activity of its subgenual portion, in mood regulation. All circuits share a basic anatomy, with an origin in the frontal lobes and sequential projections to the striatum (caudate, putamen, or ventral striatum), GP/SN, and then to specific thalamic nuclei, with a final link back to the frontal lobe (Figure 5.1). The circuits have two pathways: (1) a direct pathway, featuring a monosynaptic link between the striatum and GP interna (GPi)/SN pars reticulata (SNr) complex, and (2) an indirect pathway that projects from striatum to GP externa (GPe), linking to GPi/SNr via the subthalamic nucleus [3] (Figure 5.2). Both direct and indirect circuits project to the thalamus; the direct pathway disinhibits the thalamus, whereas the indirect pathway inhibits it. The relative influence of the two pathways therefore determines the final output of the circuit. All circuits thus share common structures and are parallel and contiguous, but remain largely segregated anatomically, despite the progressive focusing of succeeding projections onto smaller numbers of neurons. Thus, the DLPF cortex projects to the dorsolateral region of the caudate nucleus, the LOF cortex projects to the ventral 60 Figure 5.2. The general anatomy of the direct and indirect frontal-subcortical pathways. 1 – Excitatory cortico-striatal fibers. 2 – Direct loop’s inhibitory striato-pallidal (GPi) fibers. 3 – Indirect loop’s inhibitory striato-pallidal (GPe) fibers. 4 – Indirect loop’s inhibitory GPe-subthalamic nucleus fibers. 5 – Indirect loop’s excitatory subthalamic-GPi/SNr fibers. 6 – Inhibitory outflow from GPi/SNr to specific thalamic regions. 7 – Excitatory fibers returning from thalamus to cortex (shown for convenience in contralateral hemisphere). Excitatory fibers are glutaminergic, inhibitory fibers mediated via GABA. Note: these circuits are unilateral: each set of projections from cortex to subcortical structures to cortex remains within a single hemisphere. In this figure, projection from cortex to subcortical structures as well as projections between subcortical structures are illustrated on the left half of the figure whereas projections from thalamus to cortex are illustrated on the right side of the figure. This does not suggest that these circuits cross from one hemisphere to the other; instead, they are diagrammed in this manner here in order to preserve readability of the figure. caudate area, and the MOF and AC circuitry connects to the medial striatal/NAc region. Similar anatomic arrangements are maintained in the GP and thalamus (Figure 5.3). Open-loop elements While each circuit comprises a closed loop of anatomically segregated dedicated neurons, open-loop elements support the functional connectivity of FSCs. Circuit structures receive projections from non-circuit cortical areas, thalamic and amygdaloid nuclei, and also project to regions outside the circuits. Brain regions linked by these afferent or efferent projections are functionally related [15, 16]. Circuits mediating limbic functions, for example, have connections to other limbic areas whereas those involved with executive functions (EFs) interact with brain structures involved with cognition. In this manner, circuits integrate information from anatomically disparate but Chapter 5: Frontal-subcortical circuits cortex [1, 3]. These areas project principally to the putamen and thence to ventrolateral GPi, GPe, and caudolateral SNr. The GP connects to ventral lateral, ventral anterior, and centromedian nuclei of the thalamus whose major efferents are to the SMA, premotor cortex, and motor cortex, completing the circuit. Throughout the circuit, the discrete somatotopic organization of movement-related neurons is maintained, although information processing in the circuits is not strictly sequential [3]. The oculomotor circuit The oculomotor circuit originates in the frontal eye field, Brodmann’s area (BA) 8, as well as prefrontal and posterior parietal cortex and connects sequentially to the central body of the caudate nucleus, dorsomedial GPi and ventrolateral SNr, ventral anterior and medial dorsal thalamic nuclei, and back to the frontal eye field [1–3]. The dorsolateral prefrontal circuit Figure 5.3. The segregated anatomy of the frontal-subcortical circuits: dorsolateral prefrontal (horizontal stripes), lateral orbitofrontal (stippling), and anterior cingulate/medial orbitofrontal (cross-hatched) circuits in the striatum (top), pallidum (center), and mediodorsal thalamus (bottom). functionally related brain regions, the cascade of the closed circuit constituting the eventual effector mechanism [17]. Organization of individual frontal-subcortical circuits The motor circuit The motor circuit originates from neurons in the SMA, premotor cortex, motor cortex, and somatosensory Figure 5.4 illustrates the anatomy of the direct pathways of two of the behaviorally relevant FSCs. The DLPF circuit originates in BA 9 and 10 (areas reciprocally connected with adjacent BA 46) on the lateral surface of the anterior frontal lobe. Neurons in these regions project to the dorsolateral head of the caudate nucleus [18], thence to the lateral aspect of the mediodorsal GPi and rostrolateral SNr via the direct pathway [19]. The indirect pathway projects sequentially to the dorsal GPe, lateral subthalamic nucleus [20] and GPi/SNr. Output from the basal ganglia projects to the ventral anterior and mediodorsal (parvocellular portion) thalamic nuclei [21–23]. The circuit is closed by projections from these thalamic regions back to the dorsolateral frontal lobe [24, 25]. The lateral orbitofrontal circuit The LOF circuit originates in the lateral orbital gyrus of BA 11 and the medial inferior frontal gyrus of areas 10 and 47 [26]. Multiple sensory inputs, including olfactory, gustatory, visceral, somatic, and visual afferents converge on this region. Projections are to the ventrolateral caudate and dorsal edge of the NAc [27] with subsequent links to the most medial portion of the mediodorsal GPi and to the dorsomedial SNr [28]. The ventrolateral caudate also sends an indirect loop through the dorsal GPe to the lateral subthalamic 61 Section I: Structural and Functional Neuroanatomy The anterior cingulate and subgenual cingulate circuits Figure 5.4. The anatomy of the direct pathways of the dorsolateral prefrontal and lateral orbitofrontal circuits. NAc, nucleus accumbens; GPi, globus pallidus, internal segment; SNr, substantia nigra, pars reticulata; VAmc, ventral anterior nucleus, magnocellular portion; VApc, ventral anterior nucleus, parvocellular portion; MDpc, mediodorsal nucleus, parvocellular portion; MDmc, mediodorsal nucleus, magnocellular portion; MDpl, mediodorsal nucleus, paralaminar portion. nucleus, thence to GPi and SNr [20]. Neurons are sent from the GPi and SNr to the medial section of the magnocellular division of the ventral anterior thalamus as well as the paralaminar portion and inferomedial sector of the magnocellular division of the mediodorsal thalamus [18, 21, 29]. The circuit then closes with projections from these thalamic regions to the LOF cortex (Figure 5.4). The rostromedial limbic circuitry Emotional, motivational, and affective information processed by the basal ganglia is represented in the rostromedial limbic circuitry arising from the orbital and medial prefrontal cortex. Closely integrated anatomically and functionally, this comprises AC circuitry (including that related to the subgenual cingulate) and the MOF circuit (Figure 5.5). 62 There are two major subdivisions of the AC cortex, which subserve distinct functions (Figure 5.6). The dorsal cognitive division (BA 24, 24a’–b’ and 32’), also referred to as the cognitive effector region [26], is more developed in its cytoarchitecture than the rostral-ventral division, and is part of a distributed attentional network which has reciprocal interconnections with DLPF cortex (BA 46/9), parietal cortex (BA 7), and premotor and supplementary motor areas [30– 32]. This region is connected via the Muratoff bundle to dorsal and medial portions of the head and body of the caudate nucleus [7, 18, 33], with succeeding projections to the GPi/SNr and thence to the parvocellular mediodorsal thalamus as well as the ventral anterior and midline/intralaminar thalamic nuclei [25, 34]. These thalamic regions then reconnect with the cognitive division of AC via the inferior thalamic peduncle and anterior internal capsule (Figure 5.5). Functions of the cognitive effector region include modulation of attention or executive functions by influencing sensory or response selection; monitoring competition, complex motor control, motivation, novelty, error detection and working memory; and anticipation of cognitively demanding tasks [35]. The rostral-ventral affective division of the AC cortex comprises the subgenual cingulate (BA 25), adjacent (caudal) portions of BA 32 (pregenual cingulate), area 33, and the rostral portions of AC cortex, subcallosal areas 24a and 24b (Figure 5.6). These regions project to the medial part of the ventral striatum [18, 36], which includes the ventromedial caudate, ventral putamen [37], and core and shell of the NAc (the shell region receiving projections especially from the subgenual cingulate [38]). Projections from the ventral striatum and NAc core innervate the rostromedial GPi and ventral pallidum (VP, the portion of the globus pallidus inferior to the anterior commissure) as well as the rostromedial SNr and ventral tegmental area (VTA) [39–41]. The NAc shell projects to the VP, VTA and SNpc, allowing this region to influence dopaminergic inputs to other parts of the striatum [41]. The VP and GPi then project to the dorsal portion of the magnocellular mediodorsal thalamus, midline and intralaminar (parafascicular) thalamic nuclei [23, 40, 42–44], with closed-loop projections back to the anterior and subgenual cingulate (Figure 5.5) [25, 34]. Chapter 5: Frontal-subcortical circuits Figure 5.5. The anatomy of the direct pathways of the rostromedial limbic circuits. VP, ventral pallidum; Pf, parafascicular nucleus. Other abbreviations follow Figure 5.4. The pale arrows indicate open-loop connections between engaged thalamic nuclei and the lateral orbitofrontal and dorsolateral prefrontal cortices. While the OF cortex mediates information concerning the internal environment, the AC circuitry facilitates the intentional selection of environmental stimuli based on their internal relevance [45], thereby mediating motivated behavior. Through both its dorsal and rostral-ventral divisions and their connections with motor pathways, lateral PFC, and afferents from the midline thalamus and brainstem (including arousal systems), the AC cortex is ideally positioned to participate in the willed control of behavior [46]. The subgenual cingulate component of AC circuitry (Figures 5.5 and 5.6) includes substantial predominantly ipsilateral connections with the amygdala [44], MOF cortex (see below), other portions of both anterior and posterior cingulate, anterior insula, medial temporal lobe, hypothalamus, periaqueductal gray and dorsal brainstem [47]. The strong projections to the dorsal raphe suggest a role in regulating the overall function of the serotonergic system [44]. With its major outflow to autonomic, visceromotor, and endocrine systems [30, 35, 48], this circuit has been implicated in salience monitoring of emotional and motivational information, mediation of emotional and autonomic response to socially significant or provocative stimuli, affective processing, and inhibitory control [35, 49, 50]. The medial orbitofrontal circuit Arising from the gyrus rectus (areas 14r, 14c) and related areas on the medial orbital surface, including BA 11m, 13a and 13b, the MOF cortex is integrated anatomically and functionally with the 63 Section I: Structural and Functional Neuroanatomy Figure 5.7. The direct and indirect frontal-subcortical pathways. Filled arrows represent inhibitory (GABAergic) pathways; open arrows, excitatory (glutamatergic) pathways. Note that dopaminergic input from the substantia nigra pars compacta and ventral tegmental area is inhibitory, via D2 receptors, to GABA/enkephalin neurons projecting to the indirect pathway, and excitatory, via D1 receptors, to GABA/substance P neurons projecting to the direct pathway. Figure 5.6. The medial surface of the right hemisphere (anterior towards the left), showing a schematic representation of cytoarchitectural areas of the anterior cingulate cortex. The areas forming the dorsal cognitive division and the skeletomotor effector region (striped) are outlined by solid lines, the rostro-ventral affective division areas being outlined by dotted lines. specialized connections with the amygdala, the MOF circuit may serve both as an integrator of visceral drives as well as a sensor of information pertaining to emotions [51, 58]. Neurochemical organization Shared neurochemical organization infracallosal AC and subgenual cingulate (“visceral effector region”) [27, 51], with which it shares subcortical projections (Figure 5.5). Sequentially, these links are to the ventromedial caudate and NAc (core); ventromedial pallidum and dorsomedial SNr; magnocellular portions of the ventral anterior and medial mediodorsal thalamic nuclei, with a closed-loop projection back to the MOF cortex [18, 23, 38–40, 52, 53]. In addition to its connections with the infracallosal cingulate, the MOF cortex has strong reciprocal connections with the medial portion of the basal and magnocellular division of the accessory basal amygdala, as well as with the ventromedial temporal pole and rostral (agranular) insula [51, 54– 57]. The subgenual cingulate provides motivational input to gustatory, olfactory and alimentary information from anterior insular processing converging on the MOF cortex. Considering its robust connections with high-order sensory association cortices and 64 Common to all circuits is an origin in the frontal lobes with excitatory glutamatergic fibers that terminate in the striatum (caudate, putamen, and ventral striatum). These striatal cells then project inhibitory gammaaminobutyric acid (GABA) fibers both to neurons in the GPi/SNr (direct loop connection) and to the GPe (indirect loop connection). Via the indirect loop, the GPe projects inhibitory GABA fibers to the subthalamic nucleus which then connects with the GPi/SNr through excitatory glutamatergic fibers [2, 59]. The direct pathway expresses dopamine (DA) D1 receptors and utilizes substance P with its GABA projection to the pallidum while the indirect loop receives its dopaminergic influence via D2 receptors and combines GABA with enkephalin [60]. The GPi/SNr then project inhibitory GABA fibers to specific thalamic targets, which complete the circuit by sending a final excitatory connection to the cortical site of the circuit’s origin in the frontal lobe (Figure 5.7). Chapter 5: Frontal-subcortical circuits Circuit-discrete neurochemical organization The striatum is organized as two separate systems, the striosomes and the matrix. These elements are differentiated by their distinct ontological, connectional, and chemical characteristics [61]. Relative to matrix neurons, striosomal cells mature earlier, have lower concentrations of DA, serotonin and acetylcholine, and have high concentrations of limbic-associated membrane protein [62, 63]. Striosomes receive dense orbitofrontal and insular input and have high levels of D1 receptors, with dopaminergic projections from the ventral tier of the substantia nigra pars compacta (SNpc). In contradistinction, matrix cells receive afferents predominantly from the sensorimotor cortex and express primarily D2 receptors, with dopaminergic input from the dorsal tier of the SNpc [64]. The matrix stains selectively for adenylate cyclase whereas the phosphoinositide system is selectively concentrated in the striosomes of the medial and ventral striatum [65]. GABAergic output from the striosomes is to the medial portion of the SNpc, dedicated to the OF circuit, whereas the GABAergic output of the matrix is to the GPe, GPi, and SNr. Mirroring the circuit-discrete neurochemical organization of the striatum, the NAc region of the ventromedial striatum is also divided into two functionally distinct components with discrete histochemistries. Thus, the core or dorsolateral portion of the NAc, which receives projections from the MOF and AC cortex, is distinguished by a lower concentration of mu opiate receptors and a higher concentration of calcium-binding proteins. This region is virtually identical histologically to the ventromedial caudate. In distinction, the shell or ventromedial portion of NAc, which receives a segregated pool of projections from the subgenual cingulate (BA 25) and pregenual cingulate (BA 32), is inversely devoid of calcium-binding proteins and replete with mu opiate receptors [38]. Neurotransmitter systems influencing frontal-subcortical circuits Processing of the detailed information contained in the FSCs is modulated by input from dopaminergic, cholinergic, noradrenergic, and serotonergic systems. The dopamine system Dopaminergic projections from the SNpc (primarily cell group A9), the caudal extension of SNpc into the retrorubral region (cell group A8) and the VTA (A10 cell group) innervate the entire striatum, thereby influencing each of the FSCs. This provides an anatomic basis for the multifaceted effects of dopaminergic agents on motor activity, motivation, thought, and behavior. Reflecting DA’s modulatory function, DAcontaining axon terminals synapse directly on striatal output neurons, many on the necks of dendritic spines. The VTA is the primary source of DA for the ventral striatum, prefrontal cortex, and limbic targets. The nigra has inhibitory connections, via D1 receptors, with the indirect portions of the FSCs and excitatory connections, via D2 receptors, with the direct circuits (see Figure 5.2). Representing an important convergence within the otherwise segregated FSCs, the SNpc receives diffuse input from the limbic circuits, providing a means for limbic emotional input to influence both motor activity and cognition. The cholinergic system Cholinergic input to the basal ganglia and most of the thalamus is derived from the pedunculopontine nucleus and laterodorsal tegmentum of the brainstem. Second, portions of the mediodorsal, ventroanterior, and reticular nuclei of the thalamus, as well as limbic structures and neocortex, receive cholinergic input from the basal forebrain, which includes the septum, diagonal band of Broca, and the nucleus basalis of Meynert [66]. Acetylcholine facilitates thalamic activation of the cortex and also assists in the septal hippocampal pathway supporting mnemonic function. Within the striatum, there are important interactions between the cholinergic and dopaminergic systems. Activation of D2 DA receptors, located on cholinergic interneurons, inhibits acetylcholine release, whereas D1 receptor agonists enhance acetylcholine release. Conversely, acetylcholine enhances DA release via nicotinic and muscarinic receptors located on presynaptic DA terminals [67]. The norepinephrine system Noradrenergic neurons from the locus coeruleus project to the entire cortex and hippocampus as well as cerebellum and spinal cord. A ventral pathway originating below the locus coeruleus innervates the brainstem and hypothalamus. Beta 1 receptors are present in the cerebral cortex while beta 2 receptors predominate in the cerebellum. Electrophysiologic studies suggest 65 Section I: Structural and Functional Neuroanatomy that a balance between norepinephrine and DA levels may set the signal-to-noise ratio of the attentional system [68, 69], which incorporates a distributed network involving the cingulate, DLPF, and inferior parietal cortices [70]. The serotonergic system The serotonin 5-HT1 receptor is the most abundant serotonin receptor in the basal ganglia. 5-HT1C receptors are very dense in the globus pallidus and moderately dense in the caudate, putamen, and accumbens regions, which also contain intermediate levels of the 5-HT2 receptor. The highest densities of 5-HT1D receptors are found in the basal ganglia and SN. 5-HT3 is enriched in the striatal matrix and is the most abundant receptor in the ventral striatum [62, 71] and other areas functionally related to the AC circuit, including hippocampus, septum, and amygdala. This receptor is linked to a ligand-gated cation channel that also modulates the release of acetylcholine and DA. Mechanisms for interactions between motor and behavioral circuits Although the FSCs remain largely segregated throughout their course, several mechanisms may allow for complex interactions between motor and behavioral systems [52]. At higher levels, thalamic relay nuclei form not only reciprocal but also non-reciprocal cortical connections, linking multiple frontal cortical areas, allowing for information flow between cortical circuits [23] (see Figure 5.5 as an example). At the level of the striatum, striosomes interdigitate with the surrounding matrix, so that matrix-striosome borders may serve as interfaces where the sensorimotor systems of the matrix interact with the striosomal processing of prefrontal and limbic inputs [72]. Linkage between the sensorimotor and limbic related regions of the striatum can also occur through corticonigral influences on the striatum and through the overlapping efferent striatal projections to the SN. Whereas the main output from the striatal matrix is to the GPi and SNr, striosomes project primarily to the dopaminergic SNpc. In this manner, the ventral striatum is able to exert a global regulatory influence on the dopaminergic input to the entire striatum. This provides an important mechanism for limbic “motivational” input to modulate motor behavior and provides for the “anchoring” or reinforcement of successful experience. 66 A recent re-evaluation [41] supports the original concept of the NAc part of the ventral striatum as a functional interface between the limbic and motor systems [73]. Thus, its afferent connections position this region as a site for integration of signals with emotional content (amygdala); contextual information (hippocampus); motivational significance (dopaminergic inputs); information about the state of arousal (midline thalamus); and executive/cognitive information (prefrontal cortex) [41]. Outputs of the ventral striatum include projections to the brainstem dorsal raphe, the midbrain parabrachial region and to the habenula, which may also play an important role in integration of the motor and behavioral circuits [74]. The habenula, which receives input from the anterior thalamic nuclei and GP, projects to the rostral midbrain with connections to the dopaminergic VTA and to the serotonergic dorsal raphe. Both of these cell groups project diffusely to multiple tiers of the FSCs. In particular, the limbic ventral striatal and habenular input to the dorsal raphe suggests another important mechanism for cross-talk between circuits and for limbic modulation of motor function [75]. Also providing for circuit linkage between limbic and motor systems are the cholinergic interneurons of the caudate and putamen and somatostatin/neuropeptide Y-containing interneurons that ramify between compartments [76]. Differences in peptide cotransmitters and in monoaminergic, GABAergic and glutamatergic synaptic receptor expression at subcortical sites (see above) provide other mechanisms for differential response within FSCs and for a complex interplay between these circuits [75]. From a neurophysiologic perspective, integration of the temporal coincidence of processing in FSCs, occurring particularly at the level of the thalamus [74], may also contribute importantly to linkage and synthesis of information between FSCs. Prototypical frontal-subcortical circuit syndromes Three principal frontal lobe symptom complexes are recognizable: a medial frontal-AC syndrome with apathy and diminished initiative; an OF syndrome with prominent disinhibition and irritability; and a DLPF syndrome, with neuropsychological deficits involving Chapter 5: Frontal-subcortical circuits EFs. Supporting the concept of circuit-specific behavioral syndromes, similar disorders have been observed with lesions of subcortical structures of these circuits. The anterior cingulate syndrome: akinetic mutism and abulia Akinetic mutism (AM) [77] represents a wakeful state of profound apathy, with indifference to pain, thirst, or hunger, and absence of motor or psychic initiative, manifested by lack of spontaneous movement, absent verbalization, and failure to respond to questions or commands. The term abulia, derived from the Greek boul, or will [78] refers to a similar but less severe psychomotor syndrome, encompassing lack of spontaneity, apathy, and paucity of speech and movement. Akinetic mutism has been described with AC lesions, craniopharyngiomas, obstructive hydrocephalus, tumors in the region of the third ventricle, and other conditions involving the ventral striatum (NAc and ventromedial caudate), ventral GP, and medial thalamus [79]. Although reports of the clinical consequences of lesions restricted to limbic structures of the basal ganglia are scarce [57], larger lesions that include both ventral striatum and more dorsal areas of the basal ganglia are associated with severe apathetic states. In a review of patients with focal lesions of the basal ganglia [80], abulia occurred in 18 of 64 (28%) small and large caudate lesions sparing the lentiform nucleus, 15 of which were unilateral. Abulia was also seen in six of 22 (27%) restricted GP lesions, all bilateral, but did not accompany isolated lesions of the putamen (a link in the motor circuit). Lesions of the mediodorsal and anterior nuclei of the thalamus may also result in apathy [81–83]. Unilateral lesions of the AC cortex produce transient AM [84] while the most dramatic examples of AM follow bilateral AC lesions [85], particularly lesions that extend from the cognitive effector region posteriorly into the skeletomotor effector division of the cingulate [51], a region connected with primary motor and SMA [29, 86]. There are non-overlapping representations for hand movements and speech within the AC cortex, reflecting separate AC motor channels [29]. While there are no published reports of isolated speech deficits with restricted cingulate lesions, bilateral lesions in monkeys to the rostral AC cortex, located around the genu of the corpus callosum, significantly impair spontaneous vocalization whereas deficits involving limb movements in humans have been associated with lesions affecting both rostral and caudal cingulate motor areas [46]. Reflecting reciprocal connections between the AC cortex and the MOF cortex [51], orbito-medial prefrontal cortical lesions may also result in severe forms of apathy [46, 57]. Data suggest a functional continuum along the rostral-caudal axis of medial frontal lobe regions from cognitive and emotional functions to motor functions devoted to self-initiation of action and thought [57]. Reflecting this continuum, a similar disorder affecting the “drive” for willed movement and speech (“motor neglect”) has been seen in patients with lesions of the SMA [87–89]. Such patients exhibit initial global akinesia and neglect, which then lateralizes in unilateral cases. Primarily part of the motor circuit, the SMA also receives reciprocal projections from the AC (area 24c’). Positron emission tomography (PET) studies in humans have shown that regional cerebral blood flow in the rostral SMA and adjacent mesial frontal cortex is associated with the self-generation of motor actions but not with externally cued ones [90]. Conceptually distinct, DLPC circuit lesions may produce a “cognitive” rather than “emotionalaffective” form of apathy, cognitive inertia resulting from difficulties in elaborating the plan of actions necessary for ongoing or forthcoming behavior [57]. This reflects links between layer V of the dorsal cingulate cortex and superficial layers of the DLPF cortex (see Figure 5.5), a powerful avenue of communication between cognitive and motor systems [31]. Thus, while the AC can be considered broadly as the cortical gateway for limbic motivation to influence goal-directed behavior [26], subtypes of apathy may be defined based on the connections of the cingulate with other regions [30, 91]. The orbitofrontal syndrome: personality and emotional changes The OF cortex is the neocortical representation of the limbic system [92] and is involved in the determination of the appropriate time, place, and strategy for environmentally elicited behavioral responses. Lesions in this area disconnect frontal monitoring systems from limbic input [93]. In particular, visceral sensory input to OF cortex is normally used to provide information about the internal milieu and guide bodily reactions to that status (“somatic marker hypothesis”) [94]. Lesions of the LOF may then result in an “interoceptic 67 Section I: Structural and Functional Neuroanatomy agnosia,” which may provide the basis for the variety of emotional and social deficits commonly observed with such lesions. Impulsivity and behavioral disinhibition are the hallmark symptoms of OF lesions. Common manifestations include lack of judgment and social tact, improper sexual remarks or gestures and other antisocial acts [95, 96]. Patients may exhibit inappropriate jocularity (witzelsucht) or emotional lability and irritability, trivial stimuli often resulting in abrupt outbursts of anger [97]. Inattention, distractibility, and increased motor activity may be seen, as well as hypomania or mania. MOF lesions are associated with abnormal autonomic responses to socially meaningful behavior and difficulty extinguishing unreinforced behavior, which correlate with antisocial acts [98]. Large bilateral OF lobe lesions may result in enslavement to environmental cues, with automatic imitation of the gestures of others, or enforced utilization of environmental objects [99]. Typically, however, patients with OF dysfunction exhibit a dissociation between impairment of behavior necessary for activities of daily living and normal performance on psychological tests sensitive to frontal lobe dysfunction, such as the Wisconsin Card-Sorting Test (WCST) [93, 100, 101]. Although the OF syndrome usually follows bilateral OF cortex injury [93, 101], unilateral lesions may produce a similar disorder [102]. Patients with ventral caudate lesions may also appear disinhibited, euphoric, impulsive and inappropriate, reproducing the corresponding OF lobe syndrome [103]. It is likely that the early appearance of comparable personality alterations in Huntington’s disease (HD) reflects the involvement of medial caudate regions receiving OF and AC circuit projections [104]. Similarly, mania (see below) may result not only from injury to MOF cortex and caudate nuclei but also from lesions to the right thalamus [105–108]. Mixed behavioral syndromes commonly accompany focal lesions of the GP and thalamus, reflecting the progressive spatial restriction of the parallel circuits at these levels [109]. The dorsolateral prefrontal syndrome: executive function deficits Both experimental and clinical data link the DLPF cortex and its subcortical connections with EFs. Executive functions incorporate anticipation, goal selection, planning, monitoring, and use of feedback to 68 adjust task performance. Patients with restricted DLPF cortex lesions have difficulty focusing and sustaining attention, generating hypotheses, and maintaining or shifting sets in response to changing task demands, as required by the WCST [110]. Associated features include reduced verbal and design fluency, impairment of memory search strategies and of organizational and constructional strategies on learning and copying tasks, and motor programming disturbances. Similar syndromes have been reported in patients with lesions of subcortical structures of the DLPF circuit [109]. Thus, impairment on tests of memory and EF, including the WCST, have been noted in patients with dorsal caudate lesions [103], bilateral GP hemorrhages [111] and bilateral or left paramedian/medio-dorsal thalamic infarction [112, 113]. Executive functions deficits and other features of “subcortical” dementia [114] in such conditions as HD, Parkinson’s disease (PD), progressive supranuclear palsy (PSP), Wilson’s disease, neuroacanthocytosis and other subcortical disorders are believed to reflect involvement of the DLPF circuit as it projects through the basal ganglia [109, 115–117]. Movement disorders and frontal-subcortical circuits Basal ganglia dysfunction frequently results not only in disorders of movement, but also in alterations in intellectual function, mood, personality, and behavior. The nature and severity of such changes reflect the extent of involvement of the behaviorally relevant FSC structures, which project through the caudate and ventral striatum, rather than the motor circuit, which projects to the putamen. Diseases affecting primarily the putamen, such as PD, thus exhibit less striking intellectual and emotional alterations than diseases that affect primarily the caudate, such as HD. In PD, executive dysfunction and dementia is associated with involvement of the medial substantia nigra and VTA, which project to the caudate nucleus and medial frontal cortex, and is not present when changes are confined to the lateral nigral neurons, which project to the putamen [118]. While patients with PSP, a hypokinetic disease, exhibit hypoactive behaviors such as apathy, patients with HD, a hyperkinetic syndrome, exhibit predominantly hyperactive behaviors, such as agitation, irritation, euphoria, or anxiety. Such behaviors may result from an excitatory subcortical output through the medial and OF cortical circuits [119]. Chapter 5: Frontal-subcortical circuits Subcortical dementia and amnestic syndromes Subcortical dementia is generally characterized by neuropsychological deficits typical of DLPF circuit lesions [114, 120]. Such deficits may be combined with amnesia, however, when subcortical lesions involve the thalamus [109]. The thalamus is poised at the interface of the FSCs and the medial temporal-limbic circuit (incorporating the hippocampus, fornix, hypothalamus, and thalamus). While the FSCs mediate memory activation and search functions, the medial temporalthalamic circuit mediates memory storage (both recall and recognition) [113]. A related syndrome involving amnesia, fluctuating inattention, apathy and psychomotor retardation, may occur with capsular genu infarction. It has been inferred that such lesions interrupt the inferior and anterior thalamic peduncles, functionally deactivating the ipsilateral frontal cortex [121]. The anterior thalamic peduncle conveys reciprocal connections between the thalamic dorsomedial nucleus and the cingulate gyrus, as well as the prefrontal and OF cortex; the inferior thalamic peduncle carries fibers which connect with the OF, insular and temporal cortices, and amygdala. Injury to these tracts thus produces a thalamocortical disconnection syndrome combining amnesia with FSC circuit deficits. Frontal-subcortical circuit dysfunction associated with neuropsychiatric disorders Obsessive-compulsive disorder and Tourette syndrome Convergent data, including ethological and experimental observations [122, 123], clinicopathologic findings [124, 125], behavioral observations [126], magnetic resonance imaging (MRI) [127–129] and PET studies [130–133] have implicated the basal ganglia and related cortical and thalamic structures in the pathobiology of both obsessive-compulsive disorder (OCD) and Gilles de la Tourette syndrome (TS). The neurobiologic substrates for these disorders include both corticostriatothalamocortical circuits and monoaminergic pathways that modulate the activity of these circuits [134–139]. In OCD, functional imaging studies have shown increased glucose metabolism or blood flow in the medial and OF cortex and AC gyrus, in the caudate nucleus, and, to a lesser extent, in the thalamus. This has suggested aberrant OF and limbic circuits as pathophysiologic mechanisms in OCD [130, 140, 141]. The prevailing theory is that the observed cortical, striatal, and thalamic overactivity in OCD results from a relative imbalance favoring the direct versus indirect pathways within this circuitry, leading to failed striatothalamic inhibition [140, 142, 143]. In TS, studies have suggested both anatomical and functional disturbances in basal gangliathalamocortical circuits. Diffusion-tensor (DT)-MRI has shown smaller left caudate and bilateral thalamic volumes in TS children compared with controls, tic severity being positively correlated with group differences in radial water perfusion in the right thalamus [144]. Resting state fluorodeoxyglucose (FDG)–PET has suggested reduced activity in a limbic basal ganglia-thalamocortical network, with covariate decreases in caudate and thalamic metabolism associated with smaller reductions in lentiform and hippocampal activity. The expression of this metabolic pattern correlated closely with ratings on the Tourette Syndrome Global Scale [133]. Tic severity in TS has also been correlated with hypoperfusion of the left caudate and cingulate gyrus [145] and differences in D2 DA receptor binding in the head of the caudate nucleus has predicted differences in tic severity within monozygotic twin pairs [146]. The preceding observations link tics to the “associative” (non-motor) neural circuits in which the caudate nucleus (not the putamen) is a key node, and has suggested that dopaminergic dysfunction in the caudate may underlie the integrated ideational-motor symptomatology of TS and the “compulsive” quality of tics [146]. Other studies have supported the importance of sensory elements in tic pathophysiology. Thus, functional MRI (fMRI) has identified a brain network of paralimbic and sensory association areas which are activated before tic onset, analogous to movements triggered internally by unpleasant sensations, such as itch and pain [147]. It has been hypothesized that discrete sets of striatal neurons may become overactive in TS, and that the production of simple tics (via motor circuit activation), complex tics (via activation of premotor areas, SMAs, and cingulate motor areas), and compulsions (with OF circuit involvement) might be determined 69 Section I: Structural and Functional Neuroanatomy by the specific FSC that is impacted [148]. Consistent with this hypothesis, increasing complexity of cognitive and behavioral symptoms in TS has been associated with increasing, apparently dysfunctional synaptic activity within the medial, lateral, and caudal OF cortices on FDG-PET scans [149]. PET data have also suggested an alteration of cortical–subcortical interactions in TS, with increased metabolic rates in frontal motor regions and decreases in glucose utilization in paralimbic prefrontal cortices and in the ventral striatum [131]. Ventral striatal dysfunction has also been shown with PET using [11C] dihydrotetrabenazine (DTBZ) to label the type 2 vesicular monoamine transporter, TS patients showing increased DTBZ binding in the ventral striatum relative to controls [150]. Pertinent to such findings, and by analogy with animal models of stereotypy, it has been postulated that the pathophysiology of TS may be related to an imbalance between dorsal and ventral striato-pallidal systems, perhaps arising from striosomal dysfunction [148, 151, 152]. Attention-deficit hyperactivity disorder Characterized by inattention, impulsivity and hyperactivity, attention-deficit hyperactivity disorder (ADHD) shares clinical features with other neuropsychiatric conditions, including the OF and DLPF syndromes. It has been hypothesized that the neural substrates of ADHD involve disturbances in frontal-subcortical interactions involving arousal and reward systems [153] which are driven primarily by dopaminergic activity and modulated by adrenergic and serotonergic mechanisms. Dysfunction of fronto-striatal type in ADHD has been inferred from neuropsychological studies [154–156], the pattern of cognitive deficits resembling those found in “striatal” disorders such as TS [157] and HD [158]. Both MRI and quantitative morphologic studies have shown smaller caudate volumes in ADHD patients compared with controls [159, 160]. Xenon inhalation and emission tomography have revealed striatal hypoperfusion in childhood ADHD, partially reversible by methylphenidate [161], and fMRI has also shown differences between ADHD children and controls in frontal-striatal function and its modulation by methylphenidate [162]. Performance on response inhibition tasks have correlated significantly with fMRI measures of the prefrontal cortex and caudate nuclei, predominantly in the right hemisphere 70 [163]. Comparable PET studies have shown caudate hypofunction in response-inhibition paradigms [164]. The relevance of these findings may relate to the integration of the caudate nuclei not only in the DLPF (executive function) circuit but also in the OF circuit, which subserves delayed responding in primates. Impaired signaling of delayed rewards is also integral to ADHD and has been linked to disturbances in motivation processes. This implicates the AC circuit [165], which links the ventral striatum, especially NAc [166] to both AC and OF cortex [167]. Dysfunction in NAc in ADHD is consistent with the observed variability in response to psychostimulant therapy in affected children [168]. A dual pathway hypothesis [169, 170] proposes that alterations within the DLPF circuit, modulated by mesocortical DA, and the AC/reward circuit, modulated by mesolimbic DA, constitute discrete neuropsychologic bases for dissociable psychological processes in ADHD, leading to executive/inhibitory deficits and delay aversion, respectively. It is likely that both DA and norepinephrine actions contribute to the therapeutic effects of stimulants in patients with ADHD. Electrophysiologic studies in animals suggest that DA decreases “noise” through modest levels of stimulation of D1 receptors, abundantly present in prefrontal cortex, while norepinephrine enhances “signals” through post-synaptic alpha2A-adrenoceptors in prefrontal cortex [70, 171]. Alpha2-receptor stimulation increases delay-related neuronal firing [172], the cellular measure of working memory and behavioral inhibition. Depression Both structural and functional brain-imaging studies have supported an association between lesions disrupting frontostriatal or paralimbic pathways and depressed mood. OF-inferior prefrontal cortex metabolism is lower in depressed when compared with non-depressed HD patients [173], and PD patients with depression show significantly lower metabolic activity both in the orbital-inferior frontal cortex and head of the caudate nucleus compared with those without depression [174]. This is consistent with pathological evidence in depressed, cognitively impaired parkinsonian patients of disproportionate degeneration of DA neurons in the VTA, a system that is linked to motivation and reward (see below) [152] and which projects to OF and prefrontal cortex. Chapter 5: Frontal-subcortical circuits Frontolimbic DA deficiency may underlie clinical similarities between the anhedonia and “psychomotor retardation” (PsR) of major depression and the lethargy of thought, affect, and movement that constitutes “bradyphrenia” in PD [175]. Anhedonia has been linked with novelty reward, mediated by dopaminergic projections to the ventral striatum, and reward scores in response to a dopaminergic challenge correlate with activity changes in ventrolateral prefrontal cortex and caudate/putamen on fMRI in major depression [176]. Depressed patients with PsR show decreased presynaptic DA function in the left caudate [177] and elevated putaminal D2 receptor binding [178]. The DA metabolite homovanillic acid (HVA) is diminished in depressed patients with PsR [179] and levodopa improves the PsR of major depression [180]. Furthermore, clinical improvement in depressed patients with PsR parallels the dopamimetic specificity of the antidepressants administered [181]. However, DA agonists alone have limited effect on depressive symptoms in PD [182] and cerebrospinal fluid (CSF) HVA levels do not correlate with mood in PD [183]. Based on convergent findings from patients with primary and secondary depression, a model of depression has been proposed which implicates failure of the coordinated interactions of a distributed network of cortical-limbic pathways [184]. In this model, a dorsal (“attention-cognition”) compartment which includes both neocortical (DLPF) and superior limbic (AC) elements is postulated to regulate attentional and cognitive aspects of depression, such as apathy, psychomotor slowing, and impaired attention and EF. A ventral (“vegetative-circadian”) compartment, which is composed of limbic, paralimbic, and subcortical regions (anterior insula, hippocampus, subgenual cingulate, and hypothalamus) is recognized as mediating circadian and vegetative aspects of the illness, including sleep, appetite, libidinal, and endocrine disturbances. Both PET and fMRI studies have revealed decreased function of dorsal regions, such as the ventral AC cortex, and increased limbic metabolism and activation in depression [185], while fMRI has shown reciprocal effects of antidepressant treatment on activity and connectivity in these regions [186]. The rostral anterior (pregenual) cingulate is isolated from both the ventral and dorsal compartments based on its cytoarchitectural characteristics and its reciprocal connections to both compartments and may serve an important regulatory role in mediating interactions between them [187]. An important role for the subgenual cingulate (BA 25) in depression was first provided by the observation of metabolic change (overactivity) in this region in treatment-resistant depression, which uniquely predicted antidepressant response [184, 185]. Deep brain stimulation (DBS) of white matter tracts adjacent to the subgenual cingulate was subsequently shown to effectively reverse symptoms in otherwise treatmentresistant depression [188, 189] (see below). Disturbances in the subgenual cingulate circuit have been linked also with the rapid mood shifts in bipolar disorder, and may have a role in the pathophysiology of OCD [190, 191]. Mania and the lateralization of emotional behavior Affective response to brain injury may reflect the hemisphere involved. Thus, crying is more common in patients with left hemispheric lesions, while laughter occurs with right-sided lesions [192]. Left frontal and left basal ganglia infarctions are most likely to be associated with depression [103, 193] and in PD depression is more common with right hemiparkinsonism (left striato-frontal dysfunction) [194]. Conversely, mania frequently results from right-sided thalamic or right medial diencephalic lesions that may disrupt hypothalamic circuits or disturb modulating transmitters traversing the medial forebrain bundle [106, 107]. Mania has also been observed in patients with MOF cortex lesions and caudate dysfunction in basal ganglia disorders such as HD [193, 195]. The increase in appetite drives that often accompanies mania has suggested underlying hyperfunctioning of the paleocortical paralimbic belt [51]. Whereas depressed patients show bilateral temporal hypometabolism on FDG-PET scans, patients with mania exhibit unilateral, right-sided temporal hypometabolism [194]. A differential biochemical response to injury in the two hemispheres may contribute to the polarity of the expressed mood disorder. Thus, right but not left frontolateral cortical lesions in rat models produce hyperactivity, widespread depletion of brain norepinephrine, and an increased turnover of DA in the NAc. Correspondingly, right but not left hemispheric stroke in humans leads to an increase in 5-HT-2 serotonin receptor binding in both temporal and parietal cortex [194]. 71 Section I: Structural and Functional Neuroanatomy Bipolar disorder Vulnerability to bipolar disorder (BD) is linked with the gene coding for diacylglycerol (DAG) kinase eta, an enzyme that metabolizes DAG, inhibiting phosphatidylinositol-protein kinase C intracellular signaling [196–198]. The phospho-inositol second messenger system is concentrated in striosomes in limbic brain regions. This chemoarchitectural disorganization in BD is consistent with a disease model that involves dysfunction within both striato-thalamoprefrontal networks and limbic modulating regions [199–202]. Support for this model includes MRI abnormalities in prefrontal cortical areas, striatum and amygdala, and activation differences in anterior limbic regions shown by functional imaging studies. Specifically, reduced ventral and orbital prefrontal activity and increased amygdala activity has been noted, both during episodes and in remission. This suggests that dysregulation of mood in BD may result from diminished prefrontal modulation of subcortical limbic structures [199, 203]. Schizophrenia Failures of stimulus filtering and gating in schizophrenic patients have been linked with abnormalities of cortico-striato-pallido-thalamic circuitry [204–206]. Evidence implicating the DLPF circuit in schizophrenia includes the similarity of observed neuropsychological deficits to symptoms associated with DLPF lesions, decreased regional metabolism and blood flow activation, disruption of cortical subplate activity (required for connectivity of thalamocortical neurons), and decreases in major components of the GABA cortical inhibitory system [207–209]. The DLPF cortex is part of a task-related frontoparietal neuronal network, the activity of which is anticorrelated with a “default” network that is normally active at rest. The latter network, which includes medial prefrontal and posterior cortices, has been linked with internally generated “stimulusindependent” thought as well as self-monitoring and salience monitoring [210]. Using a brain-imaging technique based on low-frequency fluctuations of the blood oxygen level-dependent (BOLD) signal, schizophrenic patients exhibited striking deficits in regions associated with the default network, including the posterior cingulate and medial prefrontal regions [211]. This supports earlier data showing deficits in 72 small interneurons in cingulate and prefrontal cortices in schizophrenic patients [212]. It has been suggested that if the default network in schizophrenia is associated with decreased communication between the medial prefrontal and posterior cingulate regions, self-monitoring systems may become split, leading to the perception that auditory thoughts are externally produced [213]. Substance abuse disorders and impulse control disorders The principal components of the drug reward circuit are the A10 dopaminergic cell group of the VTA, limbic structures of the basal forebrain (OF cortex, AC cortex and ventral striatum, particularly NAc) and the dopaminergic connection between the VTA and basal forebrain limbic system (mesocorticolimbic DA system). This network links substance addiction to brain motivational and reward systems. Linked brain regions include the amygdala, which provides affective salience, and the hippocampus, relaying contextual memories. While early phases of drug-seeking behavior and addiction are probably characterized by interactions between these systems, the dorsal striatum appears to become involved in later phases when drugtaking has become a habit [214–218]. Other components of the drug reward circuit are the opioid peptide, GABA, glutamate, and serotonin systems, and other neural inputs that interact with the VTA and basal forebrain [219–221]. Impulse control disorders (ICDs), including pathological gambling, compulsive sexual behavior, and compulsive buying, are characterized by a failure to resist an impulse, drive, or temptation to perform a typically pleasurable activity. A range of ICDs occur at a greater frequency in PD than in the general population and are linked particularly with the use of direct DA agonist drugs [222]. Compared with levodopa, DA agonists have significantly greater occupation of D3 DA receptors which are concentrated in limbic brain regions, including ventral striatum, with highest concentrations in the shell of the NAc [223]. Even within the dorsal striatum, the D3 receptor is primarily localized to the patch/striosome compartment that is anatomically connected to limbic structures [224, 225]. This and other evidence suggests that pathological gambling and other ICDs in PD may result from dopaminergic overstimulation of a relatively preserved Chapter 5: Frontal-subcortical circuits mesocorticolimbic DA pathway in predisposed individuals [226]. Although considered by some as OCD-spectrum disorders, there is broader support for categorizing ICDs as behavioral addictions [227–230]. Supporting such a model, a single photon emission computed tomography (SPECT) study of PD patients with pathological gambling showed resting state overactivity in a right hemisphere network that included the OF cortex, hippocampus, amygdala, insula, and ventral pallidum [226]. The ventral pallidum is part of the limbic circuitry (see above) and is implicated in the modulation of hedonic responses to natural and drug rewards, and subsequent reward seeking-motivated drive [231]. Therapeutic interventions for frontal-subcortical circuit disorders Pharmacologic interventions The dorsolateral prefrontal syndrome: executive dysfunction In PD, specific executive functions, including working memory, cognitive sequencing, and attention shifting, may respond at least partially to dopaminergic therapies [232, 233]. This reflects the combined impact in PD of caudate nuclear DA deficiency, which creates a partial “disconnection syndrome” of subcortical origin [234] and a lesser reduction of DA in the DLPF cortex [235]. However, incomplete reversal of cognitive deficits with DA agonists is typically noted in PD [233, 236], reflecting the importance of non-dopaminergic neuronal dysfunction, especially cholinergic dysfunction, in PD dementia. Executive dysfunction in PD may also be ameliorated by the selective norepinephrine reuptake inhibitor atomoxetine [237]. This is consistent with psychopharmacologic and anatomical studies, which implicate the noradrenergic as well as the dopaminergic system as an important modulator of frontal lobe function [172, 238, 239]. Noradrenergic agents may also ameliorate executive dysfunction in a variety of other clinical states. The alpha-2 adrenergic agonists clonidine and guanfacine both enhance working memory performance in aged monkeys [240, 241] and cognitive tasks mediated by prefrontal cortex, such as Trails B, Word Fluency and Stroop tasks, are improved by clonidine in patients with schizophrenia and Korsakoff ’s syndrome [242–244]. In patients with dementia of the frontal lobe type, EF may be selectively enhanced by the alpha-2 adrenergic antagonist idazoxan [245]. In ADHD and TS, a variety of agents having important effects on the noradrenergic system, dopaminergic system, or both, may ameliorate features of both DLPF (attentional/executive) and OF (inhibitory) dysfunction (see below). Such drugs include selegiline, stimulant medications, low-dose tricyclic antidepressants, clonidine, and guanfacine [246–251]. The anterior cingulate syndrome: akinetic mutism and apathy In animals, a syndrome similar to akinetic mutism (AM) was first demonstrated by bilateral or unilateral injection of 6-hydroxydopamine into the SN, VTA, or nigrostriatal tract within the medial forebrain bundles of the lateral hypothalamus [252–254]. These behavioral deficits could be reversed by the direct DA agonist apomorphine [255, 256], and blocked by pretreatment with the DA receptor antagonist spiroperidol [257]. Similarly, in an early clinical report, AM following surgical removal of a tumor from the anterior hypothalamus responded to the DA receptor agonists lergotrile and bromocriptine but not to the presynaptic dopaminomimetics carbidopa/levodopa or methylphenidate [258]. This suggested that akinesia in this setting resulted from loss of dopaminergic input to AC or other corticolimbic structures rather than to the striatum. Clinico-pathologic correlations have subsequently suggested that isolated damage to any of the projections of brainstem dopaminergic nuclear groups may result in AM [259]. Chronic AM secondary to mesencephalic infarction, destroying ventral tegmental DA neurons at their site of origin, may also be reversed with DA agonists [260]. Where DA receptors have been lost, however, as in patients with lesions involving the AC gyri, response to direct DA agonists is typically poor. While requiring confirmation, a recent report suggests that AM may respond successfully to intramuscular olanzapine [261], a drug which may also ameliorate negative symptoms in schizophrenia. In addition to blockade of DA D2-receptors in the mesolimbic pathway, olanzapine blocks serotonin 5HT2A receptors, leading to disinhibition of D2 receptors and enhanced DA release in the mesocortical pathway. 73 Section I: Structural and Functional Neuroanatomy A preponderance of the latter action might explain observed increases in levels of DA in the medial prefrontal cortex in response to olanzapine administration [261]. Dopaminergic agents may also afford a clinically significant and sustained improvement in apathetic states encountered in a variety of neuropsychiatric disorders including Wilson’s disease, PD and human immunodeficiency virus (HIV)-associated dementia, subcortical strokes and anterior communicating artery aneurysm [209, 262–266]. Effective agents in such conditions may include direct DA agonists, particularly pramipexole and ropinirole, which have some selectivity for D3 receptors, amantadine, selegiline, bupropion, amphetamine, and methylphenidate. Apathy is the most commonly observed behavioral disturbance in Alzheimer’s disease (AD) and is associated with AC hypoperfusion [267]. The documented improvement in AD-related apathy with cholinesterase inhibitor therapy [268] may reflect partial correction of cholinergic disconnection of AC structures. The latter include the basal nucleus of the amygdala [51], innervated by cholinergic projections from basal forebrain structures, and the midline thalamic nuclei which receive input both from the basal forebrain and from cholinergic pedunculopontine projections that form part of the ascending reticular activating system. Cholinesterase inhibitors may also ameliorate apathy in traumatic brain injury [269, 270]. The orbitofrontal syndrome: personality change A variety of pharmacologic agents may ameliorate, at least partially, the disinhibited behavior of the patient with OF circuit dysfunction [271]. Such drugs include the major and minor tranquilizers, propranolol, buspirone, carbamazepine, sodium valproate, lithium, clonidine, and selective serotonin reuptake inhibitors (SSRIs). Robust data, including studies using 5HT1B receptor gene knockout mice [272], link behavioral disinhibition with central serotonergic deficiency [273–276]. The efficacy of serotonergic agonists, including fluoxetine and clomipramine, for impulsive, aggressive, or sexually disinhibited behaviors [275, 277, 278] may relate to the density of serotonin receptors in the ventral striatum and other limbic brain regions. Certain 5-HT1A agonists (“serenics”) exert a dose-dependent decrease in aggression with a concomitant increase in social interest in animal 74 paradigms [279]. In man, both propranolol and pindolol have agonist effects at limbic somato-dendritic 5-HT1A receptors at dosages used in the treatment of aggressive behavior [280–282] and the partial 5-HT1A agonist buspirone may also be effective in the treatment of aggression in a variety of neuropsychiatric conditions. In addition to their dopaminergic activity, neuroleptics may have a serotonergic mode of action in the treatment of impulsive aggression by binding to and down-regulating the 5-HT2 receptor [274], which is represented in intermediate levels in the NAc and striatum. Lithium’s mood-stabilizing action may be mediated by effects both on the serotonin system and on phosphoinositide [283, 284], which is selectively concentrated in striosomes of the medial and ventral striatum [64], regions which receive dense OF input. Clonidine is an alpha-2 noradrenergic agonist, which reduces central noradrenergic transmission by stimulating presynaptic autoreceptors [285, 286]. Its efficacy for OF syndrome is exemplified by the report of a patient with OF dysfunction including mania secondary to bilateral OF contusions [105]. The rapid response to clonidine in this case was attributed to reduction of noradrenergic overactivity induced by lesions of prefrontal areas projecting to noradrenergic systems [287] which, in turn, innervate and modulate prefrontal cortex [238, 239, 288]. Clonidine may also successfully ameliorate symptoms characteristic of OF circuit dysfunction, including distractibility, impulsivity and emotional lability, in children with ADHD and TS [246, 248, 289]. Several classes of drugs thus have the potential to favorably influence symptoms of OF circuit dysfunction, reflecting serotonergic, dopaminergic, and noradrenergic modulation of functions of the OF cortex and connected brain regions. Obsessive-compulsive disorder Serotonin is robustly implicated in the pathophysiology of OCD and SSRIs are effective treatments for this condition [290, 291]. The serotonergic innervation of the striatum is dense and is localized to those basal ganglia regions which receive input from the OF and AC cortices, via the ventromedial caudate nucleus head and ventral striatum, respectively [62]. Glucose metabolic rates in the head of the caudate nucleus diminish when OCD is treated successfully with SSRIs, a result that may be attributable to the action of serotonin afferents from the dorsal raphe on Chapter 5: Frontal-subcortical circuits caudate interneurons [130, 292]. The thalamofrontal pathways, lesioned at different sites in anterior capsulotomy and subcaudate tractotomy (see below), contain both serotonergic and dopaminergic tracts [293]. The improvement in OCD that may be observed with adjunctive DA receptor blockers, particularly in TS [294], reflects the functionally coupled interactions between brain 5-HT and DA systems [295]. Neurosurgical interventions Obsessive-compulsive disorder and Tourette syndrome Anterior cingulotomy or limbic leucotomy have been used successfully in the past to treat disabling ritualistic behaviors in selected patients with OCD [296] and TS [297–299]. The rationale for lesioning the anterior cingulate in these disorders derives from its role as the conduit for frontal cortex input to the Papez circuit and limbic system [300, 301], while limbic leucotomy selectively targets both anterior cingulate cortex and frontothalamic projections. The beneficial effect of anterior capsulotomy in OCD [302] would also be predicted by the proposed models, as this procedure severs a pathway for reciprocal tracts interconnecting the OF cortex with the dorsomedial and related thalamic nuclei [143, 303]. More recently this has been replaced effectively by DBS of the ventral anterior internal capsule [304], a procedure which has been shown to modulate activity in the dorsal and ventral striatum, subgenual cingulate cortex, MOF cortex, and thalamus [191, 305, 306]. This demonstrates how the stimulation of a single region can generate complex changes throughout the interconnected network [303]. Electrode placements in ventral caudate, STN, zona incerta (near the STN), and ventral striatum have also been effective for refractory OCD [98]. Deep brain stimulation of several targets has been used effectively to treat severe pharmacologically refractory TS. These include the nucleus ventralis oralis, the motor and limbic portions of GPi [307–311], NAc/anterior limb of the internal capsule [312], and the centromedian parafascicular complex of the median and intralaminar thalamic nuclei [311, 313–317], a unit which has significant projections into motor striatum as well as limbic and associative areas of the subthalamic nucleus [318]. Targeting of these sites often results in at least 70% long-term reductions in vocal or motor tics, with accompanying disappearance of the preceding sensory urge. Depression Five potential targets have been identified in the literature as potential surgical targets for DBS in treatmentresistant depression: (1) ventral striatum/NAc; (2) subgenual cingulate cortex (area 25); (3) inferior thalamic peduncle; (4) rostral cingulate cortex (area 24a); and (5) lateral habenula [319]. Although it has been suggested that the subgenual cingulate region may prove to be most effective, based on its anatomic connectivity [189], further studies are required in larger patient groups to assess both the safety and efficacy of these targets. Addiction In the past, procedures such as cingulotomy, hypothalamotomy, and resection of the substantia innominata and NAc have been recommended as treatments for severe addictive disorders. With expansion of knowledge concerning its neurobiology, refractory addictive states might also prove amenable to DBS of relevant brain targets [320]. Conclusion Frontal-subcortical circuits (FSCs) are effector mechanisms that allow the organism to act on the environment. The DLPF circuit allows the organization of information to facilitate a response; AC circuitry is required for motivated behavior; the LOF circuit allows the integration of limbic and emotional information into contextually appropriate behavioral responses; and mood regulation and integration of information pertaining to emotions are functions, respectively, of the subgenual cingulate and MOF circuits. Correspondingly, impaired EFs, apathy, impulsivity, and depression are hallmarks of FSC dysfunction. Movement disorders typically involve not only the motor circuit but also other FSCs as they project through the basal ganglia. A variety of other neuropsychiatric disorders may result from disturbances that impact directly or indirectly on the integrity or functioning of these circuits. Examples of such conditions include OCD and TS, ADHD, substance abuse and impulse control disorders, BD and schizophrenia. The circuits involve a number of transmitters, receptor subtypes, and second messengers that can be manipulated pharmacologically. In addition, for an increasing number of conditions, such as disabling OCD, TS and depression, discrete neurosurgical approaches to 75 Section I: Structural and Functional Neuroanatomy specific FSC dysfunctions are being explored more actively, with advances in DBS showing particular promise. Acknowledgments Special thanks are offered to John Nyquist for preparation of the figures. References 1. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9:357–81. 2. Alexander GE, Crutcher MD. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 1990;13(7):266–71. 3. Alexander GE, Crutcher MD, DeLong MR. Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, “prefrontal” and “limbic” functions. Prog Brain Res. 1990;85:119–46. 4. Pandya D, Barnes CL. Architecture and connections of the frontal lobe. In Perecman E, editor. The Frontal Lobes Revisited. New York, NY: IRBN Press; 1987, pp. 41–72. 5. Sanides F. Comparative architectonics of the neocortex of mammals and their evolutionary interpretation. Annals N Y Acad Sci. 1969;167(1):404–23. 12. Rosvold HE. The prefrontal cortex and caudate nucleus: a system for effecting correction in response mechanisms. In Rupp C, editor. Mind as a Tissue; Proceedings. New York, NY: Hoeber Medical Division; 1968, pp. 21–38. 13. Divac I. Neostriatum and functions of prefrontal cortex. Acta Neurobiol Exp (Wars.) 1972;32(2): 461–77. 14. Johnson TN, Rosvold HE, Mishkin M. Projections from behaviorally-defined sectors of the prefrontal cortex to the basal ganglia, septum, and diencephalon of the monkey. Exp Neurol. 1968;21(1):20–34. 15. Groenewegen HJ, Berendse HW, Wolters JG, Lohman AH. The anatomical relationship of the prefrontal cortex with the striatopallidal system, the thalamus and the amygdala: evidence for a parallel organization. Prog Brain Res. 1990;85:95–116;discussion 116–18. 16. Parent A. Extrinsic connections of the basal ganglia. Trends Neurosci. 1990;13(7):254–8. 17. Lichter DG, Cummings J. Introduction and overview. In Lichter DG, Cummings JL, editors. Frontalsubcortical Circuits in Psychiatric and Neurological Disorders. New York, NY: Guilford Press; 2001, pp. 1–43. 18. Selemon LD, Goldman-Rakic PS. Longitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey. J Neurosci. 1985;5(3):776–94. 6. Sanides F. Functional architecture of motor and sensory cortices in primates in the light of a new concept of neocortex evolution. In Noback CR, Montagna W, editors. The Primate Brain. New York, NY: Meredith Corp.; 1970, pp. 137–209. 19. Parent A, Bouchard C, Smith Y. The striatopallidal and striatonigral projections: two distinct fiber systems in primate. Brain Res. 1984;303(2):385–90. 7. Yeterian EH, Pandya DN. Prefrontostriatal connections in relation to cortical architectonic organization in rhesus monkeys. J Comp Neurol. 1991;312(1):43–67. 20. Smith Y, Hazrati LN, Parent A. Efferent projections of the subthalamic nucleus in the squirrel monkey as studied by the PHA-L anterograde tracing method. J Comp Neurol. 1990;294(2):306–23. 8. Yeterian EH, Van Hoesen GW. Cortico-striate projections in the rhesus monkey: the organization of certain cortico-caudate connections. Brain Res. 1978;139(1):43–63. 21. Ilinsky IA, Jouandet ML, Goldman-Rakic PS. Organization of the nigrothalamocortical system in the rhesus monkey. J Comp Neurol. 1985;236(3):315–30. 9. Selemon LD, Goldman-Rakic PS. Common cortical and subcortical targets of the dorsolateral prefrontal and posterior parietal cortices in the rhesus monkey: evidence for a distributed neural network subserving spatially guided behavior. J Neurosci. 1988;8(11): 4049–68. 10. Rosvold HE, Szwarcbart MK. Neural structures involved in delayed response performance. In Warren JM, Akert K, , editors. The Frontal Granular Cortex and Behavior. New York, NY: McGraw-Hill; 1964, pp. 1–15. 76 11. Divac I, Rosvold HE, Szwarcbart MK. Behavioral effects of selective ablation of the caudate nucleus. J Comp Physiol Psychol. 1967;63(2):184–90. 22. Kim R, Nakano K, Jayaraman A, Carpenter MB. Projections of the globus pallidus and adjacent structures: an autoradiographic study in the monkey. J Comp Neurol. 1976;169(3):263–90. 23. McFarland NR, Haber SN. Thalamic relay nuclei of the basal ganglia form both reciprocal and nonreciprocal cortical connections, linking multiple frontal cortical areas. J Neurosci. 2002;22(18):8117–32. 24. Kievit J, Kuypers HG. Organization of the thalamo-cortical connexions to the frontal lobe in the rhesus monkey. Exp Brain Res. 1977;29(3–4):299–322. Chapter 5: Frontal-subcortical circuits 25. Giguere M, Goldman-Rakic PS. Mediodorsal nucleus: areal, laminar, and tangential distribution of afferents and efferents in the frontal lobe of rhesus monkeys. J Comp Neurol. 1988;277(2):195–213. 38. Kopell BH, Greenberg BD. Anatomy and physiology of the basal ganglia: implications for DBS in psychiatry. Neurosci Biobehav Rev. 2008;32(3):408–22. 26. Mega M, Cummings J. The cingulate and cingulate syndromes. In Trimble MR, Cummings JL, editors. Contemporary Behavioral Neurology. Boston, MA: Butterworth-Heinemann; 1997, pp. 189–214. 39. Haber SN, Lynd E, Klein C, Groenewegen HJ. Topographic organization of the ventral striatal efferent projections in the rhesus monkey: an anterograde tracing study. J Comp Neurol. 1990; 293(2):282–98. 27. Ongur D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex. 2000;10(3): 206–19. 40. Haber SN, Kunishio K, Mizobuchi M, Lynd-Balta E. The orbital and medial prefrontal circuit through the primate basal ganglia. J Neurosci. 1995;15(7 Pt 1): 4851–67. 28. Johnson TN, Rosvold HE. Topographic projections on the globus pallidus and the substantia nigra of selectively placed lesions in the precommissural caudate nucleus and putamen in the monkey. Exp Neurol. 1971;33(3):584–96. 41. Groenewegen HJ, Trimble M. The ventral striatum as an interface between the limbic and motor systems. CNS Spectr. 2007;12(12):887–92. 29. Middleton F, Strick PL. Revised neuroanatomy of frontal-subcortical circuits. In Lichter DG, Cummings JL, editors. Frontal-subcortical Circuits in Psychiatric and Neurological Disorders. New York, NY: Guilford Press; 2001, pp. 44–58. 30. Devinsky O, Morrell MJ, Vogt BA. Contributions of anterior cingulate cortex to behaviour. Brain 1995;118(Pt 1):279–306. 31. Barbas H, Pandya DN. Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey. J Comp Neurol. 1989;286(3):353–75. 32. Morecraft RJ, Geula C, Mesulam MM. Architecture of connectivity within a cingulo-fronto-parietal neurocognitive network for directed attention. Arch Neurol. 1993;50(3):279–84. 33. Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford: Oxford University Press; 2006. 34. Goldman-Rakic PS, Porrino LJ. The primate mediodorsal (MD) nucleus and its projection to the frontal lobe. J Comp Neurol. 1985;242(4): 535–60. 35. Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000;4(6):215–22. 36. Kunishio K, Haber SN. Primate cingulostriatal projection: limbic striatal versus sensorimotor striatal input. J Comp Neurol. 1994;350(3):337–56. 37. Heimer L. The olfactory cortex and the ventral triatum. In Livingston KE, Hornykiewicz O, editors. Limbic Mechanisms: The Continuing Evolution of the Limbic System Concept. Proceedings of the Limbic System Symposium held at the University of Toronto, ON, Canada, November 5–6, 1976, as a satellite to the sixth annual meeting of the Society of Neuroscience. New York, NY: Plenum Press; 1978. 42. Haber SN, Lynd-Balta E, Mitchell SJ. The organization of the descending ventral pallidal projections in the monkey. J Comp Neurol. 1993;329(1):111–28. 43. Ongur D, Ferry AT, Price JL. Architectonic subdivision of the human orbital and medial prefrontal cortex. J Comp Neurol. 2003;460(3):425–49. 44. Freedman LJ, Insel TR, Smith Y. Subcortical projections of area 25 (subgenual cortex) of the macaque monkey. J Comp Neurol. 2000;421(2):172–88. 45. Tekin S, Cummings JL. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. J Psychosom Res. 2002;53(2):647–54. 46. Paus T. Primate anterior cingulate cortex: where motor control, drive and cognition interface. Nat Rev Neurosci. 2001;2(6):417–24. 47. Gutman DA, Holtzheimer PE, Behrens TE, Johansen-Berg H, Mayberg HS. A tractography analysis of two deep brain stimulation white matter targets for depression. Biol Psychiatry 2009; 65(4):276–82. 48. Vogt BA, Finch DM, Olson CR. Functional heterogeneity in cingulate cortex: the anterior executive and posterior evaluative regions. Cereb Cortex 1992;2(6):435–43. 49. Drevets WC, Price JL, Simpson JR, Jr. et al. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 1997;386(6627):824–7. 50. Yang TT, Simmons AN, Matthews SC et al. Adolescent subgenual anterior cingulate activity is related to harm avoidance. Neuroreport 2009;20(1):19–23. 51. Mega MS, Cummings JL, Salloway S, Malloy P. The limbic system: an anatomic, phylogenetic, and clinical perspective. J Neuropsychiatry Clin Neurosci. 1997;9(3):315–30. 52. Haber SN. The primate basal ganglia: parallel and integrative networks. J Chem Neuroanat. 2003; 26(4):317–30. 77 Section I: Structural and Functional Neuroanatomy 53. An X, Ongur D, Price J. Prefrontostriatal projections in relation to cortico-cortical networks in the macaque monkey. Soc Neurosci Abstr. 1997;23:901. 54. Vogt BA, Pandya DN. Cingulate cortex of the rhesus monkey: II. Cortical afferents. J Comp Neurol. 1987;262(2):271–89. 55. Morecraft RJ, Geula C, Mesulam MM. Cytoarchitecture and neural afferents of orbitofrontal cortex in the brain of the monkey. J Comp Neurol. 1992;323(3):341–58. 56. Pandya DN, Van Hoesen GW, Mesulam MM. Efferent connections of the cingulate gyrus in the rhesus monkey. Exp Brain Res. 1981;42(3–4):319–30. 57. Levy R, Dubois B. Apathy and the functional anatomy of the prefrontal cortex-basal ganglia circuits. Cereb Cortex. 2006;16(7):916–28. 58. Barbas H. Specialized elements of orbitofrontal cortex in primates. Ann N Y Acad Sci. 2007;1121: 10–32. 59. Albin RL, Young AB, Penney JB. The functional anatomy of basal ganglia disorders. Trends Neurosci. 1989;12(10):366–75. 60. Groenewegen HJ, Roeling TAP, Voorn P, Berendse HW. The parallel arrangement of basal ganglia-thalamocortical circuits: a neuronal substrate for the role of dopamine in motor and cognitive functions. In Wolters EC, Scheltens P, editors. Mental Dysfunction in Parkinson’s Disease. Amsterdam: Vrije Universiteit; 1993, pp. 3–18. 61. Graybiel AM. Neurotransmitters and neuromodulators in the basal ganglia. Trends Neurosci. 1990;13(7):244–54. 62. Lavoie B, Parent A. Immunohistochemical study of the serotoninergic innervation of the basal ganglia in the squirrel monkey. J Comp Neurol. 1990;299(1):1–16. 63. Chesselet MF, Gonzales C, Levitt P. Heterogeneous distribution of the limbic system-associated membrane protein in the caudate nucleus and substantia nigra of the cat. Neuroscience 1991;40(3):725–33. 64. Gibb WR. Melanin, tyrosine hydroxylase, calbindin and substance P in the human midbrain and substantia nigra in relation to nigrostriatal projections and differential neuronal susceptibility in Parkinson’s disease. Brain Res. 1992;581(2):283–91. 65. Fotuhi M, Dawson TM, Sharp AH et al. Phosphoinositide second messenger system is enriched in striosomes: immunohistochemical demonstration of inositol 1,4,5-trisphosphate receptors and phospholipase C beta and gamma in primate basal ganglia. J Neurosci. 1993;13(8):3300–8. 66. Parent A, Pare D, Smith Y, Steriade M. Basal forebrain cholinergic and noncholinergic projections to the 78 thalamus and brainstem in cats and monkeys. J Comp Neurol. 1988;277(2):281–301. 67. McGeer PL, McGeer EG. Neurotransmitters and their receptors in the basal ganglia. Adv Neurol. 1993;60:93–101. 68. Daniel DG, Weinberger DR, Jones DW et al. The effect of amphetamine on regional cerebral blood flow during cognitive activation in schizophrenia. J Neurosci. 1991;11(7):1907–17. 69. Sawaguchi T. Catecholamine sensitivities of neurons related to a visual reaction time task in the monkey prefrontal cortex. J Neurophysiol. 1987;58(5):1100–22. 70. Arnsten AF. Fundamentals of attention-deficit/ hyperactivity disorder: circuits and pathways. J Clin Psychiatry 2006;67 (Suppl. 8):7–12. 71. Hoyer D, Palacios JM, Mengod G. 5-HT receptor distribution in the human brain: autoradiographic studies. In Marsden CA, Heal DJ, editors. Central Serotonin Receptors and Psychotropic Drugs. London: Blackwell Scientific Publications; 1992, pp. 100–25. 72. Flaherty AW, Graybiel AM. Anatomy of the basal ganglia. In Marsden CD, Fahn S, editors. Movement Disorders. Boston: Butterworth-Heinemann Ltd.; 1994, pp. 3–27. 73. Mogenson GJ, Jones DL, Yim CY. From motivation to action: functional interface between the limbic system and the motor system. Prog Neurobiol. 1980;14(2–3): 69–97. 74. Scheibel AB. The thalamus and neuropsychiatric illness. J Neuropsychiatry Clin Neurosci. 1997; 9(3):342–53. 75. Salloway S, Cummings J. Subcortical structures and neuropsychiatric illness. Neuroscientist 1996;2(1): 66–75. 76. Gerfen CR. The neostriatal mosaic: compartmentalization of corticostriatal input and striatonigral output systems. Nature 1984; 311(5985):461–4. 77. Cairns H, Oldfield R, Pennybacker JB, Whitteridge DC. Akinetic mutism with an epidermoid cyst at the third ventricle. Brain. 1941;64:275–90. 78. Auerbach S. Beitrag zur diagnostik der geschwülste des stirnhirns. J Neurol. 1902;22(3):312–32. 79. van Domburg PH, ten Donkelaar HJ, Notermans SL. Akinetic mutism with bithalamic infarction. Neurophysiological correlates. J Neurol Sci. 1996;139(1):58–65. 80. Bhatia KP, Marsden CD. The behavioural and motor consequences of focal lesions of the basal ganglia in man. Brain 1994;117 (Pt 4):859–76. 81. Engelborghs S, Marien P, Pickut BA, Verstraeten S, De Deyn PP. Loss of psychic self-activation after Chapter 5: Frontal-subcortical circuits paramedian bithalamic infarction. Stroke 2000; 31(7):1762–5. 82. Bogousslavsky J, Regli F, Delaloye B et al. Loss of psychic self-activation with bithalamic infarction. Neurobehavioural, CT, MRI and SPECT correlates. Acta Neurol Scand. 1991;83(5):309–16. 83. Ghika-Schmid F, Bogousslavsky J. The acute behavioral syndrome of anterior thalamic infarction: a prospective study of 12 cases. Ann Neurol. 2000; 48(2):220–7. 84. Damasio H, Damasio AR. Lesion Analysis in Neuropsychology. New York, NY: Oxford University Press; 1989. 85. Barris RW, Schuman HR. [Bilateral anterior cingulate gyrus lesions; syndrome of the anterior cingulate gyri.] Neurology 1953;3(1):44–52. 86. Koski L, Paus T. Functional connectivity of the anterior cingulate cortex within the human frontal lobe: a brain-mapping meta-analysis. Exp Brain Res. 2000;133(1):55–65. 87. Damasio AR, Vanhoesen GW. Structure and function of the supplementary motor area. Neurology 1980;30(4):359. 88. Laplane D, Degos JD. Motor neglect. J Neurol Neurosurg Psychiatry 1983;46(2):152–8. 89. von Giesen HJ, Schlaug G, Steinmetz H et al. Cerebral network underlying unilateral motor neglect: evidence from positron emission tomography. J Neurol Sci. 1994;125(1):29–38. 90. Jenkins IH, Jahanshahi M, Jueptner M, Passingham RE, Brooks DJ. Self-initiated versus externally triggered movements. II. The effect of movement predictability on regional cerebral blood flow. Brain 2000;123 (Pt 6):1216–28. 91. Stuss DT. Differentiation of states and causes of apathy. In Borod JC, editor. The Neuropsychology of Emotion. New York, NY: Oxford University Press; 2000, pp. 340–63. 97. Stuss DT, Gow CA, Hetherington CR. “No longer Gage”: frontal lobe dysfunction and emotional changes. J Consult Clin Psychol. 1992;60(3):349–59. 98. Chow TW, Cummings J. Frontal-subcortical circuits. In Miller BL, Cummings JL, editors. The Human Frontal Lobes: Functions and Disorders. 2nd edition. New York, NY: Guilford Press; 2007, pp. 25–73. 99. Lhermitte F, Pillon B, Serdaru M. Human autonomy and the frontal lobes. Part I: Imitation and utilization behavior: a neuropsychological study of 75 patients. Ann Neurol. 1986;19(4):326–34. 100. Laiacona M, De Santis A, Barbarotto R et al. Neuropsychological follow-up of patients operated for aneurysms of anterior communicating artery. Cortex 1989;25(2):261–73. 101. Vanderploeg RD, Haley JA. Pseudosociopathy with intact higher-order cognitive abilities in patients with orbitofrontal cortical damage. J Clin Exp Neuropsychol. 1990;12:54–5. 102. Meyers CA, Berman SA, Scheibel RS, Hayman A. Case report: acquired antisocial personality disorder associated with unilateral left orbital frontal lobe damage. J Psychiatry Neurosci. 1992;17(3):121–5. 103. Mendez MF, Adams NL, Lewandowski KS. Neurobehavioral changes associated with caudate lesions. Neurology 1989;39(3):349–54. 104. Vonsattel JP, Myers RH, Stevens TJ et al. Neuropathological classification of Huntington’s disease. J Neuropathol Exp Neurol. 1985;44(6):559–77. 105. Bakchine S, Lacomblez L, Benoit N et al. Manic-like state after bilateral orbitofrontal and right temporoparietal injury: efficacy of clonidine. Neurology 1989;39(6):777–81. 106. Bogousslavsky J, Ferrazzini M, Regli F et al. Manic delirium and frontal-like syndrome with paramedian infarction of the right thalamus. J Neurol Neurosurg Psychiatry 1988;51(1):116–19. 92. Nauta WJ. The problem of the frontal lobe: a reinterpretation. J Psychiatr Res. 1971;8(3):167–87. 107. Cummings JL, Mendez MF. Secondary mania with focal cerebrovascular lesions. Am J Psychiatry 1984;141(9):1084–7. 93. Eslinger PJ, Damasio AR. Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR. Neurology 1985;35(12):1731–41. 108. Starkstein SE, Boston JD, Robinson RG. Mechanisms of mania after brain injury. 12 case reports and review of the literature. J Nerv Ment Dis. 1988;176(2):87–100. 94. Damasio AR. Descartes’ Error: Emotion, Reason, and the Human Brain. New York, NY: Putnam; 1994. 109. Cummings JL. Frontal-subcortical circuits and human behavior. Arch Neurol. 1993;50(8):873–80. 95. Blumer D, Benson DF. Personality changes with frontal and temporal lobe lesions. In Benson DF, Blumer D, editors. Psychiatric Aspects of Neurological Disease. New York, NY: Grune & Stratton; 1975. 110. Milner B. Effects of different brain lesions on card sorting: the role of the frontal lobes. Arch Neurol. 1963;9(1):90–100. 96. Reitman F. Orbital cortex syndrome following leucotomy. Am J Psychiatry 1946;103(2):238–41. 111. Strub RL. Frontal lobe syndrome in a patient with bilateral globus pallidus lesions. Arch Neurol. 1989;46(9):1024–7. 79 Section I: Structural and Functional Neuroanatomy 112. Sandson TA, Daffner KR, Carvalho PA, Mesulam MM. Frontal lobe dysfunction following infarction of the left-sided medial thalamus. Arch Neurol. 1991;48(12): 1300–3. 113. Stuss DT, Guberman A, Nelson R, Larochelle S. The neuropsychology of paramedian thalamic infarction. Brain Cogn. 1988;8(3):348–78. 114. Albert ML, Feldman RG, Willis AL. The ‘subcortical dementia’ of progressive supranuclear palsy. J Neurol Neurosurg Psychiatry 1974;37(2):121–30. 115. Peinemann A, Schuller S, Pohl C et al. Executive dysfunction in early stages of Huntington’s disease is associated with striatal and insular atrophy: a neuropsychological and voxel-based morphometric study. J Neurol Sci. 2005;239(1):11–19. 116. Sawamoto N, Piccini P, Hotton G et al. Cognitive deficits and striato-frontal dopamine release in Parkinson’s disease. Brain 2008;131(Pt 5):1294–302. 117. Owen AM. Cognitive dysfunction in Parkinson’s disease: the role of frontostriatal circuitry. Neuroscientist 2004;10(6):525–37. 118. Rinne JO, Rummukainen J, Paljarvi L, Rinne UK. Dementia in Parkinson’s disease is related to neuronal loss in the medial substantia nigra. Ann Neurol. 1989;26(1):47–50. 119. Litvan I, Paulsen JS, Mega MS, Cummings JL. Neuropsychiatric assessment of patients with hyperkinetic and hypokinetic movement disorders. Arch Neurol. 1998;55(10):1313–19. 120. Cummings J (editor). Introduction. In Subcortical Dementia. New York, NY: Oxford University Press; 1990, pp. 3–16. 121. Tatemichi TK, Desmond DW, Prohovnik I et al. Confusion and memory loss from capsular genu infarction: a thalamocortical disconnection syndrome? Neurology 1992;42(10):1966–79. 122. Kolb B. Studies on the caudate-putamen and the dorsomedial thalamic nucleus of the rat: implications for mammalian frontal-lobe functions. Physiol Behav. 1977;18(2):237–44. 80 evidence for cortico-striatal network dysfunction? J Psychopharmacol. 2000;14(1):37–9. 127. Robinson D, Wu H, Munne RA et al. Reduced caudate nucleus volume in obsessive-compulsive disorder. Arch Gen Psychiatry 1995;52(5):393–8. 128. Peterson B, Riddle MA, Cohen DJ et al. Reduced basal ganglia volumes in Tourette’s syndrome using three-dimensional reconstruction techniques from magnetic resonance images. Neurology 1993; 43(5):941–9. 129. Singer HS, Reiss AL, Brown JE et al. Volumetric MRI changes in basal ganglia of children with Tourette’s syndrome. Neurology 1993;43(5):950–6. 130. Baxter LR, Jr., Schwartz JM, Bergman KS et al. Caudate glucose metabolic rate changes with both drug and behavior therapy for obsessive-compulsive disorder. Arch Gen Psychiatry 1992;49(9):681–9. 131. Braun AR, Stoetter B, Randolph C et al. The functional neuroanatomy of Tourette’s syndrome: an FDG-PET study. I. Regional changes in cerebral glucose metabolism differentiating patients and controls. Neuropsychopharmacology 1993;9(4):277–91. 132. Jeffries KJ, Schooler C, Schoenbach C et al. The functional neuroanatomy of Tourette’s syndrome: an FDG PET study III: functional coupling of regional cerebral metabolic rates. Neuropsychopharmacology 2002;27(1):92–104. 133. Eidelberg D, Moeller JR, Antonini A et al. The metabolic anatomy of Tourette’s syndrome. Neurology 1997;48(4):927–34. 134. Gerard E, Peterson BS. Developmental processes and brain imaging studies in Tourette syndrome. J Psychosom Res. 2003;55(1):13–22. 135. Singer HS, Minzer K. Neurobiology of Tourette’s syndrome: concepts of neuroanatomic localization and neurochemical abnormalities. Brain Dev. 2003;25 (S1):S70–84. 123. Rapoport JL. Recent advances in obsessive-compulsive disorder. Neuropsychopharmacology 1991;5(1):1–10. 136. Peterson B, Leckman JF, Arnsten AF. Neuroanatomical circuitry. In Leckman JF, Cohen DJ, editors. Tourette’s Syndrome – Tics, Obsessions, Compulsions: Developmental Psychopathology and Clinical Care. New York, NY: John Wiley & Sons; 1998, pp. 230–60. 124. Cummings JL, Cunningham K. Obsessive-compulsive disorder in Huntington’s disease. Biol Psychiatry 1992;31(3):263–70. 137. Leckman JF, Knorr AM, Rasmusson AM, Cohen DJ. Basal ganglia research and Tourette’s syndromes. Trends Neurosci. 1991;14(3):94. 125. Swedo SE, Rapoport JL, Cheslow DL et al. High prevalence of obsessive-compulsive symptoms in patients with Sydenham’s chorea. Am J Psychiatry 1989;146(2):246–9. 138. Leckman JF, Pauls DL, Peterson BS et al. Pathogenesis of Tourette syndrome. Clues from the clinical phenotype and natural history. Adv Neurol. 1992;58:15–24. 126. Dursun SM, Burke JG, Reveley MA. Antisaccade eye movement abnormalities in Tourette syndrome: 139. Leckman JF, Walker DE, Goodman WK, Pauls DL, Cohen DJ. “Just right” perceptions associated with Chapter 5: Frontal-subcortical circuits compulsive behavior in Tourette’s syndrome. Am J Psychiatry 1994;151(5):675–80. 140. Modell JG, Mountz JM, Curtis GC, Greden JF. Neurophysiologic dysfunction in basal ganglia/ limbic striatal and thalamocortical circuits as a pathogenetic mechanism of obsessive-compulsive disorder. J Neuropsychiatry Clin Neurosci. 1989;1(1): 27–36. 141. Insel TR. Toward a neuroanatomy of obsessive-compulsive disorder. Arch Gen Psychiatry 1992;49(9):739–44. 152. Canales JJ, Graybiel AM. A measure of striatal function predicts motor stereotypy. Nat Neurosci. 2000;3(4):377–83. 153. Wise RA. Catecholamine theories of reward: a critical review. Brain Res. 1978;152(2):215–47. 154. Barkley RA, Grodzinsky G, DuPaul GJ. Frontal lobe functions in attention deficit disorder with and without hyperactivity: a review and research report. J Abnorm Child Psychol. 1992;20(2):163–88. 155. Shue KL, Douglas VI. Attention deficit hyperactivity disorder and the frontal lobe syndrome. Brain Cogn. 1992;20(1):104–24. 142. Rauch SL. Neuroimaging and neurocircuitry models pertaining to the neurosurgical treatment of psychiatric disorders. Neurosurg Clin N Am. 2003;14(2):213–23, vii–viii. 156. Kemp SL, Kirk U. An investigation of frontal executive dysfunction in attention deficit disorder subgroups. Ann N Y Acad Sci. 1993;682:363–5. 143. Masterman DL, Cummings JL. Frontal-subcortical circuits: the anatomic basis of executive, social and motivated behaviors. J Psychopharmacol. 1997; 11(2):107–14. 157. Towbin KE, Riddle MA. Attention deficit hyperactivity disorder. In Kurlan R, editor. Handbook of Tourette’s Syndrome and Related Tic and Behavioral Disorders. New York, NY: M. Dekker; 1993, pp. 89–109. 144. Makki MI, Behen M, Bhatt A, Wilson B, Chugani HT. Microstructural abnormalities of striatum and thalamus in children with Tourette syndrome. Mov Disord. 2008;23(16):2349–56. 158. Bamford KA, Caine ED, Kido DK, Plassche WM, Shoulson I. Clinical-pathologic correlation in Huntington’s disease: a neuropsychological and computed tomography study. Neurology 1989; 39(6):796–801. 145. Moriarty J, Costa DC, Schmitz B et al. Brain perfusion abnormalities in Gilles de la Tourette’s syndrome. Br J Psychiatry 1995;167(2):249–54. 146. Wolf SS, Jones DW, Knable MB et al. Tourette syndrome: prediction of phenotypic variation in monozygotic twins by caudate nucleus D2 receptor binding. Science 1996;273(5279): 1225–7. 147. Bohlhalter S, Goldfine A, Matteson S et al. Neural correlates of tic generation in Tourette syndrome: an event-related functional MRI study. Brain 2006;129(8):2029–37. 148. Mink JW. Neurobiology of basal ganglia and Tourette syndrome: basal ganglia circuits and thalamocortical outputs. Adv Neurol. 2006;99:89–98. 149. Braun AR, Randolph C, Stoetter B et al. The functional neuroanatomy of Tourette’s syndrome: an FDG-PET Study. II: Relationships between regional cerebral metabolism and associated behavioral and cognitive features of the illness. Neuropsychopharmacology 1995;13(2):151–68. 150. Albin RL, Koeppe RA, Bohnen NI et al. Increased ventral striatal monoaminergic innervation in Tourette syndrome. Neurology 2003;1(3):310–15. 151. Groenewegen HJ, van den Heuvel OA, Cath DC, Voorn P, Veltman DJ. Does an imbalance between the dorsal and ventral striatopallidal systems play a role in Tourette’s syndrome? A neuronal circuit approach. Brain Dev. 2003;25(S1):S3–S14. 159. Hynd GW, Hern KL, Novey ES et al. Attention deficit-hyperactivity disorder and asymmetry of the caudate nucleus. J Child Neurol. 1993;8(4):339–47. 160. Castellanos FX, Giedd JN, Eckburg P et al. Quantitative morphology of the caudate nucleus in attention deficit hyperactivity disorder. Am J Psychiatry 1994;151(12):1791–6. 161. Lou HC, Henriksen L, Bruhn P, Borner H, Nielsen JB. Striatal dysfunction in attention deficit and hyperkinetic disorder. Arch Neurol. 1989;46(1):48–52. 162. Vaidya CJ, Austin G, Kirkorian G et al. Selective effects of methylphenidate in attention deficit hyperactivity disorder: a functional magnetic resonance study. Proc Natl Acad Sci USA 1998;95(24):14 494–9. 163. Casey BJ, Castellanos FX, Giedd JN et al. Implication of right frontostriatal circuitry in response inhibition and attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 1997;36(3):374–83. 164. Dickstein SG, Bannon K, Castellanos FX, Milham MP. The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis. J Child Psychol Psychiatry 2006;47(10):1051–62. 165. McClure SM, York MK, Montague PR. The neural substrates of reward processing in humans: the modern role of FMRI. Neuroscientist 2004;10(3): 260–8. 166. Zink CF, Pagnoni G, Martin-Skurski ME, Chappelow JC, Berns GS. Human striatal responses to monetary 81 Section I: Structural and Functional Neuroanatomy reward depend on saliency. Neuron 2004;42(3): 509–17. 167. Kringelbach ML, Rolls ET. The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog Neurobiol. 2004;72(5):341–72. 168. Voeller KK. What can neurological models of attention, intention, and arousal tell us about attention-deficit hyperactivity disorder? J Neuropsychiatry Clin Neurosci. 1991;3(2): 209–16. 169. Sonuga-Barke EJ. Psychological heterogeneity in AD/HD – a dual pathway model of behaviour and cognition. Behav Brain Res. 2002;130(1–2): 29–36. 170. Sonuga-Barke EJ. The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics. Neurosci Biobehav Rev. 2003;27(7):593–604. 171. Brennan AR, Arnsten AF. Neuronal mechanisms underlying attention deficit hyperactivity disorder: the influence of arousal on prefrontal cortical function. Ann N Y Acad Sci. 2008;1129:236–45. 172. Li BM, Mao ZM, Wang M, Mei ZT. Alpha-2 adrenergic modulation of prefrontal cortical neuronal activity related to spatial working memory in monkeys. Neuropsychopharmacology 1999;21(5):601–10. 173. Mayberg HS, Starkstein SE, Peyser CE et al. Paralimbic frontal lobe hypometabolism in depression associated with Huntington’s disease. Neurology 1992;42(9): 1791–7. 174. Mayberg HS, Starkstein SE, Sadzot B et al. Selective hypometabolism in the inferior frontal lobe in depressed patients with Parkinson’s disease. Ann Neurol. 1990;28(1):57–64. 175. Rogers D. Bradyphrenia in Parkinson’s disease. In Huber SJ, Cummings JL, editors. Parkinson’s Disease: Neurobehavioral Aspects. New York, NY: Oxford University Press; 1992, pp. 86–96. 176. Tremblay LK, Naranjo CA, Graham SJ et al. Functional neuroanatomical substrates of altered reward processing in major depressive disorder revealed by a dopaminergic probe. Arch Gen Psychiatry 2005; 62(11):1228–36. 82 179. Praag HM, Korf J, Lakke J, Schut T. Dopamine metabolism in depressions, psychoses, and Parkinson’s disease: the problem of the specificity of biological variables in behaviour disorders. Psychol Med. 1975;5(2):138–46. 180. Goodwin FK, Murphy DL, Brodie HK, Bunney WE, Jr. L-DOPA, catecholamines, and behavior: a clinical and biochemical study in depressed patients. Biol Psychiatry 1970;2(4):341–66. 181. Rampello L, Nicoletti G, Raffaele R. Dopaminergic hypothesis for retarded depression: a symptom profile for predicting therapeutical responses. Acta Psychiatr Scand. 1991;84(6):552–4. 182. Marsh GG, Markham CH. Does levodopa alter depression and psychopathology in Parkinsonism patients? J Neurol Neurosurg Psychiatry 1973; 36(6):925–35. 183. Mayeux R, Stern Y, Williams JB et al. Clinical and biochemical features of depression in Parkinson’s disease. Am J Psychiatry 1986;143(6):756–9. 184. Mayberg HS. Limbic-cortical dysregulation: a proposed model of depression. J Neuropsychiatry Clin Neurosci. 1997;9(3):471–81. 185. Mayberg HS, Liotti M, Brannan SK et al. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry 1999;156(5):675–82. 186. Anand A, Li Y, Wang Y, Gardner K, Lowe MJ. Reciprocal effects of antidepressant treatment on activity and connectivity of the mood regulating circuit: an FMRI study. J Neuropsychiatry Clin Neurosci. 2007;19(3):274–82. 187. Mayberg HS. Depression and frontal-subcortical circuits: focus on prefrontal-limbic interactions. In Lichter DG, Cummings JL, editors. Frontal-subcortical Circuits in Psychiatric and Neurological Disorders. New York, NY: Guilford Press; 2001, pp. 177–206. 188. Mayberg HS, Lozano AM, Voon V et al. Deep brain stimulation for treatment-resistant depression. Neuron 2005;45(5):651–60. 189. Johansen-Berg H, Gutman DA, Behrens TE et al. Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression. Cereb Cortex 2008;18(6):1374–83. 177. Martinot M, Bragulat V, Artiges E et al. Decreased presynaptic dopamine function in the left caudate of depressed patients with affective flattening and psychomotor retardation. Am J Psychiatry 2001; 158(2):314–16. 190. Drevets WC, Savitz J, Trimble M. The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008;13(8):663–81. 178. Meyer JH, McNeely HE, Sagrati S et al. Elevated putamen D(2) receptor binding potential in major depression with motor retardation: an [11C]raclopride positron emission tomography study. Am J Psychiatry 2006;163(9):1594–602. 191. Van Laere K, Nuttin B, Gabriels L et al. Metabolic imaging of anterior capsular stimulation in refractory obsessive-compulsive disorder: a key role for the subgenual anterior cingulate and ventral striatum. J Nucl Med. 2006;47(5):740–7. Chapter 5: Frontal-subcortical circuits 192. Sackeim HA, Greenberg MS, Weiman AL et al. Hemispheric asymmetry in the expression of positive and negative emotions. Neurologic evidence. Arch Neurol. 1982;39(4):210–18. 193. Starkstein SE, Robinson RG, Price TR. Comparison of cortical and subcortical lesions in the production of poststroke mood disorders. Brain 1987;110 (Pt 4):1045–59. 194. Starkstein SE, Preziosi TJ, Bolduc PL, Robinson RG. Depression in Parkinson’s disease. J Nerv Ment Dis. 1990;178(1):27–31. and cerebral cortex in schizophrenia. Arch Gen Psychiatry 1991;48(10):881–90. 207. Grebb JA, Weinberger DR, Wyatt RJ. Schizophrenia. In Asbury AK, McKahann GM, McDonald WI, editors. Diseases of the Nervous System: Clinical Neurobiology. 2nd edition. Philadelphia, PA: W.B. Saunders; 1992, pp. 839–48. 208. Goldman-Rakic PS, Selemon LD. Functional and anatomical aspects of prefrontal pathology in schizophrenia. Schizophr Bull. 1997;23(3):437–58. 195. Jorge RE, Robinson RG, Starkstein SE et al. Secondary mania following traumatic brain injury. Am J Psychiatry 1993;150(6):916–21. 209. Bunney WE, Bunney BG. Evidence for a compromised dorsolateral prefrontal cortical parallel circuit in schizophrenia. Brain Res Brain Res Rev. 2000; 31(2–3):138–46. 196. Newberg AR, Catapano LA, Zarate CA, Manji HK. Neurobiology of bipolar disorder. Expert Rev Neurother. 2008;8(1):93–110. 210. Gusnard DA, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci. 2001;2(10):685–94. 197. Arnsten AF. Ameliorating prefrontal cortical dysfunction in mental illness: inhibition of phosphotidyl inositol-protein kinase C signaling. Psychopharmacology (Berl.) 2009;202(1–3): 445–55. 211. Garrity AG, Pearlson GD, McKiernan K et al. Aberrant “default mode” functional connectivity in schizophrenia. Am J Psychiatry 2007;164(3): 450–7. 198. Manji HK, Lenox RH. Protein kinase C signaling in the brain: molecular transduction of mood stabilization in the treatment of manic-depressive illness. Biol Psychiatry 1999;46(10):1328–51. 199. Strakowski SM, Delbello MP, Adler CM. The functional neuroanatomy of bipolar disorder: a review of neuroimaging findings. Mol Psychiatry 2005;10(1):105–16. 200. DelBello MP, Adler CM, Strakowski SM. The neurophysiology of childhood and adolescent bipolar disorder. CNS Spectr. 2006;11(4):298–311. 212. Benes FM, McSparren J, Bird ED, SanGiovanni JP, Vincent SL. Deficits in small interneurons in prefrontal and cingulate cortices of schizophrenic and schizoaffective patients. Arch Gen Psychiatry 1991;48(11):996–1001. 213. Williamson P. Are anticorrelated networks in the brain relevant to schizophrenia? Schizophr Bull. 2007; 33(4):994–1003. 214. Everitt BJ, Wolf ME. Psychomotor stimulant addiction: a neural systems perspective. J Neurosci. 2002; 22(9):3312–20. 201. Adler CM, DelBello MP, Strakowski SM. Brain network dysfunction in bipolar disorder. CNS Spectr. 2006;11(4):312–20. 215. Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci. 2005; 8(11):1481–9. 202. Caetano SC, Olvera RL, Glahn D et al. Fronto-limbic brain abnormalities in juvenile onset bipolar disorder. Biol Psychiatry 2005;58(7):525–31. 216. Volkow ND, Wang GJ, Telang F et al. Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J Neurosci. 2006;26(24):6583–8. 203. Haldane M, Frangou S. New insights help define the pathophysiology of bipolar affective disorder: neuroimaging and neuropathology findings. Prog Neuropsychopharmacol Biol Psychiatry 2004; 28(6):943–60. 217. Yin HH, Knowlton BJ. The role of the basal ganglia in habit formation. Nat Rev Neurosci. 2006;7(6):464–76. 204. Swerdlow NR, Koob GF. Toward a unified hypothesis of cortico-striato-pallido-thalamus function. Behav Brain Sci. 1990;13(1):172–177. 205. Braff DL, Geyer MA. Sensorimotor gating and schizophrenia. Human and animal model studies. Arch Gen Psychiatry 1990;47(2):181–8. 206. Jernigan TL, Zisook S, Heaton RK et al. Magnetic resonance imaging abnormalities in lenticular nuclei 218. Belin D, Everitt BJ. Cocaine seeking habits depend upon dopamine-dependent serial connectivity linking the ventral with the dorsal striatum. Neuron 2008; 57(3):432–41. 219. Koob GF, Nestler EJ. The neurobiology of drug addiction. J Neuropsychiatry Clin Neurosci. 1997;9(3):482–97. 220. Gardner E. Brain reward mechanisms. In Lowinson JH, editor. Substance Abuse: A Comprehensive Textbook. 3rd edition. Baltimore, MD: Williams & Wilkins; 1997, pp. 51–85. 83 Section I: Structural and Functional Neuroanatomy 221. Ivanov IS, Schulz KP, Palmero RC, Newcorn JH. Neurobiology and evidence-based biological treatments for substance abuse disorders. CNS Spectr. 2006;11(11):864–77. 222. Weintraub D. Dopamine and impulse control disorders in Parkinson’s disease. Ann Neurol. 2008; 64(Suppl. 2):S93–100. 235. Scatton B, Javoy-Agid F, Rouquier L, Dubois B, Agid Y. Reduction of cortical dopamine, noradrenaline, serotonin and their metabolites in Parkinson’s disease. Brain Res. 1983;275(2):321–8. 223. Joyce JN, Gurevich EV. D3 receptors and the actions of neuroleptics in the ventral striatopallidal system of schizophrenics. Ann N Y Acad Sci. 1999;877:595–613. 236. Portin R, Rinne UK. Predictive factors for cognitive deterioration and dementia in Parkinson’s disease. Adv Neurol. 1987;45:413–16. 224. Murray AM, Ryoo HL, Gurevich E, Joyce JN. Localization of dopamine D3 receptors to mesolimbic and D2 receptors to mesostriatal regions of human forebrain. Proc Natl Acad Sci USA 1994;91(23): 11 271–5. 237. Marsh L, Biglan K, Gerstenhaber M, Williams JR. Atomoxetine for the treatment of executive dysfunction in Parkinson’s disease: a pilot open-label study. Mov Disord. 2009;24(2):277–82. 225. Chen J, Marmur R, Paredes W, Pulles A, Gardner EL. Systemic cocaine challenge after chronic cocaine treatment reveals sensitization of extracellular dopamine levels in nucleus accumbens but direct cocaine perfusion into nucleus accumbens does not: implications for the neural locus of cocaine sensitization. Life Sci. 1996;58(8):PL139–46. 226. Cilia R, Siri C, Marotta G et al. Functional abnormalities underlying pathological gambling in Parkinson disease. Arch Neurol. 2008;65(12):1604–11. 227. Blanco C, Moreyra P, Nunes EV, Saiz-Ruiz J, Ibanez A. Pathological gambling: addiction or compulsion? Semin Clin Neuropsychiatry 2001;6(3):167–76. 228. Potenza MN. Should addictive disorders include non-substance-related conditions? Addiction 2006;101(Suppl. 1):142–51. 229. Hollander E. Behavioral and substance addictions: A new proposed DSM-V category characterized by impulsive choice, reward sensitivity, and fronto-striatal circuit impairment. CNS Spectr. 2006;11(11):814. 230. Potenza MN. The neurobiology of pathological gambling and drug addiction: an overview and new findings. Philos R Soc B. 2008;363(1507):3181–9. 231. Smith KS, Berridge KC. Opioid limbic circuit for reward: interaction between hedonic hotspots of nucleus accumbens and ventral pallidum. J Neurosci. 2007;27(7):1594–605. 232. Lange KW, Robbins TW, Marsden CD et al. L-dopa withdrawal in Parkinson’s disease selectively impairs cognitive performance in tests sensitive to frontal lobe dysfunction. Psychopharmacology (Berl.) 1992; 107(2–3):394–404. 233. Cooper JA, Sagar HJ, Doherty SM et al. Different effects of dopaminergic and anticholinergic therapies on cognitive and motor function in Parkinson’s disease. A follow-up study of untreated patients. Brain 1992;115(Pt 6):1701–25. 84 234. Taylor AE, Saint-Cyr JA, Lang AE. Frontal lobe dysfunction in Parkinson’s disease. The cortical focus of neostriatal outflow. Brain 1986;109(Pt 5):845–83. 238. Arnsten AF, Contant TA. Alpha-2 adrenergic agonists decrease distractibility in aged monkeys performing the delayed response task. Psychopharmacology (Berl.) 1992;108(1–2):159–69. 239. Goldman-Rakic PS, Lidow MS, Gallager DW. Overlap of dopaminergic, adrenergic, and serotoninergic receptors and complementarity of their subtypes in primate prefrontal cortex. J. Neurosci. 1990;10(7): 2125–38. 240. Arnsten AF, Cai JX, Goldman-Rakic PS. The alpha-2 adrenergic agonist guanfacine improves memory in aged monkeys without sedative or hypotensive side effects: evidence for alpha-2 receptor subtypes. J Neurosci. 1988;8(11):4287–98. 241. Arnsten AF, Goldman-Rakic PS. Alpha 2-adrenergic mechanisms in prefrontal cortex associated with cognitive decline in aged nonhuman primates. Science 1985;230(4731):1273–6. 242. Fields RB, Van Kammen DP, Peters JL et al. Clonidine improves memory function in schizophrenia independently from change in psychosis. Preliminary findings. Schizophr Res. 1988;1(6):417–23. 243. Mair RG, McEntee WJ. Cognitive enhancement in Korsakoff’s psychosis by clonidine: a comparison with L-dopa and ephedrine. Psychopharmacology (Berl.) 1986;88(3):374–80. 244. Moffoot A, O’Carroll RE, Murray C et al. Clonidine infusion increases uptake of 99mTc-Exametazime in anterior cingulate cortex in Korsakoff’s psychosis. Psychol Med. 1994;24(1):53–61. 245. Sahakian BJ, Coull JJ, Hodges JR. Selective enhancement of executive function by idazoxan in a patient with dementia of the frontal lobe type. J Neurol Neurosurg Psychiatry 1994;57(1):120–1. 246. Denckla MB, Reader MJ. Education and psychosocial interdentions: executive dysfunction and its consequences. In Kurlan R, editor. Handbook of Tourette’s Syndrome and Related Tic and Behavioral Chapter 5: Frontal-subcortical circuits Disorders. New York, NY: M. Dekker; 1993, pp. 431–51. 247. Golden GS. Treatment of attention deficit hyperactivity disorder. In Kurlan R, editor. Handbook of Tourette’s Syndrome and Related Tic and Behavioral Disorders. New York, NY: M. Dekker; 1993, pp. 423–30. 248. Leckman JF, Hardin MT, Riddle MA et al. Clonidine treatment of Gilles de la Tourette’s syndrome. Arch Gen Psychiatry 1991;48(4):324–8. 249. Tannock R, Schachar RJ, Carr RP, Chajczyk D, Logan GD. Effects of methylphenidate on inhibitory control in hyperactive children. J Abnorm Child Psychol. 1989;17(5):473–91. 250. Hunt RD, Arnsten AF, Asbell MD. An open trial of guanfacine in the treatment of attention-deficit hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 1995;34(1):50–4. 251. Arnsten AF, Steere JC, Hunt RD. The contribution of alpha 2-noradrenergic mechanisms of prefrontal cortical cognitive function. Potential significance for attention-deficit hyperactivity disorder. Arch Gen Psychiatry 1996;53(5):448–55. 252. Ungerstedt U. Is interruption of the nigro-striatal dopamine system producing the “lateral hypothalamus syndrome”? Acta Physiol Scand. 1970;80(4): 35A–6A. 253. Ungerstedt U. Adipsia and aphagia after 6-hydroxydopamine induced degeneration of the nigro-striatal dopamine system. Acta Physiol Scand Suppl. 1971;367:95–122. 254. Marshall JF, Richardson JS, Teitelbaum P. Nigrostriatal bundle damage and the lateral hypothalamic syndrome. J Comp Physiol Psychol. 1974;87(5):808–30. 255. Ljungberg T, Ungerstedt U. Reinstatement of eating by dopamine agonists in aphagic dopamine denervated rats. Physiol Behav. 1976;16(3):277–83. 256. Marshall JF, Ungerstedt U. Apomorphine-induced restoration of drinking to thirst challenges in 6-hydroxydopamine-treated rats. Physiol Behav. 1976;17(5):817–22. 257. Marshall JF, Gotthelf T. Sensory inattention in rats with 6-hydroxydopamine-induced degeneration of ascending dopaminergic neurons: apomorphineinduced reversal of deficits. Exp Neurol. 1979;65(2):398–411. 258. Ross ED, Stewart RM. Akinetic mutism from hypothalamic damage: successful treatment with dopamine agonists. Neurology 1981;31(11):1435–9. 259. Nemeth G, Hegedus K, Molnar L. Akinetic mutism and locked-in syndrome: the functional-anatomical basis for their differentiation. Funct Neurol. 1986;1(2):128–39. 260. Alexander MP. Reversal of chronic akinetic mutism after mesencephalic infarction with dopaminergic agents. Neurology 1995;45(4):A330. 261. Spiegel DR, Casella DP, Callender DM, Dhadwal N. Treatment of akinetic mutism with intramuscular olanzapine: a case series. J Neuropsychiatry Clin Neurosci. 2008;20(1):93–5. 262. Parks RW, Crockett DJ, Manji HK, Ammann W. Assessment of bromocriptine intervention for the treatment of frontal lobe syndrome: a case study. J Neuropsychiatry Clin Neurosci. 1992;4(1):109–11. 263. Barrett K. Treating organic abulia with bromocriptine and lisuride: four case studies. J Neurol Neurosurg Psychiatry 1991;54(8):718–21. 264. Holmes VF, Fernandez F, Levy JK. Psychostimulant response in AIDS-related complex patients. J Clin Psychiatry 1989;50(1):5–8. 265. Czernecki V, Schupbach M, Yaici S et al. Apathy following subthalamic stimulation in Parkinson disease: a dopamine responsive symptom. Mov Disord. 2008;23(7):964–9. 266. Marin RS, Fogel BS, Hawkins J, Duffy J, Krupp B. Apathy: a treatable syndrome. J Neuropsychiatry Clin Neurosci. 1995;7(1):23–30. 267. Craig AH, Cummings JL, Fairbanks L et al. Cerebral blood flow correlates of apathy in Alzheimer disease. Arch Neurol. 1996;53(11):1116–20. 268. Kaufer DI, Cummings JL, Christine D. Effect of tacrine on behavioral symptoms in Alzheimer’s disease: an open-label study. J Geriatr Psychiatry Neurol. 1996;9(1):1–6. 269. Masanic CA, Bayley MT, VanReekum R, Simard M. Open-label study of donepezil in traumatic brain injury. Arch Phys Med Rehabil. 2001;82(7):896–901. 270. Morey CE, Cilo M, Berry J, Cusick C. The effect of Aricept in persons with persistent memory disorder following traumatic brain injury: a pilot study. Brain Inj. 2003;17(9):809–15. 271. Cummings J. Behavioral disorders associated with frontal lobe injury. In Cummings JL, editor. Clinical Neuropsychiatry. Orlando, FL: Grune & Stratton; 1985, pp. 57–67. 272. Lesch KP, Merschdorf U. Impulsivity, aggression, and serotonin: a molecular psychobiological perspective. Behav Sci Law 2000;18(5):581–604. 273. Brown GL, Linnoila MI. CSF serotonin metabolite (5-HIAA) studies in depression, impulsivity, and violence. J Clin. Psychiatry 1990;51(Suppl.):31–41; discussion 2–3. 274. Coccaro EF. Central serotonin and impulsive aggression. Br J Psychiatry Suppl. 1989;8:52–62. 85 Section I: Structural and Functional Neuroanatomy 275. Stein DJ, Hollander E, Liebowitz MR. Neurobiology of impulsivity and the impulse control disorders. J Neuropsychiatry Clin Neurosci. 1993;5(1):9–17. 276. Dougherty DM, Moeller FG, Bjork JM, Marsh DM. Plasma L-tryptophan depletion and aggression. Adv Exp Med Biol. 1999;467:57–65. 277. Leo RJ, Kim KY. Clomipramine treatment of paraphilias in elderly demented patients. J Geriatr Psychiatry Neurol. 1995;8(2):123–4. 278. Hollander E, Wong CM. Body dysmorphic disorder, pathological gambling, and sexual compulsions. J Clin Psychiatry 1995;56(Suppl. 4):7–12; discussion 3. 279. Olivier B, Mos J. Serenics and aggression. Stress Medicine 1986;2(3):197–209. 291. Goodman WK, Price LH, Delgado PL et al. Specificity of serotonin reuptake inhibitors in the treatment of obsessive-compulsive disorder. Comparison of fluvoxamine and desipramine. Arch Gen Psychiatry 1990;47(6):577–85. 292. Greybiel AM, Ragsdale CW. Biochemical anatomy of the striatum. In Emson PC, editor. Chemical Neuroanatomy. New York, NY: Raven Press; 1983, pp. 427–507. 293. Nieuwenhuys R. Chemoarchitecture of the Brain. Berlin: Springer-Verlag; 1985. 280. Pazos A, Palacios JM. Quantitative autoradiographic mapping of serotonin receptors in the rat brain. I. Serotonin-1 receptors. Brain Res. 1985;346(2):205–30. 294. McDougle CJ, Goodman WK, Price LH. Dopamine antagonists in tic-related and psychotic spectrum obsessive compulsive disorder. J Clin Psychiatry 1994;55(Suppl.):24–31. 281. Hjorth S, Carlsson A. Is pindolol a mixed agonist-antagonist at central serotonin (5-HT) receptors? Eur J Pharmacol. 1986;129(1–2):131–8. 295. Korsgaard S, Gerlach J, Christensson E. Behavioral aspects of serotonin-dopamine interaction in the monkey. Eur J Pharmacol. 1985;118(3):245–52. 282. Bel N, Romero L, Celada P et al. editors. Neurobiological basis for the potentiation of the antidepressant effect of 5-HT reuptake inhibitors by the 5-HT1A antagonist pindolol. In Monitoring Molecules in Neuroscience: Proceeding of the 6th International Conference of in vivo Methods; 1994. Bordeaux: University of Bordeaux. 296. Ballantine HT, Jr., Bouckoms AJ, Thomas EK, Giriunas IE. Treatment of psychiatric illness by stereotactic cingulotomy. Biol Psychiatry 1987;22(7):807–19. 283. Bunney WE, Garland-Bunney BL. Mechanisms of action of lithium in affective illness: basic and clinical implications. In: Meltzer HY, editor. Psychopharmacology: The Third Generation of Progress. New York, NY: Raven Press; 1987, pp. 553–65. 298. Robertson M, Doran M, Trimble M, Lees AJ. The treatment of Gilles de la Tourette syndrome by limbic leucotomy. J Neurol Neurosurg Psychiatry 1990; 53(8):691–4. 284. Snyder SH. Second messengers and affective illness. Focus on the phosphoinositide cycle. Pharmacopsychiatry 1992;25(1):25–8. 285. Jouvent R, Lecrubier Y, Puech AJ, Simon P, Widlocher D. Antimanic effect of clonidine. Am J Psychiatry 1980;137(10):1275–6. 286. Shen WW. Mania, clonidine, and dopamine. Am J Psychiatry 1986;143(1):127. 287. Arnsten AF, Goldman-Rakic PS. Selective prefrontal cortical projections to the region of the locus coeruleus and raphe nuclei in the rhesus monkey. Brain Res. 1984;306(1–2):9–18. 288. Cummings JL. Organic psychoses. Delusional disorders and secondary mania. Psychiatr Clin North Am. 1986;9(2):293–311. 289. Jankovic J. Deprenyl in attention deficit associated with Tourette’s syndrome. Arch Neurol. 1993;50(3):286–8. 290. Zohar J, Insel TR. Obsessive-compulsive disorder: psychobiological approaches to diagnosis, treatment, 86 and pathophysiology. Biol Psychiatry 1987;22(6):667–87. 297. Sawle GV, Lees AJ, Hymas NF, Brooks DJ, Frackowiak RS. The metabolic effects of limbic leucotomy in Gilles de la Tourette syndrome. J Neurol Neurosurg Psychiatry 1993;56(9):1016–19. 299. Kurlan R, Kersun J, Ballantine HT, Jr., Caine ED. Neurosurgical treatment of severe obsessive-compulsive disorder associated with Tourette’s syndrome. Mov Disord. 1990;5(2): 152–5. 300. Papez JW. A proposed mechanism of emotion. Arch Neurol Psychiatry 1937;38(4):725–43. 301. Mesulam M. Patterns in behavioral neuoranatomy: association areas, the limbic system, and hemispheric specialization. In Mesulam MM, editor. Principles of Behavioral Neurology. Philadelphia. PA: F.A. Davis; 1985, pp. 1–70. 302. Bingley T, Leksell L, Meyerson BA, Rylander G. Long-term results of stereotactic anterior capsulotomy in chronic obsessive-compulsive neurosis. In Sweet WH, Obrador S, Martı́n-Rodrı́guez JG, editors. Neurosurgical Treatment in Psychiatry, Pain, and Epilepsy. Proceedings of the Fourth World Congress of Psychiatric Surgery, September 7–10, 1975, Madrid, Spain. Baltimore, MD: University Park Press; 1977, pp. 287–99. Chapter 5: Frontal-subcortical circuits 303. Lujan JL, Chaturvedi A, McIntyre CC. Tracking the mechanisms of deep brain stimulation for neuropsychiatric disorders. Front Biosci. 2008; 13:5892–904. 304. Greenberg BD, Malone DA, Friehs GM et al. Three-year outcomes in deep brain stimulation for highly resistant obsessive-compulsive disorder. Neuropsychopharmacology 2006;31(11):2384–93. 305. Rauch SL, Dougherty DD, Malone D et al. A functional neuroimaging investigation of deep brain stimulation in patients with obsessive-compulsive disorder. J Neurosurg. 2006;104(4):558–65. 306. Nuttin BJ, Gabriels LA, Cosyns PR et al. Long-term electrical capsular stimulation in patients with obsessive-compulsive disorder. Neurosurgery 2003;52(6):1263–72; discussion 72–4. 307. Ackermans L, Temel Y, Cath D et al. Deep brain stimulation in Tourette’s syndrome: two targets? Mov Disord. 2006;21(5):709–13. 308. Shahed J, Poysky J, Kenney C, Simpson R, Jankovic J. GPi deep brain stimulation for Tourette syndrome improves tics and psychiatric comorbidities. Neurology 2007;68(2):159–60. 312. Flaherty AW, Williams ZM, Amirnovin R et al. Deep brain stimulation of the anterior internal capsule for the treatment of Tourette syndrome: technical case report. Neurosurgery 2005;57(4 Suppl.):E403; discussion E. 313. Servello D, Porta M, Sassi M, Brambilla A, Robertson MM. Deep brain stimulation in 18 patients with severe Gilles de la Tourette syndrome refractory to treatment: the surgery and stimulation. J Neurol Neurosurg Psychiatry 2008;79(2):136–42. 314. Bajwa RJ, de Lotbiniere AJ, King RA et al. Deep brain stimulation in Tourette’s syndrome. Mov Disord. 2007;22(9):1346–50. 315. Maciunas RJ, Maddux BN, Riley DE et al. Prospective randomized double-blind trial of bilateral thalamic deep brain stimulation in adults with Tourette syndrome. J Neurosurg. 2007;107(5):1004–14. 316. Visser-Vandewalle V. DBS in Tourette syndrome: rationale, current status and future prospects. Acta Neurochir Suppl. 2007;97(Pt 2):215–22. 317. Temel Y, Visser-Vandewalle V. Surgery in Tourette syndrome. Mov Disord. 2004;19(1):3–14. 309. Welter ML, Mallet L, Houeto JL et al. Internal pallidal and thalamic stimulation in patients with Tourette syndrome. Arch Neurol. 2008;65(7):952–7. 318. Hamani C, Saint-Cyr JA, Fraser J, Kaplitt M, Lozano AM. The subthalamic nucleus in the context of movement disorders. Brain 2004;127(Pt 1): 4–20. 310. Dehning S, Mehrkens JH, Muller N, Botzel K. Therapy-refractory Tourette syndrome: beneficial outcome with globus pallidus internus deep brain stimulation. Mov Disord. 2008;23(9):1300–2. 319. Hauptman JS, DeSalles AA, Espinoza R, Sedrak M, Ishida W. Potential surgical targets for deep brain stimulation in treatment-resistant depression. Neurosurg Focus 2008;25(1):E3. 311. Houeto JL, Karachi C, Mallet L et al. Tourette’s syndrome and deep brain stimulation. J Neurol Neurosurg Psychiatry 2005;76(7):992–5. 320. Stelten BM, Noblesse LH, Ackermans L, Temel Y, Visser-Vandewalle V. The neurosurgical treatment of addiction. Neurosurg Focus 2008;25(1):E5. 87 Section I Structural and Functional Neuroanatomy Chapter Arousal 6 C. Alan Anderson, Christopher M. Filley, David B. Arciniegas, and James P. Kelly Arousal refers to the physiological state of wakefulness and alertness. Whereas the term has been used interchangeably with consciousness, arousal in fact has a more specific meaning as a neural foundation of what is required to be conscious. Arousal denotes the level of consciousness [1], which, in combination with the content of consciousness (attention, language, memory, praxis, gnosis, visuospatial skills, and executive function) creates the capacity for awareness and behavior. The arousal system involves multiple distributed neural networks working in harmony to permit normal sleep–wake cycles, satisfy internal drive states, and respond to environmental demands. Diseases and injuries that affect these systems represent some of the most serious and devastating conditions encountered in clinical medicine. The assessment of arousal therefore is as crucial in patient care as the details of higher cognitive function. Arousal represents a fundamental requirement for consciousness, providing the wakefulness and alertness that enable all other cognitive operations. It makes no sense, for example, to describe a patient as aphasic because of failure to produce meaningful language while he or she is asleep or in deep coma. Disorders of arousal highlight this core function of the brain while challenging the clinician to address a host of medical, neurologic, ethical, and social issues. Neuroanatomy and neurophysiology of arousal and awareness Disorders of arousal typically involve pathology of the brainstem, thalamus, or widespread areas of both cerebral hemispheres. This useful generalization structures the consideration of how arousal is organized in the normal brain. It has been long recognized that the ascending reticular activating system in the brainstem forms the foundation of the arousal system [2]. From these early observations, the understanding of the arousal system has been significantly refined through expanded knowledge of its anatomy and physiology (Figures 6.1 and 6.2). Key features of the distributed neural networks supporting arousal are their multiplicity and redundancy. These characteristics permit fine-tuning of the organism’s response to its environment, as well as provide protection against failure of any single component [3]. A parallel series of distinct neural networks using dopamine, histamine, serotonin, acetylcholine, and norepinephrine as neurotransmitters originates in the brainstem or in the basal forebrain [4]. The arousal systems then converge in the thalamus, and from there support and modulate arousal through widespread projections throughout the cerebral cortex [3]. These distributed reticulocortical, reticulothalamic, and thalamocortical networks are the foundation of consciousness, and comprise a highly integrated anatomy that matches the level of arousal to the requirements of the organism [5]. While there is a general level of arousal maintained during waking states, these networks also serve to modulate specific arousal states related to motivational systems including hunger, thirst, pain, fear, and sexual behavior [6]. The brainstem nuclei for these ascending arousal systems receive afferent sensory input including somatosensory, auditory, vestibular, and gustatory information. Somatosensory signals related to pain or sexual signals are amplified as they reach arousal networks [3]. Other forms of sensory information modulating arousal, however, enter the system through alternative Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 88 Chapter 6: Arousal Figure 6.1. Key components of the ascending arousal system. A major input to the relay and reticular nuclei of the thalamus originates from cholinergic (ACh) cell groups in the upper pons, the pedunculopontine (PPT) and laterodorsal tegmental nuclei (LDT). These inputs facilitate thalamocortical transmission. A second pathway activates the cerebral cortex to facilitate the processing of inputs from the thalamus; this arises from neurons in the monoaminergic cell groups, including the tuberomammillary nucleus (TMN) containing histamine (His), ventral periaqueductal gray (vPAG) dopamine-containing cell groups (DA), the dorsal and median raphe nuclei containing serotonin (5-HT), and the noradrenergic locus coeruleus (LC) containing noradrenaline (NA). This pathway also receives contributions from peptidergic neurons in the lateral hypothalamus (LH) containing orexin (ORX) or melanin-concentrating hormone (MCH), and from basal forebrain (BF) neurons that contain gamma-aminobutyric acid (GABA) or ACh. Reproduced from Saper CB, Scammell TE, Lu J. Hypothalamic regulation of sleep and circadian rhythms. Nature 2005;437(7063): 1257–63, with permission from Nature Publishing Group. pathways. Olfactory input, for example, enters the system via the basal forebrain, which is tightly connected to the amygdala and modulates arousal based on relevant odors and their associated emotional valence. The visual system also has unique connections with the arousal system and, given the importance of visual input in humans, it is not surprising that multiple pathways exist by which vision can influence arousal. Projections from visual cortex to the superior colliculus in the midbrain are relayed to thalamic nuclei including the reticular and medial cell groups as well as the pulvinar [3]. Through widespread cortical connections these thalamic cell groups modulate arousal based on salient visual stimuli. Higher cortical processing of Figure 6.2. A schematic drawing to show the key projections of the ventrolateral preoptic nucleus (VLPO) to the main components of the ascending arousal system. It includes the monoaminergic cell groups such as the tuberomammillary nucleus (TMN), the A10 cell group, the raphe cell groups and the locus coeruleus (LC). It also innervates neurons in the lateral hypothalamus, including the perifornical (PeF) orexin (ORX) neurons, and interneurons in the cholinergic (ACh) cell groups, the pedunculopontine (PPT) and laterodorsal tegmental nuclei (LDT). Additional abbreviations: 5-HT – serotonin; GABA – gamma-aminobutyric acid; gal – galanin; NA – noradrenaline; His – histamine. Reproduced from Saper CB, Scammell TE, Lu J. Hypothalamic regulation of sleep and circadian rhythms. Nature 2005;437(7063):1257–63, with permission from Nature Publishing Group. visual input influences arousal in a top-down fashion through reciprocal connections back to the thalamus as well as to upper brainstem nuclei, thus tailoring an appropriate level of arousal based on the biological significance of visual information [3, 7]. The ascending arousal systems include cholinergic, noradrenergic, dopaminergic, glutamatergic, and histaminic nuclei [6]. The pendunculopontine and laterodorsal tegmental nuclei in the brainstem send cholinergic projections to the reticular nuclei of the thalamus, forming the reticulothalamic component of the ascending arousal system, and modulating cortical engagement and information processing. Glutamatergic projections from the thalamus are broadly distributed to the cerebral cortex, activating these areas in preparation for information processing. Dopaminergic projections originating in the ventral tegmental area of the midbrain project widely throughout the 89 Section I: Structural and Functional Neuroanatomy cerebral cortex and serve a similar activating function. These connections are bidirectional, providing for feedback in the system, and their bilateral distribution provides additional safety through redundancy against the failure of any single component [6]. Brainstem projections from serotonergic nuclei in the median and dorsal raphe nuclei, histamine-producing neurons in the hypothalamus, noradrenergic nuclei in the locus coeruleus, and additional cholinergic projections originating in the ventral forebrain nuclei also participate in the modulation of cortical activation [6]. The combination of reticulothalamic and reticulocortical projections, as well as ascending tracts originating in the ventral forebrain, provide balance and feedback mechanisms that help modulate the activity of diencephalic and cortical targets, and match the overall level of arousal and awareness to the everchanging needs for self- and environmental awareness, and behavior [4, 8]. The brainstem and basal forebrain structures are so closely linked that some investigators consider the basal forebrain nuclei a rostral extension of the reticular activating system in the brainstem rather than a separate functional entity [9]. The intralaminar and midline thalamic nuclei form a critical relay of the arousal system, receiving ascending arousal fibers originating in the brainstem and in turn projecting diffusely throughout the cerebral cortex [7, 10]. In addition to the input from the brainstem reticular activating system, thalamic intralaminar nuclei receive fibers from the spinothalamic tract and cerebellum, and have reciprocal connections to medial prefrontal and anterior cingulate cortex [10]. These nuclei serve more than a simple relay function. The thalamic nuclei are anatomically and functionally distinct, receiving targeted input from specific brainstem nuclei and projecting to specific sites in the striatum and cerebral cortex, thus allowing the integration of sensory input and internal drive states [7]. As such, these nuclei serve as part of a thalamic matrix that underlies cortical synchronization in support of awareness and higher cognitive functions [11]. In addition to their role in arousal and other cognitive functions, these thalamic nuclei modulate basal ganglia function and participate in the transmission of nociceptive inputs to the cerebral cortex [10]. Thus, the thalamic nuclei are positioned to integrate internal drive states, cognitive function, somatic and external sensory information, and motor systems into the modulation of arousal. 90 Disturbances at any level of this system can affect arousal. A wide variety of pathological processes including vascular disease, the compressive effects of tumors or other mass lesions, and the direct mechanical effects of trauma can injure brainstem nuclei and their ascending fibers. For example, white matter tracts that interconnect cortical and subcortical structures are particularly vulnerable to traumatic brain injury (TBI) and anoxia. The midline and intralaminar nuclei of the thalamus are also affected in TBI [10]. Hypoxic– ischemic brain injury (HI–BI) can produce a wide range of lesions involving the rostral brainstem [12], the cerebral white matter (with special vulnerability in areas between the major cerebral artery territories and subcortical regions irrigated by the distal branches of deep and superficial penetrating vessels), and the cerebral cortex, producing laminar necrosis that is usually most severe in layers three, four, and five [13–15]. Disorders of arousal and awareness Brain death Brain death represents the most severe disturbance of arousal, with total and irreversible cessation of any brain function, including that associated with the brainstem. In most of the USA, with the exception of a few state governments that amended the Uniform Determination of Death Act [16] to address physician qualifications or religious considerations, an individual determined to meet the criteria for brain death is clinically and legally dead. Determining the presence of brain death is not just an academic exercise, since once the diagnosis of brain death is established, it is permissible to discontinue patient care [17]. Current criteria for brain death include the absence of responsiveness, including any motor response to painful stimulation, and the loss of all brainstem reflexes (including respiratory drive). The diagnosis must be made in the absence of confounding medical problems that could affect the evaluation, including centrally active medications and hypothermia [18]. Complicating the assessment, some patients meeting the criteria for brain death are noted to retain autonomic responses including sweating, blushing, and tachycardia, intact limb reflexes, and they may even have spontaneous limb movements. Diagnostic studies used to confirm the diagnosis of brain death include conventional angiography, transcranial Doppler ultrasonography, and radionuclide Chapter 6: Arousal brain scans used to establish the absence of cerebral blood flow. Electroencephalographic (EEG) monitoring, typically for at least 30 minutes, can be performed to demonstrate the absence of cerebral electrical activity. Similarly, the absence of somatosensory evoked potentials with stimulation of the median nerve is a confirmatory finding [18]. Once the diagnosis of brain death is established, issues surrounding termination of care, the potential suitability of the patient as an organ donor, and postmortem examination must be decided. The complexity of these considerations make the diagnosis and management of brain death a challenging problem for medical personnel, staff, families, and other parties with vested interests in the outcome (e.g., organ transplant teams). Moving beyond the complete cessation of brain function constituting brain death, the discussion leads to patients with severe impairments of arousal and awareness but with at least some residual brain function. Coma, the vegetative state, and the minimally conscious state are disorders of consciousness with disturbances in both arousal and awareness. All of these states imply major brain dysfunction and, unlike typical portrayals in motion pictures and television programs, most patients with cardiac arrest, severe TBI, or other severe insult to the brain remain comatose for some time following the insult and their resuscitation. As patients begin the slow process of recovery, they emerge from coma and progress through levels of improving arousal and awareness; the eventual outcome varies considerably and is largely dependent on age, medical comorbidities, and the mechanism of injury [19]. Coma Coma is the state of neurological unconsciousness exhibited by unarousable unawareness of the external environment that is due to extensive damage to or depressed function of both cerebral hemispheres, bilateral diencephalic structures, or the ascending reticular activating system [1]. This state can last for several weeks or a matter of seconds. The loss of consciousness seen with concussion is due to rapid and widespread neuronal depolarization across the hemispheres, which resolves quickly but can initiate a neurochemical cascade, which further impairs brain function [20]. Coma patients typically (but not invariably) have their eyes closed, are unresponsive to verbal or painful physical stimuli, and lack sleep–wake cycles; while there may be reflex motor activity or purposeless restless movements, they lack any goal-directed motor activity [1, 21]. This state represents the effects of structural injury, pharmacologic effects, or metabolic disturbances of the brain’s arousal mechanisms, resulting in an unconscious and unarousable clinical condition [22]. In addition to making the diagnosis and recommending treatment, establishing the prognosis for patients in coma is a frequent clinical question. Depending on the mechanism and severity of the insult that results in coma, some patients may progress to higher levels of consciousness very quickly, while others remain in coma for prolonged periods of time. The setting where clinicians are most accurate in making prognostic determinations is in the aftermath of hypoxic–ischemic events. For example, validated neurological examination and laboratory findings support early predictions, within the first five to seven days after HI–BI, of survival and neurological outcome following cardiac arrest in adults [17, 23]. Established clinical markers associated with death or unconsciousness at one month, or unconsciousness or severe disability at six months following the hypoxic–ischemic event, include the absence of corneal reflexes or the pupillary light response, an absent or extensor motor response to noxious stimulation 72 hours following cardiac arrest, and the presence of myoclonic status epilepticus. Validated laboratory data predicting poor outcome following hypoxic–ischemic events include a serum neuron-specific enolase level of greater than 33 mcg per liter and the absence of cortical somatosensory evoked potentials performed at least 72 hours following resuscitation [23]. Prognosis based on these clinical measures, however, may be influenced by treatments administered as part of the resuscitation following cardiac arrest. Cooling protocols may change the predictive value of diagnostic tests as well as extend the time period for potential recovery [24, 25]. Coma in other settings – including trauma, infections, poisonings, metabolic disturbances, and hemorrhagic or ischemic cerebral vascular injury – presents greater prognostic challenges, with more variability in both short-term and long-term recovery. For these conditions, the prognosis is largely determined by the patient’s response to specific treatments aimed 91 Section I: Structural and Functional Neuroanatomy at addressing the underlying process. This additional variable limits the development and application of prognostic criteria similar to that available for anoxic ischemic injury for these causes of coma. Finally, regardless of the mechanism of injury, accurate diagnosis, management, and prognostication may be complicated by ongoing treatments including paralytics, sedation, mechanical ventilation, and hypothermia, as well as the confounding effects of comorbid injury and medical conditions. Vegetative state Vegetative states (VS) are distinguished from coma by the presence of spontaneous eye opening and sleep– wake cycles. Patients in VS demonstrate no response to verbal, visual, or physical stimulation and no awareness of self or the environment [26]. Patients in VS have no purposeful activity, and demonstrate no effort (or ability) to communicate (e.g., express or comprehend language). Diagnostic criteria have been established for VS and include the absence of any awareness of self or environment, the absence of purposeful voluntary responses to external stimuli, the absence of language expression or comprehension, adequate autonomic function to survive with medical and nursing care, bowel and bladder incontinence, the presence of sleep–wake cycles, and variable preserved cranial nerve and spinal reflexes [26]. Laboratory findings among patients in VS include polymorphic delta or theta activity on electroencephalogram (EEG), and evidence of diffuse gray and white matter injury and global volume loss on structural neuroimaging [27]. Progression from coma to VS represents recovery of some or nearly all brainstem arousal mechanisms with continued failure of the distal target networks; the persistent dysfunction of rostral brain networks can occur because of thalamic injury, disruption of white matter tracts, and diffuse cortical injury, alone or in combination. Positron emission tomography (PET) measurements of cerebral blood flow in VS patients demonstrate that there is actually hypermetabolism in the ascending reticular activating system with impaired functional connectivity between the brainstem arousal system and higher cortical structures [28]. Pathologically, patients in VS demonstrate variable findings, including injury in the upper brainstem, the central thalamus, the subcortical white 92 matter (often diffuse axonal injury), and widespread cortical injury, depending on the individual patient and mechanism(s) of injury [29–31]. The degree of brain injury in VS patients is typically greater than that of other severely brain-injured patients who demonstrate any degree of conscious behavior [30]. As described above, the diagnostic criteria for VS are based on clinical observation, meaning the assessment of the patient for the absence of any form of awareness, spontaneous communication, or responsiveness to the environment. This evaluation requires careful serial examinations over a period of time, and the recognition of subtle clinical signs can be difficult. The diagnostic process is subject to error, and a significant number of patients are incorrectly diagnosed [32]. Specific bedside assessment tools have been developed to measure neurological function as patients emerge from coma, which aids in the diagnosis, prognosis, and understanding of an individual patient’s level of consciousness [33–35]. Of particular concern is misdiagnosis of VS among individuals who are conscious and aware of their surroundings – including those whose clinical conditions are most accurately regarded as a “locked-in syndrome” (LIS) [36]. A subset of individuals with LIS – in which consciousness and higher cognitive abilities are intact – superficially appear to be in VS simply as a result of lost capacity for a motor response [37–39]. Functional magnetic resonance imaging (fMRI) and PET studies assessing language, facial recognition, and other cognitive functions in patients with VS have demonstrated cortical regions of preserved activation in some subjects [37]. The prognosis for patients in VS is to a large degree dependent on the mechanism(s) of injury. Establishing a prognosis in patients in VS has crucial medical–legal implications. Important decisions regarding ongoing care, end-of-life decisions including termination of care, and withdrawal of nutritional support and hydration are commonly based on these prognostications [40]. Whereas the overall prognosis for patients with brain insult of this severity is poor, there are patients who emerge from VS and recover to varying degrees while others remain in VS indefinitely [1, 40]. This condition has been referred to variably as a persistent or a permanent VS (PVS); given that these terms are not interchangeable, it is important to be clear in our use of these terms. Criteria for considering a patient to be in PVS is controversial, with proposed criteria varying based on Chapter 6: Arousal the cause: 30 days is considered adequate for patients following cardiac arrest and 12 months is required after TBI [1, 40]. The recommendation of the Aspen Neurobehavior Conference Working Group was to limit the diagnosis to the indefinite term “vegetative state” and include the cause of the injury (i.e., anoxic versus metabolic versus TBI) and the length of time that the patient had been in VS. Whereas overall rates of recovery from VS are poor, the likelihood of recovery is better for younger patients and for those with TBI than for patients with HI–BI [41]. The longer the time interval after onset of VS, the less likely the patient will see any significant recovery. It is unlikely for patients with TBI to return to consciousness after 12 months, and following non-TBI it is unlikely to see a return to consciousness after three months. While there are patients with late recovery from VS, even years after the onset, these patients are typically severely impaired neurologically [41]. Minimally conscious state The minimally conscious state (MCS) differs from VS by virtue of the patient having minimal and inconsistent evidence for conscious behavior and some awareness of self and the environment [40, 42]. While MCS represents a spectrum of severely altered consciousness, patients with MCS demonstrate inconsistent purposeful behavior including following commands, responding to physical stimulation, manifesting appropriate emotional responses, and making efforts to initiate communication. When patients begin to interact with their environment and communicate consistently, even if still functionally severely impaired, they are considered to have made the transition from MCS to a higher level of consciousness. While VS is defined by the absence of any purposeful behaviors, efforts to communicate, and unresponsiveness to the environment, MCS is defined by the presence of these features, albeit at an inconsistent and minimal level. Making the diagnosis of MCS requires careful patient observation and examination as well as consideration of the confounding effect of specific cognitive deficits including aphasia and apraxia to try to determine the true level of awareness for self and environment. This process is critical because the prognostic implications of MCS are quite different from those of the VS. Because MCS includes a broader spectrum of function as well as a wider range of injury producing the condition, overall the prognosis is usually better than for VS [43]. Patients in MCS show slightly higher levels of cerebral metabolism compared to patients in VS, and functional imaging studies assessing responsiveness to auditory and tactile stimuli demonstrate large-scale activation of cortical networks in some MCS patients, to a much greater extent than is typically seen with similar paradigms applied to patients in VS [19, 44]. This distinction in the level of consciousness between MCS and VS has important medical–legal ramifications for decisions about ongoing management as well as termination of care, including withdrawal of nutritional support and hydration. While the distinction between VS and MCS is clear in principle, if not always clinically, it is still difficult to determine when patients have emerged from MCS. Similarly, given the severity of the cognitive dysfunction, it is sometimes difficult to gauge clinical improvement within the spectrum of MCS. In an effort to clarify this issue, the Aspen Neurobehavior Conference Working Group defined emergence from MCS as the ability to consistently demonstrate functional interactive communication, the functional use of objects, or both [42]. It is not surprising that the most useful prognostic measures for recovery from MCS are the patient’s age, intact auditory evoked potentials, functional level when evaluated, the duration of the condition, and the rate of improvement shown by the patient up to that point [43, 45]. In contrast to VS, in which late recovery is rare, in a recent study of patients with MCS, a third (including both traumatic and non-traumatic etiologies) emerged from MCS [43]. However, it is important to note that despite their late improvement, they all remained severely or totally disabled. The recent advances in functional imaging of aspects of arousal and awareness demonstrate the limitations of our current clinical approach to the assessment and prognosis of patients with disorders of consciousness, and highlight the need for better ways of evaluating and treating these challenging patients [39]. In summary, much has been clarified recently about the fundamental differences in arousal and awareness that distinguish brain death, coma, VS, and MCS. Brain death is the complete failure of brain function, including all aspects of cortical and brainstem function. This condition implies the complete and irreversible loss of arousal and consciousness, as well as all 93 Section I: Structural and Functional Neuroanatomy other neurological functions of the brain and brainstem. In coma, elementary centrally mediated functions are preserved with an absence of arousal and awareness. Vegetative state is defined by wakefulness without any awareness of self or the environment and the absence of any purposeful behaviors. With the return of minimal awareness and interaction with the environment, the patient is defined as being in MCS. Principles of treatment in the disorders of consciousness Most of what is known about the disorders of consciousness considered in this chapter relates to etiology, diagnosis, and prognosis. Given the broad range of clinical disorders that can produce these disturbances of arousal and awareness, there is a vast literature addressing specific interventions (i.e., the management of metabolic disorders, interventions for ischemic stroke and intracranial hemorrhage, and cooling protocols after cardiac arrest). Specific therapies aimed at the fundamental disturbance of arousal and awareness, however, remain an understudied area in clinical medicine. Regardless of the mechanism of injury, it is important to identify and aggressively treat ongoing medical problems, provide adequate nutritional support and hydration, and limit as much as possible the use of medications that reduce arousal (e.g., anticonvulsants, antipsychotics, pain medications, and sedatives). Because the diagnosis of the specific disorders of consciousness described above depends on careful assessments, and improvement, while clinically relevant, may be subtle, it is important to have systematic serial examinations on which to base treatment decisions. A number of clinical scales for use in this setting are available. These include the Coma Recovery Scale-Revised [46], the Coma–Near Coma Scale [47], the Glasgow Coma Scale [48], and the Rancho Los Amigos Levels of Cognitive Functioning [49]. In our experience, the Coma Recovery Scale and the Coma–Near Coma Scale have proved particularly useful in the evaluation of treatment response and recovery. The available literature, much of it related to patients with TBI, offers some evidence to help guide pharmacologic interventions and specific rehabilitative strategies in patients with disorders of arousal and consciousness. General considerations include providing the best possible environment for their 94 ongoing care. Specific steps include optimizing light– dark cycles, protecting time for sleep at night and encouraging wakefulness throughout the day, and providing regularly scheduled meals (by whatever means necessary, including tube feedings) in order to entrain circadian rhythms, limit distractions and other sources of unwanted stimulation such as treating pain and minimizing painful procedures, and provide necessary stimuli to help facilitate interaction with others and the environment. For coma patients, a specific rehabilitative strategy is coma stimulation, in which structured sensory stimulation is administered for the purposes of improving sensory awareness and facilitating improvements in arousal and awareness. A time-limited trial of this therapy represents a specific intervention with limited risk for patients in coma, and can also be considered for those in VS and MCS. There is mounting evidence supporting the use of this therapy when provided by well-trained and competent therapists in the proper setting [50]. The evidence for pharmacologic interventions in disorders of consciousness is limited and it is important to note that at present there are no FDA-approved treatment options for these patients. All proposed treatment options are based on off-label uses of these medications. Amantadine has been used in the treatment of severe disturbances of arousal, with two double-blind, placebo-controlled studies suggesting that it may be beneficial [45, 51]. A variety of other medications have demonstrated benefit in small case series and individual case reports. These medications include modafinil [52], methylphenidate [53– 55], bromocriptine [56], pramipexole [57], levodopa [58], and zolpidem [58, 59]. These drugs represent additional off-label treatment options as second-line therapies in patients where amantadine was either not tolerated or ineffective. Deep brain stimulation (DBS), in common use for the treatment of a variety of movement disorders, may offer an alternative to pharmacologic therapy in selected patients in the future. Deep brain stimulation targeting specific nuclei in the thalamus in a patient in MCS following TBI led to improvement in arousal, interactions with the environment, and the ability to communicate [60]. Bilateral DBS leads were placed in the anterior intralaminar nuclei and the adjacent paralaminar regions of the thalamus. This patient was selected for the procedure because fMRI studies indicated that large-scale bihemispheric language Chapter 6: Arousal networks remained intact and responsive to activation despite the marked functional impairment. The premise behind the DBS procedure was that increasing activation of neocortical and basal ganglia neurons through stimulation of the thalamus would compensate for the loss of arousal modulation normally provided by these structures in the intact brain [31]. Whereas these studies suggest great potential for DBS in this severely impaired population (in whom there are limited therapeutic options), the available evidence and experience with the procedure limits its application to selected research subjects. Whether the risks and benefits of this procedure warrant broader application remains to be seen. Conclusion Arousal is a central function of the brain and, as such, makes possible all the higher functions characterizing the human species and collectively known as consciousness. Disorders of arousal affect consciousness at its most fundamental level, and serve well to illustrate how the capacity for wakefulness and awareness is so critical for all conscious human behavior. For clinicians engaged in the care of these severely compromised patients, clinical management commonly involves not only complex neurologic issues but also difficult medical–legal, ethical, and social concerns. Although specific treatments for these disorders are limited, a range of rehabilitative, pharmacologic, and environmental strategies can improve outcomes. As neuroscientific advances continue, a more informed approach to the topic of arousal and its disorders can be anticipated. References 1. Posner JB, Saper CB, Schiff ND, Plum F. Plum and Posner’s Diagnosis of Stupor and Coma. 4th edition. New York, NY: Oxford University Press; 2007. 2. Moruzzi G, Magoun HW. Brain stem reticular formation and activation of the EEG. Electroencephalogr Clin Neurophysiol. 1949;1(4): 455–73. 3. Pfaff D. Brain Arousal and Information Theory. Cambridge, MA: Harvard University Press; 2006. 4. Parvizi J, Damasio A. Consciousness and the brainstem. Cognition 2001;79(1–2):135–60. 5. Mesulam M. Attentional networks, confusional states, and neglect syndromes. In Mesulam MM, editor. Principles of Behavioral and Cognitive Neurology. 2nd edition. Oxford: Oxford University Press; 2000, pp. 174–256. 6. Pfaff D, Ribeiro A, Matthews J, Kow LM. Concepts and mechanisms of generalized central nervous system arousal. Ann N Y Acad Sci. 2008;1129:11–25. 7. Steriade M, Glenn LL. Neocortical and caudate projections of intralaminar thalamic neurons and their synaptic excitation from midbrain reticular core. J Neurophysiology 1982;48(2):352–71. 8. Kinomura S, Larsson J, Gulyas B, Roland PE. Activation by attention of the human reticular formation and thalamic intralaminar nuclei. Science 1996;271(5248):512–15. 9. Gray TS. Functional and anatomical relationships among the amygdala, basal forebrain, ventral striatum, and cortex. An integrative discussion. Ann N Y Acad Sci. 1999;877:439–44. 10. Benarroch EE. The midline and intralaminar thalamic nuclei: anatomic and functional specificity and implications in neurologic disease. Neurology 2008;71(12):944–9. 11. Jones EG. The thalamic matrix and thalamocortical synchrony. Trends Neurosci. 2001;24(10):595–601. 12. Hopkins RO, Tate DF, Bigler ED. Anoxic versus traumatic brain injury: amount of tissue loss, not etiology, alters cognitive and emotional function. Neuropsychology 2005;19(2):233–42. 13. Arbelaez A, Castillo M, Mukherji SK. Diffusion-weighted MR imaging of global cerebral anoxia. AJNR: Am J Neuroradiol. 1999;20(6): 999–1007. 14. Chalela JA, Wolf RL, Maldjian JA, Kasner SE. MRI identification of early white matter injury in anoxic-ischemic encephalopathy. Neurology 2001;56(4):481–5. 15. Greer DM. Mechanisms of injury in hypoxic-ischemic encephalopathy: implications to therapy. Semin Neurol. 2006;26(4):373–9. 16. Guidelines for the determination of death. Report of the medical consultants on the diagnosis of death to the President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research. J Am Med Assoc. 1981;246(19):2184–6. 17. Geocadin RG, Eleff SM. Cardiac arrest resuscitation: neurologic prognostication and brain death. Curr Opin Crit Care 2008;14(3):261–8. 18. Wijdicks EF. Determining brain death in adults. Neurology 1995;45(5):1003–11. 19. Laureys S, Perrin F, Schnakers C, Boly M, Majerus S. Residual cognitive function in comatose, vegetative and minimally conscious states. Curr Opin Neurol. 2005;18(6):726–33. 95 Section I: Structural and Functional Neuroanatomy 20. Giza CC, Hovda DA. The neurometabolic cascade of concussion. J Athl Train. 2001;36(3):228–35. 21. Stevens RD, Bhardwaj A. Approach to the comatose patient. Crit Care Med. 2006;34(1):31–41. 22. Schiff ND, Plum F. The role of arousal and “gating” systems in the neurology of impaired consciousness. J Clin Neurophysiol. 2000;17(5):438–52. 23. Wijdicks EFM, Hijdra A, Young GB et al. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2006;67(2):203–10. 35. Pape TL, Senno RG, Guernon A, Kelly JP. A measure of neurobehavioral functioning after coma. Part II: Clinical and scientific implementation. J Rehabil Res Dev. 2005;42(1):19–27. 36. Andrews K, Murphy L, Munday R, Littlewood C. Misdiagnosis of the vegetative state: retrospective study in a rehabilitation unit. Br Med J. 1996; 313(7048):13–16. 37. Owen AM, Coleman MR. Detecting awareness in the vegetative state. Annals N Y Acad Sci. 2008;1129: 130–8. 24. Al Thenayan E, Savard M, Sharpe M, Norton L, Young B. Predictors of poor neurologic outcome after induced mild hypothermia following cardiac arrest. Neurology 2008;71(19):1535–7. 38. Laureys S, Boly M, Schnakers C et al. Revelations from the unconscious: studying residual brain function in coma and related states. Bull Mem Acad R Med Belg. 2008;163(7–9):381–8; discussion 8–90. 25. Leithner C, Ploner CJ, Hasper D, Storm C. Does hypothermia influence the predictive value of bilateral absent N20 after cardiac arrest? Neurology 2010;74(12):965–9. 39. Monti MM, Vanhaudenhuyse A, Coleman MR et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med. 2010;362(7):579–89. 26. The Multi-Society Task Force on PVS. Medical aspects of the persistent vegetative state (1). N Engl J Med. 1994;330(21):1499–508. 27. American Academy of Neurology. Practice parameters: assessment and management of patients in the persistent vegetative state (summary statement). Neurology 1995;45(5):1015–18. 28. Silva S, Alacoque X, Fourcade O et al. Wakefulness and loss of awareness: brain and brainstem interaction in the vegetative state. Neurology 2010;74(4):313–20. 29. Adams JH, Graham DI, Jennett B. The neuropathology of the vegetative state after an acute brain insult. Brain 2000;123(Pt 7):1327–38. 30. Jennett B, Adams JH, Murray LS, Graham DI. Neuropathology in vegetative and severely disabled patients after head injury. Neurology 2001;56(4): 486–90. 31. Schiff ND, Fins JJ. Deep brain stimulation and cognition: moving from animal to patient. Curr Opin Neurol. 2007;20(6):638–42. 32. Schnakers C, Vanhaudenhuyse A, Giacino J et al. Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment. BMC Neurol. 2009;9:35. 33. Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004;85(12):2020–9. 34. Pape TL, Heinemann AW, Kelly JP, Hurder AG, Lundgren S. A measure of neurobehavioral functioning after coma. Part I: Theory, reliability, and 96 validity of Disorders of Consciousness Scale. J Rehabil Res Dev. 2005;42(1):1–17. 40. Giacino J, Whyte J. The vegetative and minimally conscious states: current knowledge and remaining questions. J Head Trauma Rehabil. 2005;20(1): 30–50. 41. Estraneo A, Moretta P, Loreto V et al. Late recovery after traumatic, anoxic, or hemorrhagic long-lasting vegetative state. Neurology 2010;75(3):239–45. 42. Giacino JT, Ashwal S, Childs N et al. The minimally conscious state: definition and diagnostic criteria. Neurology 2002;58(3):349–53. 43. Luaute J, Maucort-Boulch D, Tell L et al. Long-term outcomes of chronic minimally conscious and vegetative states. Neurology 2010;75(3):246–52. 44. Schiff ND, Rodriguez-Moreno D, Kamal A et al. fMRI reveals large-scale network activation in minimally conscious patients. Neurology 2005;64(3):514–23. 45. Whyte J, Katz D, Long D et al. Predictors of outcome in prolonged posttraumatic disorders of consciousness and assessment of medication effects: a multicenter study. Arch Phys Med Rehabil. 2005;86(3):453–62. 46. Kalmar K, Giacino JT. The JFK Coma Recovery Scale–Revised. Neuropsychology 2005;15(3–4): 454–60. 47. Rappaport M, Dougherty AM, Kelting DL. Evaluation of coma and vegetative states. Arch Phys Med Rehabil. 1992;73(7):628–34. 48. Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet 1974;2(7872):81–4. 49. Hagen C, Malkmus D, Durham P. Levels of Cognitive Functioning. Downey, CA: Ranchos Los Amigos Hospital; 1972. Chapter 6: Arousal 50. Lombardi F, Taricco M, De Tanti A, Telaro E, Liberati A. Sensory stimulation for brain injured individuals in coma or vegetative state. Cochrane Database Syst Rev. 2002;2:CD001427. 51. Meythaler JM, Brunner RC, Johnson A, Novack TA. Amantadine to improve neurorecovery in traumatic brain injury-associated diffuse axonal injury: a pilot double-blind randomized trial. J Head Trauma Rehabil. 2002;17(4):300–13. 52. Rivera VM. Modafinil for the treatment of diminished responsiveness in a patient recovering from brain surgery. Brain Injury 2005;19(9):725–7. 53. Hornyak JE, Nelson VS, Hurvitz EA. The use of methylphenidate in paediatric traumatic brain injury. Pediatr Rehabil. 1997;1(1):15–17. 54. Plenger PM, Dixon CE, Castillo RM et al. Subacute methylphenidate treatment for moderate to moderately severe traumatic brain injury: a preliminary double-blind placebo-controlled study. Arch Phys Med Rehabil. 1996;77(6): 536–40. 55. Worzniak M, Fetters MD, Comfort M. Methylphenidate in the treatment of coma. J Fam Pract. 1997;44(5):495–8. 56. Passler MA, Riggs RV. Positive outcomes in traumatic brain injury-vegetative state: patients treated with bromocriptine. Arch Phys Med Rehabil. 2001; 82(3):311–15. 57. Patrick PD, Buck ML, Conaway MR, Blackman JA. The use of dopamine enhancing medications with children in low response states following brain injury. Brain Injury 2003;17(6):497–506. 58. Clauss R, Nel W. Drug induced arousal from the permanent vegetative state. NeuroRehabilitation 2006;21(1):23–8. 59. Cohen SI, Duong TT. Increased arousal in a patient with anoxic brain injury after administration of zolpidem. Am J Phys Med Rehabil. 2008;87(3):229–31. 60. Schiff ND. Central thalamic deep-brain stimulation in the severely injured brain: rationale and proposed mechanisms of action. Annals N Y Acad Sci. 2009;1157:101–16. 97 Section I Structural and Functional Neuroanatomy Chapter Sleep 7 Martin L. Reite The core function of sleep in maintaining the integrity of brain and bodily function in animals is still under debate [1], but its absolute necessity in humans is unequivocal. Total sleep deprivation leads to death [2]. In humans, sleep restriction and insufficient sleep lead to decreased cognitive and psychomotor performance [3], impaired antibody production following immunizations [4], increased C-reactive protein [5], decreased leptin, and increased grehlin production with concomitant higher risk for insulin resistance and type 2 diabetes [6, 7]. Recognition of the personal and economic cost of disturbed sleep has contributed to the development of the specialty of sleep medicine and improved the definition of the many sleep pathologies. Four major brain systems, and two state switching mechanisms, underlie the appearance and control of sleep. The major systems are the arousal system, the non-rapid eye movement (REM) slow-wave sleep (SWS) system, the REM sleep system, and the circadian timing system. The switching mechanisms are bistable neurophysiological mechanisms, one of which controls the wake/SWS transition, the other controlling the REM-on vs. REMoff state. A basic knowledge of these systems and switching mechanisms provides an important background for assessment and treatment of most sleep disorders. In the parlance of sleep medicine, the sleep homeostatic systems are encompassed by the term Process S, and the circadian timing systems by the term Process C [8]. Most sleep pathologies can be conceptualized as representing disturbances in Process S, Process C, or both. Historically, an important event in sleep medicine was the encephalitis lethargica epidemic of 1917– 1918, which was associated with profound changes in sleep behavior, including severe hypersomnia, and, less frequently, profound insomnia. The Austrian neuropsychiatrist and World War I pilot Constantin von Economo examined the brains of deceased patients and found that lesions in the regions now known to encompass the ascending reticular activating system (ARAS) led to profound sleepiness and even coma, whereas lesions in regions now known to encompass the sleep-promoting ventrolateral pre-optic (VLPO) region led to profound insomnia. The cause was never found, and explanations ranged from viral to, more recently, delayed destructive autoimmune responses following streptococcal infection. Since that time, much progress in the understanding of sleep has been made. In this chapter, the organization of human sleep and the brain mechanisms that regulate it are reviewed, and the manner in which disturbances in those mechanisms lead to specific sleep disorders is discussed. For convenience we will group the sleep pathologies into three broad categories: (1) the insomnias, (2) the hypersomnias, and (3) the parasomnias. While there is much overlap between syndromes, patients often present with complaints representing these three categories (e.g., “I can’t sleep,” “I sleep too much,” or “strange things happen while I sleep”). Basic wake–sleep organization and mechanisms Wake-promoting systems The ARAS lies in the upper brainstem and sends projections rostrally through two major pathways, one to the thalamus-activating thalamic relay neurons Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 98 Chapter 7: Sleep which gate transmission of information to the cortex, and a second more direct system that bypasses the thalamus and activates neurons in the hypothalamus, basal forebrain, and cerebral cortex. These pathways, when active, promote wakefulness. The first pathway originates largely in cholinergic cell groups active during wakefulness and REM sleep that are located in the pedunculopontine (PPT) and laterodorsal tegmental (LDT) nuclei. Whereas downregulation of this system during sleep might be thought to prevent sensory information from reaching the cortex, clinical and laboratory evidence show otherwise. For example, neurons in the auditory cortex respond to auditory input during sleep [9], and complex parasomnia behavior including driving can occur during sleep, indicating that the brain can respond appropriately to sensory input while not conscious of it. Several additional regions in the upper brainstem, predominantly monoaminergic, are involved in promoting wakefulness, including histaminergic nuclei (important in maintaining vigilance, as discussed below), the tubero-mammillary region (modulated by orexin/hypocretin), and serotonergic influences (primarily from the raphe nuclei). The second branch of the ARAS originates primarily in monoaminergic neurons in the upper brainstem and caudal hypothalamus, including the noradrenergic locus coeruleus (LC). The ARAS thus depends on acetylcholine (ACh), monoamines, and some neuropeptide neurotransmitter systems for its function. Overactivation of the ARAS leads to complaints of insomnia, suggesting stress, anxiety, or the general state of hyperarousal thought to underlie primary insomnia. Agents that inhibit the ARAS promote decreased arousal or sleep. It has been hypothesized that disturbances in ARAS function may be related to sleep–wake control problems found in a number of neurological and mental disorders [10]. The neurophysiology of arousal is discussed in more detail in Chapter 6. Sleep-promoting systems As noted above, von Economo described a small percentage of patients with encephalitis lethargica whose response was to develop profound insomnia, sleeping only a few hours each day. Autopsy studies demonstrated that these patients had lesions involving the basal ganglia and adjacent hypothalamus. Subsequent animal studies identified sleep-promoting neural systems located primarily in hypothalamic and contiguous regions, and emphasizing neuronal activity in the median pre-optic nucleus (MnPN) and VLPO region of the hypothalamus, the activity of which promotes non-REM or SWS and inhibits wakefulness. VLPO and MnPN neurons are primarily active during sleep, and send inhibitory output to all major cell groups of the hypothalamus and brainstem that participate in arousal [11, 12]. These neuronal systems depend significantly on the inhibitory neurotransmitter gamma-amino butyric acid (GABA) and the inhibitory neuropeptide galanin. They appear to be activated by preceding wakefulness, and may be modulated by the build-up of adenosine associated with the wakeful state. Adenosine appears to be an important homeostatic sleep factor acting through the adenosine (Ado) A1 and A2 receptors, whose release is triggered by inducible nitric oxide synthesis in the basal forebrain secondary to prolonged wakefulness [13]. Thus the longer one has been awake, the more likely sleep will be triggered, and the adenosine antagonist caffeine tends to prolong wakefulness. The VLPO hypothalamic areas in humans is a sexually dimorphic region, with volume and cell number peaking about age 2–4 and declining thereafter. In males, decreases do not begin until later in adulthood; men between 45 and 60 years of age show a decrease in cell number of about 3% per year until age 60, after which further decreases are not noted. In females, cell numbers decrease until the teen years, then remain fairly stable until after age 50 when a gradual decrease begins, dramatically increasing after age 75. Between the ages of 75 and 85 years, cell number decreases in females at a rate of 4–8% per year, leading to cell numbers only 10–15% of the peak seen between ages 2 and 4 [14]. These findings imply increasing difficulty in initiating and maintaining sleep in both men and women beginning around age 50, stabilizing in men by 60, but continuing to worsen in women with increasing age. Paralleling these neuroanatomic changes, it is notable that insomnia is a complaint that increases with age, especially in women, leading Gaus and colleagues to suggest that “ . . . shrinkage of the VLPO with advancing age may explain sleep deficits in elderly humans” [15]. The genetic regulation of SWS is not yet well understood, but advances are being made that should illuminate this likely important area [16, 17]. 99 Section I: Structural and Functional Neuroanatomy Bi-stable wake/sleep switch The ARAS and VLPO systems are mutually inhibitory, and can therefore set up a self-reinforcing loop such that activity in one decreases activity in the other, similar to electrical circuits termed “flip-flop” switches [11]. Increasing activity in one side of the circuit can abruptly switch the circuit and cause abrupt sleep/wake transitions. This flip-flop switch may be modulated by orexin neurons, which when depleted, as in narcolepsy (see below), lead to instability of the switch. Diminished strength of either side of the switch can increase its instability, and thus elderly individuals who have lost some VLPO neurons may also exhibit more frequent wake/sleep transitions. REM sleep REM sleep is controlled independently by brainstem oscillators whose activation leads to the multiple physiological accompaniments of the REM state, including low-voltage fast, awake-like electroencephalographic (EEG) patterns, skeletal muscle paralysis, rapid eye movements, temporary suspension of thermoregulation, and EEG sharp waves termed PGO spikes, sharp complexes arising in the pons and transmitted through the lateral geniculate to the occipital cortex (hence PGO). The REM state normally appears only during SWS, although evidence suggests the underlying neurophysiological oscillator may run continuously. In narcolepsy, the REM state can break into the waking state with varying degrees of loss of skeletal muscle tone, termed cataplexy; this disorder, often triggered by emotional arousal, can result in the person falling to the ground and entering a full-blown REM episode complete with dream-like mentation. Brainstem neuronal systems appear to account for the periodic generation of the REM state in all mammals, including humans. Cholinergic systems residing in the laterodorsal and pedunculopontine (LDT/PPT) nuclei projecting to the mesencephalic and pontine reticular formation (mPRF) appear to play a prominent role in activating REM sleep [18]. These systems largely reside in the pontine tegmentum, and may constitute a separate component of the ARAS. The frequencies of these independent oscillators appear to be a function of body size (the smaller the animal, the faster the oscillator). Cholinergic systems appear involved in activating REM states, and monoamines in suppressing them. Agents that increase Ach activity, such as the Ach inhibitor physostigmine, increase 100 REM sleep, and agents that increase monoamine activity, such as certain antidepressants (most notably the monoamine oxidase inhibitors) decrease REM sleep. REM flip-flop switch It has recently been suggested that a type of neurophysiological “flip-flop” switch also exists for controlling transitions into and out of the REM state (the “REM-on/REM-off” switch), consisting of mutually inhibitory GABAergic neurons with independent pathways mediating EEG and atonia effects [19]. In the cat, the REM-on neurons are thought to be concentrated in the sublaterodorsal nucleus and modulated by glutamatergic neurons in the lateral and ventrolateral periaqueductal gray. This switch is thought to be subsidiary to the putative wake–sleep flip-flop switch, preventing transitions into REM during wakefulness, unless narcolepsy is present and leads to a weakened wake side of the wake–sleep switch due to loss of orexin neurons. Dreaming The dream is the unusual mental content that often accompanies REM sleep. Thought by Freud to encompass significant symbolism, the explanation of Hobson and McCarley that the dream is random mental content accompanying an activated cortex generated by brainstem structures led to a fundamental re-thinking of dreaming [20]. Several lines of evidence support this idea. Selective activation of the amygdalae and other emotion-generating limbic regions occurs during REM sleep, along with deactivation of the dorsolateral prefrontal cortex. Several aminergic systems, including noradrenergic, serotonergic, and histaminergic, are essentially shut down in REM sleep, while dopaminergic systems remain active. With the recognition that dopamine antagonists are used in the treatment of psychosis, it has been suggested that the relative predominance of dopamine activity during REM sleep may account for the psychotic quality of a typical dream [21]. Sleep timing regulation – the circadian system The primary body “clock” regulating circadian rhythms is located in the suprachiasmatic nucleus (SCN) of the hypothalamus, and has an intrinsic Chapter 7: Sleep rhythmicity of about 24 hours. This clock is primarily synchronized by light exposure. A set of photoreceptors distinct from rods and cones contain a vitamin A-based photoreceptor called melanopsin and project via the retino-hypothalamic pathway to the SCN to provide light information to the brain. A number of factors that affect the role of light in modulating circadian rhythms have been described, including light intensity and duration (more photons equals more activation), previous light exposure (less previous light exposure increases the effect of light, and vice versa) [22], timing (there is a phase response curve), and frequency (short wavelength more effective than longer wavelengths) of light [23]. Output from the SCN includes efferent projections to contiguous hypothalamic regions, as well as a major sympathetic pathway from the SCN via the intermediolateral cell column of the spinal cord and superior cervical ganglion to the pineal gland, which regulates production of melatonin, the primary circadian hormone. Melatonin production is activated when light diminishes as day transitions to night. Measurement of blood or salivary melatonin can assist in deriving a metric termed dim-light melatonin onset (DLMO), a primary circadian timing signal. The circadian system is largely under genetic control [24]. A number of circadian clock genes have been identified (e.g., Per1, Per2, Per3, Cry1, Cry2) that are transcriptional regulators thought to underlie circadian rhythm generation at the cellular level, and may also have a role in sleep homeostasis [25]. Disturbances in the regulation of these genes is thought to underlie many sleep disturbances [17]. Disturbances in circadian regulation, as seen in jet lag or shift work, and the circadian rhythm sleep disorders (e.g., delayed and advanced sleep phase syndrome, and free-running rhythms in blind individuals) constitute an important component of sleep medicine. These disorders usually present clinically as insomnia, as discussed below. Sleep morphology and architecture The original Rechtschaffen and Kales sleep stage terminology [26] has recently been supplanted by a new scoring system developed by the American Academy of Sleep Medicine [27], which is used in this chapter. The EEG, however, remains central to the characterization of sleep morphology and architecture (see Chapter 28). Non-REM sleep typically progresses from wakefulness to stage N1 (loss of alpha rhythm, lower voltage theta with some sharp wave activity), to N2 (development of sleep spindles, K-complexes, increasing ⬍1 to ∼4 Hz delta activity), to N3 (greater than 20% delta activity with diminution of spindles and K-complexes). The term SWS has been variably and confusingly used to represent non-REM sleep, stage N3 sleep, or sometimes simply delta activity ⬍2Hz. Sleep spindles result from recurrent inhibitory circuits in the reticular thalamic nuclei leading to hyperpolarization of thalamocortical neurons [28]. Sleep spindles can be divided into subtypes based upon both frequency and topography. ∼12 Hz spindle activity is dominant over precentral and frontal areas, and ∼14 Hz spindles are dominant post-centrally [29, 30]. Fast spindles have been related to visuomotor learning [31]. K-complexes are large (high voltage), sharp wave complexes, often followed by spindle bursts that are maximally seen over high central and central parietal regions. They are thought to represent a type of EEG “evoked response” triggered by external or internal stimuli, and may have, as their source, deeper brain structures. Spectral-based measurement of sleep EEG activity in the 0.75 Hz to 4.5 Hz band has been termed slow wave activity (SWA) and has proven very useful in measuring sleep homeostatic pressure. Slow wave activity varies regionally over the brain as a function of experience preceding sleep, suggesting a local regulation of SWA in addition to that relating to circadian timing and duration of wakefulness prior to sleep [32]. Conceptually, the term “Process S” was coined by Borbely and Achermann [8] to refer to the homeostatic sleep drive. The strength of this homeostatic drive, sometimes termed “sleep pressure,” is measured by computing the amount of SWA in the sleep EEG following varying periods of wakefulness. Longer periods of wakefulness result in substantial increases in SWA during the first several sleep cycles of recovery sleep. Animal studies suggest that the adenosine receptor Ado A1 gene is involved in regulating the efficiency of the SWA response and associated recovery of central nervous system (CNS) function following sleep loss [33]. Other EEG patterns are often seen during sleep, but their significance is not well understood and they are usually neither formally scored nor commented upon in typical clinical sleep recordings. One 101 Section I: Structural and Functional Neuroanatomy such pattern is the cyclic alternating pattern (CAP), which consists of spontaneous, periodic, somewhat stereotypical interruptions of background activity during non-REM sleep and thought to be a measure of sleep stability [34, 35]. It has been suggested that CAP EEG waveforms may be related to learning [36], parasomnias in children [37], and sleep disturbances in other disorders, including chronic fatigue [38], depression [39], and developmental disabilities [37]. The suggestion has been made that it may be more meaningful to examine the wake and sleep EEG based on the coalescence of complex oscillatory phenomena, a metric that might index neuronal interactions in corticothalamic systems with greater neurobiological and functional meaning than that obtained with most current sleep staging based on analytic methods [40]. Local sleep While earlier it was thought that sleep reflects a state encompassing the entire brain, emerging evidence suggests that this is not so. It is now thought that the specific brain regions most used during the preceding period of wakefulness require a greater intensity and/or duration of sleep during a subsequent sleep period, thus sleep, especially SWS, will be differentially distributed across brain regions in a use-dependent fashion [41]. Krueger and colleagues suggest that sleep may be a fundamental property of cortical networks, dependent upon prior activity in each, and such difference may be expressed at the level of the cortical column [42]. Sleep physiology As humans transition from wakefulness to sleep, characteristic physiological changes include decreases in muscle tone (as measured by electromyographic activity), respiratory rate, heart rate, and blood pressure. Body temperature also decreases, and this change is related not only to the decreased metabolic activity accompanying sleep but also to the central circadian temperature regulation system. Sleep also modulates hormonal secretion, with decreases in cortisol and thyrotropin and increases in prolactin and growth hormone production. REM sleep is accompanied by increased variability in the heart rate, respiratory rate, and blood pressure. Adverse cardiac events such as arrhythmias and infarctions seem to cluster in the early morning hours 102 when REM sleep is more prominent, possibly indicating an increased vulnerability of those with impaired cardiac perfusion to this physiological activation and variability. Body temperature regulation temporarily ceases during REM sleep, and for a short time humans become essentially poikilothermic animals. REM sleep has other unique physiological signatures including penile or clitoral tumescence and the characteristic sharp occipital EEG waves called PGO spikes. Most pronounced of all, perhaps, is the general paralysis of descending skeletal muscle (with the exception of the diaphragm) that accompanies REM sleep, which not only prevents the acting out of dreams but also increases the probability of apneas, hypopneas, and hypoventilation because of hypotonia of the accessory muscles of respiration (e.g., the intercostal muscles) and the upper airway dilator muscles. Prominent disturbances in the regulation of REM sleep physiology are also seen in narcolepsy and REM behavior disorder (see below). Development of sleep across the lifespan Human sleep patterns change dramatically during development (see [43] for review). At birth, sleep consists of two types, each occurring in 50% of sleep time: active sleep, an immature form of REM sleep, and quiet sleep, a prelude to non-REM SWS. Active sleep rapidly decreases such that by 6 months of age it represents only 25% of total sleep, approaching adult REM values. The two major processes termed homeostatic and circadian (Process S and Process C, respectively) are not developed at birth. Process S in infants consists of quiet sleep with gradual maturation into adult EEG sleep patterns as the neocortex develops. Process C is not apparent at birth, but begins to appear at about 16 weeks when a 24-hour sleep–wake cycle begins to emerge with sleep being consolidated in the night and waking in the day. At birth, quiet and active sleep are interspersed, but by 3 months of age adult sleep cycle patterns emerge and REM (active) sleep comes to constitute a greater percentage of later sleep periods, with non-REM or SWS being more prominent in earlier cycles. REMs are present at birth in active sleep; the skeletal muscle inhibition of adult REM sleep begins to appear at 6 months. Total sleep time is about 16–18 hours/day at birth, diminishing to 14–15 hours at one year and to more adult values as maturation continues. Chapter 7: Sleep There is an increase in SWS sleep during adolescence, likely associated with the onset of programmed synaptic pruning associated with brain maturation during this period [44]. Recent experimental data demonstrate the close association of SWA during sleep supporting the synaptic plasticity (“pruning and tuning”) associated with learning and memory consolidation [45]. During the nocturnal sleep period, “sleep cycles” usually beginning with non-REM sleep and ending in a REM period, repeat with about a 90minute periodicity, to be repeated four to five times during a typical night. Most SWS sleep occurs during the first several sleep cycles; REM periods become longer as the night progresses. After a period of sleep deprivation, SWS is the first sleep epoch to recover. Sleep morphology is generally reasonably stable during adulthood (late adolescence to the mid-50s or 60s); however, changes are prominent in the elderly. The amplitude of SWS diminishes such that formal criteria for Stage N3 may not be met, leaving primarily stages N1 and N2. The loss of VLPO neurons (see above) and their support of Process S lead to sleep fragmentation with frequent awakenings, less restorative sleep, and resultant insomnia complaints in the elderly. With respect to Process C, aging is accompanied by a decrease in the amplitude of body temperature and other circadian rhythms as well as a decrease in the ability to synchronize the circadian system to changes such as those associated with jet lag [46]. Adults over the age of 65 have decreased sensitivity to the phasedelaying effects of bright light exposure [47]. The nocturnal production of melatonin also decreases with age, so after age 60 this may be related to difficulties with sleep onset [48]. These changes may relate to functional or structural alterations within the SCN itself [46]. Complicating such sleep loss in the elderly is the fact that those genetic mechanisms subserving recovery from sleep deprivation may also be impaired in older individuals. Animal studies have shown that the endoplasmic reticulum (ER) is a major component of a quality control system that removes abnormal or misfolded proteins accumulated during the stress of sleep deprivation. The adaptive response of the ER to coping with these abnormal proteins appears to be attenuated in older individuals, and thus they may be disproportionately impacted by sleep deprivation as a result of impaired abnormal protein recovery systems [49]. Sleep disorders Insomnias The domain of the insomnias is the largest in terms of sleep complaints, and benefits from thoughtful differential diagnosis, as the causation may be both multiple and obscure. The 2005 National Institutes of Health (NIH) State-of-the-Science Conference on the Manifestations and Management of Chronic Insomnia in Adults estimated that 30% of people in the general population experience symptoms consistent with insomnia. Often essentially written off in the past with the attitude that “no one dies from insomnia” this disorder has been recently shown to be associated with significant daytime functional decrements, emphasizing that it is a 24-hour disorder, not just a sleep disorder. Problems include sleepiness and fatigue, cognitive and psychomotor impairments, worsened anxiety and depression, increased risk for some medical disorders, activation of the hypothalamic– pituitary–adrenal axis, impairment in immune function, and impairment in global functioning and/or quality of life. For a recent review of functional impairments associated with insomnia see [50]. Our conceptualization of insomnia has undergone a dramatic shift during the past few years, from being an annoying but not particularly serious symptom to the recognition that (1) sleep loss has serious consequences; (2) chronic insomnia and its associated impaired sleep are highly comorbid with (or indeed may cause) many other medical and psychiatric disorders; and (3) chronic insomnia in some cases may represent a separate medical disorder of its own with an independent neurobiological basis. Our primary concern is not the transient and short-term insomnias, which are generally related to stress, short-term illness, jet lag, or shift work (although clearly medically significant in its own right), but the chronic insomnias that require a systematic differential diagnosis. The diagnosis of an insomnia complaint should include a systematic review of the following areas, some or all of which may be contributory: (1) medical disorders and their treatments; (2) psychiatric disorders and substance misuse; (3) circadian rhythm disorders; (4) movement disorders; and (5) the conditioned insomnia, sleep state misperception, primary insomnia group. A systematic review of each of these categories is likely to encompass the potential contributions to most 103 Section I: Structural and Functional Neuroanatomy insomnia complaints. It is important not to stop with the first potential causation encountered, as more than one cause is the rule rather than the exception. A brief discussion of each area follows. Medical disorders The insomnia complaints that are comorbid with medical disorders include both sleep disturbances caused by medical symptoms (e.g., breathing difficulties, heartburn from gastroesophageal reflux disease, nocturia, pain), but also those sleep disturbances caused by the pathophysiology underlying the medical condition. Major entries on this list include the dementias, rheumatological disorders, chronic fatigue syndrome (CFS), and fibromyalgia. Sleep complaints are very common in CFS, and can include insomnia, hypersomnia, non-restorative sleep, and sleeping at the wrong time of the 24-hour period (e.g., circadian rhythm abnormalities). Conventional polysomnogram (PSG) findings are generally non-specific and include decreased sleep efficiency, decreased SWS, increased sleep latency, and alpha-delta sleep EEG patterns. CFS-related disturbances in regulation of underlying sleep control mechanisms are supported by several studies. One recent study found an increase in cyclic alternating pattern in the PSG of CFS patients complaining of non-restorative sleep [38], and there is also evidence of decreased sleep drive (Process S) in CFS [51]. Sleep disturbances, usually insomnia and non-restorative sleep, are also common components of fibromyalgia, and are not entirely explained by the pain and depression associated with this syndrome [52]. Chronic pain is of special concern, as it has been demonstrated that sleep loss lowers the pain threshold, and self-reported restorative sleep is associated with the resolution of pain complaints [53]. Treatments of sleep complaints in these disorders has been challenging, although recently positive results have been found in fibromyalgia using sodium hydroxybutyrate, which enhances GABAergic activity and increases delta sleep. Dementias including Alzheimer’s disease (AD) are often associated with severe insomnia that is quite disruptive to patients and families, and may be one of the factors precipitating institutional care. Neuropathologic changes of AD in the sleep and circadian rhythm control centers of the hypothalamus and SCN may contribute to this problem. Sleep is also disturbed in dementia with Lewy bodies (DLB), which has been found in up to 20% of dementia cases referred to autopsy [54]. This disturbance often 104 takes the form of increased motor activity and suggests REM behavior disorder. Body temperature and activity circadian rhythms can also be altered in the dementing disorders as a result of characteristic brain lesions. Alzheimer’s disease has been associated with a phase delay in both body temperature and activity rhythms, whereas frontotemporal dementia appears to produce fragmented sleep with a phase advance of the activity rhythm, apparently uncoupled from the body temperature rhythm [55]. Degeneration of cholinergic neurons in the nucleus basalis of Meynert may contribute to rest–activity disturbances and the “sundowning” syndrome in AD patients [56]. Pharmacotherapy of AD may contribute as well to sleep complaints, as tacrine and donepezil are centrally acting cholinesterase inhibitors, and may cause insomnia. Psychiatric disorders Sleep complaints are very frequently comorbid with psychiatric disorders. Most common is insomnia, although hypersomnia may accompany atypical depression. In the case of mood disorders, dysregulation of hypothalamic systems controlling mood likely overlaps with systems controlling sleep. The striking and rapid antidepressive response to sleep deprivation, possibly due to a reduction in brain glutamatergic systems involving the cingulate, demonstrates the close relationship between sleep and mood regulation systems [57, 58]. Schizophrenia has also been associated with a wide variety of sleep disorders, including both Process S and Process C dysregulation [17]. Sleep spindles have been found to be deficient in schizophrenia, possibly reflecting dysfunction of the thalamic reticular nucleus and thalamocortical mechanisms [59]. Circadian rhythm disorders The circadian rhythm disorders include a number of phenotypes. Patients usually present with an insomnia complaint, but in fact these disorders are caused by abnormal function of the circadian timing system. Most common is the delayed sleep phase syndrome (DSPS), with onset usually in adolescence or early adulthood, significant difficulty getting to sleep at a normal bedtime, and a tendency to sleep late in the morning. If these patients are required to arise early, they become sleep-deprived, and complain of insomnia. Advanced sleep phase syndrome (ASPS), more common in older persons, is associated with Chapter 7: Sleep early waking and early bedtime. Both DSPS and ASPS are strongly familial, and to date, at least ten abnormalities in the clock genes regulating circadian timing have been found in these disorders [17]. Lack of circadian entrainment (free-running rhythms) is found in 50% of blind individuals, although light can be utilized as a zeitgeber in cortical-blind individuals with intact retinas and retino-hypothalamic tracts. Appropriately timed melatonin administration may re-entrain many blind free-runners [60]. Circadian components of chronic insomnia complaints should be independently identified and treated, primarily with proper sleep hygiene, light therapy, appropriately timed melatonin, or possibly the melatonin receptor MT1 and MT2 agonist ramelteon. Typical hypnotic agents are usually not particularly helpful. Movement disorders Restless leg syndrome (RLS) and periodic limb movements of sleep (PLMS) are usually considered sleep disorders, but are perhaps more appropriately thought of as movement disorders that interfere with sleep. Restless leg syndrome is associated with both disordered brain iron homeostasis and altered CNS dopaminergic systems, with genetic influences considered likely [61]. Restless leg syndrome can also be secondary to a wide variety of other illnesses including renal failure, iron deficiency, neuropathy, pregnancy, multiple sclerosis, and other illnesses [62]. Periodic limb movements of sleep is closely associated with RLS, but occurs widely in the general population without sleep complaints, and may imply enhanced spinal cord excitability [63]. Periodic limb movements of sleep may be aggravated by a number of antidepressant agents, a point to be kept in mind during its evaluation. Conditioned insomnia, sleep state misperception, and primary insomnia Insomnia complaints that remain after evaluation and treatment of the causes outlined above suggest a group including conditioned insomnia, sleep state misperception syndrome (SSMS), and primary insomnia. These disorders are grouped together under the term “psychophysiological insomnia” in the latest International Classification of Sleep Disorders [64]. Conditioned insomnia is precisely as it sounds – susceptible individuals are conditioned to arouse in their normal sleep environment, whereas they may sleep normally in a sleep laboratory. Sleep state misperception syndrome is sometimes termed “paradoxical insomnia,” since patients are unaware they have been sleeping. Some evidence suggests an increase in fast activity in the sleep EEG of SSMS patients [65, 66] might account for a preservation of some level of awareness of mentation during sleep in these patients. Primary insomnia includes those subjects with insomnia complaints at least 30 days in duration, not explained by any other etiology, and associated with significant impairment of daytime function. Evidence supporting the notion that primary insomnia represents a state of physiological hyperarousal comes from a number of studies in such patients that include elevated metabolic activity [67], increased fast EEG activity during sleep [65, 68, 69], and auditory evoked potential findings supportive of impaired inhibitory mechanisms during sleep [70]. Additional evidence includes increased evening cortisol levels [71], elevations in inflammatory markers [72], and increases in waking whole-brain glucose metabolism [73]. Coortoos et al. have suggested that primary insomnia might best be considered a state of hyperarousal affecting both sleep and daytime function throughout the entire 24-hour period and not just during sleep [74]. Treatment considerations for the insomnias Assuming comorbid conditions have been optimally treated, and Process C components appropriately addressed, two options remain for treatment of continuing insomnia: behavioral, primarily cognitive behavioral therapy (CBT), and pharmacologic. Cognitive behavioral therapy has been reported as significantly effective for many forms of insomnia, including primary insomnia, and should be considered wherever possible [75–78]. Pharmacologic treatments have improved greatly over the years, with safe and effective medications now available. A decision should be made as to whether pharmacologic efforts will be directed at up-regulating the VLPO GABAergic sleep control systems, down-regulating the ARAS, or both. Unfortunately, adequate information is not yet available to enable this decision without some trial and error. GABAergic supplementation can be provided by benzodiazepine (BZ) hypnotics (triazolam, temazepam, estazolam, flurazepam, and quazepam) and newer non-BZ omega-1 agonists (zolpidem, zolpidem ER, zaliplon, and eszopiclone). With both BZ 105 Section I: Structural and Functional Neuroanatomy and non-BZ hypnotics, the half-life is a major consideration in choosing specific agents. Benzodiazepine hypnotic agents are appropriate in patients who also have anxiety; for primary insomnia, the newer non-BZ agents have a better risk–benefit profile. Other GABAergic agents have been shown to influence sleep, but are not yet approved for use as hypnotics. Sodium oxybate, approved for narcolepsy, increases SWS. Tiagabine, a GABA reuptake inhibitor that increases synaptic GABA through selective inhibition of the GAT-1 GABA transporter, and is approved for epilepsy, has been shown to increase SWS in a dose-dependent fashion in primary insomnia [79]. Gaboxadol is a selective extrasynaptic GABAa agonist, possibly working at the thalamic level, that has been shown to improve sleep in a phase-advance model of insomnia [80], but has many adverse side effects. Alternative strategies include down-regulation of the ARAS. Sedative antidepressants have been used at low doses likely for their antihistaminic and antiserotonergic properties, but have not formally been studied as hypnotics and are used off-label. Promising drugs are histamine H1 antagonists such as lowdose (3–6 mg) doxepin, which improves sleep in primary insomnia, but has few anticholinergic or antinoradrenergic side effects at this dose [81]. Atypical antipsychotic agents are used off-label at low doses likely for the H1 and 5HT2a antagonism, but are probably not drugs of first choice. Several 5HT2a antagonists, which may actually increase SWS, are in clinical trials, including epilvanserin, volinanserin, and pruvanserin [82]. Orexin antagonists are also in development as possible hypnotic agents [83], based upon the yet unsubstantiated premise that since underactivity of the orexin systems leads to hypersomnia with state instability, overactivity of orexin systems may lead to insomnia. A good general rule is to consider GABAergic hypnotics, and CBT, as first-line approaches for most insomnias, and switching to and/or adding additional ARAS active sedative hypnotic agents is recommended only if the initial therapeutic response is inadequate. It may be necessary to use more than one agent, addressing different mechanisms (e.g., GABA up-regulation as well as ARAS down-regulation) in difficult cases. Using the lowest dose possible for the shortest time possible is standard practice, but considering the adverse consequences of sleep loss, 106 effective treatment should not be withheld when necessary. Hypersomnias Excessive daytime sleepiness (EDS) is the primary symptom of the hypersomnias. It is important to distinguish true EDS from fatigue, which can present with EDS, although those complaining of fatigue will often not demonstrate short sleep latency on the Multiple Sleep Latency Test (MSLT). Any disorder (or lifestyle) that interferes with adequate sleep, or results in fragmented sleep, may present with an EDS complaint, but most germane to this chapter are the hypersomnia disorders that are thought to result from primary CNS dysfunction, which include narcolepsy, primary hypersomnia, and the periodic hypersomnias (e.g., Kleine–Levin syndrome). A number of neurological disorders may have EDS as an associated complaint, including Parkinson’s disease (PD) [84], and various infectious diseases, possibly related to cytokine production [85]. The most common cause of EDS (aside from not getting enough sleep at night – encountered surprisingly often) is a sleep-related breathing disorder, primarily obstructive sleep apnea (OSA). The hypersomnia of OSA is not considered to be of central origin, but rather secondary to the physiological disturbances associated with repeated respiratory obstructions. Decreased cerebrospinal fluid (CSF) histamine levels, a likely marker of central hypersomnia (see below), is not seen in OSA [86]. Evidence of altered cognition or affective regulation associated with the breathing disorder may indicate CNS involvement, but is more likely secondary to the OSA syndrome. Narcolepsy Narcolepsy is a rare (worldwide prevalence estimated at 0.02% with some racial and national variation) but well-known cause of EDS. The primary characteristics of narcolepsy are EDS with uncontrollable sleep attacks, and evidence of abnormal REM sleeprelated phenomena such as cataplexy, sleep paralysis, and hypnagogic hallucinations (the so-called narcoleptic tetrad). There are thought to be two forms of narcolepsy, which have been termed idiopathic narcolepsy and symptomatic narcolepsy [87]. The more common form, idiopathic narcolepsy, has been linked Chapter 7: Sleep to an orexin/hypocretin deficiency from neuronal loss in the hypothalamus, possibly autoimmune in origin. Narcolepsy is closely associated with the HLA system, supporting a possible autoimmune basis. The main predisposing allele is DQB1∗ 0602, which is found in 95% of patients who have narcolepsy with cataplexy across all ethnic groups. However, this allele is neither necessary nor sufficient for developing narcolepsy [16]. Genetic variants of narcolepsy with cataplexy also exist with normal CSF hypocretin levels [88, 89]. Narcolepsy usually appears in late adolescence or early adulthood in previously healthy individuals. Early symptoms often begin with EDS, and irresistible sleep attacks may occur in school or other inappropriate places. Within several years episodes of cataplexy may appear, with loss of skeletal muscle tone in response to anger or emotional excitement. Patients may collapse in a chair or fall to the ground during a cataplectic episode, and if undisturbed a full-blown REM sleep episode may ensue. Loss of orexin/hypocretin neurons in the hypothalamus may result in instability of the sleep/wake and REM-on/REM-off bi-stable switches, thought to be modulated by the orexins, such that stable wakefulness cannot be maintained, and REM onset may not be restricted to non-REM sleep periods. Symptomatic narcolepsy is rare and may accompany or be seemingly caused by a large number of other disturbances of brain function [87]. The nocturnal sleep in most narcoleptic patients is often significantly disrupted. Patients are prone to frequent nocturnal spontaneous arousals as well as a greater incidence of PLMS and sleep apnea. The sleep disruption may be related to impaired function of the sleep/wake and REM-off/REM-on neurophysiological switches as a result of loss of the orexin/hypocretin controlling system. Narcolepsy is diagnosed by history and a PSG followed by MSLT. The MSLT will show decreased mean sleep latency (⬍5 minutes) and requires two sleep-onset REM periods for diagnosis. Narcolepsy without cataplexy will not have symptoms of REM breakthrough, but will have similar PSG/MSLT findings [90]. Histamine, a major contributor to vigilance, has been shown to be deficient in the CSF in narcolepsy, and may be related to the EDS. The histaminergic system is modulated by orexin/hypocretin, and loss of neurons may result in decreased histamine and resultant EDS [86]. However, decreased CSF histamine has also been found in narcolepsy without hypocretin deficiency, thus decreased histamine may also be an index of central hypersomnia independent of hypocretins [89]. There is at this time no cure for narcolepsy. However, in most cases the symptoms can be adequately managed with alerting and, if necessary, wakepromoting agents [91]. Primary hypersomnia Idiopathic hypersomnia (IH) is a syndrome of persistent daytime somnolence. Patients with this disorder note an increasingly irresistible need to sleep during the day that leads to prolonged naps. These naps are lengthy, often 60 minutes or longer, and not very refreshing. When these patients are not sleeping, they are drowsy and have difficulty concentrating. This excessive sleepiness occurs after sufficient or even increased amounts of nocturnal sleep. Two forms of IH have been described, IH with a long sleep time and IH without a long sleep time. In the former, patients have documented EDS in spite of sleep durations greater than 10 hours, whereas in the latter, patients have excessive sleepiness documented after a preceding night’s sleep duration of 6–10 hours. Patients with either form of IH frequently have complaints of “sleep drunkenness” on awakening. Idiopathic hypersomnia can usually be differentiated from narcolepsy by the absence of cataplexy, hypnagogic hallucinations, and sleep paralysis. A PSG and MSLT are usually necessary to differentiate this disorder from other causes of EDS. Patients with IH will usually demonstrate short mean sleep latency on the MSLT, but not sleep-onset REM periods as commonly seen in narcolepsy. They also do not have the HLA genetic markers of narcolepsy or decreased CSF orexin/hypocretin levels, but they may have low CSF histamine levels [86]. The etiology remains obscure. Periodic hypersomnias Kleine–Levin syndrome is an uncommon periodic hypersomnia disorder that is most common in males and often begins in the teenage years. Typically, the patient has one or more episodes yearly that are characterized by periods of excessive sleepiness often lasting for weeks. During these hypersomnolent periods the patient can be aroused from sleep, but when awake, 107 Section I: Structural and Functional Neuroanatomy he or she is confused and agitated and has a loss of sexual inhibitions [92]. While in this state, patients can have insatiable appetites, especially when presented with food. The patient has minimal recollection of the hypersomnolent period after the episode clears, and appears to function normally between attacks. Usually, this disorder spontaneously remits by age 40. The etiology of Kleine–Levin syndrome is unknown, but disorders of several brain regions, including the thalamus, brainstem, frontal lobes, and hypothalamus, have been suggested [93]. Infections, trauma, and autoimmune conditions have also been proposed as etiologies [94]. Electroencephalograms performed during the wakeful portions of a hypersomnolent episode have shown mild intermittent slowing of brain-wave activity. Nocturnal sleep has been reported to lack Stage III and Stage IV (N3) sleep. Also, shortened REM latency has been reported, and even occasional sleep-onset REM periods [95]. Although the results of CSF analysis are usually normal in these patients, one study reported that levels of 5-hydroxyindoleacetic acid are elevated [96], and CSF hypocretin levels appear normal in asymptomatic patients but were reported as low in one patient while symptomatic [97]. The periodic nature of the somnolence, along with the abnormal behavior, confusion, and compulsive eating, differentiate Kleine–Levin syndrome from other common causes of excessive somnolence. The clinician should consider other psychiatric disorders (especially bipolar disorder and schizophrenia), drug-induced states, and metabolic and inflammatory disorders in the differential diagnosis. Because Kleine–Levin syndrome is self-limited, many patients are not treated. Stimulant medication has been useful to treat the somnolence but can worsen the behavioral problems. Lithium has had some success in prophylaxis of the hypersomnolent episodes [98]. Menstruation-associated hypersomnia is an uncommon condition occurring in women who become periodically hypersomnolent around the time of their menses. They may awaken only for bathroom visits and often act uncharacteristically (e.g., exhibiting withdrawal, apathy, and irritability). After menstruation, these women resume their regular behavior and daytime alertness. The etiology is not known, but hypothalamic dysfunction is hypothesized. 108 Parasomnias The third major category of sleep disorders is the parasomnias, or the strange things that happen during the night. These events range from the mundane (e.g., bruxism (tooth grinding)) or sleeptalking, to the spectacular (e.g., the rare but well-publicized homicidal incidents occurring during a somnambulistic state). They clearly represent interesting and sometimes clinically significant yet peculiar interactions of brain function and behavior. Parasomnias are often grouped into two major categories: (1) primary sleep parasomnias which include both non-REM (e.g., bruxism, night terrors, somnambulism (sleepwalking)) and REM sleep-related parasomnias (e.g., nightmares, REM behavior disorder), and (2) secondary parasomnias which represent disorders of function of other organ systems that manifest themselves during sleep. The former category is emphasized in this chapter, as its underlying disordered sleep mechanisms are beginning to be understood. Non-REM sleep parasomnias The most common non-REM parasomnias – night terrors, confusional arousals, and somnambulism – are usually grouped together as “disorders of arousal” (which should not be confused with the disorders of consciousness discussed in Chapter 6) since they are thought to reflect partial but not complete activation of arousal systems such that complex motor behaviors can occur without conscious awareness or intent [99]. These parasomnias occur mainly in Stage N3 sleep and thus cluster during the first several hours of sleep. Night terrors usually begin with a cry at the onset of a period of intense agitation including motor behavior (even jumping out of bed) and evidence of physiological arousal including sweating, tachycardia, increased blood pressure, and dilated pupils. These disorders of arousal are more common in the immature CNS. Night terrors begin in early childhood, and somnambulism later in childhood, with most of both resolving by adulthood, although it is estimated that up to 5% of normal adults may still experience occasional parasomnias. Recall for such events is usually lacking, and they are not accompanied by typical complex dream imagery. Sleepwalking is highly hereditary; if both parents sleepwalk, there is a 60% chance that any child of the couple will sleepwalk [100]. If only one parent sleepwalks, the risk is still 45%. Chapter 7: Sleep Grillner suggested the term “central pattern generators” (CPG) for ensembles of neurons located in the mesencephalon, pons, and spinal cord, that subserve complex behavioral patterns, both motoric and emotional, when activated [101]. Tassinari and colleagues have suggested that such CPG neuronal systems, normally under the control of neocortical inhibition, may be released with suppression of neocortical inhibition, such as during sleep parasomnias and epileptic seizures [102]. Such a mechanism may help explain phenomena such as bruxism, sleep terrors and somnambulism, although the more complex parasomnias, i.e., those involving driving a car, are clearly also associated with modulation of motor activity by concurrent sensory input, even in the absence of consciousness. Treatment of most disorders of arousal remains in the domain of protecting the patient from injury, alerting others if possible, and avoiding factors known to increase frequency (e.g., sleep deprivation, sedatives such as alcohol). Anticipatory awakenings have been effectively used in children [103]. Considerable recent interest has been shown in the “night eating syndrome” first reported by Stunkard over half a century ago [104]. Two types are described: the night eating syndrome (NES), characterized by evening hyperphagia, nocturnal eating, and morning anorexia, and sleep-related eating disorder (SRED), characterized by recurrent episodes of eating after arousal from nighttime sleep with or without amnesia [105]. Night eating syndrome is thought to represent an abnormality in the circadian rhythm of meal timing with preservation of normal timing of sleep onset, rather than a true parasomnia, and has been reported to respond to sertraline [106]. Sleep-related eating disorder often accompanies other parasomnias, has been associated with use of zolpidem as well as other sedative hypnotic agents, and has been reported to respond to topiramate [105]. REM sleep parasomnias Nightmares are the most common REM sleep parasomnia, but REM behavior disorder is the most serious. Nightmares are frightening dreams usually accompanied by intense fear and anxiety. They are most common in children, and tend to decrease in frequency with age. They are seen in about 5–8% of the general adult population, and are more common in women. Some recent evidence has suggested that persons with low serum lipid levels (total cholesterol, LDL, and triglycerides) may be especially susceptible to nightmares [107]. Severe cases may qualify for a diagnosis of nightmare disorder, and nightmares are common accompaniments of the post-traumatic stress disorder (PTSD) syndrome. Pharmacologic management has not been systematically evaluated, although recent evidence suggests prazosin [108], and possibly topiramate [109], to be effective in some cases. A manualized CBT program for chronic nightmares has recently been described with positive long-term results [110]. Imagery rehearsal therapy has also been described as successful in treatment of PTSD-induced nightmares [111, 112]. Of more concern is REM behavior disorder (RBD), representing an impairment of the descending skeletal muscle inhibition normally accompanying REM sleep such that affected individuals may sit up or jump out of bed and engage in vigorous physical activity during REM sleep as though they are acting out their dreams. Such motor activity may result in injury to themselves or others. REM behavior disorder is most commonly encountered in older men. A careful history will help in differential diagnosis to distinguish RBD from seizure disorders or non-REM disorders of arousal, as dream content synonymous with the unusual behavior can usually be elicited. Polysomnogram evidence of increased motor activity during REM sleep is required for definitive diagnosis, however. Boeve and colleagues have recently proposed a pathophysiology of RBD as indicative of an impairment in the operation of the putative flip-flop switch involved in REM sleep control [113]. REM behavior disorder has been associated with a variety of neurological disorders (e.g., autoimmune, neoplastic, vascular, infectious), often following the onset of the other disorder to suggest a pathophysiological commonality. Of concern is the evidence that RBD can frequently be a harbinger of a subsequent neurodegenerative disorder, most often PD or DLB, with an estimated risk of developing one of these disorders of 40% at 10 years [114]. Neurological evaluation and close follow-up may be warranted in these cases. Secondary parasomnias There are a number of additional so-called “secondary parasomnias” that will not be considered here, including phenomena such as vascular headaches, 109 Section I: Structural and Functional Neuroanatomy “exploding head syndrome,” “hypnic headache syndrome,” and several cardiopulmonary and gastrointestinal parasomnias that interested readers can find described in more comprehensive textbooks. Conclusion This chapter provided a review of the basic neurophysiological mechanisms of wake/sleep systems, and discussed the relationships between disorders in the functioning of these complex systems and sleep pathologies observed in clinical medicine. If disorders of sleep are approached from the standpoint of the basic system disturbances that may be contributing to the phenomenology at issue, more rational treatment programs may be developed that can improve on approaching the problems from the standpoint of symptom alleviation alone. References 1. Cirelli C, Tononi G. Is sleep essential? PLoS Biol. 2008;6(8):e216. 2. Rechtschaffen A, Bergmann BM. Sleep deprivation in the rat: an update of the 1989 paper. Sleep 2002; 25(1):18–24. 12. Szymusiak R, McGinty D. Hypothalamic regulation of sleep and arousal. Ann N Y Acad Sci. 2008;1129: 275–86. 13. Stenberg D. Neuroanatomy and neurochemistry of sleep. Cell Mol Life Sci. 2007;64(10):1187–204. 14. Hofman MA, Swaab DF. The sexually dimorphic nucleus of the preoptic area in the human brain: a comparative morphometric study. J Anat. 1989; 164:55–72. 15. Gaus SE, Strecker RE, Tate BA, Parker RA, Saper CB. Ventrolateral preoptic nucleus contains sleep-active, galaninergic neurons in multiple mammalian species. Neuroscience 2002;115(1):285–94. 16. Hamet P, Tremblay J. Genetics of the sleep-wake cycle and its disorders. Metabolism 2006;55(10 Suppl 2): S7–12. 17. Wulff K, Porcheret K, Cussans E, Foster RG. Sleep and circadian rhythm disturbances: multiple genes and multiple phenotypes. Curr Opin Genet Dev. 2009;19(3):237–46. 18. McCarley RW. Mechanisms and models of REM sleep control. Arch Ital Biol. 2004;142(4):429–67. 3. Dawson D, Reid K. Fatigue, alcohol and performance impairment. Nature 1997;388(6639):235. 19. Lu J, Sherman D, Devor M, Saper CB. A putative flip-flop switch for control of REM sleep. Nature 2006;441(7093):589–94. 4. Lange T, Perras B, Fehm HL, Born J. Sleep enhances the human antibody response to hepatitis A vaccination. Psychosom Med. 2003;65(5):831–5. 20. Hobson JA, McCarley RW. The brain as a dream state generator: an activation-synthesis hypothesis of the dream process. Am J Psychiatry 1977;134(12):1335–48. 5. Meier-Ewert HK, Ridker PM, Rifai N et al. Effect of sleep loss on C-reactive protein, an inflammatory marker of cardiovascular risk. J Am Coll Cardiol. 2004;43(4):678–83. 21. Hobson A. A model for madness? Nature 2004; 430(6995):21. 6. Spiegel K, Tasali E, Penev P, Van Cauter E. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med. 2004;141(11):846–50. 7. Spiegel K, Knutson K, Leproult R, Tasali E, Van Cauter E. Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes. J Appl Physiol. 2005; 99(5):2008–19. 8. Borbely AA, Achermann P. Sleep homeostasis and models of sleep regulation. J Biol Rhythms 1999; 14(6):557–68. 9. Issa EB, Wang X. Sensory responses during sleep in primate primary and secondary auditory cortex. J Neurosci. 2008;28(53):14,467–80. 10. Garcia-Rill E. Disorders of the reticular activating system. Med Hypotheses 1997;49(5):379–87. 110 11. Saper CB, Scammell TE, Lu J. Hypothalamic regulation of sleep and circadian rhythms. Nature 2005;437(7063):1257–63. 22. Nelson DE, Takahashi JS. Integration and saturation within the circadian photic entrainment pathway of hamsters. Am J Physiol. 1999;277(5 Pt 2):R1351–61. 23. Lockley SW, Evans EE, Scheer FA et al. Short-wavelength sensitivity for the direct effects of light on alertness, vigilance, and the waking electroencephalogram in humans. Sleep 2006; 29(2):161–8. 24. Cirelli C. The genetic and molecular regulation of sleep: from fruit flies to humans. Nat Rev Neurosci. 2009;10(8):549–60. 25. Franken P, Dijk DJ. Circadian clock genes and sleep homeostasis. Eur J Neurosci. 2009;29(9):1820–9. 26. Rechtschaffen A, Kales A (editors). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Bethesda, MD: U.S. National Institutes of Health, U. S. National Institute of Neurological Diseases and Blindness, Neurological Information Network; 1968. Chapter 7: Sleep 27. Iber C. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Westchester, IL: American Academy of Sleep Medicine; 2007. 28. Steriade M, Amzica F. Sleep oscillations developing into seizures in corticothalamic systems. Epilepsia 2003;44 (Suppl. 12):9–20. 29. Urakami Y. Relationships between sleep spindles and activities of cerebral cortex as determined by simultaneous EEG and MEG recording. J Clin Neurophysiol. 2008;25(1):13–24. 30. Bodizs R, Kormendi J, Rigo P, Lazar AS. The individual adjustment method of sleep spindle analysis: methodological improvements and roots in the fingerprint paradigm. J Neurosci Methods 2009;178(1):205–13. 31. Tamaki M, Matsuoka T, Nittono H, Hori T. Activation of fast sleep spindles at the premotor cortex and parietal areas contributes to motor learning: a study using sLORETA. Clin Neurophysiol. 2009; 120(5):878–86. 32. Dijk DJ. Regulation and functional correlates of slow wave sleep. J Clin Sleep Med. 2009;5(2 Suppl): S6–15. 33. Bjorness TE, Kelly CL, Gao T, Poffenberger V, Greene RW. Control and function of the homeostatic sleep response by adenosine A1 receptors. J Neurosci. 2009;29(5):1267–76. 34. Terzano MG, Parrino L, Sherieri A et al. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med. 2001;2(6):537–53. 35. Terzano MG, Parrino L, Smerieri A. [Neurophysiological basis of insomnia: role of cyclic alternating patterns]. Rev Neurol. (Paris) 2001;157(11 Pt 2):S62–6. 40. Steriade M. Grouping of brain rhythms in corticothalamic systems. Neuroscience 2006; 137(4):1087–106. 41. Rector DM, Schei JL, Van Dongen HP, Belenky G, Krueger JM. Physiological markers of local sleep. Eur J Neurosci. 2009;29(9):1771–8. 42. Krueger JM, Rector DM, Roy S et al. Sleep as a fundamental property of neuronal assemblies. Nat Rev Neurosci. 2008;9(12):910–19. 43. Heraghty JL, Hilliard TN, Henderson AJ, Fleming PJ. The physiology of sleep in infants. Arch Dis Child. 2008;93(11):982–5. 44. Feinberg I, Higgins LM, Khaw WY, Campbell IG. The adolescent decline of NREM delta, an indicator of brain maturation, is linked to age and sex but not to pubertal stage. Am J Physiol Regul Integr Comp Physiol. 2006;291(6):R1724–9. 45. Huber R, Ghilardi MF, Massimini M, Tononi G. Local sleep and learning. Nature 2004;430(6995):78–81. 46. Weinert D. Age-dependent changes of the circadian system. Chronobiol Int. 2000;17(3):261–83. 47. Duffy JF, Zeitzer JM, Czeisler CA. Decreased sensitivity to phase-delaying effects of moderate intensity light in older subjects. Neurobiol Aging 2007;28(5):799–807. 48. Haimov I, Lavie P, Laudon M et al. Melatonin replacement therapy of elderly insomniacs. Sleep 1995;18(7):598–603. 49. Naidoo N, Ferber M, Master M, Zhu Y, Pack AI. Aging impairs the unfolded protein response to sleep deprivation and leads to proapoptotic signaling. J Neurosci. 2008;28(26):6539–48. 50. Krystal AD. Treating the health, quality of life, and functional impairments in insomnia. J Clin Sleep Med. 2007;3(1):63–72. 36. Ferri R, Huber R, Arico D et al. The slow-wave components of the cyclic alternating pattern (CAP) have a role in sleep-related learning processes. Neurosci Lett. 2008;432(3):228–31. 51. Armitage R, Landis C, Hoffmann R et al. The impact of a 4-hour sleep delay on slow wave activity in twins discordant for chronic fatigue syndrome. Sleep 2007;30(5):657–62. 37. Bruni O, Ferri R, Vittori E et al. Sleep architecture and NREM alterations in children and adolescents with Asperger syndrome. Sleep 2007;30(11):1577–85. 52. Belt NK, Kronholm E, Kauppi MJ. Sleep problems in fibromyalgia and rheumatoid arthritis compared with the general population. Clin Exp Rheumatol. 2009;27(1):35–41. 38. Guilleminault C, Poyares D, Rosa A et al. Chronic fatigue, unrefreshing sleep and nocturnal polysomnography. Sleep Med. 2006;7(6):513–20. 39. Lopes MC, Quera-Salva MA, Guilleminault C. Non-REM sleep instability in patients with major depressive disorder: subjective improvement and improvement of non-REM sleep instability with treatment (Agomelatine). Sleep Med. 2007;9(1): 33–41. 53. Davies KA, Macfarlane GJ, Nicholl BI et al. Restorative sleep predicts the resolution of chronic widespread pain: results from the EPIFUND study. Rheumatology (Oxford) 2008;47(12):1809–13. 54. McKeith IG. Clinical Lewy body syndromes. Ann N Y Acad Sci. 2000;920:1–8. 55. Harper DG, Stopa EG, McKee AC et al. Differential circadian rhythm disturbances in men with Alzheimer 111 Section I: Structural and Functional Neuroanatomy disease and frontotemporal degeneration. Arch Gen Psychiatry 2001;58(4):353–60. 56. Klaffke S, Staedt J. Sundowning and circadian rhythm disorders in dementia. Acta Neurol Belg. 2006; 106(4):168–75. 57. Benedetti F, Calabrese G, Bernasconi A et al. Spectroscopic correlates of antidepressant response to sleep deprivation and light therapy: a 3.0 Tesla study of bipolar depression. Psychiatry Res. 2009;173(3): 238–42. 58. Wirz-Justice A, Benedetti F, Berger M et al. Chronotherapeutics (light and wake therapy) in affective disorders. Psychol Med. 2005;35(7):939–44. 59. Ferrarelli F, Huber R, Peterson MJ et al. Reduced sleep spindle activity in schizophrenia patients. Am J Psychiatry 2007;164(3):483–92. 60. Sack RL, Auckley D, Auger RR et al. Circadian rhythm sleep disorders: part II, advanced sleep phase disorder, delayed sleep phase disorder, free-running disorder, and irregular sleep-wake rhythm. An American Academy of Sleep Medicine review. Sleep 2007; 30(11):1484–501. 61. Winkelmann J, Polo O, Provini F et al. Genetics of restless legs syndrome (RLS): state-of-the-art and future directions. Mov Disord. 2007;22(Suppl. 18):S449–58. 62. Ondo WG. Restless legs syndrome. Neurol Clin. 2009;27(3):779–99, vii. 63. Bara-Jimenez W, Aksu M, Graham B, Sato S, Hallett M. Periodic limb movements in sleep: state-dependent excitability of the spinal flexor reflex. Neurology 2000;54(8):1609–16. 64. American Academy of Sleep Medicine. The International Classification of Sleep Disorders: Diagnostic and Coding Manual. 2nd edition. Westchester, IL: American Academy of Sleep Medicine; 2005. 65. Krystal AD, Edinger JD, Wohlgemuth WK, Marsh GR. NREM sleep EEG frequency spectral correlates of sleep complaints in primary insomnia subtypes. Sleep 2002;25(6):630–40. 66. Edinger JD, Krystal AD. Subtyping primary insomnia: is sleep state misperception a distinct clinical entity? Sleep Med Rev. 2003;7(3):203–14. 67. Bonnet MH, Arand DL. 24-hour metabolic rate in insomniacs and matched normal sleepers. Sleep 1995;18(7):581–8. 68. Perlis ML, Kehr EL, Smith MT et al. Temporal and stagewise distribution of high frequency EEG activity in patients with primary and secondary insomnia and in good sleeper controls. J Sleep Res. 2001;10(2):93–104. 112 69. Merica H, Blois R, Gaillard JM. Spectral characteristics of sleep EEG in chronic insomnia. Eur J Neurosci. 1998;10(5):1826–34. 70. Bastien CH, St-Jean G, Morin CM, Turcotte I, Carrier J. Chronic psychophysiological insomnia: hyperarousal and/or inhibition deficits? An ERPs investigation. Sleep 2008;31(6):887–98. 71. Vgontzas AN, Bixler EO, Lin HM et al. Chronic insomnia is associated with nyctohemeral activation of the hypothalamic-pituitary-adrenal axis: clinical implications. J Clin Endocrinol Metab. 2001;86(8): 3787–94. 72. Vgontzas AN, Chrousos GP. Sleep, the hypothalamic-pituitary-adrenal axis, and cytokines: multiple interactions and disturbances in sleep disorders. Endocrinol Metab Clin North Am. 2002;31(1):15–36. 73. Nofzinger EA, Buysse DJ, Germain A et al. Functional neuroimaging evidence for hyperarousal in insomnia. Am J Psychiatry 2004;161(11):2126–8. 74. Cortoos A, Verstraeten E, Cluydts R. Neurophysiological aspects of primary insomnia: implications for its treatment. Sleep Med Rev. 2006;10(4):255–66. 75. Edinger JD, Means MK. Cognitive-behavioral therapy for primary insomnia. Clin Psychol Rev. 2005; 25(5):539–58. 76. Morin CM, Bootzin RR, Buysse DJ et al. Psychological and behavioral treatment of insomnia: update of the recent evidence (1998–2004). Sleep 2006;29(11): 1398–414. 77. Rybarczyk B, Stepanski E, Fogg L et al. A placebocontrolled test of cognitive-behavioral therapy for comorbid insomnia in older adults. J Consult Clin Psychol. 2005;73(6):1164–74. 78. Sivertsen B, Nordhus IH. Management of insomnia in older adults. Br J Psychiatry 2007;190:285–6. 79. Walsh JK, Perlis M, Rosenthal M et al. Tiagabine increases slow-wave sleep in a dose-dependent fashion without affecting traditional efficacy measures in adults with primary insomnia. J Clin Sleep Med. 2006;2(1):35–41. 80. Walsh JK, Deacon S, Dijk DJ, Lundahl J. The selective extrasynaptic GABAA agonist, gaboxadol, improves traditional hypnotic efficacy measures and enhances slow wave activity in a model of transient insomnia. Sleep 2007;30(5):593–602. 81. Goforth HW. Low-dose doxepin for the treatment of insomnia: emerging data. Expert Opin Pharmacother. 2009;10(10):1649–55. 82. Estivill E, Leger D, Soubrane C. A randomized double-blind placebo-controlled 12-week trial of Chapter 7: Sleep eplivanserin with long-term open extension in insomniac patients with sleep maintenance difficulties: results from the double-blind treatment period. Sleep 2009;32:A41-A. 83. Dingemanse J, Dorffner G, Goran H et al. Proof-of-concept study in primary insomnia patients with ACT-078573, a dual orexin receptor antagonist. Sleep Biol Rhythms 2007;5(Suppl 1; Late breaking abstracts):A194. 84. Monderer R, Thorpy M. Sleep disorders and daytime sleepiness in Parkinson’s disease. Curr Neurol Neurosci Rep. 2009;9(2):173–80. 85. Kapsimalis F, Basta M, Varouchakis G et al. Cytokines and pathological sleep. Sleep Med. 2008;9(6):603–14. 86. Kanbayashi T, Kodama T, Kondo H et al. CSF histamine contents in narcolepsy, idiopathic hypersomnia and obstructive sleep apnea syndrome. Sleep 2009;32(2):181–7. 87. Nishino S, Kanbayashi T. Symptomatic narcolepsy, cataplexy and hypersomnia, and their implications in the hypothalamic hypocretin/orexin system. Sleep Med Rev. 2005;9(4):269–310. 88. Khatami R, Maret S, Werth E et al. Monozygotic twins concordant for narcolepsy-cataplexy without any detectable abnormality in the hypocretin (orexin) pathway. Lancet 2004;363(9416):1199–200. 89. Nishino S, Okuro M, Kotorii N et al. Hypocretin/orexin and narcolepsy: new basic and clinical insights. Acta Physiol. (Oxford) 2010; 198(3):209–22. 90. Black JE, Brooks SN, Nishino S. Conditions of primary excessive daytime sleepiness. Neurol Clin. 2005;23(4): 1025–44. 91. Mignot E, Nishino S. Emerging therapies in narcolepsy-cataplexy. Sleep 2005;28(6):754–63. 92. Arnulf I, Zeitzer JM, File J, Farber N, Mignot E. Kleine-Levin syndrome: a systematic review of 186 cases in the literature. Brain 2005;128(Pt 12):2763–76. 93. Huang YS, Guilleminault C, Kao PF, Liu FY. SPECT findings in the Kleine–Levin syndrome. Sleep 2005;28(8):955–60. 94. Dauvilliers Y, Mayer G, Lecendreux M et al. Kleine–Levin syndrome: an autoimmune hypothesis based on clinical and genetic analyses. Neurology 2002;59(11):1739–45. 95. Huang YS, Lin YH, Guilleminault C. Polysomnography in Kleine–Levin syndrome. Neurology 2008;70(10):795–801. 96. Koerber RK, Torkelson R, Haven G et al. Increased cerebrospinal fluid 5-hydroxytryptamine and 5-hydroxyindoleacetic acid in Kleine–Levin syndrome. Neurology 1984;34(12):1597–600. 97. Dauvilliers Y, Baumann CR, Carlander B et al. CSF hypocretin-1 levels in narcolepsy, Kleine–Levin syndrome, and other hypersomnias and neurological conditions. J Neurol Neurosurg Psychiatry 2003;74(12):1667–73. 98. Poppe M, Friebel D, Reuner U et al. The Kleine–Levin syndrome – effects of treatment with lithium. Neuropediatrics 2003;34(3):113–19. 99. Broughton RJ. Sleep disorders: disorders of arousal? Enuresis, somnambulism, and nightmares occur in confusional states of arousal, not in “dreaming sleep”. Science 1968;159(819):1070–8. 100. Mahowald MW, Schenck CH. Non-rapid eye movement sleep parasomnias. Neurol Clin. 2005;23(4):1077–106, vii. 101. Grillner S. The motor infrastructure: from ion channels to neuronal networks. Nat Rev Neurosci. 2003;4(7):573–86. 102. Tassinari CA, Rubboli G, Gardella E et al. Central pattern generators for a common semiology in fronto-limbic seizures and in parasomnias. A neuroethologic approach. Neurol Sci. 2005; 26(Suppl. 3):s225–32. 103. Mason TB, 2nd, Pack AI. Pediatric parasomnias. Sleep 2007;30(2):141–51. 104. Stunkard AJ, Grace WJ, Wolff HG. The night eating syndrome; a pattern of food intake among certain obese patients. Am J Med. 1955;19(1):78–86. 105. Howell MJ, Schenck CH, Crow SJ. A review of nighttime eating disorders. Sleep Med Rev. 2009; 13(1):23–34. 106. O’Reardon JP, Stunkard AJ, Allison KC. Clinical trial of sertraline in the treatment of night eating syndrome. Int J Eat Disord. 2004;35(1):16–26. 107. Agargun MY, Gulec M, Cilli AS et al. Nightmares and serum cholesterol level: a preliminary report. Can J Psychiatry 2005;50(6):361–4. 108. Dierks MR, Jordan JK, Sheehan AH. Prazosin treatment of nightmares related to posttraumatic stress disorder. Ann Pharmacother. 2007;41(6): 1013–17. 109. Aalbersberg CF, Mulder JM. [Topiramate for the treatment of post traumatic stress disorder. A case study]. Tijdschr Psychiatr. 2006;48(6):487–91. 110. Davis JL, Wright DC. Randomized clinical trial for treatment of chronic nightmares in trauma-exposed adults. J Trauma Stress 2007;20(2):123–33. 111. Krakow B, Hollifield M, Johnston L et al. Imagery rehearsal therapy for chronic nightmares in sexual assault survivors with posttraumatic stress disorder: a randomized controlled trial. J Am Med Assoc 2001;286(5):537–45. 113 Section I: Structural and Functional Neuroanatomy 112. Moore BA, Krakow B. Imagery rehearsal therapy for acute posttraumatic nightmares among combat soldiers in Iraq. Am J Psychiatry 2007;164(4): 683–4. 113. Boeve BF, Silber MH, Saper CB et al. Pathophysiology of REM sleep behaviour disorder and relevance to 114 neurodegenerative disease. Brain 2007;130(Pt 11):2770–88. 114. Postuma RB, Gagnon JF, Vendette M et al. Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 2009;72(15):1296–300. Section I Structural and Functional Neuroanatomy Chapter Attention 8 Joshua Cosman and Matthew Rizzo The ability to effectively deal with the overwhelming amount of information present in the environment at any given time requires humans to focus on some things and ignore others. For example, imagine attempting to read a book in a crowded public place. This requires the reader to focus on the words on the page and ignore the sounds of conversations going on in the background, allowing an effective focus on the task at hand. The process by which people can both select relevant and suppress irrelevant environmental information refers to a number of processes collectively referred to as “attention.” From an information-processing standpoint, attention can be conceptualized as operating not as a single, monolithic process, but rather a group of more fragmented, domain-specific processes. For instance, in the example above, attention is required to select or suppress information across more than one sensory modality. In addition, attention can select information based on its location in space, its identity, or its relevance to current goals. For this reason, research in the cognitive and brain sciences has typically focused on specific subcomponents of attentional processing. One broad distinction that has been made in the study of attention has been between the control of attention (i.e., how attention selects stimuli) and the subsequent effects of attention (i.e., what is the fate of stimuli once attended to). Within the domain of control, attentional selection can occur as the result of cognitive (top-down) or stimulus-driven (bottom-up) processes. In turn, these selection processes can bias the way in which information provided by the environment is interpreted. Since the majority of research on attention has focused on the visual system, the discussion of attention in this chapter will center primarily on the control of different aspects of visual attention. However, many of the principles discussed below hold true for the selection of information across other sensory domains. This chapter will outline relevant behavioral measures related to the control of attention, and functional theories of attention based on such measures. The major focus will be on the control of visual attention in both normal and neurologically impaired individuals, mapping functional theories of attention onto what is known about the cerebral structures subserving this process. Control of attention One of the most important issues in attention research concerns how attention is controlled. At a basic level, attention can be considered a sensory gatekeeper, allowing humans to select and act upon only the subset of sensory information that is most relevant to carrying out specific goals. A familiar example illustrates this point. When conversing with a friend in a noisy room, one is able to carry on a normal conversation despite the milieu of irrelevant sensory information – in other words, to selectively attend to the conversation. However, if another friend shouts one’s name from across the room, attention is captured in a nearly automatic manner, putting on hold the conversation in which one was engaged. This scenario highlights two ways in which attention can be controlled. On one hand, you are able to voluntarily attend to a conversation with your friend – an example in which you exercise “goal-directed,” or top-down, control of attention. However, this voluntary focus of attention can be overridden if a sufficiently important stimulus (in this case, the shouting of your name by another friend) is detected in the environment – a case of “stimulus-driven,” or bottom-up, Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 115 Section I: Structural and Functional Neuroanatomy (A) (B) Ti m Reaction time (milliseconds) Reaction time (milliseconds) e Valid Invalid Cue validity Valid Invalid Cue validity Figure 8.1. The order of events in Posner’s spatial cueing paradigm. Observers are asked to detect the appearance of a target that has been validly or invalidly pre-cued. (A) Peripheral pre-cue that automatically summons spatial attention to the cued region, and typical results. (B) Central, symbolic pre-cue that can be used to voluntarily shift spatial attention to the cued region, and typical results. Note that in both cases, subjects respond more quickly to targets that have been validly cued. However, central pre-cues typically elicit a slower response to the target, reflecting the voluntary nature of the attentional orienting produced by such cues. factors controlling the allocation of attention. This distinction between the bottom-up and top-down control of attention has served as an important concept informing the study of attention in both cognitive psychology and neuroscience. The following section will outline the concepts of bottom-up and top-down control of attention, their interaction, and their consequences for subsequent sensory processing. Bottom-up vs. top-down selection: evidence from spatial cueing and visual search Two experimental paradigms have contributed the most to the understanding of the control of visual attention: visual search and spatial cueing. In a spatial cueing paradigm, a stimulus or instruction precedes the presentation of a target stimulus. This stimulus or instruction is referred to as a “cue,” and this cue typically either predicts or does not predict the location of a subsequently presented target stimulus. One widely used spatial cueing task is that developed by Posner [1]. In Posner’s cueing task, depicted in Figure 8.1, each trial begins with a cue intended to orient an observer’s 116 attention to one of several possible locations. The cue can take the form of either a peripherally presented “flicker” appearing in a location where a subsequent target may appear, or may appear as a centrally presented symbol such as an arrow, or a directionally related word (“left”). After a delay, a target is presented and observers indicate that they detect the target (e.g., by pressing a button as soon as the target appears) or they discriminate among several targets (e.g., reporting if the target is a “T” or an “L”). On “valid” trials, the cue correctly predicts the target’s location; on “invalid” trials, the cue is misleading. Observers typically respond to valid trials fastest and invalid trials slowest, representing a “validity effect” of the cue. Each of the cues mentioned above are designed to direct attention to locations in space, but each does so through different mechanisms. Specifically, peripherally presented cues tap bottom-up attentional control processes, whereas centrally presented cues recruit top-down processes. This distinction allows for the examination of bottom-up and top-down influences on attention independently of one another, and data from these types of cueing tasks have provided useful information regarding differences between these two types of attentional control: (1) Observers typically cannot ignore peripheral cues, and these cues attract attention to the cued location more or less automatically. However, observers can ignore central cues when instructed, demonstrating that central cues do not direct attention in a stimulus-driven manner and are instead under voluntary control. (2) Peripheral cues operate more quickly than central cues, with reaction time differences between validly and invalidly cued trials emerging sooner with peripheral cues. This phenomenon reflects greater processing time required to use central cues, indicating that these cues require voluntary and effortful cognitive control. (3) Peripheral cues have the capacity to interrupt attentional orienting produced by a central cue, but central cues exert little effect on orienting from peripheral cues. This observation indicates that peripheral cues attract attention more or less automatically, in a stimulus-driven manner. (4) Studies that use central cues tend to present more valid than invalid trials, in an effort to encourage observers to attend to locations predicted by the cue. For example when using central cues, 75% of Chapter 8: Attention (C) (D) Reaction time (milliseconds) (B) Reaction time (milliseconds) (A) 0 4 8 12 Set size (# of items) 0 4 8 12 Set size (# of items) Figure 8.2. (A, B) Visual search displays and (C, D) typical results from a visual search task. Panel C shows an efficient search for a target that differs from distracters on a single feature dimension, such as color (A). D shows an inefficient search for a target that differs from distracters on two feature dimensions (B). trials may include a valid cue, with only 25% of cues being invalid. By contrast, peripheral cues attract attention to a location regardless of validity, even if valid trials are less frequent than invalid (e.g., 25% valid, 75% invalid). Again, peripheral cues are shown to summon attention in an automatic manner. The other paradigm that has provided insight into the control of visual attention is the visual search task (Figure 8.2). Visual search refers to the act of looking for a visual target among distracters – similar to the process encountered when trying to “find Waldo” in the popular book series. In a typical visual search task, the number of distracters, or “set size,” is varied across trials, and reaction time (RT) to detect a target item is measured as a function of the set size. An “efficient” visual search results in shallow search slopes (i.e., set size has little influence on an efficient search), and “inefficient” searches result in steep search slopes (i.e., set size affects the time taken to detect a target). These differences in search functions can be conceptualized as representing the differential recruitment of bottom-up, stimulus-driven attention mechanisms and top-down, goal-directed attention mechanisms. In the case of an efficient search, the target is typically perceptually distinct from the distracter items. An example of an efficient search would be a case where observers are asked to search for a red bar among green distracters (Figure 8.2A). In this case, attention would be attracted more or less automatically to the target based on bottom-up factors – in this example, the bar’s distinctive color. Since the target is defined on the basis of a distinct perceptual attribute, this type of search would remain efficient regardless of the number of distracters present in the array, resulting in the characteristic shallow slope seen during efficient search. By contrast, in the case of an inefficient search, the target is typically less perceptually distinct from the distracter items. For instance, if an observer were asked to search for a target based on a conjunction of multiple features (i.e., a red vertical bar among red horizontal and green vertical distracters, Figure 8.2B), the observer would be forced to carry out a more effortful search, requiring a greater amount of cognitive control. In this case, bottom-up information is not sufficient to define a target, and observers are required to adopt a strategy in which each item in the display is treated as a possible target, with each item or a subset of items being examined until the target is identified. Functional models of attentional control An early explanation for the differential efficiency during visual search was based on serial and parallel processing models, with efficient searches being classified as “parallel” and inefficient searches “serial.” This conceptualization of search being either serial or parallel is the core of Treisman and Gelade’s [2] feature integration theory of visual search. In their original model [2], these investigators proposed that during an efficient search, all items in the array are processed preattentively in parallel – all incoming visual sensory information is processed simultaneously, and the target is detected in a more or less automatic manner, “popping out” based on its bottom-up salience. This “parallel search” would lead to the shallow search slopes described during efficient search, since all items in the display could be processed simultaneously regardless of the number of distracters present. Conversely, a search in which bottom-up information alone is not sufficient to identify the target requires subjects to perform a more effortful “serial search.” It was hypothesized that during inefficient searches, observers are forced to direct attention to each item in the display, with attention to each item being required to “bind” the two features and identify the stimulus [3]. 117 Section I: Structural and Functional Neuroanatomy “Feature maps” Vertical Orientation Horizontal “Saliency map” Black Top-down information Color White “Search for black vertical bar” Consequently, set size would have a large impact on target detection RTs, as increasing the number of serially searched items theoretically increases RTs with each distracter item added to the array. Although this feature integration model of visual search provides a straightforward account of search slope differences between efficient and inefficient searches, it does not fully account for some findings in the visual search literature. Specifically, it has been shown that some “serial” looking processes can arise from parallel processing mechanisms. For instance, RT patterns that resemble those seen in serial search can be produced by limited capacity parallel search mechanisms. To illustrate this point, imagine that multiple items in a display can be processed in parallel. However, due to capacity limitations not all items in the display can be processed at once. Since not all items in the display can be searched through in parallel, searching through many items (large set size) in a display takes longer than searching through only a few items (small set size), producing RT slopes that resemble those seen in “serial” searches. In addition, searches that would be deemed “serial” by feature integration theory can be surprisingly efficient, resulting in RT slopes that resemble parallel searches [4]. Therefore, search is typically discussed with respect to its efficiency, where efficient search leads to shallow search functions (slopes ⬍10 ms/item) and inefficient search leads to steeper search functions (slopes ⬎20 ms/item), rather than in terms of serial or conjunction search. 118 Figure 8.3. An illustration of the visually guided search model of attentional allocation during visual search. In this model of attentional control, observers organize bottom-up information from the environment into “feature maps” that code for the identity and location of features in visual space. This information regarding stimulus features present in a scene is combined with top-down information based on the goals of the observer to create a “saliency map.” The saliency map then provides information on which areas of space are most likely to contain a particular stimulus in the environment, preferentially directing attention to these areas over others. This model allows the interaction of bottom-up and top-down information to bias the control of attention toward relevant visual stimuli. To account for results that appeared to be inconsistent with feature integration theory, Wolfe [4, 5] proposed a two-stage “guided search” model of attentional control (Figure 8.3). As with feature integration theory, the initial stage of processing is carried out preattentively and in parallel across the entire visual field. From this processing, independent parallel representations of items in the search array are created based on basic visual features such as color, shape, or orientation. These representations are termed “feature maps,” and code for all of the features that are present in a given visual scene. Importantly, these maps also code for the location that the different features occupy. For example, if a subject were asked to search for a red bar among green distracters, all items in the display would be preattentively processed in parallel. From this processing, a feature map for “color” would be generated that included representations for items that were both red and green (since both types of items are present in the display). In addition to this color information, the location at which this information was detected would be represented in the map, providing the observer with a spatial map of features present in the scene. In contrast to feature integration theory, in the second stage of processing the bottom-up information represented in the feature map is combined with topdown information based on the goals of the observer. This combination, in turn, serves to bias attention toward particular elements of a visual scene [6]. In the case of the simple feature search described above, topdown information regarding target identity (i.e., the Chapter 8: Attention target is red) is combined with bottom-up information regarding the location of red items in the display. This produces an “activation map” or “saliency map” that is used to direct the limited capacity resources of attention to a location or locations that are most likely to contain the target item. The guided search model is important for three reasons. First, it clears up the serial vs. parallel issues that are not easily explained by feature integration theory, providing a more plausible explanation of the control of attention during search. Second, the underlying mechanisms of the model are made explicit and can be studied empirically. Third, and most importantly, the model is transparent at a neural level: although it is based on behavioral research and computer simulation, it maps well onto what is known about the structural organization of brain regions involved in attentional control (to be discussed in detail later). This point illustrates an important principle in neuroscientific research, which is that functional accounts of cognition should correspond with what is known about the anatomy of cognitive functions and vice versa. The following section will focus on the major forms of attention, and how functional accounts of these constructs map on to neuropsychological and neuroanatomical data. Major types of attention Having considered how attention is controlled, it is now important to turn to the types of information that attention can select. In this section, four different classes of selection will be considered: (1) spatial attention, in which stimuli are selected based on their position in space; (2) object-based attention, in which stimuli are selected based on their identity; (3) attentional selection in visual working memory, in which attention selects items that will be remembered; and (4) executive attention, in which attention is involved in choosing which task or behavior an observer will perform. Spatial attention Attention can be selectively directed toward different regions in space, a concept traditionally referred to as “spatial attention.” Spatial attention selects stimuli based on their location in space, allowing stimuli at a particular location to receive further processing. One of the first and most widely used paradigms in the study of spatial attention is the spatial cueing task mentioned in the previous section. Recall that when observers are directed to a specific location in space, subsequent stimuli appearing in this location are detected or discriminated better than those appearing in other locations. This phenomenon suggests that once attention selects a location, stimuli appearing in that location receive processing benefits over other stimuli. One way that spatial attention may exert an influence on stimulus processing at particular locations is by prioritizing these locations, so that stimuli located within a particular region are processed before those in other regions. In a standard spatial cueing task, the pre-cue draws attention to a particular location, putting stimuli falling within that region first in line for further processing. This sequence would result in the response time patterns seen in spatial cueing trials, with validly cued targets being detected or discriminated more quickly than those at invalidly cued locations based on the priority settings established by the cues. Another way that spatial attention may exert an influence on stimulus processing at particular locations is by enhancing the perceptual representation of stimuli at those locations. Since there is a great deal of noise in the visual system, effective selection of incoming stimuli requires a mechanism that increases the signal-to-noise ratio in favor of relevant sensory information. At a neural level, this perceptual enhancement may be achieved through increased firing amplitude of neurons coding for stimuli at the selected location. In other words, attention acts as a sensory gain control that effectively “turns up” the neural representation of stimuli in an attended location versus those in unattended locations, causing these attended stimuli to stand out [7]. For example, spatial attention has been shown to change the appearance of items, actually making them more perceptible. Carrasco and colleagues [8] showed that directing attention to a location in a display effectively increased contrast for the item falling within the attended region, thus leading to increased performance on a visual discrimination task. From this observation it was concluded that attention intensified the sensory representation of the attended item, producing a stronger sensory impression of the stimulus. In this way, spatial attention can actually alter the phenomenological perception of objects occupying a particular location. 119 Section I: Structural and Functional Neuroanatomy Figure 8.4. Diagram indicating the region of the brain in which damage most commonly results in symptoms of visual neglect, the temporoparietal junction. It has also been demonstrated that spatial attention can influence what information is allowed into visual working memory. If the appearance of multiple visual objects must be retained in working memory during a delay period, entry into visual working memory is necessary for these items to be remembered following the delay. If the location of one of the visual objects is cued either before or directly following its presentation, it is more easily remembered than other, uncued items [9]. Thus it appears that by directing attention to the location of one of the objects in a display, working memory is better able to encode the objects for later recall. These effects of spatial attention are contingent on the ability of the attention system to effectively orient to particular locations in space. This process has been shown to be deficient in patients with focal cerebral damage, and these patients have enabled the study of spatial attention defects. More recently, these investigations have been complemented by functional imaging studies, shedding further light on the neural mechanisms responsible for the control of spatial attention. The parietal lobes, spatial attention, and neglect One of the most extensively studied cortical regions contributing to spatial attention processes is the posterior parietal lobe. Unilateral damage to the human parietal lobe, especially in the vicinity of the temporoparietal junction (or TPJ, see Figure 8.4), results in a profoundly disabling syndrome referred to as neglect or hemineglect [10, 11]. Because neglect 120 most often follows right parietal damage, clinical symptoms are most evident for the left side of extrapersonal space or the left side of the patient (i.e., left hemineglect). Neglect has been known to occur following focal lesions to areas other than the parietal lobes, such as the frontal lobes, but most studies of attention in patient populations have focused on damage to parietal lobe structures. Within the parietal lobe, there has been debate over what regions are most crucial to the control of attention. A number of studies have implicated damage to the TPJ in neglect, but others have associated symptoms of neglect with damage to the superior parietal lobe (SPL). Friedrich and colleagues [11] directly compared the effects of focal damage to either the TPJ or the SPL, and showed that patients with damage to the TPJ were more likely to display symptoms characteristic of neglect, supporting a central role of the TPJ in the control of spatial attention. Patients with neglect typically fail to attend to stimuli falling within the region of space contralateral to the lesion (the contralesional side of space). In many cases, individuals with neglect will fail to read words on the left side of the page, eat food on the left side of the plate, or shave the left side of the face. Importantly, this failure to attend to stimuli in the hemifield opposite to the lesion is not the result of visual sensory deficits such as scotoma or hemianopia. Patients with sensory disturbances alone are aware of their defects, and therefore will orient to a contralesional hemifield stimulus in order to compensate for their impairment. However, patients with hemineglect are generally unaware of their deficit, and if confronted with a defect on the impaired side (such as a hemiparesis) may even deny it, a phenomenon known as anosognosia. Insights into the nature of the attentional impairments seen in neglect have come from studies using the attention paradigms discussed above. Posner and colleagues [12] were among the first to study patients with right parietal lobe damage using the guidance of an explicit cognitive theory of attention. Using a spatial cueing task, they found asymmetries in attentional orienting in patients with right parietal lobe damage, who were slower to detect invalidly cued targets presented in the contralesional field. In other words, patients with right parietal damage were slower to detect targets appearing in the left hemifield following an invalid cue in the right hemifield than they were to detect targets Chapter 8: Attention Target in right hemifield Neglect subjects Normal subjects Reaction time (milliseconds) Reaction time (milliseconds) Target in left hemifield Valid Invalid Cue validity Valid Invalid Cue validity Figure 8.5. Hypothetical data depicting a typical “disengage” pattern of results on a spatial cueing task. Compared with normal subjects, patients with parietal lobe damage show slightly slower overall reaction times to targets. Importantly, these patients show a disproportionate slowing in their reaction time to invalidly cued targets following cues presented in their intact hemifield (note the disproportionate cost of invalid cues when the subsequent target appeared in the damaged hemifield). These results have been taken as evidence for a role of the parietal lobe in the disengagement of attention. appearing in their right hemifield following an invalid cue in the left hemifield field (Figure 8.5). However, these patients were nearly as fast to detect validly cued targets in their contralateral hemifield as they were to detect targets in their ipsilesional hemifield, suggesting a disproportionate cost for reorienting attention following invalid cues in the ipsilesional hemifield. Based on this response asymmetry, Posner and colleagues [12] suggested that the parietal lobes allow disengagement of attention and that right parietal lesions cause a “disengage deficit,” that hinders disengagement from the ipsilesional visual field. Thus, when a cue appears in the ipsilesional field and a target follows in the contralesional field, a right parietal lobe patient would have difficulties detecting the target. This interpretation would predict that patients with damage to both the left and right parietal lobes would be equally likely to show symptoms of neglect. However, as discussed above, this is not the case, and severe or lasting neglect typically results only from damage to the right parietal lobe. Therefore, alternative theories of the deficits present in neglect have emphasized competitive interactions between left and right parietal regions and how this competition affects the control of attention. For example, Cohen and colleagues [13] presented a neural network model as an alternative explanation of the disengage deficit (Figure 8.6). In their model [13], neural representations of each visual hemifield compete with each other for attentional selection. If the representation of the left field is damaged, it competes less effectively with the representation of the right field. As a consequence, when attention is directed to the right (good) field, a target appearing in the left (damaged) field is at a competitive disadvantage. Thus, detecting an invalidly cued target appearing in the left field would be difficult. In contrast, when attention is directed to the disordered left field, a target appearing in the intact right field could compete effectively for attention, allowing this target to be detected relatively quickly. Under this account, no mention of “attentional disengaging” is required. Patients’ behavior appears as though there is a disengagement of attention, but the mechanism underlying the patients’ behavior is based on competition between damaged and intact representations of space, not on an “attentional disengager.” Effects of right parietal damage on visual search are consistent with results from spatial cueing paradigms. Eglin and colleagues [14] asked patients with parietal damage to perform a conjunction search (e.g., color and shape, see Figure 8.2B) across a number of set sizes. The patients were much slower to detect target items when the distracters appeared in the ipsilesional field compared with when they appeared in the contralesional field. In the context of Cohen and colleagues’ model, the presence of ipsilesional distracters may have prevented the contralesional representation of the target from competing effectively for attention. Taken together, these data suggest that the attention deficits seen following parietal lobe damage are the result of impaired processing of bottom-up inputs to the attention system. This interpretation does not necessarily mean that neglect is an elemental sensory 121 Section I: Structural and Functional Neuroanatomy Response unit Attention units (left hemispace) Perception units (left hemispace) Attention units (right hemispace) Inhibitory connections responsible for competition Perception units (right hemispace) Figure 8.6. An illustration based on the neural network that simulates the “disengage deficit” seen in patients with damage to the parietal lobe by performing a simulated version of Posner’s spatial cueing task. The perception units provide parallel input to both attention units and a response unit. Spatial cues and targets are presented as input to the model by “turning on” one of the perception units. This activation propagates through the network and activates the attention and response units. Thus, if a target is then presented to the right perception unit – an invalidly cued target – the model takes a long time to respond because the right pool of attention units has been inhibited. This model shows a disengage-like pattern of results because damage to one of the attentional pools impairs these units’ ability to compete with the intact pool of attentional units. If the right pool of attention units is damaged (e.g., in parietal damage) a spatial cue on the left (which activates the left pool of attention units) is able to inhibit the right pool of attention units more than if the model was undamaged. deficit, but rather that it may involve an inability of the attention system to effectively use bottom-up sensory information in the allocation of attention. Recall Wolfe’s guided search model, where bottom-up and top-down inputs are combined to create a salience map of the visual environment. If the attention system is less able to use bottom-up information from contralesional space, salient items in that portion of the visual environment would be less able to compete for attentional selection. As a result, these items would not be readily detected and it would take the observer with neglect longer to respond to them, resulting in the types of search and detection deficits outlined above. Therefore, rather than conceptualizing the posterior parietal lobe as an “attentional disengager,” it may be more accurate to think of this region as being responsible for detecting salient aspects of the environment, allowing the attention system to reorient toward them. Frontal lobe influences on spatial attention Although neglect has typically been studied in patients with parietal lobe damage, neglect can also occur in patients with damage to areas of the frontal lobes. Specifically, damage to the regions of the dorsolateral prefrontal cortex (DLPFC) has been implicated in neglect [15, 16]. Husain and colleagues [17] performed a lesion overlap analysis on lesion information 122 from patients with focal frontal lobe damage who showed symptoms of neglect. The region of greatest overlap in this analysis was located in a specific area of the DLPFC, the frontal eye field (FEF). This region has been implicated in both overt attentional orienting (attention requiring eye movements) and covert orienting (attentional orienting that does not require eye movements). Many studies of damage to the FEF have focused on the ability of patients with lesions to this region to overtly direct attention in space. Typically, directing the eyes to a region of space is preceded by directing covert spatial attention to the target region [18], and lesions to the FEF seem to disrupt particular types of eye movements. In a study by Henik and colleagues [19], performance on a spatial cueing task was compared between a group of patients with damage to the FEF and a group of patients with frontal lobe damage that did not include the FEF. In one portion of the experiment, patients performed a “saccade task” in which they were instructed to make eye movements to a peripheral location, indicated by either a central cue (an arrow) or a peripheral cue (a brief peripherally located flicker). In another portion of the experiment, patients performed a detection task in which they were told to respond to a target by pressing a particular key, without making saccades. As in the first task, subjects were presented with either a central arrow cue Chapter 8: Attention or a peripheral flicker cue, which in this task was followed by the presentation of a target, which observers responded to with a button press. In both tasks, half of the cues (central and peripheral) were valid and half were neutral; there were no invalid cues. It was shown that FEF lesions disrupted eye movements to peripheral locations, but not all eye movements were disrupted equally [19]. The FEF patients were slower to make eye movements into the contralesional field than into the ipsilesional field following central cues. Conversely, following peripheral cues, the patients with frontal damage that included the FEF made faster eye movements into the contralesional field than into the ipsilesional field. However, frontal lobe patients with an intact FEF made eye movements into the contra- and ipsilesional field approximately equally following both central and peripheral cues. The results from the FEF patients indicate that overt, voluntary orienting to central cues is impaired in this group, as these patients are only slowed in directing eye movements to the contralesional field following the symbolic arrow cue. Thus the FEF appears to play a role in the voluntary, or top-down, orienting of attention. Further evidence implicating the frontal lobes in the voluntary orienting of attention comes from a study by Vecera and Rizzo [20]. In this study patient E.V.R., a well-known patient with bilateral frontal lobe damage, performed two spatial cueing tasks. In both tasks, E.V.R. was instructed to press a key as quickly as possible in response to the detection of a target appearing on either the left or right side of the screen. In one task, peripheral cues were used to direct attention to the location of the target, on either the left or right side of the display. In the other task, central word cues (left, right) were used to direct attention to the target location. It was shown that E.V.R. could use peripheral cues, as evidenced by quicker reaction times to validly vs. invalidly cued target locations. However, performance in the central cue task did not reflect any reaction time differences between validly and invalidly cued trials, indicating that E.V.R. could not use the directional information provided by the central cue. These results suggest that regions of the frontal lobes are critical for voluntary, top-down attentional control, with the FEFs being particularly important. Functional imaging of spatial attention As already mentioned, top-down or “goal-directed” attentional processes are those that rely on an observer’s knowledge about the environment to guide attention. This knowledge can include previously learned information (e.g., looking for a friend’s face in a crowd) or can be based on a particular goal state (e.g., searching for an empty chair in a crowded auditorium). Conversely, bottom-up, “stimulus-driven” attentional processes rely on the properties of the stimuli present in a scene and attract attention on the basis of some unique visual quality rather than any cognitive factors. Whereas the patient studies discussed above provided early insights into brain regions involved in the control of attention, a number of recent functional imaging investigations have attempted to more precisely elucidate the mechanisms involved in both stimulus-driven and goal-directed attentional orienting. In an early event-related functional magnetic resonance imaging (fMRI) study by Hopfinger and colleagues [21], hemodynamic responses of observers were recorded while they performed a cueing task that used a central cue. In this study, observers were presented with a central arrow cue that always indicated the location of the target to-be-discriminated. By using a predictive central cue, it was possible to isolate the activation of brain structures involved in voluntary allocation of attention in space. Analyses of hemodynamic responses to these stimuli revealed that a number of discrete regions of parietal and frontal cortex were differentially active during attentional orienting and response. Specifically, regions in superior frontal and parietal cortex showed increased activity in response to the presentation of the central cue. This increased activity was greatest for the FEF and intraparietal sulcus (IPs), suggesting that these regions were linked in controlling the voluntary orienting of attention to locations indicated by the cue. In contrast to the more dorsal activation in response to the top-down information provided by cue, ventral regions of the prefrontal and parietal cortex showed greater activation in response to the presentation of the target. In particular, the inferior frontal gyrus (IFG) and inferior parietal lobule (IPL) showed greater activity during target presentation, indicating that these regions may be involved in the selective processing of stimuli and subsequent response processes rather than attentional orienting in general. Further evidence for a dorsal-ventral distinction between structures supporting voluntary attentional orienting and stimulus detection comes from a study in which the neural activity in response to 123 Section I: Structural and Functional Neuroanatomy peripheral and central cues was directly compared. Kincade and colleagues [22] used a spatial cueing task in which observers monitored a display for a target letter appearing on either the left or right side of the display, responding to the identity of the letter once presented. Before target presentation, a spatial cue was presented that could be either valid or invalid with respect to the subsequent target presentation. Critically, across blocks of trials the cue could be peripheral, central, or neutral (no cue), allowing for a comparison between cues that relied on voluntary and stimulus-driven shifts of attention. When central cues were used to direct attention, there was significantly higher activation in bilateral areas of the FEF and IPs than in conditions where either peripheral or neutral cues were presented, consistent with previously discussed findings. Moreover, these same dorsal regions showed greater activation in response to peripheral cues as well, suggesting that this dorsal frontoparietal network mediates both stimulus-driven (bottomup) and goal-directed (top-down) allocation of attention. Object-based attention To this point, our discussion has focused on the control of attention in space. However, attention also may be directed toward objects. In particular situations, object selection can take place regardless of where the object appears, suggesting that object-based and space-based attention are dissociable processes. When studying object-based attention, it is important to use designs that rule out selection by spatial attention, since by necessity objects occupy locations in space. For this reason, most studies of object-based attention have used experimental designs that eliminate or hold constant the spatial separation between objects. Although a number of object-based attention paradigms have been employed in the attention literature, this discussion will be limited to two of the most widely used tasks. In the “object attribute” task developed by Duncan [23], observers view a stimulus which includes two overlapping objects: a box and a line (Figure 8.7). Each object has two features: the box can be tall or short, and has a gap on either the left or the right side; the line can be either dashed or dotted, and can be tilted either to the left or the right. A box/line stimulus is presented briefly (∼100 ms) and followed by a masking stimulus that disrupts perception. Observers are asked to report two of the 124 Figure 8.7. An illustration of stimuli used to study object-based attention. Participants report features from the same object (e.g., box height and side of gap) or from different objects (e.g., box height and line tilt). four features mentioned above, and the features can come from the same object (e.g., box height and side of gap) or from different objects (e.g., box height and type of line). Observers are typically more accurate when reporting two features from the same objects than when reporting two features from different objects. A second paradigm developed for studies of objectbased attention uses an adaptation of the spatial cueing method already discussed. In a task developed by Egly and colleagues [24], observers view two rectangles, and the end of one of the rectangles is cued with a brief flash (i.e., a peripheral cue), followed by a target item (Figure 8.8). On most trials, the cue is valid and the target appears in the same location as the cue. Critically, on some trials the target appears at an uncued location, either within the same object that was cued or in another, uncued object. Even when uncued targets appear at the same distance from the cued region of the rectangle, observers are faster to respond to targets appearing in the uncued end of the cued rectangle than at any location in the uncued rectangle. This seems to suggest that attention automatically spreads across an entire object, conferring the benefits of attention to any region located within an attended object. Results from these studies suggest that space- and object-based attention can be dissociated from one another at the behavioral level. A number of patient and functional imaging studies seem to support this dissociation, although it appears that portions of the same attention system responsible for carrying out space-based attention are recruited during objectbased attention. The next section reviews relevant data from patient populations and functional imaging studies regarding the neural mechanisms of object-based selection. Chapter 8: Attention e m Ti or Invalid same object Invalid diff. object Reaction time (milliseconds) Valid or Figure 8.8. An illustration of the object cueing paradigm and typical results. Following a pre-cue, a target appears at either the cued location (left), at an un-cued location in the same (cued) object (center), or at an un-cued location in the other (un-cued) object (right). Response times are faster to targets that appear in the cued object (Invalid Same) than in the un-cued object (Invalid Different [Diff.]), even though these two locations are the same spatial distance from the cued region. Valid Invalid same Invalid diff. Cue validity Object-based attention in neglect As discussed above, patients with neglect due to parietal lobe damage often fail to attend to stimuli falling within the region of space contralateral to the lesion, neglecting to read words on the left side of the page or eat food on the left side of the plate. In addition to deficits in spatial attention, patients with neglect may also show deficits in object-based attention. Although it is possible that some object-based attention deficits in neglect patients arise as a secondary effect of their spatial attention deficits, a number of studies have shown that these deficits can be dissociated in these patients. An early study of object-based attention in parietal patients was carried out by Egly and colleagues [24], as a portion of the study described above. In their study, patients were placed into two groups based on the laterality of their parietal lobe lesion; one group consisted of patients with damage to the left parietal lobe, and the other group had damage to the right parietal lobe. With the use of a cued detection task identical to that described above (see Figure 8.8), objectbased attention effects were measured between the two groups. Results showed that the two groups exhibited different types of attentional impairment in the task. Both groups showed evidence of impaired disengagement following invalid cues, which would be expected based on the site of their lesions. However, the two groups differed in their object-based results. Patients with damage to the right parietal lobe showed a typical object effect, responding faster to invalid targets appearing in the cued object than those appearing in the uncued object. In contrast, patients with damage to the left parietal lobe showed a larger object effect in their contralesional field than in their ipsilesional field, indicating that they had trouble shifting attention between objects when the objects fell within their contralesional field. The authors suggested that the performance differences between patients with damage to left and right parietal lobes was due to differential recruitment of left and right parietal lobes during object-based attentional selection. Specifically, it was hypothesized that the right parietal lobe may be more involved in spatial attention processes (since damage here results in space- but not object-based attention effects), whereas the left parietal lobe may be more involved in object-based attention processes. Functional imaging of object-based attention The neural mechanisms underlying object-based attention effects have received a great deal of study using functional imaging in recent years. In many of these studies, locations of to-be-attended items are held constant, as described above, allowing the control 125 Section I: Structural and Functional Neuroanatomy processes responsible for attention to objects to be isolated from those involved in attention to spatial locations. In a functional imaging study reported by Serences and colleagues [25], observers viewed a continuous stream of superimposed houses and faces, presented in the same location at the center of the display. Observers were asked to selectively attend to either the house stream or the face stream, monitoring the streams for one of four possible targets (two houses, two faces). One of the face or house targets signaled that attention should remain on the current stream of objects (the “hold” condition), and the other target signaled that attention should be shifted to the other object stream (the “shift” condition). By comparing activations in the hold and shift conditions, it was possible to isolate regions involved in object-based shifts of attention. It was shown that relative to the hold condition, there was increased activity in bilateral regions of the superior parietal lobule (SPL) during trials that required a shift of attention between object streams. The authors concluded that this response reflected a transient signal that indicated an object-based shift of attention. Further support for the involvement of the SPL in object-based shifts of attention comes from another study using an object-based cueing task similar to that in Figure 8.8 [26]. Observers viewed two rectangles oriented perpendicular to each other, with a color patch located in both ends of each rectangle, and were initially instructed to attend to a single color patch. The color patches changed in color synchronously every 250 ms, and observers were asked to monitor the stream for one of three particular target colors. One color indicated that observers should hold attention on the current patch, another indicated that they should shift attention to the color patch at the other end of the same object, and another indicated that they should shift attention to the color patch at the same end of the other object. Overall, activation in bilateral SPL regions was greater for shift trials versus hold trials, consistent with the results of Serences and colleagues [25]. In addition, activation in the left SPL showed object-based modulation; in trials that required within object shifts, left SPL activations were increased relative to trials requiring betweenobject shifts. These data indicate that regions of the left SPL may be specifically involved in object-based shifts of attention, a finding consistent with that reported 126 in Egly and colleagues’ [24] study of object-based attention in a patient with left parietal lobe damage. Taken together, these results show that some parietal regions involved in spatial attention processes may also be responsible for mediating object-based attention. Additionally, there appears to be a bias toward regions in the left SPL in mediating the control of attention to objects. Attention and visual working memory Both spatial and object attention involve selection of perceptual characteristics that do not persist beyond the duration of the attentional operation. Visual working memory provides a mechanism for the storage of three to four objects in a more durable form over a longer period of time [9]. Recent research suggests that attention is vital for allowing information to enter into visual working memory, gating incoming sensory information, and keeping the working memory system from becoming overloaded. Evidence for this function of attention – as a gatekeeper for working memory – comes from studies of an event referred to as the “attentional blink.” In a typical attentional blink task, observers are presented with a rapid serial visual presentation (RSVP) of stimuli in which they are asked to detect two targets from the stream, responding at the end of the stream [27]. Figure 8.9 depicts a typical attentional blink task. In this example, observers are asked to identify two letters within a stream of numbers, with letter one being target one (T1) and letter two being target two (T2). There is a window of time following the detection of target one where observers fail to detect the presentation of a second target (see Figure 8.9A). However, if T2 is presented a sufficient time after T1, observers typically have little problem detecting the target (see Figure 8.9B). The period of time following the presentation of T1 where subjects are unable to detect T2 is referred to as the attentional blink because, as in an eyeblink, there is a brief period during which targets cannot be detected. Typical results from an attentional blink task are shown in Figure 8.9C. Note that the recognition of T2 depends upon the occurrence of T1. If no T1 target appears, observers are accurate at reporting T2, and there is no attentional blink. These results suggest that the attentional blink arises from an inability to store T2 in visual working Chapter 8: Attention (A) (B) + + F e m Ti T1 T1 A A T C C F X T2 X T Percent reported (C) T2 Figure 8.9. Attentional blink paradigm and typical results from this paradigm. In this example, subjects are asked to monitor the stream of letters and respond to two targets – the letters A and X. (A) shows a rapid serial visual presentation (RSVP) stream of letters and numbers, with T1 and T2 separated by a single item. (B) shows the same RSVP task, but T1 and T2 are separated by a greater number of intervening letters. (C) shows a typical attentional blink result: After identifying the first target digit (T1), observers fail to correctly identify the second target digit (T2) when it appears shortly after T1, as in (A). However, if T2 is presented sufficiently long after T1, as in (B), observers can correctly identify both targets. T1 80 70 60 50 T2 40 30 1 2 3 4 5 Number of items presented between T1 and T2 memory. This incapacity is due to the fact that attending to T1 delays the allocation of attention to the second target for a short period of time. Therefore, if the second target is presented soon after the first, it cannot be immediately processed and decays before being stored in working memory. However, as the time between T1 and T2 increases, processing of T1 is more likely to be completed by the time T2 appears, allowing attention resources to be allocated to T2 and resulting in detection of the second target. The attentional blink paradigm has been used in patient populations to shed light on the neural structures responsible for the type of memory-based attention outlined above. For example, Husain and colleagues [17] showed that an increased attentional blink can accompany visual neglect. Eight subjects with a mean age of 64 years were studied one month (on average) after a right hemisphere stroke affecting IPL, ventral frontal cortex (VFC), or the basal ganglia. All had clinically defined visual neglect and performed an attentional blink task similar to that in Figure 8.9. It was shown that the neglect patients could not identify the second target in the visual stream until 1.4 s had elapsed after the identification of the first target, an attentional blink that was nearly twice that of non-brain-damaged subjects (540 ms). Based on these results, the authors concluded that visual neglect is a disorder that affects the patient’s ability to direct attention in time as well as space. Furthermore, this study implicated the same cortical regions involved in attention in space and attention to objects in higher-level memory-based attention. However, Rizzo and colleagues [28] provided further details in 13 subjects with chronic focal brain lesions on MRI and nine control subjects without neurological impairments performing an RSVP task that used letters as targets. The results showed that an abnormal attentional blink could occur with lesions in either hemisphere and persist for years. The abnormality affected both length and magnitude of the attentional blink; did not require a lesion in the parietal lobe, frontal lobe, or basal ganglia; occurred independently of spatial neglect, and persisted after spatial neglect resolved. The authors concluded that an increased attentional blink has no special status 127 Section I: Structural and Functional Neuroanatomy in neglect, and that the neural mechanisms of spatial attention that are disrupted in the visual hemineglect syndrome differ from the neural mechanisms that underlie the attentional blink. Elucidation of the neural structures responsible for the type of temporal selection seen during the attentional blink is an active area of research, with a number of recent functional imaging studies providing insights into the neural correlates of memory-based attentional control [29]. Executive attention and task selection The last form of attentional selection to be discussed is the selection of one task from among many possible ones, which also implicates the coordination of multiple tasks [30, 31]. In all of the paradigms discussed above, observers perform the same attention task throughout, yet in real life, humans often perform different tasks concurrently or in series, such as rehearsing a phone number that was looked up, dialing the phone number, and conversing with the person just called. In general, executive functions control the focus of attention [32] and the executive system permits the awareness of marked changes in an object or a scene. The failure of executive control over attention has important real-world implications for noticing changes in the environment, particularly when information load is high. For example, automobile drivers navigating through complex driving environments with high traffic and visual clutter would be required to use the executive system to recognize and cope with changes in the driving environment [33, 34]. Driver errors occur when attention is focused away from a critical roadway event in which vehicles, traffic signals, and signs are seen but not acted upon, or are missed altogether [35]. Sometimes eye gaze is captured by irrelevant distracters [36] that may prevent a driver from seeing a critical event [37], such as an incurring vehicle or a child chasing a ball. Drivers with cerebral lesions disrupting the executive system are liable to be “looking but not seeing” despite low information load [38, 39]. Considering the multiple tasks involved in a complex task such as driving, executive control is needed to switch the focus of attention between various critical tasks such as tracking the road terrain, monitoring the changing locations of neighboring vehicles, 128 reading signs, maps, traffic signals, and dashboard displays, and checking the mirrors. This requires switching attention between disparate spatial locations, local and global object details, and different visual tasks. Drivers must also switch attention between modalities when they drive while conversing with other vehicle occupants, listening to the radio, using a mobile phone, and interacting with in-vehicle devices [40]. These attentional abilities can fail in drivers with visual processing impairments caused by cerebral lesions or fatigue [32, 41]. Neural systems involved in the control of attention Before the advent of functional imaging techniques, research on patients with focal brain damage provided a great deal of evidence for brain regions involved in the control of attention. From this work, a number of cortical areas involved in attentive processing were revealed, allowing an early classification of the neural systems involved in the control of attention [12, 42]. These theories paved the way for functional imaging studies that attempted to classify the large-scale operations of attention systems involved in the performance of specific attention-demanding tasks. To this point, the account has focused on distinct regions of the brain that play a role in the control of attention. This section will center on the relationships between these various regions, describing how specific brain areas directly interact to form large-scale neural systems responsible for the control of attention. A useful organizing concept is Corbetta and Shulman’s [43] model of selective attention, which was developed by synthesizing large amounts of data from patient and functional imaging studies, as well as information from early models of attentional selection. Based on this information, Corbetta and colleagues have developed a theoretical framework for the neural mechanisms of attentional selection. In their model, two separate attentional systems are posited that rely on differential frontoparietal connectivity, each system being involved in a different aspect of attentional control. The bilateral dorsal frontoparietal system is hypothesized to be involved in the overall control of attention, and consists of the IPs-FEF network shown to be responsible for the voluntary orienting of attention in response to relevant top-down information. This system is complemented by a right-lateralized Chapter 8: Attention IPs/SPL FEF VFC (IFg/MFg) TPJ (IPL/STG) Top-down control L FEF R FEF Novelty R VFC L VFC Circuit breaker Stimulusresponse selection L TPJ R IPs L IPs R TPJ Behavioral valence Visual areas Stimulus-driven control Figure 8.10. Illustration of a neuroanatomic model of attentional control. Top: diagram indicating brain regions involved in the control of attention. Bottom: Schematic of the mechanisms of a model of attentional control [43]. The dorsal network (IPs-FEF), indicated by the black arrows, is involved in the top-down, or “goal-directed,” control of attention. The ventral network (TPJ-VFC), indicated by the gray arrows, is involved in bottom-up, or “stimulus-driven,” control of attention. The dorsal system is also modulated by bottom-up information, with the TPJ communicating with the IPs and acting as a “circuit breaker” allowing salient bottom-up information to interrupt voluntary, top-down orienting, in turn reorienting attention to salient aspects of the environment. Abbreviations: IPs: intraparietal sulcus; SPL: superior parietal lobule; FEF: frontal eye field; TPJ: temporoparietal junction; IPL: inferior parietal lobule; STG: superior temporal gyrus; VFC: ventral frontal cortex; IFg: inferior frontal gyrus: MFg: middle frontal gyrus; L: left; R: right. Adapted from Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201–15, with permission from Macmillan Publishers Ltd. This figure is presented in color in the color plate section. ventral-frontoparietal system that includes regions of VFC and the TPJ. The ventral-frontoparietal system is involved in the detection of salient or novel bottomup information, especially with regard to behaviorally relevant stimuli, and can act as a circuit breaker for the dorsal system. Specifically, it is hypothesized that the right TPJ is involved in detecting task-relevant or salient stimuli outside of the focus of processing, and through interactions with the IPs in the dorsal system can cause attention to re-orient in response to these stimuli (Figure 8.10). To illustrate this model, consider another familiar example. Imagine searching the night sky for a particular constellation. In an example such as this, one uses previously learned information such as the constellation’s location or the way that it typically appears to guide one’s search. At a neural level, this process would rely on structures in the dorsal-frontoparietal circuit. Now imagine that, suddenly, a shooting star appears in the corner of one’s eye, capturing attention. This event would be detected by structures in the ventral-frontoparietal circuit, and a signal would be sent to the IPs to re-orient attention toward the shooting star. By this account, the dorsal attention system is responsible for the overall control of attention, but the ventral-frontoparietal system acts as an environmental sensor that picks up on relevant bottom-up information. 129 Section I: Structural and Functional Neuroanatomy An appealing feature of this neural model of attentional control is that it maps well onto Wolfe’s guided search model of attentional control during visual search [4, 5]. Recall that according to guided search theory, salient bottom-up information from the visual field is represented by category-specific feature maps, with separate maps for features such as color and orientation. In addition, these maps include not only information about physical features present in the visual field, but also for the locations of these features, marking possible areas of interest for the allocation of attention. This information is combined with topdown information regarding current goals or previous knowledge, forming an overall salience map of the entire visual scene. This salience map is then used to guide attention to regions of a scene most likely to include stimuli of interest. In the context of Corbetta and Shulman’s model, the ventral frontoparietal system may act as a saliency detector that, through reciprocal connections with visual and association cortices, uses bottom-up information to alert the dorsal system to salient aspects of the environment. This arrangement would allow the ventral system to direct attention in a stimulusdriven manner to highly salient stimuli in the visual field through its interactions with the dorsal system. Based on the functional imaging findings discussed above, Kincade and colleagues [22] hypothesized that the dorsal frontoparietal system is an ideal candidate for the saliency map described in guided search theory. Through interactions with the ventral system, this map receives bottom-up information from the environment. In addition, its involvement in goal-directed attentional control indicates that it directly uses topdown information to direct attention. Therefore, in the context of guided search components, the dorsal system (especially the FEF) appears to be a candidate region responsible for overall saliency coding, representing a possible neural correlate of the saliency map [44]. Dorsal and ventral attention systems and neglect As we have already described in detail, damage to regions of the parietal lobe, particularly on the right, results in profound neglect of contralesional space. How does Corbetta and Shulman’s model of attention map onto the deficits seen in patients with neglect? 130 Mesulam [45] has hypothesized that the deficits seen in neglect result from damage to regions that control the deployment of attention in response to top-down signals, which in the context of Corbetta and Shulman’s model would imply that neglect arises following damage to the FEF or IPs, or the dorsal attention system. However, data previously discussed show that neglect is most often the result of damage to the TPJ [11], a component of the ventral attention system. Therefore, Corbetta and Shulman have suggested that neglect more likely reflects damage to the ventral TPJVFC system, an assertion based on the following findings from neglect studies: (1) Neglect most often arises following damage to regions of the TPJ, which is part of the ventral attention circuit [11, 46]. (2) Neglect is more frequent, severe, and lasting following right parietal lesions. Recall that the functions of the ventral TPJ-VFC system are right lateralized, whereas the functions of the dorsal system are bilateral. The stronger association of neglect with right parietal damage is consistent with ventral-system damage. (3) Neglect patients show impaired inability to effectively use bottom-up information in the control of attention, as evidenced by the “disengage” deficits described above – a function attributed to the ventral system. However, they retain the ability to effectively use top-down information to guide attention, indicating an intact dorsal attention system [47]. Although this model fits many of the data regarding the deficits seen during neglect, one point of divergence from the literature should be noted. In this model the ventral frontoparietal system is hypothesized to be right lateralized, with no homologous function attributed to the TPJ-VFC system in the left hemisphere. Therefore, damage to this system would predict bilateral deficits in attentional orienting to salient bottom-up sensory information by the dorsal IPs-FEF system. However, this is not typically the case: there is a disproportionate deficit in directing attention to stimuli appearing in contralesional space, with a relative sparing of the ability to orient to stimuli in the ipsilesional field. It is possible that damage to the right TPJ or underlying white matter could impair communication between the ventral saliency detector and dorsal Chapter 8: Attention orienting system, resulting in a functional inactivation of the right dorsal IPs-FEF system. This would lead to the same types of “disengage” deficits described above, with the intact left dorsal attention system more effectively competing for attentional resources. Although Corbetta and Shulman’s model is tentative, it provides an intuitive framework for understanding the allocation of attention in response to bottom-up and topdown information. Importantly, this model maps well onto a widely accepted functional theory of attention, and is supported by a large amount of converging evidence from patient and functional imaging studies. Conclusion Attention is required for focusing on relevant information in the environment, simultaneously suppressing irrelevant information. By restricting what stimuli are and are not processed, attention acts as a gating system that allows us to function efficiently in a highly complex, ever-changing environment. Although the term attention has traditionally been considered to represent a single, monolithic process, it is clear that attention can and does operate across a number of different functionally defined levels. Whereas it is evident from the above discussion that many forms of attention have been defined, further research is needed to better understand both the functional and anatomic mechanisms involved in the control of these processes. Using behavioral techniques provided by cognitive psychology, the processes of attention can be studied rigorously across these multiple domains. By using well-defined behavioral measures in conjunction with neuropsychological and neurophysiological techniques, it is also possible to study the multiple components of attention at a structural level, providing further insights into how the brain carries out attentive processing. This chapter has provided evidence for a number of cerebral sites that appear to be involved in the overall control of attention. Understanding how these sites interact and how they relate to functional theories of attentional control will greatly increase our understanding of normal and disordered attentional control processes. References 1. Posner MI. Orienting of attention. Q J Exp Psychol. 1980;32(1):3–25. 2. Treisman AM, Gelade G. A feature-integration theory of attention. Cogn Psychol. 1980;12(1):97–136. 3. Treisman A. The binding problem. Curr Opin Neurobiol. 1996;6(2):171–8. 4. Wolfe JM, Cave KR, Franzel SL. Guided search: an alternative to the feature integration model for visual search. J Exp Psychol Hum Percept Perform. 1989; 15(3):419–33. 5. Wolfe JM. Guided Search 2.0 – a revised model of Visual-Search. Psychon B Rev. 1994;1(2):202–38. 6. Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annu Rev Neurosci. 1995;18:193–222. 7. Hillyard SA, Vogel EK, Luck SJ. Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. Philos Trans R Soc Lond B Biol Sci. 1998;353(1373): 1257–70. 8. Carrasco M, Penpeci-Talgar C, Eckstein M. Spatial covert attention increases contrast sensitivity across the CSF: support for signal enhancement. Vision Res. 2000;40(10–12):1203–15. 9. Vogel EK, Woodman GF, Luck SJ. Storage of features, conjunctions and objects in visual working memory. J Exp Psychol Hum Percept Perform. 2001;27(1):92–114. 10. Bisiach E, Vallar G. Hemineglect in humans. In Boller F, Grafman J, editors. Handbook of Neuropsychology. Amsterdam: Elsevier; 1988. 11. Friedrich FJ, Egly R, Rafal RD, Beck D. Spatial attention deficits in humans: a comparison of superior parietal and temporal-parietal junction lesions. Neuropsychology 1998;12(2):193–207. 12. Posner MI, Walker JA, Friedrich FJ, Rafal RD. Effects of parietal injury on covert orienting of attention. J Neurosci. 1984;4(7):1863–74. 13. Cohen JD, Romero RD, Servanschreiber D, Farah MJ. Mechanisms of spatial attention – the relation of macrostructure to microstructure in parietal neglect. J Cogn Neurosci. 1994;6(4):377–87. 14. Eglin M, Robertson LC, Knight RT. Visual search performance in the neglect syndrome. J Cogn Neurosci. 1989;1(4):372–85. 15. Heilman KM, Valenstein E. Frontal lobe neglect in man. Neurology 1972;22(6):660–4. 16. Heilman KM, Valenstein E, Watson RT. Localization of neglect. In Kertesz A, editor. Localization in Neuropsychology. New York, NY: Academic Press; 1983, pp. 471–92. 17. Husain M, Shapiro K, Martin J, Kennard C. Abnormal temporal dynamics of visual attention in spatial neglect patients. Nature 1997;385(6612):154–6. 131 Section I: Structural and Functional Neuroanatomy 18. Colby CL, Goldberg ME. Space and attention in parietal cortex. Annu Rev Neurosci. 1999;22:319–49. 19. Henik A, Rafal R, Rhodes D. Endogenously generated and visually guided saccades after lesions of the human frontal eye fields. J Cogn Neurosci. 1994;6(4):400–11. 20. Vecera SP, Rizzo M. Attention: normal and disordered processes. In Rizzo M, Eslinger PJ, editors. Principles and Practice of Behavioral Neurology and Neuropsychology. Philadelphia, PA: W.B. Saunders; 2004, pp. 223–45. 21. Hopfinger JB, Buonocore MH, Mangun GR. The neural mechanisms of top-down attentional control. Nat Neurosci. 2000;3(3):284–91. 22. Kincade JM, Abrams RA, Astafiev SV, Shulman GL, Corbetta M. An event-related functional magnetic resonance imaging study of voluntary and stimulus-driven orienting of attention. J Neurosci. 2005;25(18):4593–604. 23. Duncan J. Selective attention and the organization of visual information. J Exp Psychol Gen. 1984; 113(4):501–17. 24. Egly R, Driver J, Rafal RD. Shifting visual attention between objects and locations: evidence from normal and parietal lesion subjects. J Exp Psychol Gen. 1994;123(2):161–77. 25. Serences JT, Schwarzbach J, Courtney SM, Golay X, Yantis S. Control of object-based attention in human cortex. Cereb Cortex 2004;14(12):1346–57. 26. Shomstein S, Behrmann M. Cortical systems mediating visual attention to both objects and spatial locations. Proc Natl Acad Sci USA 2006;103(30):11,387–92. 27. Raymond JE, Shapiro KL, Arnell KM. Temporary suppression of visual processing in an RSVP task: an attentional blink? J Exp Psychol Hum Percept Perform. 1992;18(3):849–60. 28. Rizzo M, Akutsu H, Dawson J. Increased attentional blink after focal cerebral lesions. Neurology 2001;57(5):795–800. 29. Hommel B, Kessler K, Schmitz F et al. How the brain blinks: towards a neurocognitive model of the attentional blink. Psychol Res. 2006;70(6): 425–35. 30. Allport DA, Styles EA, Hsieh ST. Shifting intentional set: Exploring the dynamic control of tasks. In Umiltà C, Moscovitch M, editors. Attention and Performance XV: Conscious and Nonconscious Information Processing. Cambridge, MA: MIT Press; 1994, pp. 396–419. 31. Rogers RD, Monsell S. Costs of a predictable switch between simple cognitive tasks. J Exp Psychol Gen. 1995;124(2):207–31. 132 32. Vecera SP, Rizzo M. Spatial attention: normal processes and their breakdown. Neurol Clin. 2003;21(3):575–607. 33. Caird JK, Edwards CJ, Creaser J. The effect of time constraints on older and younger driver decisions to turn at intersections using a modifed change blindness paradigm. 1st International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design; Aspen, CO: University of Iowa; 2001. 34. Batchelder S, Rizzo M, Vanderleest R, Vecera SP. Traffic scene related change blindness in older drivers. In Rizzo M, Lee JD, McGehee D, editors. Proceedings of Driving Assessment 2003: 2nd International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Park City, UT, July 21–24, 2003; Iowa City, IA: University of Iowa; 2003, pp. 177–81. 35. Treat JR. A Study of Pre-crash Factors Involved in Traffic Accidents. Ann Arbor, MI: University of Michigan; 1980. 36. Theeuwes J. Exogenous and endogenous control of attention: the effect of visual onsets and offsets. Percept Psychophys. 1991;49(1):83–90. 37. O’Regan JK, Rensink RA, Clark JJ. Change-blindness as a result of ‘mudsplashes’. Nature 1999;398(6722): 34. 38. Rizzo M, Reinach S, McGehee D, Dawson J. Simulated car crashes and crash predictors in drivers with Alzheimer disease. Arch Neurol. 1997;54(5):545–51. 39. Rizzo M, McGehee DV, Dawson JD, Anderson SN. Simulated car crashes at intersections in drivers with Alzheimer disease. Alzheimer Dis Assoc Disord. 2001;15(1):10–20. 40. Kantowitz BH. Using microworlds to design intelligent interfaces that minimize driver distraction. 1st International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design; Aspen, CO: University of Iowa; 2001, pp. 42–57. 41. Rizzo M. Safe and unsafe driving. In Rizzo M, Eslinger PJ, editors. Principles and Practice of Behavioral Neurology and Neuropsychology. Philadelphia, PA: W.B. Saunders; 2004, pp. 197–222. 42. Mesulam MM. A cortical network for directed attention and unilateral neglect. Ann Neurol. 1981;10(4):309–25. 43. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201–15. 44. Schall JD, Thompson KG. Neural selection and control of visually guided eye movements. Annu Rev Neurosci. 1999;22:241–59. Chapter 8: Attention 45. Mesulam MM. Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci. 1999;354(1387):1325–46. 46. Heilman KM, Watson RT, Valenstein E. Neglect I: Clinical and anatomic issues. In Feinberg TE, Farah MJ, editors. Patient-based Approaches to Cognitive Neuroscience. Cambridge, MA: MIT Press; 2000. 47. Losier BJ, Klein RM. A review of the evidence for a disengage deficit following parietal lobe damage. Neurosci Biobehav Rev. 2001;25(1): 1–13. 133 Section I Structural and Functional Neuroanatomy Chapter Motivation 9 Brian D. Power and Sergio E. Starkstein The English term “motivation” was first recorded in 1873, and is defined as “the (conscious or unconscious) stimulus for action towards a desired goal” [1]. In neurobiological terms, motivation refers to the behaviorally relevant process that helps to regulate the organism’s internal (e.g., thirst, loneliness) and external environment (e.g., proximity to water, proximity to people) [2]. Motivation implies activation of the organism by external or internal stimuli resulting in goal-directed behaviors (e.g., drinking, companionship) [3]. A scheme for the motivated behavior is considered to involve the following steps: (a) formulating a goal and an intention to act; (b) response selection; (c) programming; and (d) initiation that precedes the execution of an action [4]. Cognitive operations of motivated behavior consist of the following necessary components: (1) attention and conscious awareness; (2) choice and control; and (3) intentionality. Disorders of motivation in humans have been reported since the nineteenth century [5]. Loss of motivation constitutes the core symptom of apathy, a syndrome frequently found among patients with acute or chronic neurological conditions such as stroke, traumatic brain injury, and dementia. This chapter will discuss the neurobiological basis of motivation. We will first review the most important findings obtained from animal research (mostly in rodents and non-human primates) and also discuss the putative mechanism of motivation in humans. We will then discuss conceptual issues that underlie the study of motivation in humans, and finish with a brief discussion of disorders of motivation. Neurobiological basis of motivation The mechanism of motivated behavior is based on neural structures that attach salience (i.e., allocate importance) and valence (i.e., allocate positive or negative value) to a given stimulus, and activate and direct an appropriate behavior in response to that stimulus [6]. Goal-oriented behavior is thought to mostly depend on the neural connectivity of three relevant brain regions – the amygdala, nucleus accumbens, and the prefrontal cortex – with the neurotransmitter dopamine exerting an important influence on the activity of this neural circuitry [6]. This section will address the proposed role of each of these neural networks in motivation; before doing so, consideration will be given to some of the general concepts of the organization of these networks. First, it is important to note that motivation or intentional behavior does not depend on a single brain structure. Stimulation of a localized brain region may elicit a variety of motivated behaviors, which is partially related to contextual cues, individual predisposition, and exposure to previous events [7]. For instance, Berridge [8] demonstrated that motivated behavior evoked by brain stimulation may be changed gradually by manipulating the animal’s experiences during the stimulation process [7]. Second, with regard to the hierarchy of structures involved in motivation, the forebrain is considered to mediate the “higher” motivational functions and to interact with “lower” centers in the brainstem that mediates core functions, such as locomotion, autonomic changes, and emotional responses [9]. Given the complexity of re-entrant loops in forebrain limbic Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 134 Chapter 9: Motivation Figure 9.1. Schema showing the mesolimbic and mesocortical DA systems. wiring [10], Berridge [8] suggested that the relationship is likely to be more complex than the connections explained by a hierarchical system (e.g., from cortical centers to subcortical effectors), and proposed a heterarchical organization (i.e., non-hierarchical) to accommodate those circuits involved in motivated behavior. Dopaminergic systems in the mechanism of motivation Dopaminergic systems in the brain have been implicated in numerous neurological and psychiatric disorders, from Parkinson’s disease to schizophrenia. Dopamine (DA) is considered to play a central role in the mechanism of motivation and regulation of effort-related processes, despite the fact that significant discussion is presently underway on the hypotheses related to DA function in this domain [2]. Below we shall discuss the structure and function of dopaminergic systems and how they relate to the neural circuitry of motivation. Brainstem reticular formation and monoaminergic systems The brainstem in humans is a small structure that lies between the spinal cord and diencephalic structures (including the thalamus, where all sensory modalities, with the exception of olfaction, feature a synaptic connection between the periphery and the cerebral cortex). The brainstem could be considered as a more sophisticated spinal cord, for it contains somatic and visceral sensory and motor fibers, as well as the nuclei of the cranial nerves that primarily subserve the head and neck. However, surrounding the major tracts and nuclei of the brainstem are the cells of the reticular formation that make more extensive connections, and which confer on the brainstem a functional importance that far outweighs its small size. Within the reticular formation are three major monoaminergic systems – the noradrenergic, serotonergic, and dopaminergic systems – whose efferent axons have widespread connections with most parts of the neuraxis. Although the serotonergic system is the most extensive monoaminergic system in the brainstem, the dopaminergic neurons are by far more numerous than noradrenergic neurons. Further, dopaminergic cells are highly organized topographically, and, on the basis of their efferent projections, are classified into two main systems, as illustrated in Figure 9.1. The mesostriatal system consists of dopaminergic cell bodies located in the retrorubal nucleus, substantia nigra, and ventral tegmental area (VTA), which project to striatal and related structures, including the caudate, putamen, subthalamic nucleus, and nucleus accumbens. 135 Section I: Structural and Functional Neuroanatomy The mesolimbocortical system consists of dopaminergic cell bodies in the VTA, which project to limbic and cortical areas such as the habenula, amygdala, locus coeruleus, cingulate cortex, and piriform and entorhinal cortices. Dopaminergic neurons are also found in other parts of the central nervous system, such as the zona incerta, hypothalamus, the olfactory bulb, the retina, and pre-optic areas. It is postulated that dopaminergic neurons in the VTA are activated in response to a motivationally relevant event [11]. The dopaminergic system is therefore thought to have an enhancing or energizing effect on goal-oriented behavior by affecting the target structures innervated by the mesolimbic, mesocortical, and mesostriatal pathways, thereby placing the behavioral responses in a state of preparedness [12]. Further, it is thought that the release of DA at these sites facilitates cellular changes that establish learned associations with the event, thereby reinforcing the behavioral response should the event re-occur [13]. Experimental work in animals implicating dopaminergic systems in motivation The classical studies linking DA to motivation were based on stereotaxic injections of neurotoxin into the afferent projections of the mesolimbic and mesocortical pathways, which produced severe aphagia and adipsia [14]. Since the time of these studies, numerous means (e.g., pharmacologic, electrophysiological) have been used to study the effects of altering DA systems on motivated behaviors, such as hunger, thirst, and sex; some data remain inconclusive, while the interpretation of other results is the focus of much debate (see [15], [2], and [16] for review). Pharmacologic studies suggest a role for DA in motivation. Pharmacologic enhancement of DA release in the ventral striatum has been shown to increase the control by the animal over behavior exerted by conditioned reinforcing stimuli previously paired in a Pavlovian fashion with appetite reinforcement [17]. Other pharmacologic studies that implicate DA in motivation include infusion of DA into the nucleus accumbens altering animal approach behaviors [18], blockade of DA receptors by neuroleptics impairing intracranial self-stimulation by the animal [19], and observations of DA increases occurring during goal-directed behaviors for cocaine and natural reinforcers [20]. It should be noted, however, that there 136 is no clear evidence that antagonism of DA receptors blocks drug-induced euphoria [21]. Brain dialysis studies of DA in the nucleus accumbens and prefrontal cortex (i.e., sampling DA efflux over minutes) showed an increase in DA efflux in both of these structures before contact with food [22] or sexual reward stimuli [23]. Electrophysiological studies also suggest a role for DA in motivation. Neurons in the VTA projecting to the nucleus accumbens fire selectively in response to the presentation of reward cues [24]. Animals are shown to emit very high rates of responding for intracranial self-stimulation of the VTA [25], eliciting DA release in the nucleus accumbens [26], and self stimulation is suppressed by the injection of DA antagonist drugs [27]. Using a combination of electrochemistry, electrophysiology, and iontophoresis, Cheer and colleagues [28] demonstrated changes in firing and increased DA release in the nucleus accumbens shell, occurring for cues signaling award availability, and this was functionally linked to DA receptor activation. Further data supporting the role of DA in motivation are the findings that the ventral subiculum powerfully increases DA neuronal activity and elevates DA in terminal regions of the nucleus accumbens, prefrontal cortex, and amygdala, possibly via its connections with the prefrontal cortex [29]. Nucleus accumbens The nucleus accumbens (originally named, for its physical location in the brain, the nucleus accumbens septi, meaning the nucleus leaning against the septum), together with adjacent parts of the caudate and putamen, constitutes a striatal subdivision termed the ventral striatum. Whereas the dorsal striatum is considered to be involved in motor processes, the ventral striatum is considered to be involved in affective and goal-related behavior [15]. The nucleus accumbens may be regarded as a striatal nucleus with predominantly limbic connections, and hence a critical structure linking motor and motivational processes [2]. The nucleus accumbens seems to mediate the primary motivational characteristics of feeding and reproductive behavior as well as reward-motivated behaviors [30]. The nucleus accumbens contains two functionally distinct sectors, designated the shell and core [31]. The shell is connected to the hypothalamus and the VTA, Chapter 9: Motivation and plays an important role in unconditioned motivated behavior [32]. For instance, rises of dopamine in the accumbal shell has been found to precede goal-directed behavior [33]. On the other hand, the core is connected with the anterior cingulate and the orbitofrontal cortex, and may mediate the expression of learned behaviors in response to stimuli predicting motivationally relevant events [31]. Depletion of DA in the nucleus accumbens can interfere with instrumental behaviors in some conditions, including feeding [2], sexual behavior [34], and maternal behavior [35]. Further evidence from animal experimentation implicating the nucleus accumbens in motivation includes electrophysiological recordings of neurons that were shown to react to novel and previously reinforced stimuli, and the fact that DA release increases more robustly in the nucleus accumbens during reward anticipation than during reward consumption [36]. Neurons in the nucleus accumbens show response to impending rewards that are altered in DA receptor knock-out mice [37], and by VTA inactivation [38]. Further, DA efferents from the VTA to the nucleus accumbens become active with a motivationally relevant event [39]. Lesions of the nucleus accumbens have been shown to impair the ability of the animal to increase the rate of food-reinforced instrumental responding to a conditioned stimulus previously paired with food [36]. Taken together, these findings suggest that the nucleus accumbens plays a pivotal role in the motivation circuits, such as mediating the primary motivational characteristics of feeding and reproductive behavior as well as reward-motivated behaviors. Prefrontal cortex Another critical region in the neural circuit of motivation is the prefrontal cortex, mainly the anterior cingulate and the orbitofrontal cortices [6]. To better understand the role of the prefrontal cortex in motivation, one must first consider some of the wiring paradigms that have been defined, the socalled frontal-subcortical circuits. Frontal-subcortical circuits are thought to form the main network by which motor activity and behavior in humans are mediated, and help explain the similarity of behavioral changes in frontal cortical and subcortical disorders [40]. Anterior cingulate Cortex Gyrus rectus/ medial orbital gyrus Striatum Nucleus accumbens Globus pallidus substantia nigra Globus pallidus/ substantia nigra Ventral pallidum Ventral anterior Thalamus nucleus Mediodorsal Ventral striatum Figure 9.2. Schema showing frontal-subcortical loops related to motivation. There are five well-defined frontal-subcortical circuits named according to their function or site of origin in the cortex: the motor circuit, the oculomotor circuit, the dorsolateral prefrontal circuit, the lateral and medial orbitofrontal circuit, and the anterior cingulate circuit (see Chapter 5). The circuits share many features in common. For instance, they originate in the prefrontal cortex, project to the striatum (caudate, putamen, and ventral striatum); connect to the globus pallidus and substantia nigra, project to the thalamus, and are finally closed via projections back to the frontal cortex. There are also open-loop connections of each of these circuits, which are thought to integrate information from functionally related sites. Within each of these circuits there are both direct and indirect pathways, which can modulate input to the thalamus, the function of which is thought to modulate overall circuit activities in response to different inputs [40]. Limbic system connections involve both the anterior cingulate and medial orbitofrontal cortex (Figure 9.2). The anterior cingulate circuit originates in the anterior cingulate cortex and projects to the ventral striatum (ventromedial caudate, ventral putamen, nucleus accumbens, and olfactory tubercle). Efferents from this limbic part of the striatum go to the rostromedial globus pallidus interna, ventral pallidum, and rostrodorsal substantia nigra. The ventral pallidum in turn projects to the ventral anterior nucleus of the thalamus, and from the thalamus, neurons project back to the anterior cingulate cortex, thus closing the loop. The open connections 137 Section I: Structural and Functional Neuroanatomy of the anterior cingulate circuit include inputs from the perirhinal area and hippocampus, and outputs to the substantia nigra, lateral hypothalamus, and subthalamic nucleus. The medial orbitofrontal circuit originates in the gyrus rectus and the medial orbital gyrus, and projects to the nucleus accumbens, ventral pallidum, and the mediodorsal thalamic nucleus in a closed loop fashion; open connections include the ventral striatum and amygdala. This prefrontal cortical region has a primary role in goal-directed behavior and affective processing [41]. More specifically, the prefrontal cortex may regulate the motivational salience of stimuli and determine the type of behavioral response [42]. Furthermore, the nucleus accumbens and the prefrontal cortex have reciprocal connections by which the prefrontal cortex may engage the nucleus accumbens to process motivationally salient stimuli [43]. Ventura and coworkers [44] demonstrated that norepinephrine projections from the prefrontal cortex modulate DA levels in the nucleus accumbens, which, in conjunction with the DA mesolimbic system, may play an important role in attaching motivational attribution to award- and aversion-related stimuli. Amygdala The amygdala (derived from the Greek word for “almond” and named for its size and shape) is a collection of nuclei originally considered to be part of the basal ganglia, as in the temporal lobe it is adjacent to striatal structures (the tail of the caudate is continuous with the amygdala, which in turn is continuous with the putamen). Although the amygdala does have some connectivity with the striatum, its pattern of connectivity is more typical of limbic structures, and the amygdala forms one of the central components of the so-called lateral limbic system. The amygdala is situated below the uncus, at the anterior end of the hippocampus, and at the inferior horn of the lateral ventricle. It receives its main afferents from the inferior visual temporal cortex, the superior temporal auditory cortex, the cortex of the temporal pole (involved in the regulation of behavior), as well as from taste and auditory cortical centers. The main efferents of the amygdala are to the hypothalamus, autonomic centers of the medulla oblongata, the nucleus accumbens in the ventral striatum, and areas of the temporal insular and orbitofrontal cortices. The amygdala is 138 thus positioned to receive polymodal information from sensory regions and influence motor, autonomic, and endocrine systems. These connections allow the amygdala to influence drive-related behaviors and the subjective feelings that accompany them. Experimental data obtained from animal models support the role of the amygdala in motivation, with lesions of the central nucleus impairing the acquisition of learned orienting response to visual conditioned stimuli paired with food [45], and impairing the ability of a cue previously paired with food to increase the rate of food-reinforced instrumental responding [46]. Further, inactivation of the basolateral amygdala abolishes the increase in nucleus accumbens DA resulting from presentation of a stimulus previously associated with a reward [47]. The amygdala is thus considered a relevant region in the mechanism of goal-oriented behavior [6], and is traditionally considered to be primarily involved in fear-motivated behavior. The amygdala may mediate those processes that associate motivationally relevant events with otherwise neutral stimuli, which later become predictors of that specific event (i.e., the conditioned stimuli) [48]. The role of the amygdala in motivated behaviors may be inferred by the behavioral manifestations of the Klüver–Bucy syndrome produced by bilateral lesions of the amygdala, which usually results in tameness and lack of emotional responsivity. The physiology of motivation Jahanshahi and Frith [4] proposed a “willed action route,” through which “goals and plans lead to formulation of intentions, which result in initiation of appropriate actions . . . and production of a response.” The dorsolateral prefrontal cortex, the anterior cingulate, and the supplementary motor area were suggested as the key cortical components of this route, with the thalamus and basal ganglia as its main subcortical components. Using functional magnetic resonance imaging (fMRI), Epstein and co-workers [12] found that depressed patients had less activation of ventral striatal regions compared with healthy individuals, and there was a significant correlation between patients’ failure to show brain activation to positive stimuli and loss of interest and motivation. The authors speculated Chapter 9: Motivation that the relatively low ventral activation in depressed patients may be related to deficits translating motivational information into behavior. Habib [49] proposed that a subset of the basal ganglia may subserve motivational functions in humans through previously described frontal-subcortical loops underlying motor acts, emotional expression, and cognitive activity. The main circuit considered to be involved in human motivation consists of projections from the anterior cingulate to the ventral striatum, the ventral globus pallidus, the dorsomedial thalamus, and back to the anterior cingulate. This striato-pallidal circuit is considered “an interface between motivation and action” [50], as well as the site of “conversion of motivational processes into behavioural output” [51]. Habib suggested that the crucial role of the “limbic striatopallidum” depends on relevant inputs from the amygdala (involved in “emotional labelling” of sensory stimuli) and the hippocampus (involved with comparing new information with biographical data). In turn, output from the striato-pallidum projects to sections of the basal ganglia involved in initiating and organizing motor behavior. Habib [49] explained loss of spontaneous action and poverty of thinking (which he labeled “athymormia”) as due to cortico-subcortical lesions disconnecting the anterior striatum from cortical afferents. The main connection runs from the anterior cingulate to the nucleus accumbens, and lesions of these structures may result in loss of initiative and spontaneous action, decreased interest and drive, and emotional blunting [52]. Bilateral lesions of the thalamus may also result in loss of motivation, but this is usually accompanied by cognitive deficits [49]. Laplane and Dubois used the term “loss of psychic auto-activation” [53] to refer to a deficit in spontaneous activation of mental processing, observed in behavioral, cognitive or affective domains, which can be totally reversed by external stimulation that activates normal patterns of response. Brown and Pluck [54] stressed that loss of motivation in humans does not result from lesions in any single structure, given that motivated behavior results from complex cortico-subcortical networks. They suggested that the auto-activation deficit usually results from damage to pathways linking the ventral striatum to the anterior cingulate, caudate nucleus, and the orbitofrontal cortex. Limitations of current models of the neurobiology of motivation The fronto-subcortical circuit proposed to mediate motivation is not fully segregated, and connections between different circuits are the rule. Complex reentrant loops connect the nucleus accumbens, ventral pallidum, hypothalamus, amygdala, septum, hippocampus, VTA, cingulate and prefrontal cortex, with other forebrain limbic structures [8]. From a conceptual standpoint, it is important to consider how internal and external triggers “motivate” behavior. This interaction may not be problematic for basic or instinctive behaviors (e.g., feeding and sexual behaviors), although these behaviors are better considered reflex or automatic rather than motivated. More difficult is to conceptualize how complex goals and projects may engage a “motivation system,” given that goals, plans, and projects are all psychological concepts while behavior is instantiated by complex neural networks. Another concern is that motivation circuits should be in close functional association with systems mediating mood and arousal, given the well-known impact of mood and anxiety disorders upon motivated behavior. However, little is yet known about this association. Loss of motivation Diminished motivation is a common finding of the aging process and neurodegenerative brain conditions. In 1991, Robert Marin [55] reported a series of patients with stroke, Parkinson’s disease, or Alzheimer’s disease who had comorbid loss of motivation in the absence of depression. He proposed apathy as an independent syndrome characterized by (1) deficits in goal-directed behaviors [55] as manifested by lack of effort, initiative, and productivity, (2) reduced goaldirected cognition, as manifested by decreased interests, lack of plans and goals, and lack of concern about one’s own health or functional status, and (3) reduced emotional concomitants of goal-directed behaviors, as manifested by flat affect, emotional indifference, and restricted emotional responses to important life events [55]. Starkstein organized these symptoms into a set of diagnostic criteria, which were validated for use in Alzheimer’s disease and stroke [56] (Box 9.1). Similarly, van Reekum and co-workers divided apathy into emotional, cognitive, and behavioral domains [57]. 139 Section I: Structural and Functional Neuroanatomy Box 9.1 Diagnostic criteria for apathy. Adapted from Starkstein SE, Leentjens AF. The nosological position of apathy in clinical practice. J Neurol Neurosurg Psychiatry 2008;79(10):1088–92, with permission from BMJ Publishing Group Ltd. A. Lack of motivation relative to the patient’s previous level of functioning or the standards of his or her age and culture, as indicated either by subjective account or observation by others. B. Presence for at least four weeks, during most of the day, of at least one symptom belonging to each of the following three domains: Diminished goal-directed behavior 1. Lack of effort or energy to perform everyday activities. 2. Dependency on prompts from others to structure everyday activities. Diminished goal-directed cognition 1. Lack of interest in learning new things, or in new experiences. 2. Lack of concern about one’s personal problems. Diminished concomitants of goal-directed behavior 1. Unchanging or flat affect 2. Lack of emotional responsivity to positive or negative events. A. The symptoms cause clinically significant distress or impairment in social, occupational, or other important areas of functioning. B. The symptoms are not due to diminished level of consciousness or the direct physiological effects of a substance. Diagnosis of loss of motivation One of the main limitations to the diagnosis of apathy is that structured clinical interviews and valid diagnostic criteria have only recently been developed. Thus, most assessments of apathy in clinical samples use scales to measure the severity of this condition. The Apathy Evaluation Scale was developed by Marin and co-workers [58], and consists of three sections, rated by an informant, the examiner, and the patient. The Apathy Scale developed by Starkstein and co-workers [59] is an examiner-rated scale based on Marin’s instrument. Robert and co-workers developed 140 the Apathy Inventory [60], which is structured similar to the Neuropsychiatric Inventory [61] and has been used to rate apathy in Parkinson’s disease. Strauss and Sperry developed the Dementia Apathy Interview and Rating [62] to assess apathy among individuals with cognitive decline. To our knowledge, the Structured Clinical Interview for Apathy [63] is the only standardized assessment for this condition. This instrument is assessed with the patient and an appropriate informant, but information from other sources, such as patients’ medical records or information from other medical providers, is also considered. This instrument has been validated for use in dementia, but it is currently used in Parkinson’s disease and stroke as well. Finally, diagnosis of apathy may be carried out using the diagnostic criteria proposed by Marin and modified by Starkstein and Leentjens [64] (Box 9.1). This set of criteria has been already validated for use in dementia. Conclusion Motivation is a complex construct that is expressed in a wide variety of behaviors, ranging from instinctive behavior in mammals (e.g., feeding, fight or flight reactions), to complex human behaviors. The neuroanatomy of motivation is being increasingly clarified. Critical regions for motivation include the amygdala, nucleus accumbens, and prefrontal cortex, and the neurotransmitter dopamine plays an important role. Loss of motivation is a cardinal symptom of depression, but is also a conspicuous finding in many acute and chronic neuropsychiatric disorders such as traumatic brain injury, stroke, dementia, and Parkinson’s disease. The diagnosis of apathy is still fraught with methodological limitations, but valid and reliable structured interviews and diagnostic criteria are now being developed. A better knowledge of the brain mechanisms of motivation coupled with adequate diagnostic instruments will enhance the search for better treatments for this condition. References 1. Berrios GE, Gili M. Abulia and impulsiveness revisited: a conceptual history. Acta Psychiatr Scand. 1995;92(3):161–7. 2. Salamone JD, Correa M, Farrar A, Mingote SM. Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits. Psychopharmacology (Berl.) 2007;191(3):461–82. Chapter 9: Motivation 3. Salamone JD. Functions of mesolimbic dopamine: changing concepts and shifting paradigms. Psychopharmacology (Berl.) 2007;191(3):389. 4. Jahanshahi M, Frith CD. Willed action and its impairments. Cognitive Neuropsych. 1998;15(6–8):483–533. 5. Berrios GE. The History of Mental Symptoms: Descriptive Psychopathology since the Nineteenth Century. Cambridge: Cambridge University Press; 1996. 6. Kalivas PW, Volkow ND. The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry 2005;162(8):1403–13. 7. Valenstein ES, Cox VC, Kakolewski JW. Reexamination of the role of the hypothalamus in motivation. Psychol Rev. 1970;77(1):16–31. 8. Berridge KC. Motivation concepts in behavioral neuroscience. Physiol Behav. 2004;81(2):179–209. 9. Gallistel CR. The Organization of Action: A New Synthesis. Hillsdale, NJ: Lawrence Erlbaum Associates; distributed by Halsted Press; 1980. 10. Heimer L, Alheid GF, de Olmos JS et al. The accumbens: beyond the core-shell dichotomy. J Neuropsychiatry Clin Neurosci. 1997;9(3):354–81. 11. McClure SM, Daw ND, Montague PR. A computational substrate for incentive salience. Trends Neurosci. 2003;26(8):423–8. 12. Epstein J, Pan H, Kocsis JH et al. Lack of ventral striatal response to positive stimuli in depressed versus normal subjects. Am J Psychiatry 2006;163(10):1784–90. 13. Keitz M, Martin-Soelch C, Leenders KL. Reward processing in the brain: a prerequisite for movement preparation? Neural Plast. 2003;10(1–2):121–8. 14. Ungerstedt U. Adipsia and aphagia after 6-hydroxydopamine induced degeneration of the nigro-striatal dopamine system. Acta Physiol Scand Suppl. 1971;367:95–122. 15. Robbins TW, Everitt BJ. A role for mesencephalic dopamine in activation: commentary on Berridge (2006). Psychopharmacology (Berl.) 2007;191(3): 433–7. 16. Phillips AG, Vacca G, Ahn S. A top-down perspective on dopamine, motivation and memory. Pharmacol Biochem Behav. 2008;90(2):236–49. 17. Taylor JR, Robbins TW. 6-Hydroxydopamine lesions of the nucleus accumbens, but not of the caudate nucleus, attenuate enhanced responding with reward-related stimuli produced by intra-accumbens d-amphetamine. Psychopharmacology (Berl.) 1986;90(3):390–7. 18. Ikemoto S, Panksepp J. Dissociations between appetitive and consummatory responses by pharmacological manipulations of reward-relevant brain regions. Behav Neurosci. 1996;110(2):331–45. 19. Wise RA. Addictive drugs and brain stimulation reward. Annu Rev Neurosci. 1996;19:319–40. 20. Phillips PE, Stuber GD, Heien ML, Wightman RM, Carelli RM. Subsecond dopamine release promotes cocaine seeking. Nature 2003;422(6932):614–18. 21. Wachtel SR, Ortengren A, de Wit H. The effects of acute haloperidol or risperidone on subjective responses to methamphetamine in healthy volunteers. Drug Alcohol Depend. 2002;68(1):23–33. 22. Ahn S, Phillips AG. Dopaminergic correlates of sensory-specific satiety in the medial prefrontal cortex and nucleus accumbens of the rat. J Neurosci. 1999;19(19):RC29. 23. Fiorino DF, Coury A, Phillips AG. Dynamic changes in nucleus accumbens dopamine efflux during the Coolidge effect in male rats. J Neurosci. 1997;17(12):4849–55. 24. Schultz W, Tremblay L, Hollerman JR. Reward processing in primate orbitofrontal cortex and basal ganglia. Cereb Cortex 2000;10(3):272–84. 25. Crow TJ. A map of the rat mesencephalon for electrical self-stimulation. Brain Res. 1972;36(2):265–73. 26. Fiorino DF, Coury A, Fibiger HC, Phillips AG. Electrical stimulation of reward sites in the ventral tegmental area increases dopamine transmission in the nucleus accumbens of the rat. Behav Brain Res. 1993;55(2):131–41. 27. Mogenson GJ, Takigawa M, Robertson A, Wu M. Self-stimulation of the nucleus accumbens and ventral tegmental area of Tsai attenuated by microinjections of spiroperidol into the nucleus accumbens. Brain Res. 1979;171(2):247–59. 28. Cheer JF, Heien ML, Garris PA, Carelli RM, Wightman RM. Simultaneous dopamine and single-unit recordings reveal accumbens GABAergic responses: implications for intracranial self-stimulation. Proc Natl Acad Sci USA 2005;102(52):19,150–5. 29. Floresco SB, Todd CL, Grace AA. Glutamatergic afferents from the hippocampus to the nucleus accumbens regulate activity of ventral tegmental area dopamine neurons. J Neurosci. 2001;21(13):4915–22. 30. Kluver H, Bucy PC. Preliminary analysis of functions of the temporal lobes in monkeys. 1939. J Neuropsychiatry Clin Neurosci. 1997;9(4):606–20. 31. Kelley AE. Ventral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning. Neurosci Biobehav Rev. 2004;27(8):765–76. 32. Pecina S, Smith KS, Berridge KC. Hedonic hot spots in the brain. Neuroscientist 2006;12(6):500–11. 141 Section I: Structural and Functional Neuroanatomy 33. Cheer JF, Aragona BJ, Heien ML et al. Coordinated accumbal dopamine release and neural activity drive goal-directed behavior. Neuron 2007;54(2): 237–44. 46. Cardinal RN, Parkinson JA, Hall J, Everitt BJ. Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci Biobehav Rev. 2002;26(3):321–52. 34. Hull EM, Weber MS, Eaton RC et al. Dopamine receptors in the ventral tegmental area affect motor, but not motivational or reflexive, components of copulation in male rats. Brain Res. 1991;554(1–2): 72–6. 47. Louilot A, Besson C. Specificity of amygdalostriatal interactions in the involvement of mesencephalic dopaminergic neurons in affective perception. Neuroscience 2000;96(1):73–82. 35. Pereira M, Uriarte N, Agrati D, Zuluaga MJ, Ferreira A. Motivational aspects of maternal anxiolysis in lactating rats. Psychopharmacology (Berl.) 2005;180(2):241–8. 36. Berridge KC, Robinson TE. What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res Brain Res Rev. 1998;28(3):309–69. 48. Everitt BJ, Cardinal RN, Parkinson JA, Robbins TW. Appetitive behavior: impact of amygdala-dependent mechanisms of emotional learning. Ann N Y Acad Sci. 2003;985:233–50. 49. Habib M. Athymhormia and disorders of motivation in basal ganglia disease. J Neuropsychiatry Clin Neurosci. 2004;16(4):509–24. 50. Mogenson GJ, Jones DL, Yim CY. From motivation to action: functional interface between the limbic system and the motor system. Prog Neurobiol. 1980;14(2–3): 69–97. 37. Tran AH, Tamura R, Uwano T et al. Dopamine D1 receptors involved in locomotor activity and accumbens neural responses to prediction of reward associated with place. Proc Natl Acad Sci USA 2005;102(6):2117–22. 51. Apicella P, Ljungberg T, Scarnati E, Schultz W. Responses to reward in monkey dorsal and ventral striatum. Experimental Brain Res. 1991;85(3):491–500. 38. Yun IA, Wakabayashi KT, Fields HL, Nicola SM. The ventral tegmental area is required for the behavioral and nucleus accumbens neuronal firing responses to incentive cues. J Neurosci. 2004;24(12):2923–33. 52. Stuss DT, Benson DF. Emotional concomitants of psychosurgery. In Satz P, Heilman KM, editors. Neuropsychology of Human Emotion. New York, NY: Guilford Press; 1983, pp. 111–40. 39. Schultz W. Predictive reward signal of dopamine neurons. J Neurophysiol. 1998;80(1):1–27. 53. Laplane D, Dubois B. Auto-activation deficit: a basal ganglia related syndrome. Mov Disord. 2001; 16(5):810–14. 40. Tekin S, Cummings JL. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. J Psychosom Res. 2002;53(2):647–54. 41. Bechara A, Tranel D, Damasio H. Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain 2000;123 (Pt 11):2189–202. 42. Bush G, Vogt BA, Holmes J et al. Dorsal anterior cingulate cortex: a role in reward-based decision making. Proc Natl Acad Sci USA 2002;99(1): 523–8. 43. Ventura R, Cabib S, Alcaro A, Orsini C, Puglisi-Allegra S. Norepinephrine in the prefrontal cortex is critical for amphetamine-induced reward and mesoaccumbens dopamine release. J Neurosci. 2003;23(5):1879–85. 44. Ventura R, Morrone C, Puglisi-Allegra S. Prefrontal/accumbal catecholamine system determines motivational salience attribution to both reward- and aversion-related stimuli. Proc Natl Acad Sci USA 2007;104(12):5181–6. 45. Gallagher M, Graham PW, Holland PC. The amygdala central nucleus and appetitive Pavlovian conditioning: lesions impair one class of conditioned behavior. J Neurosci. 1990;10(6):1906–11. 142 54. Brown RG, Pluck G. Negative symptoms: the ‘pathology’ of motivation and goal-directed behaviour. Trends Neurosci. 2000;23(9):412–17. 55. Marin RS. Apathy: a neuropsychiatric syndrome. J Neuropsychiatry Clin Neurosci. 1991;3(3):243–54. 56. Starkstein SE. Apathy and withdrawal. Int Psychogeriatr. 2000;12(S1):135–7. 57. van Reekum R, Stuss DT, Ostrander L. Apathy: why care? J Neuropsychiatry Clin Neurosci. 2005;17(1): 7–19. 58. Marin RS, Butters MA, Mulsant BH, Pollock BG, Reynolds CF, 3rd. Apathy and executive function in depressed elderly. J Geriatr Psychiatry Neurol. 2003; 16(2):112–16. 59. Starkstein SE, Mayberg HS, Preziosi TJ et al. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 1992;4(2):134–9. 60. Robert PH, Clairet S, Benoit M et al. The apathy inventory: assessment of apathy and awareness in Alzheimer’s disease, Parkinson’s disease and mild cognitive impairment. Int J Geriatr Psychiatry 2002; 17(12):1099–105. Chapter 9: Motivation 61. Cummings JL, Mega M, Gray K et al. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 1994;44(12):2308–14. 63. Starkstein SE, Ingram L, Garau ML, Mizrahi R. On the overlap between apathy and depression in dementia. J Neurol Neurosurg Psychiatry 2005;76(8): 1070–4. 62. Strauss ME, Sperry SD. An informant-based assessment of apathy in Alzheimer disease. Neuropsychiatry Neuropsychol Behav Neurol. 2002;15(3):176–83. 64. Starkstein SE, Leentjens AF. The nosological position of apathy in clinical practice. J Neurol Neurosurg Psychiatry 2008;79(10):1088–92. 143 Section I Structural and Functional Neuroanatomy Chapter Perception and recognition 10 Benzi M. Kluger and Gila Z. Reckess The wind was flapping a temple flag. Two monks were arguing about it. One said the flag was moving; the other said the wind was moving. Arguing back and forth they could come to no agreement. The Sixth Patriarch said, “It is neither the wind nor the flag that is moving. It is your mind that is moving.” The two monks were struck with awe. Zen Koan In this chapter we will discuss the processes by which we come to know the environment, namely sensation, perception, and recognition. Although seemingly effortless, these computational tasks are remarkably complex. For instance, we can recognize the face of a friend in the midst of a crowd or distinguish their voice despite significant background noise. While we typically take these abilities for granted, there are many symptoms and syndromes in Behavioral Neurology & Neuropsychiatry (BN&NP) in which specific aspects of perception or recognition are disrupted. The study of perceptual processes is historically one of the oldest fields of inquiry within psychology and neuroscience, and thus is one of the most highly developed. A comprehensive overview of what is currently known about perception would not be feasible within the confines of a single chapter. Therefore, we have endeavored to provide a concise, broad overview of the functional anatomy underlying perception and recognition within each of the five primary perceptual systems. To facilitate discussion of each system, we will first begin with an overview of shared terminology and general organizational principles, followed by an introduction to classes of perceptual disorders. Clinical cases have provided valuable insight into normal and pathologic mechanisms subserving each sensory system. Conversely, understanding the structural architecture of sensory systems and behavioral correlates of normal and abnormal connectivity is essential for localizing anatomic and behavioral abnormalities. Therefore, for each sensory system we will first review the general functional neuroanatomy, followed by an overview of related syndromes that readers may encounter in the literature or clinic. Defining perception and recognition Rather than engaging in a philosophical or semantic debate about the definitions of perception and recognition, we define these processes on the basis of their relatively concrete neuroanatomic and neuropsychological elements. The first of these is sensation, which involves transduction of external stimuli to neural signals and transmission of this information to the central nervous system (CNS). Basic sensory information includes stimulus timing (onset and offset), magnitude, location, and basic qualitative information related to the sensory modality. Perception builds upon basic sensation by extracting more complex attributes from sensory elements. For example, visual perception includes the ability to detect motion, differentiate colors, and distinguish basic forms. Primary sensation, however, is not a necessary precondition for perception, as demonstrated by internally generated perceptual phenomena such as hallucinations or imagery. Recognition involves identification of a sensory stimulus via access to and integration of stored representations of previously encountered stimuli. Recognition may be seen as a form of memory, and clearly involves learning and access to prior knowledge [1]. Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 144 Chapter 10: Perception and recognition Moreover, the role of learning in perception requires a certain degree of plasticity within these systems, which may allow for improved rehabilitation strategies [2]. We purposefully avoid conscious awareness as a criterion for our definitions of perception and recognition given that stimuli do not have to be consciously perceived in order to influence behavior or other neurophysiologic outcomes. Perceptual priming, for example, is a well-established phenomenon in which behavior is influenced by stimuli that are not consciously perceived [3]. Many aspects of perception and recognition are not passive, however, and require active allocation of attention and coordination of perceptual and motor systems. For example, the ability to recognize complex objects such as faces requires elaborate visual scanning to identify both global and local features [4]. In contrast, autistic individuals have a disorganized scanning pattern and spend less time fixating on critical facial areas such as the eyes [5]. Organizational principles All of our sensory and perceptual systems share certain organizational principles. Knowledge of these principles is helpful in understanding both the basic neuroscience of these systems, as well as understanding pathological syndromes. Sensation begins with the transduction of energy (in the case of hearing and vision), pressure (in the case of touch) or the detection of molecules (in the case of chemosensation – i.e., taste and smell) to neural signals by specialized receptor neurons. These receptor neurons then transmit sensory information to the CNS. This information is quantitatively coded in that stimuli of larger magnitudes typically induce higher firing rates (also known as frequency coding). It is also qualitatively coded based on the location of the receptor type that originated the signal (also known as location coding). This is true across modalities as well as within modalities. For example, sensory pathways mediating light touch remain distinct from those mediating vibration from peripheral neurons all the way through the primary sensory cortex. At the earliest stages of perception, sensory input is refined through center-surround inhibition. For example, if a beam of light hits the retina, stimulated cells will also inhibit neighboring cells. This mechanism allows for detection of edges (in vision and touch), fine-tuning of sensory input, and progressive refinement of accuracy. Neurons at progressively higher levels of the CNS are tuned to detect more complex features, typically through combining input from multiple lower level neurons. For instance, while retinal neurons may detect light in a particular area, their projections in primary visual cortex are tuned to detect lines in a particular orientation anywhere on the retina. Sensory input is typically transmitted via a combination of serial and parallel circuitry. Most sensory systems project to the contralateral thalamus en route to primary cortical sensory areas. Mesulam [6] proposed a schema for dividing cortical areas into primary sensory cortex, unimodal cortex, and transmodal cortex (including heteromodal, limbic, and paralimbic cortices). In this model, the primary sensory cortices are the entry point of sensory information into cortical circuits and initiate modality specific perceptual processing. Information then travels in parallel to multiple unimodal association specialized to accomplish a particular perceptual or recognition task. Transmodal processing areas receive input from multiple sensory modalities to accomplish higher-order recognition, emotional or behavioral goals. In this chapter we will focus primarily on primary sensory and unimodal cortex. Examples of transmodal cortex include Wernicke’s area, the entorhinal-hippocampal complex, and posterior parietal cortices. Although traditional texts suggest that information flow is unidirectional in sensory systems, progressing from sensory neurons through polymodal association areas, more recent evidence demonstrates that there are multiple layers of both top-down and bottom-up reciprocal communication between primary sensory and unimodal areas [7]. Similarly, top-down influences from the prefrontal cortex have been demonstrated to modify many aspects of perception and recognition [8]. In addition to specialized cortical modules dedicated to certain perceptual or sensory functions, there are extensive connections between these modules. As we will discuss below, perceptual dysfunction may occur without damage to modules but from their disconnection from either afferent sensory input or efferent output to higher-order stores of verbal and nonverbal information [9, 10]. More recent research suggests that certain forms of information processing depend not only on the basic architecture of intermodal circuitry, but also on its temporal properties. For example, lesions that disrupt the timing of reciprocal communication between cortical areas, or cortical 145 Section I: Structural and Functional Neuroanatomy and subcortical areas, may be sufficient to disturb normal perception and recognition. These clinical observations are consistent with basic research on the binding problem, which asks how functionally and structurally modular perceptual systems become integrated [11, 12]. Disorders of perception and recognition Acquired brain damage and developmental abnormalities may affect each level of processing discussed above, including primary sensation, cortically mediated perception, or higher-order aspects of perception or recognition. A useful distinction can be made between negative syndromes, those in which there is a failure to correctly perceive some element of the environment, and positive syndromes, those in which a patient perceives stimuli or stimulus features in the absence of associated external sensory input. Although relatively rare, the agnosias are among the more extensively studied negative perceptual syndromes and have provided invaluable insight into the functional neuroanatomy of perception and recognition. Agnosias are characterized by profound impairment in perceiving, identifying, and/or recognizing stimuli in the context of relatively unimpaired language, attention, and primary sensation. Therefore, an important part of making this diagnosis is to exclude other more general causes of perceptual disturbances. Additionally, agnosias are most commonly modality specific, which allows one to demonstrate that higherorder cortical processes are intact. For instance, a patient with a tactile agnosia may be unable to recognize a key placed in their hand but can recognize the sound of jangling keys or a picture of a key. Lissauer [13] drew a distinction between apperceptive agnosias, in which patients are limited by perceptual disturbances, and associative agnosias, in which patients are unable to attach appropriate concepts or meaning to perceptual objects. These syndromes are often difficult to distinguish, but can be differentiated if one tests the perceptual abilities necessary for recognition. For example, patients with visual associative agnosia can copy drawings of objects but cannot identify, name, or categorize objects; those with apperceptive agnosia are often unable to even copy simple object drawings, or do so in a manner that disregards the natural groupings of parts of objects. Lissauer’s initial model continues to form the basis of theories on 146 agnosia. However, it is now clear that this dichotomous distinction is an oversimplification. For example, there is now considerable evidence of perceptual deficits in patients with associative agnosia as well [14]. A further categorization of agnosias is integrative agnosias, characterized by a recognition deficit attributed to the inability to incorporate the parts of an object into a global percept. A classic example is simultanagnosia (as seen in Balint’s syndrome), in which patients with bilateral lesions of the occipitoparietal cortices are able to identify individual objects but are unable to recognize a larger scene. For example, when shown a picture of a picnic they may report seeing a blanket, a sandwich, or ants. There is some controversy about a final class of agnosia, termed category-specific agnosias, such as selective recognition impairment for living versus non-living things. Determining the characteristics of objects affected by category-specific agnosias remains elusive, as patients with agnosia for “living things” often have difficulties with musical instruments but not body parts [15]. Positive symptoms may be classified as either illusions, which are misinterpretations of existing stimuli, or hallucinations, which are perceptions that arise independently of external stimuli. Some authors further distinguish between hallucinations and pseudohallucinations, the latter of which are associated with preserved insight into the hallucination’s disconnection from objective reality; however, pseudohallucinations is not a universally accepted term. Hallucinations may be divided into release hallucinations, which are believed to arise from the release of perceptual areas from inhibitory input, and hyperexcitable or irritative hallucinations, which arise from excessive stimulation. Based on recent, sophisticated physiological models, Behrendt and Young [16] proposed that hallucinations are due to aberrant thalamocortical communications, leading to under-constrained perceptual processing. Release hallucinations may arise from destructive lesions at any level of the CNS, ranging from environmental sensory deprivation and peripheral nerve lesions to higher-cortical transmodal areas. Irritative hallucinations classically arise in the setting of ictal phenomena in epilepsy. Similar principles may apply to the balance of excitatory and inhibitory neurotransmitters impinging on perceptual areas in the setting of psychiatric disorders and pharmacologically induced hallucinations [17]. From a clinical standpoint, ictal hallucinations are characterized by their Chapter 10: Perception and recognition brevity (seconds to a few minutes), stereotypic nature, and frequent association with other alterations in consciousness or post-ictal confusion; in contrast, release hallucinations are of longer duration, more variable with regard to their content, and may be associated with other fixed perceptual deficits. Although we will not discuss the physiology of perception in dreams, there appears to be a strong relationship between alterations in sleep and arousal systems and hallucinations. In peduncular hallucinosis, patients with strokes in the midbrain develop hallucinations (predominantly visual) in association with disturbances of arousal [18]. Similarly, hallucinations may be associated with primary sleep disorders (including narcolepsy) [19], secondary sleep disorders as in the setting of Parkinson’s disease (PD) [20], and even in normal individuals who are sleep-deprived [21]. Vision The paramount importance and inherent complexity of visual perception is evident in the sheer magnitude of cortical representation of this sensory system. More than one quarter of the cerebral cortex is involved in vision and vision-related processes, and more than 30 cortical areas with vision-related function had been identified by 1991 [22], only some of which will be highlighted in this section. Multiple systems of nomenclature are often used in reference to subregions within the visual cortex. Brodmann’s area (BA) 17, located along the banks of the calcarine fissure, is the primary visual cortex. It is also referred to as striate cortex due to the visibly pale striation of myelinated axons within the fourth of its six principal cortical layers. Consequently, visual association areas (BA18 and 19), which do not contain myelinated striations, are collectively referred to as extrastriate cortex. Additionally, subdivisions within human visual cortex are often referenced based on functional mapping studies in macaque monkeys, although the functional and structural correspondence of these areas may not be conserved across species [23]. Based on functional mapping, BA17 is equivalent to V1, BA18 includes V2 and V3, and BA19 includes V3a, V4, and V5. The primary visual pathway The central, or primary, visual pathway refers to the series of connections through which input is relayed from the retina to the visual cortex. Stimulus-initiated, or veridical, visual perception begins when light passes through the cornea, iris, and lens and is detected by photoreceptors (rods and cones) in the outermost layer of the retina. Visual input is inverted and reversed by the lens as it is projected onto the retina. Therefore, cells in the lower left portion of each retina process light from the upper-right portion of visual space. There is considerable overlap between the visual fields of each eye, which enables binocular vision and facilitates depth perception. Axons of retinal ganglion cells exit the retina via the optic disk and form the optic nerve (though technically not a “nerve” since the retina is actually part of the CNS). Axons from the medial half of each retina, or nasal hemiretina, decussate at the optic chiasm and project to the opposite cerebral hemisphere; axons from the temporal hemiretina (lateral to the fovea) do not cross. As a result, each cerebral hemisphere receives visual input from both eyes, with each hemisphere exclusively processing information from the opposite visual field. Posterior to the optic chiasm, retinal ganglion fibers are referred to as the optic tract. The majority of these fibers terminate in the lateral geniculate nucleus (LGN) of the thalamus as part of the central visual pathway. Although traditionally viewed as a simple relay station, the LGN may itself have a more active role in perception [24]. In fact, retinal input only accounts for 10–20% of input to the LGN [25]. Optic tract fibers also project to at least nine other targets [26] that contribute to functions, such as regulation of circadian rhythms (the hypothalamus), pupillary reflexes (Edinger–Westphal nucleus of midbrain) and oculomotor functions (superior colliculus). Extra-geniculate visual pathways have also been implicated in some visual functions, including collicular contribution to visually guided action [27] and visual motion detection functions in the pulvinar [28]. Within the geniculostriate pathway, axons of postsynaptic neurons in the LGN form the optic radiations, which arc around the lateral ventricles. The inferior radiations (also known as Meyer’s loop) transmit input from the superior visual field through the temporal lobe while the superior radiations transmit information from the inferior visual field through the parietal lobe. As its name implies, the geniculostriate pathway terminates in striate cortex (area V1), typically considered to be the final point along the central visual 147 Section I: Structural and Functional Neuroanatomy pathway. Visual input from LGN to V1 maintains general topographic integrity such that input from the upper-right visual field is transmitted to the leftinferior portion of V1. Note, however, that the relative dimensions of this retinotopic map are not preserved. A disproportionately large portion (25–50%) of V1 is devoted to the very small amount of visual input processed by the fovea. V1 relays visual input to extrastriate visual areas for further processing. Additionally, evidence suggests that V1 also projects to subcortical regions, including the superior colliculus and feedback fibers to the LGN [29]. Functional specialization within the visual system In 1890, Lissauer proposed that there are three perceptual processes – color, motion, and form – that are differentially affected by brain damage and that therefore are functionally and neuroanatomically distinct (see [30] for review). These three functional distinctions correspond to input from at least three parallel pathways, beginning with distinct classes of retinal ganglion cells [31]. M ganglion cells are relatively large and respond to gross visual features, including luminance contrast, temporal frequency, and movement. P cells comprise the vast majority (approximately 80%) of ganglion cells and are most sensitive to finer visual details, including spatial frequency and stimulus orientation. Additionally, there appear to be two subdivisions within the P cell pathway, one of which transmits color input, whereas the other contributes to form/shape perception. Although some cross-projections have been identified, the M and P pathways remain largely segregated throughout the visual system, including formation of synapses within different layers of the LGN (e.g., magnocellular and parvocellular layers) and projection to different portions of primary and association cortices [32]. M and P pathways also serve as bases for the distinct ventral (“what”) and dorsal (“where”) visual streams originally proposed by Ungerleider and Mishkin [33]. V1 and V2 are the largest visual cortical subregions and contribute to early processing for both dorsal and ventral visual systems, although evidence suggests that each is also involved in higherorder functions [34]. The ventral visual stream processes object properties such as color and shape and is closely associated with the two P pathways [35]. 148 Within extrastriate cortex, the ventral system includes V4, located in the lingual and fusiform gyri, and adjacent area V8, both of which are highly sensitive to chromatic input and serve as the main loci for color perception [36]. The fusiform gyrus, located anterior to V8, is also part of the ventral visual stream and contributes to object and form perception. Movement and spatially relevant visual features relayed via the M pathway predominantly contribute to the dorsal visual system [35]. Motion perception in the extrastriate cortex is largely attributed to areas V5 (motion-sensitive middle temporal cortex in humans, designated hMT+) [37] and V3, followed by projection through the medial superior temporal area (MST, or V5a). The dorsal stream projects through the parietal cortex, bridging between the visual and sensorimotor cortices. Although colloquially referred to as the “where” pathway due to its role in spatial perception [33, 38], the complex and nuanced functions of the parietal portion of the dorsal visual stream continue to be a matter of debate. Milner and Goodale [39] emphasized the system’s contribution to visually guided action and contrasted this with the ventral visual stream’s more perceptually based function. Jeannerod [40] characterized the dorsal stream as “pragmatic” and proposed that the parietal portion serves multiple visually related functions [41]. The authors argue that these functional distinctions correspond to differentiable contributions of superior and inferior parietal lobules, such that the superior lobule contributes to non-lateralized visuomotor processing whereas the inferior lobule subserves visuospatial perception (right hemisphere) and goal-directed action (left hemisphere). Rizzolatti and Matelli [42] also suggest that the dorsal visual stream is comprised of two functionally independent subsystems: the dorsodorsal and ventro-dorsal streams both contribute to coordination of action but only the ventrodorsal stream plays a pivotal role in visuospatial perception. At the level of recognition, both dorsal and ventral visual streams project to the medial temporal lobe, where visual information is translated into stored representations for future access. However, even at this point, spatial and object input appear to remain largely segregated in primates, such that dorsal visual input projects to the parahippocampal cortex whereas ventral visual input primarily projects to the perirhinal cortex [43]. Chapter 10: Perception and recognition Disorders of visual perception and recognition Visual field cuts (anopsias) may result from lesions at any point within the central visual pathway, and, based on the serial trajectory, visual field defects often facilitate localization of CNS lesions. Lesions to the optic nerve result in monocular visual loss; damage affecting the optic chiasm classically results in bitemporal hemianopia; and lesions posterior to the optic chiasm result in loss of vision in the contralateral visual field (contralateral homonymous hemianopia). Unilateral lesions within superior or inferior V1 or V2 result in contralateral homonymous quadrantanopia [44]. Cortically mediated vision loss is not functionally identical to vision loss from peripheral visual pathway lesions. Severe bilateral damage to V1 results in cortical blindness. This syndrome often presents as Anton’s syndrome, which, unlike direct retinal damage, is accompanied by anosognosia and confabulation of visual experiences [45]. Patients with visual cortical lesions may also present with blindsight, a curious combination of impaired conscious visual perception despite residual implicit visual abilities, including localization of visual stimuli. Recall that a minority of retinal ganglion cells do not synapse within the LGN, and instead project to other structures including the hypothalamus, superior colliculus, and pretectal areas. Functions subserved by these additional pathways are not necessarily disrupted by damage at the level of V1. In a recent review [46], Weiskrantz provides compelling evidence that blindsight cannot be simply explained as degraded vision. Rather, it is most likely that residual vision-related functions are attributable to unaffected cortical and subcortical brain regions with vision-related functionality, including collicular input to the dorsal visual stream [27]. In contrast to lesions of V1 and V2, extrastriatal damage may result in apperceptive agnosias that predominantly affect perception of one aspect of visual input. Patients with akinetopsia consequent to damage in V5 demonstrate selectively impaired motion perception relative to intact color and form perception [47, 48]. In contrast, lesions to V4 typically result in selective impairment of color perception, called central achromatopsia. Since V4 is located within the inferior visual cortex, central achromatopsia is characteristically accompanied by visual field defects restricted to the superior visual field. Both color and motion perception can also be impaired consequent to lesions elsewhere in the brain. For example, achromatopsia may result from central visual pathway damage [49], and motion perception may be impaired in patients with cerebellar, midbrain, or vestibular lesions [48]. Another category of visual deficit is simultanagnosia, which refers to impaired perception or recognition of a stimulus or group of stimuli despite comparatively unimpaired processing of subcomponents of the stimulus or array. Although features of simultanagnosia can be associated with ventral damage [14], it most commonly occurs in the context of bilateral occipitoparietal (dorsal) lesions. Simultanagnosia is a hallmark feature of Balint’s syndrome, which is additionally characterized by optic ataxia (impaired visually guided reaching) and oculomotor apraxia (impaired voluntary saccadic movement to visual targets). Ventral visual stream lesions may result in associative visual object agnosias characterized by impaired object identification, categorization and naming, despite relatively unimpaired visual object perception. Prosopagnosia was originally considered to be an example of a category-specific associative agnosia for faces and resulting from damage to the fusiform face area in the medial occipitotemporal gyrus, analogous to inferotemporal cortex (area IT) in non-human primates [50]. Deficits no longer appear to be constrained to face perception/recognition, and may include other aspects of configural processing and non-face exemplars [51]. Multiple variants of prosopagnosia also have been identified, including both apperceptive and associative forms [50]. Additionally, other brain regions appear to contribute to facial perception and recognition, including the left inferior occipital gyrus (i.e., “occipital face area”) and the right posterior superior temporal sulcus [52]. Recent evidence also suggests that category-specific representations within the ventral visual pathway may not be vision-specific, but rather represent object forms based on multi modal input [53]. All of the above are examples of negative symptoms, or deficits, resulting from damage to stimulusinduced (veridical) sensory-perceptual systems. In contrast, non-veridical, or positive, perceptual abnormalities are not directly triggered by external stimulation of the retina. Visual hallucinations are less common in psychosis than their auditory counterparts, but they are prominent features in numerous other disorders ranging from neurodegenerative dementias to migraines and epilepsy [54, 55]. Simple, or 149 Section I: Structural and Functional Neuroanatomy elementary, hallucinations typically arise from pathology within the occipital lobe or central visual pathway and are characterized by nondescript (e.g., phosphenes) and/or simple geometric visual experiences. Visual auras with migraines and ictal visual phenomena in occipital lobe epilepsy typically present as elementary visual hallucinations, although evidence suggests they can be clinically differentiated based, for example, on the tendency for visual seizures to be shorter in duration, more frequent, and characterized by circular, colored patterns [56]. Complex, or formed, hallucinations include perception of complete objects, animals, or people. The nature of complex hallucinations strongly implicates aberrant neuronal activity within visual association cortex, particularly within the temporal lobe. For example, Bien and colleagues [57] found that complex hallucinations occurred in temporal lobe epilepsy patients but not in patients with occipital lobe seizure onset. Complex hallucinations can also occur in the context of lesions to early visual pathways (e.g., retina and optic nerve), as is the case of the Charles Bonnet syndrome [58]. Finally, neurodegenerative disorders may also lead to visual symptoms. Posterior cortical atrophy (PCA) is a progressive neurodegenerative disease characterized by prominent visual deficits that affect higherorder visual functions over time, eventually leading to deficits including Balint’s syndrome, Gerstmann’s syndrome, and/or visual object agnosia [59]. This clinical profile has been identified most often as a variant of Alzheimer’s disease (AD), and a visual variant of AD associated with asymmetric cortical degeneration has been proposed [54]; however PCA also develops as a result of other neurological diseases, including progressive subcortical gliosis [60]. Consistent with the role of subcortical regions in visual processing, neurodegenerative disorders with subcortical pathology may also result in positive or negative visual abnormalities. For example, up to 60% of patients with PD experience positive visual abnormalities including complex hallucinations [61]. Audition Auditory pathways are activated when sound waves are transmitted to the cochlea through the temporal bone and the tympanic membrane and ossicles of the inner ear. The cochlea is a fluid-filled structure shaped like a snail’s shell. Separating the cochlea into two chambers 150 is the basilar membrane, which is a specialized structure designed to mechanically capture sound on the basis of pitch and loudness. This is accomplished by the physical characteristics of the basal membrane, which vary from a broad flexible quality at its base to a narrower more rigid quality at the apex. Sound is transduced to neural signals in the organ of Corti, which lies along the basilar membrane. The organ of Corti contains hair cells that have mechanically gated ion channels activated when their region of the basilar membrane is moved by a sound wave (see [62] for additional details). Thus there is an inherent spatial organization to pitch even at the level of the cochlea, and this tonotopic organization is maintained even at the level of the primary auditory cortex. Hair cells transmit information to neurons of the cochlear (i.e., spiral) ganglion, which, in turn, reaches the CNS as the eighth cranial nerve and terminates in the cochlear nucleus at the pontomedullary junction. From here information is transmitted to the superior olivary complex in the ventral pons (via the trapezoid body), the nucleus of the lateral lemniscus in the superior pons, and the inferior colliculus (via the lateral lemniscus). The superior olivary complex is notably important in sound localization. The inferior colliculus is important in some multisensory processing, including the startle reflex, and may also be important in auditory vigilance [63]. From the inferior colliculus, information passes to the medial geniculate nucleus (MGN) of the thalamus and then to the primary auditory cortex (also known as A1 or Brodmann’s areas 41 and 42) located in the superior temporal gyrus or Heschl’s gyrus. The primary auditory cortex is organized tonotopically, with low frequencies located anterolateral to higher frequencies and with segregated but bilateral representation of sound from both ears. Two other tonotopic maps exist in the temporal cortex adjacent to A1, namely areas R (rostrolateral to A1), which receives direct input from the MGN, and CM (centromedial), which is dependent on A1 for auditory and tonal information [64]. Similar to visual processing, there is increasing evidence in primates and humans of distinct “what” and “where” pathways of higher unimodal cortices that lie adjacent to primary auditory cortex (often referred to as belt and parabelt regions as they form a belt around A1). The “where” auditory system is primarily involved in sound localization, although it may play some role in other auditory tasks including language, Chapter 10: Perception and recognition and is located posteriorally to primary auditory areas [65]. The “what” auditory system is involved primarily in sound discrimination, including speech. Functional imaging studies and lesion studies in humans suggest that the “what” pathway proceeds anterolaterally from A1, with higher-order discrimination of speech being processed in the left superior temporal gyrus (STG), and higher-order pitch discrimination in both speech and music being processed in the right STG. Results from both functional imaging and lesion studies suggest that posterior portions of the middle temporal gyrus, particularly on the right, are involved in the recognition of environmental sounds [66]. Disorders of auditory perception and recognition As with the visual system, negative perceptual symptoms involving hearing may affect primary sensory processes, secondary perceptual abilities, or recognition. Hearing loss or deafness may occur on the basis of either peripheral or central lesions. Because of the extensive crossing of tracts in the brainstem, most cases of unilateral deafness reflect peripheral causes or brainstem lesions at the level of the cochlear nucleus. However, subtle signs of decreased auditory discrimination can be detected contralateral to temporal lobe lesions [67]. Cases of cortical deafness typically involve bilateral damage to the temporal cortex and frequently extend beyond A1. In fact, the majority of cases of bilateral lesions to auditory cortices have auditory agnosias rather than true cortical deafness as demonstrated by intact hearing abilities [68]. Analogous to blindsight, two patients have been reported to demonstrate “deaf-hearing” through either acoustic startle responses [69] or the ability to determine tone onset and offset when specifically directed to do so [70]. In one patient, these abilities appeared to be mediated by residual temporal cortex and prefrontal cortex when studied using functional imaging [70]. Cortical auditory disorder or auditory agnosia refers to a non-specific loss of the ability to discriminate both speech and environmental auditory stimuli. As noted above, this is a more frequent outcome from bilateral damage to auditory cortices. Pure word deafness (PWD) or auditory verbal agnosia is a rare disorder that refers to the specific loss of the ability to discriminate auditory speech in the face of otherwise intact language and ability to recognize non-speech sounds. Lesions typically involve bilateral temporal cortex, although isolated left STG lesions [71] suggest that this area may have a unique contribution to speech analysis. Similarly, isolated left subcortical lesions have been described, which presumably interrupted auditory input to Wernicke’s area [72]. Auditory sound or auditory object agnosia refers to a deficit in the ability to recognize environmental sounds in the face of intact speech recognition. Pure auditory object agnosia has been reported most frequently with right temporal lesions, although bilateral and left-sided lesions have also been reported [73]. Phonagnosia refers to the loss of the ability to recognize or discriminate voices, similar to prosopagnosia for faces. Van Lancker and colleagues [74] have distinguished between an inability to discriminate between unfamiliar voices, associated with lesions of the STG, and the ability to recognize familiar voices, which occurs with lesions of the right inferior parietal lobe. Receptive amusia refers to impairments in the ability to recognize or appreciate music. Music appreciation is a complex function that depends on the subject’s level of musical expertise and the precise aspects of music that are assessed. In general, research suggests that global aspects of music (such as melody) are mediated through the right hemisphere, while more local aspects (such as rhythm) are related to the left hemisphere. However, lesion analyses only partially support this division (showing a disruption of rhythm with left hemisphere lesions) and there are increasing data to suggest that music perception relies on crosshemispheric networks [75]. While basic science studies support the notion of auditory “what” and “where” systems, deficits of sound localization in humans do not reliably localize to these “where” pathways [76]. The complexity of the auditory agnosias is further displayed by observations that, in addition to cortical lesions, subcortical [77], thalamic [78], and brainstem lesions [79] may also be responsible. Positive auditory symptomatology includes palinacusis, tinnitus, and auditory hallucinations. Palinacusis refers to an auditory illusion in which an external sound is perseverated internally, typically for seconds but occasionally as long as hours. It is most frequently associated with temporal lobe seizures and may be an aura, ictal event, or post-ictal event [80]. Tinnitus, which is experienced most commonly as ringing in the ears, may be associated with peripheral lesions or damage to the ear. However, increasing evidence suggests 151 Section I: Structural and Functional Neuroanatomy that most chronic tinnitus results from neuroplastic changes within primary and secondary auditory pathways; these models of chronic tinnitus are analogous to those proposed as explanations for chronic central pain [81]. Chronic tinnitus is frequently difficult to treat and may have severe neuropsychiatric sequelae including impaired attention, depression, and an increased risk for suicide. Recent studies have suggested the repetitive transcranial magnetic stimulation to the primary auditory cortex may have some potential for treating this condition [82]. Whereas auditory hallucinations are one of the classic symptoms of schizophrenia, they may also be seen in many other psychiatric disorders as well as with lesions of the peripheral and central auditory pathways. Similar to the Charles Bonnet syndrome in vision, hearing loss from any cause may result in auditory release hallucinations [17]. With regard to central etiologies, Lampl and colleagues [83] reviewed over 600 stroke cases and found four patients with acute auditory hallucinations following right temporal lobe infarcts. This phenomenon may be due to release of the left temporal lobe, which may be more strongly associated with auditory hallucinations in schizophrenia [84], and which may be the site of auditory hallucinations associated with negative affect in epilepsy [85]. As for musical hallucinations, these may be seen in psychiatric disease, hearing loss, epilepsy and focal brain lesions of either hemisphere, mostly involving the temporal lobes [86]. Somatosensation Although “touch” is considered colloquially to be one of the five main senses, it is just one element of a group of sensations processed by somesthesis (also referred to as somatic sensation or somatosensation). Somesthesis is unique in that it incorporates mechanical, thermal, and chemical sensory transduction, and receptors are distributed throughout the body rather than within one discrete organ/location. Tactile (or haptic) sensation is one of three primary cutaneous senses, along with temperature (thermoreception) and pain (nociception). Another prominent somatic sense is proprioception, which includes perception of static limb and body position, and perception of limb movement (kinesthesia). Dorsal root ganglion neurons serve as the primary sensory receptor for each modality, although there is a range of receptor subtypes. For example, 152 mechanoreceptors mediate proprioception and fine touch via large, myelinated fibers whereas nociceptors transmit pain via small myelinated or unmyelinated fibers. Regarding serial connectivity, most somesthesis is accomplished via a three-neuron system: primary afferent fibers from skin, muscles, joints, and internal organs relay sensory input to second-order neurons, which then synapse within the contralateral thalamus. Post-synaptic, tertiary neurons project to four cytoarchitecturally differentiable regions within the primary somatosensory cortex (S-I), including BA 1, 2, 3a, and 3b. In addition to internal cross-projections, S-I projects to the secondary somatosensory cortex (S-II). Central somatosensory pathways also include parallel circuitry. Input with high spatial and temporal resolution, including proprioception and some aspects of tactile sensation (e.g., discriminative (fine) touch and vibration), is relayed via the dorsal column-medial lemniscal pathway; crude touch, pain, temperature, and visceroreception are transmitted via the anterolateral pathways, which include the spinothalamic tract. Both pathways terminate in the ventral posterior lateral (VPL) nucleus, and the spinothalamic tract also projects to medial and intralaminar thalamic nuclei. Tactile sensation is relayed by multiple pathways and this redundancy ensures that selective damage to one pathway does not result in complete loss of haptic sensation. Somatosensory input from the face is transmitted from the trigeminal nerve (cranial nerve V) and ascends to the ventral posterior medial (VPM) nuclei of the thalamus via the ventral trigeminal thalamic tract (VTTT). Segregation by submodality and by input location appears to be conserved throughout the ascending pathways and within primary somatosensory cortex (S-I). Each of the four subdivisions of S-I (BA 1, 2, 3a, and 3b) contains a full somatotopic representation of input (homunculus), with proportional allocation of cortical and subcortical maps based on density of input receptors. Areas 1 and 3b process cutaneous input, whereas proprioceptive information is processed in areas 2 and 3a [87]. Both S-I and S-II project to the posterior parietal and insular cortices, which are both involved in higher-order and multimodal sensory processing. Dijkerman and Haan [88] recently proposed that the posterior parietal and posterior insular cortices receive projections from the anterior parietal cortex via two Chapter 10: Perception and recognition somatosensory processing streams, similar to the differentiation between dorsal and ventral visual streams. In this model, the stream projecting to posterior insular cortex is akin to the ventral visual stream and primarily contributes to object perception and recognition. In contrast, the role of the posterior parietal cortex in somesthesis is closely related to its role in visual perception, including spatial and actionoriented functions. In addition to direct pathways to somatosensory cortex, there are also indirect pathways through which spinal afferents project to areas other than primary somatosensory cortex, some of which also have somesthetic functions. For example, proprioceptive input to the cerebellum relayed via the spinocerebellar tracts was traditionally thought to subserve motor planning and/or reflexive action, but recent evidence suggests that it may also contribute to tactile perception and non-motor sensory support functions [89]. Visual cortical regions have also been implicated in tactile perception in healthy individuals, including haptic shape discrimination [90] and gating orientation discrimination [91, 92]. Evidence for occipital involvement in tactile perception is even more compelling in early blind individuals, including recent evidence for somatotopic representation of tactile finger sensations in early blind Braille readers [93]. Disorders of somatosensory perception and recognition Sensory loss may arise from damage at any point within the somatosensory system. Since afferents decussate within the spinal cord or brainstem and synapse within the contralateral thalamus, lesions within or above the level of the thalamus result in contralateral deficits. Cortical sensory loss is classically associated with “cortical sensory deficits,” namely agraphesthesia (impaired recognition of numbers or letters traced on the palm or fingers) and astereognosis (impaired spatiotemporal discrimination, typically evaluated via recognition and discrimination of objects placed in the hand). These deficits are frequently associated with stroke but may also present in the context of neurodegenerative disorders, most notably corticobasal degeneration [94]. Bauer and Demery [49] suggest that astereognosis is primarily a deficit in perception and may be differentiated from associative forms of tactile agnosia in which marked object recognition deficits occur in the absence of notable somatosensory impairment. Agraphesthesia and astereognosis may also be found consequent to damage elsewhere in the dorsal columnlemniscal system, including damage restricted to the dorsal columns. Somatosensation is critically important for normal motor function, and thus lesions affecting somatosensory systems are often associated with motor deficits as well. Positive somatic symptoms, called paresthesias, also may develop consequent to lesions at any level within the somatosensory systems. The Dejerine– Roussy syndrome is characterized by contralateral pain associated with unilateral thalamic lesions. Tactile, or haptic, hallucinations are physical sensations in the absence of cutaneous input. Formications are one subtype of tactile hallucinations, and are characterized by the sensation of insects or snakes crawling along the skin. In the general population, one study found that haptic hallucinations were the most frequent form of hypnagogic (while falling asleep) and hypnopompic (while waking up) hallucinations, and the prevalence of daytime haptic hallucinations was 2.6% [95]. Haptic hallucinations may also occur in the context of neurological or neuropsychiatric dysfunction, including substance withdrawal and lesions affecting the thalamus or parietal cortex. Phantom limb sensations are present in the vast majority of amputees, a subset of whom also experience pain in the amputated or deafferented body part. The precise etiology of these phantom phenomena is not known, but clinical and animal studies suggest possible contributory roles throughout the somatosensory pathway, including injured nerve endings, abnormal activity within the dorsal root ganglia, and both spinal and supraspinal CNS mechanisms. Additionally, research suggests that the S-I undergoes neuroplastic changes after amputation or deafferentation of a limb, and phantom sensations at least in part reflect invasion of cortical regions previously dedicated to perceptual processes for the amputated limb (for a review, see [96]). The proximity and overlap of higher-order somatosensory processing with the dorsal visual stream may have clinical implications as well. In addition to its role in somesthesis, the posterior parietal cortex is involved in spatial perception and visuomotor integration (see Vision section above), and evidence suggests that it contributes to attention orientation [97]. Hemineglect consequent to posterior parietal lesions may therefore present with tactile 153 Section I: Structural and Functional Neuroanatomy symptoms, including tactile extinction (impaired tactile perception during simultaneous, bilateral stimulation). Finally, clinical symptoms may arise from deficient integration of somatosensory modalities and/or coordination with motor execution. For example, movement disorders primarily characterized by motor impairment may present with features of somatic abnormalities as well. Aberrant somatosensory evoked potentials have been demonstrated in patients with Parkinson’s disease (PD) or Huntington’s disease, and patients with dystonia often present with sensory symptoms in addition to their primary motor impairment [98]. Chemosensation: olfaction and gustation Olfaction begins with specialized neurons in the olfactory epithelium of the nose. These neurons are unique in that they have a relatively short lifespan (30–60 days) and are continuously being replaced by basal cells in the olfactory epithelium. Olfactory neurons project cilia into the nasal cavity. On the surface of these cilia are specialized odor receptors that detect specific molecular configurations and cause depolarization through a second messenger system. Each olfactory neuron expresses one of approximately 350 unique odorant receptor genes [99]. Unlike other sensory modalities, these neurons do not have a relay in the thalamus, but proceed to an area directly above the olfactory epithelium on the ventral surface of the prefrontal cortex known as the olfactory bulb. Some authors, however, have argued that the olfactory bulb serves many functions similar to those of the thalamus, including inhibitory modulation and oscillatory communication with higher olfactory cortical areas [100]. From the olfactory bulb, information is transmitted to several structures including the primary olfactory cortex (the piriform and peri-amygdaloid cortex), the amygdala, the olfactory tubercle, and the anterior entorhinal cortex. From these structures there are direct projections to the orbitofrontal cortex (OFC) olfactory area, as well as indirect projections that pass through the mediodorsal nucleus of the thalamus. Similar to other sensory systems, the olfactory system has several higher-order functions including odor discrimination and odor localization [101]. In 154 humans, odor discrimination appears to rely mainly on the piriform cortex and OFC. At least one functional imaging study suggests that in humans there may be hemispheric asymmetry such that the right OFC is more strongly associated with higher-order odor discrimination [102]. With regard to localization of odors, functional imaging studies in humans suggest a role for the piriform cortex (left greater than right) and in the posterior superior temporal gyrus near areas previously identified in the dorsal visual stream [103]. The strong connections and proximity of olfactory cortex to limbic and paralimbic areas seem to facilitate the ability of olfactory stimuli to trigger both autobiographical memories and emotional reactions [104]. Taste is a complex sensation that incorporates stimuli not only from primary taste receptors (taste buds), but also from olfaction and somatosensory information including the presence of capsaicin (found in spicy hot foods such as chili peppers), and the temperature and consistency of ingested food or liquids. Basic taste begins with taste cells, which are located primarily within taste buds on the tongue but also the pharynx and upper esophagus. Microvilli from taste cells interact with chemicals in foods and liquids either through specific receptors and second messenger systems (e.g., sweet) or ion channels (e.g., salt) that then lead to depolarization of the taste cell [105]. These interactions result in the five primary tastes of humans: sweet, salt, bitter, sour, and umami (derived from the combined Japanese words umai (delicious) and mi (taste), and translated into English as “savory,” this taste is related to glutamate in many high-protein foods such as meat). The facial, glossopharyngeal, and vagal nerves carry taste information to the rostral portion of the nucleus tractus solitarius (NTS). Somatosensory information from the trigeminal nerve also converges on the NTS. From the NTS information travels to the ventroposterior medial parvicellular (VPMpc) portion of the thalamus and from the VPMpc to the primary taste cortex, which lies just anterior to the face areas of the post-central gyrus as well as the anterior insula. Unlike other primary sensory modalities, there is evidence of polymodal integration (e.g., olfaction and taste) in the anterior insula. From the primary taste cortex, information is transmitted to the amygdala, hypothalamus, and the secondary taste cortex located in the caudolateral OFC. Pathways exist from all of these secondary taste areas Chapter 10: Perception and recognition to the NTS, and are able to modulate brainstem taste function [106]. Disorders of olfactory and gustatory perception and recognition Although disorders of olfaction are rarely brought to medical attention, they may affect nutritional status through their interaction with taste and are often associated with neuropsychiatric diseases. Anosmia refers to the loss of smell and is most frequently associated with diseases of the nasal cavity. Hyposmia refers to an incomplete deficit in olfaction. The term olfactory agnosia is rarely used as most researchers test only for odor discrimination ability, rather than other primary aspects of smell such as stimulus onset/offset or intensity. Anosmia or hyposmia is frequently seen following traumatic brain injury as a result of shearing injury of the olfactory nerve at the cribriform plate, as well as possible secondary injuries to other nasal structures or the olfactory cortex in the basal forebrain. Deficits in olfactory discrimination are seen following lesions of the temporal lobe, particularly the uncinate region. Jones-Gotman and colleagues [107] reported that olfactory dysfunction is common in patients who have had temporal lobe resection for intractable epilepsy. Consistent with the functional imaging literature, lesions of the right OFC have also been associated with central anosmia [108]. Olfactory deficits may also be seen in other neuropsychiatric disorders that affect the medial temporal lobe and olfactory bulbs including AD and mild cognitive impairment [109], PD [110], and schizophrenia [111]. The easy accessibility of neural tissue and neural stem cells within the olfactory epithelium has spurred efforts to use nasal biopsies as a means of diagnosing neuropsychiatric conditions with pathological specimens including Creutzfeldt–Jakob disease [112], AD [113], and PD [114]. Olfactory testing is also being studied as a means of determining preclinical risk for AD, which involves mediotemporal olfactory cortex as an early event, and PD, which affects the olfactory bulbs [109, 114]. Olfactory hallucinations are typically unpleasant and have been described with temporal lobe epilepsy (uncinate fits), AD, parkinsonism, schizophrenia, post-traumatic stress disorder, and depression [115]. Ageusia and hypogeusia refer to lack and loss of taste respectively. In addition to changes in olfactory function, these syndromes may be associated with peripheral lesions, brainstem lesions interrupting ascending pathways from the NTS [116], and occasionally from medications (notably phenytoin and chemotherapy). Gustatory agnosia has rarely been reported with one case demonstrating bilateral anterior medial temporal lobe lesions [117]. Gustatory hallucinations have been noted in schizophrenia, temporal lobe lesions, and temporal lobe epilepsy [17]. Crossmodal integration Humans typically do not experience the world in terms of individual sensory modalities, but rather as a seamlessly integrated whole based on information from multiple sensory sources. Crossmodal integration may change perception within a sensory modality through information from a separate modality, as may be seen through the influence of visual information on our sense of taste or smell [118]. Alternatively, crossmodal integration may be necessary to accomplish a truly multisensory task such as sound localization or lip reading. Crossmodal integration depends on the communication between unimodal and transmodal sensory areas, particularly the OFC, hippocampal-entorhinal complex, and posterior parietal lobe. Although patients with lesions in these areas do not typically present with deficits in crossmodal integration, these deficits may be demonstrated through neuropsychological testing [119]. Many conditions in BN&NP, including AD [120] and schizophrenia [121], demonstrate crossmodal integration deficits when clinical examinations sensitive to such impairments are performed. Synesthesia refers to a phenomenon in which a stimulus presented in one sensory modality automatically invokes a distinct perception in a secondary sensory modality. This condition is most commonly seen in otherwise normal (and often artistic) individuals, although a case of “feeling sounds” has been reported following a thalamic stroke [122]. The most common type of synesthesia involves the linking of graphemic forms (often numbers) with the experience of particular colors. Other types of synesthesia may involve crossmodal perception, as in seeing the color of certain auditory tones. Experiments in individuals with synesthesia have demonstrated that synesthesia occurs at relatively early perceptual processing stages and is 155 Section I: Structural and Functional Neuroanatomy not dependent on conscious associations. Synesthesia is proposed to arise from abnormal connectivity between either unimodal perceptual areas or within transmodal cortex (see [123] for an excellent review). Self-perception and recognition A final topic of importance to BN&NP is the unique human ability to recognize self-generated actions, sounds, images, and touch as distinct from environmental stimuli from other sources. This ability involves the generation of a feed-forward or efferent copy of our motor actions to be sent to transmodal sensory areas to tag the ensuing stimuli as self-generated. Research from both lesion and functional imaging studies in humans implicates a predominantly right cerebral network for feelings of agency for actions, particularly the inferior parietal lobule and anterior cingulate cortex [124], and a predominantly left cerebral network for the perception of agency regarding inner voice, particularly the left inferior frontal lobe [125]. Disturbances in these networks have been hypothesized to underlie several neuropsychiatric symptoms, particularly hallucinations and delusions of control [126]. Support for this hypothesis comes from functional imaging studies demonstrating disruptions within these networks in individuals with hallucinations and passivity experiences, as well as the surprising ability of these individuals to tickle themselves [127]. Conclusion The study of perceptual processes is among the oldest fields of inquiry in neurology and also one of the most highly developed. In this chapter, a concise, broad overview of the functional neuroanatomy underlying perception and recognition within each of the five primary perceptual systems was provided. Perception and recognition were defined in terms of their neuroanatomic and neuropsychological elements. The organizational principles common to all human sensory and perceptual processes were reviewed, and the general types of perception and recognition disorders were presented. Specific discussion of the neuroanatomy and neuropsychology of vision, audition, somatosensation, and chemosensation (i.e., olfaction and gustation) was offered, and specific types of perception and recognition disturbances for each sensory modality were presented. Finally, the importance of crossmodal integration as well as self-perception and recognition were discussed. Throughout this review, 156 understanding the structural architecture of sensory systems and behavioral correlates of normal and abnormal connectivity was emphasized and identified as prerequisite knowledge for clinical examination and lesions localization of persons with disorders of perception and recognition. References 1. Rugg MD, Yonelinas AP. Human recognition memory: a cognitive neuroscience perspective. Trends Cogn Sci. 2003;7(7):313–19. 2. Anderson SW. Neuropsychologic rehabilitation for visuoperceptual impairments. Neurologic Clinics 2003;21(3):729–40. 3. Murphy ST, Zajonc RB. Affect, cognition, and awareness: affective priming with optimal and suboptimal stimulus exposures. J Pers Soc Psychol. 1993;64(5):723–39. 4. Heisz JJ, Shore DI. More efficient scanning for familiar faces. J Vis. 2008;8(1):9.1–10. 5. Pelphrey KA, Sasson NJ, Reznick JS et al. Visual scanning of faces in autism. J Autism Dev Disord. 2002;32(4):249–61. 6. Mesulam MM. From sensation to cognition. Brain 1998;121(Pt 6):1013–52. 7. Pascual-Leone A, Walsh V. Fast backprojections from the motion to the primary visual area necessary for visual awareness. Science 2001;292(5516): 510–12. 8. Bar M, Kassam KS, Ghuman AS et al. Top-down facilitation of visual recognition. Proc Natl Acad Sci USA 2006;103(2):449–54. 9. Geschwind N. Disconnexion syndromes in animals and man. I. Brain 1965;88(2):237–94. 10. Geschwind N. Disconnexion syndromes in animals and man. II. Brain 1965;88(3):585–644. 11. Buzsáki G. Rhythms of the Brain. Oxford: Oxford University Press; 2006. 12. Tsodyks M, Gilbert C. Neural networks and perceptual learning. Nature 2004;431(7010):775–81. 13. Shallice T, Jackson M. Lissauer on agnosia. Cogn Neuropsych. 1988;5(2):153–6. 14. Bauer RM. The agnosias. In Snyder PJ, Nussbaum PD, Robins DL, editors. Clinical Neuropsychology: A Pocket Handbook for Assessment. 2nd edition. Washington, DC: American Psychological Association; 2006, pp. 508–33. 15. Barbarotto R, Capitani E, Laiacona M. Living musical instruments and inanimate body parts? Neuropsychologia 2001;39(4):406–14. Chapter 10: Perception and recognition 16. Behrendt RP, Young C. Hallucinations in schizophrenia, sensory impairment, and brain disease: a unifying model. Behav Brain Sci. 2004;27(6):771–87; discussion 787–830. 32. Nealey TA, Maunsell JH. Magnocellular and parvocellular contributions to the responses of neurons in macaque striate cortex. J Neurosci. 1994; 14(4):2069–79. 17. Brasic JR. Hallucinations. Percept Mot Skills 1998; 86(3 Pt 1):851–77. 33. Ungerleider L, Mishkin M. Two cortical visual systems. In Ingle D, Goodale MA, Mansfield RJW, editors. Analysis of Visual Behavior. Cambridge, MA: MIT Press; 1982. 18. Benke T. Peduncular hallucinosis: a syndrome of impaired reality monitoring. J Neurol. 2006;253(12): 1561–71. 19. Black JE, Brooks SN, Nishino S. Conditions of primary excessive daytime sleepiness. Neurol Clin. 2005;23(4): 1025–44. 20. Kulisevsky J, Roldan E. Hallucinations and sleep disturbances in Parkinson’s disease. Neurology 2004;63(8 Suppl. 3):S28–30. 21. Babkoff H, Sing HC, Thorne DR, Genser SG, Hegge FW. Perceptual distortions and hallucinations reported during the course of sleep deprivation. Percept Mot Skills 1989;68(3 Pt 1):787–98. 22. Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1991;1(1):1–47. 23. Eysenck MW, Keane MT. Basic processes in visual perception. In Eysenck MW, Keane MT, editors. Cognitive Psychology: A Student’s Handbook. 5th edition. Hove: Psychology Press; 2005, pp. 33–67. 24. Wunderlich K, Schneider KA, Kastner S. Neural correlates of binocular rivalry in the human lateral geniculate nucleus. Nat Neurosci. 2005;8(11): 1595–602. 25. Wurtz RH, Kandel ER. Central visual pathways. In Kandel ER, Schwartz JH, Jessell TM, editors. Principles of Neural Science. 4th edition. New York, NY: McGraw-Hill; 2000, pp. 523–47. 26. Zillmer EA, Spiers MV. Sensory-perceptual and motor systems. In Zillmer E, Spiers M, Culbertson WC, editors. Principles of Neuropsychology. Belmont, CA: Wadsworth Pub. Co.; 2001. 27. Ro T. Unconscious vision in action. Neuropsychologia 2008;46(1):379–83. 28. Casanova C, Merabet L, Desautels A, Minville K. Higher-order motion processing in the pulvinar. Prog Brain Res. 2001;134:71–82. 29. Sillito AM, Jones HE. Corticothalamic interactions in the transfer of visual information. Philos Trans R Soc Lond B Biol Sci. 2002;357(1428):1739–52. 30. Riddoch MJ, Humphreys GW. Visual agnosia. Neurol Clin. 2003;21(2):501–20. 31. Livingstone M, Hubel D. Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 1988;240(4853):740–9. 34. Super H. Cognitive processing in the primary visual cortex: from perception to memory. Rev Neurosci. 2002;13(4):287–98. 35. Wurtz RH, Kandel ER. Perception of motion, depth, and form. In Kandel ER, Schwartz JH, Jessell TM, editors. Principles of Neural Science. 4th edition. New York, NY: McGraw-Hill; 2000, pp. 548–71. 36. Stoerig P. The neuroanatomy of phenomenal vision: a psychological perspective. Ann N Y Acad Sci. 2001; 929:176–94. 37. Grill-Spector K, Malach R. The human visual cortex. Annu Rev Neurosci. 2004;27:649–77. 38. Mishkin M, Ungerleider LG, Macko KA. Object vision and spatial vision: two cortical pathways. Trends Neurosci. 1983;6:414–17. 39. Milner AD, Goodale MA. The Visual Brain in Action. Oxford: Oxford University Press; 1995. 40. Jeannerod M. The Cognitive Neuroscience of Action. Oxford: Blackwell Publishing; 1997. 41. Jeannerod M, Jacob P. Visual cognition: a new look at the two-visual systems model. Neuropsychologia 2005;43(2):301–12. 42. Rizzolatti G, Matelli M. Two different streams form the dorsal visual system: anatomy and functions. Exp Brain Res. 2003;153(2):146–57. 43. Burwell RD. The parahippocampal region: corticocortical connectivity. Ann N Y Acad Sci. 2000; 911:25–42. 44. Horton JC, Hoyt WF. Quadrantic visual field defects. A hallmark of lesions in extrastriate (V2/V3) cortex. Brain 1991;114(Pt 4):1703–18. 45. Bresch D. Beyond Wernicke’s: a lexicon of eponyms in psychiatry. J Neuropsychiatry Clin Neurosci. 2002; 14(2):155–60. 46. Weiskrantz L. Is blindsight just degraded normal vision? Exp Brain Res. 2009;192(3):413–16. 47. Zeki S. Cerebral akinetopsia (visual motion blindness). A review. Brain 1991;114(Pt 2):811–24. 48. Nawrot M. Disorders of motion and depth. Neurol Clin. 2003;21(3):609–29. 49. Bauer RM, Demery JA. Agnosia. In Heilman KM, Valenstein E, editors. Clinical Neuropsychology. 157 Section I: Structural and Functional Neuroanatomy 4th edition. Oxford: Oxford University Press; 2003, pp. 236–95. 50. Barton JJ. Disorders of face perception and recognition. Neurol Clin. 2003;21(2):521–48. 51. Gauthier I, Behrmann M, Tarr MJ. Can face recognition really be dissociated from object recognition? J Cogn Neurosci. 1999;11(4):349–70. 52. Sorger B, Goebel R, Schiltz C, Rossion B. Understanding the functional neuroanatomy of acquired prosopagnosia. Neuroimage 2007;35(2): 836–52. 53. Pietrini P, Furey ML, Ricciardi E et al. Beyond sensory images: object-based representation in the human ventral pathway. Proc Natl Acad Sci USA 2004;101(15): 5658–63. 54. Caselli RJ. Visual syndromes as the presenting feature of degenerative brain disease. Semin Neurol. 2000; 20(1):139–44. 55. Norton JW, Corbett JJ. Visual perceptual abnormalities: hallucinations and illusions. Semin Neurol. 2000;20(1):111–21. 56. Panayiotopoulos CP. Visual phenomena and headache in occipital epilepsy: a review, a systematic study and differentiation from migraine. Epileptic Disord. 1999;1(4):205–16. 57. Bien CG, Benninger FO, Urbach H, et al. Localizing value of epileptic visual auras. Brain 2000;123(Pt 2): 244–53. 58. Plesnicar BK, Zalar B, Bocic MB. The Charles Bonnet syndrome: a case report. Wien Klin Wochenschr. 2004;116(Suppl. 2):75–7. 59. Kirshner HS, Lavin PJ. Posterior cortical atrophy: a brief review. Curr Neurol Neurosci Rep. 2006;6(6): 477–80. 60. Caine D. Posterior cortical atrophy: a review of the literature. Neurocase 2004;10(5):382–5. 158 65. Scott SK, Blank CC, Rosen S, Wise RJ. Identification of a pathway for intelligible speech in the left temporal lobe. Brain 2000;123(Pt 12):2400–6. 66. Lewis JW, Wightman FL, Brefczynski JA et al. Human brain regions involved in recognizing environmental sounds. Cereb Cortex 2004;14(9):1008–21. 67. Duane D. A neurologic perspective of central auditory dysfunction. In Keith RW, editor. Central Auditory Dysfunction. New York, NY: Grune & Stratton; 1977, p. 1. 68. Kaga K, Shindo M, Tanaka Y. Central auditory information processing in patients with bilateral auditory cortex lesions. Acta Otolaryngol Suppl. 1997; 532:77–82. 69. Garde MM, Cowey A. “Deaf hearing”: unacknowledged detection of auditory stimuli in a patient with cerebral deafness. Cortex 2000;36(1): 71–80. 70. Engelien A, Huber W, Silbersweig D et al. The neural correlates of ‘deaf-hearing’ in man: conscious sensory awareness enabled by attentional modulation. Brain 2000;123(Pt 3):532–45. 71. Stefanatos GA, Gershkoff A, Madigan S. On pure word deafness, temporal processing, and the left hemisphere. J Int Neuropsychol Soc. 2005;11(4): 456–70; discussion 5. 72. Hayashi K, Hayashi R. Pure word deafness due to left subcortical lesion: neurophysiological studies of two patients. Clin Neurophysiol. 2007;118(4):863–8. 73. Clarke S, Bellmann A, Meuli RA, Assal G, Steck AJ. Auditory agnosia and auditory spatial deficits following left hemispheric lesions: evidence for distinct processing pathways. Neuropsychologia 2000; 38(6):797–807. 74. Van Lancker DR, Kreiman J, Cummings J. Voice perception deficits: neuroanatomical correlates of phonagnosia. J Clin Exp Neuropsychol. 1989;11(5): 665–74. 61. Diederich NJ, Goetz CG, Stebbins GT. Repeated visual hallucinations in Parkinson’s disease as disturbed external/internal perceptions: focused review and a new integrative model. Mov Disord. 2005;20(2): 130–40. 75. Schuppert M, Munte TF, Wieringa BM, Altenmuller E. Receptive amusia: evidence for cross-hemispheric neural networks underlying music processing strategies. Brain 2000;123(Pt 3):546–59. 62. Hudspeth AJ. Hearing. In Kandel ER, Schwartz JH, Jessell TM, editors. Principles of Neural Science. 4th edition. New York, NY: McGraw-Hill; 2000, pp. 590–613. 76. Adriani M, Maeder P, Meuli R et al. Sound recognition and localization in man: specialized cortical networks and effects of acute circumscribed lesions. Exp Brain Res. 2003;153(4):591–604. 63. Li L, Yue Q. Auditory gating processes and binaural inhibition in the inferior colliculus. Hear Res. 2002;168(1–2):98–109. 77. Tanaka Y, Nakano I, Obayashi T. Environmental sound recognition after unilateral subcortical lesions. Cortex 2002;38(1):69–76. 64. Rauschecker JP, Tian B. Mechanisms and streams for processing of “what” and “where” in auditory cortex. Proc Natl Acad Sci USA 2000;97(22): 11,800–6. 78. Biedermann F, Bungert P, Dorrscheidt GJ, von Cramon DY, Rubsamen R. Central auditory impairment in unilateral diencephalic and Chapter 10: Perception and recognition telencephalic lesions. Audiol Neurootol. 2008;13(2): 123–44. 79. Johkura K, Matsumoto S, Hasegawa O, Kuroiwa Y. Defective auditory recognition after small hemorrhage in the inferior colliculi. J Neurol Sci. 1998;161(1):91–6. 80. Di Dio AS, Fields MC, Rowan AJ. Palinacousis – auditory perseveration: two cases and a review of the literature. Epilepsia 2007;48(9):1801–6. 95. Ohayon MM. Prevalence of hallucinations and their pathological associations in the general population. Psychiatry Res. 2000;97(2–3):153–64. 96. Flor H, Nikolajsen L, Staehelin Jensen T. Phantom limb pain: a case of maladaptive CNS plasticity? Nat Rev Neurosci. 2006;7(11):873–81. 81. Saunders JC. The role of central nervous system plasticity in tinnitus. J Commun Disord. 2007;40(4): 313–34. 97. Verfaellie M, Heilman KM. Neglect syndromes. In Snyder PJ, Nussbaum PD, Robins DL, editors. Clinical Neuropsychology: A Pocket Handbook for Assessment. 2nd edition. Washington, DC: American Psychological Association; 2006, pp. 489–507. 82. Langguth B, Hajak G, Kleinjung T et al. Repetitive transcranial magnetic stimulation and chronic tinnitus. Acta Otolaryngol Suppl. 2006;(556):102–5. 98. Abbruzzese G, Berardelli A. Sensorimotor integration in movement disorders. Mov Disord. 2003;18(3): 231–40. 83. Lampl Y, Lorberboym M, Gilad R, Boaz M, Sadeh M. Auditory hallucinations in acute stroke. Behav Neurol. 2005;16(4):211–16. 99. Mombaerts P. The human repertoire of odorant receptor genes and pseudogenes. Annu Rev Genomics Hum Genet. 2001;2:493–510. 84. Bogerts B. The temporolimbic system theory of positive schizophrenic symptoms. Schizophr Bull. 1997;23(3):423–35. 100. Kay LM, Sherman SM. An argument for an olfactory thalamus. Trends Neurosci. 2007;30(2): 47–53. 85. Florindo I, Bisulli F, Pittau F et al. Lateralizing value of the auditory aura in partial seizures. Epilepsia 2006; 47(Suppl. 5):68–72. 101. Wilson DA, Kadohisa M, Fletcher ML. Cortical contributions to olfaction: plasticity and perception. Semin Cell Dev Biol. 2006;17(4):462–70. 86. Evers S, Ellger T. The clinical spectrum of musical hallucinations. J Neurol Sci. 2004;227(1):55–65. 102. Zatorre RJ, Jones-Gotman M, Evans AC, Meyer E. Functional localization and lateralization of human olfactory cortex. Nature 1992;360(6402): 339–40. 87. Gardner EP, Kandel ER. Touch. In Kandel ER, Schwartz JH, Jessell TM, editors. Principles of Neural Science. 4th edition. New York, NY: McGraw-Hill; 2000, pp. 451–71. 88. Dijkerman HC, de Haan EH. Somatosensory processes subserving perception and action. Behav Brain Sci. 2007;30(2):189–201; discussion 201–39. 89. Parsons LM. Integrating cognitive psychology, neurology and neuroimaging. Acta Psychol. (Amsterdam) 2001;107(1–3):155–81. 90. Peltier S, Stilla R, Mariola E et al. Activity and effective connectivity of parietal and occipital cortical regions during haptic shape perception. Neuropsychologia 2007;45(3):476–83. 91. Sathian K, Zangaladze A, Hoffman JM, Grafton ST. Feeling with the mind’s eye. Neuroreport 1997;8(18): 3877–81. 92. Zangaladze A, Epstein CM, Grafton ST, Sathian K. Involvement of visual cortex in tactile discrimination of orientation. Nature 1999;401(6753):587–90. 93. Ptito M, Schneider FC, Paulson OB, Kupers R. Alterations of the visual pathways in congenital blindness. Exp Brain Res. 2008;187(1):41–9. 94. Mahapatra RK, Edwards MJ, Schott JM, Bhatia KP. Corticobasal degeneration. Lancet Neurol. 2004;3(12):736–43. 103. Porter J, Anand T, Johnson B, Khan RM, Sobel N. Brain mechanisms for extracting spatial information from smell. Neuron 2005;47(4):581–92. 104. Willander J, Larsson M. Olfaction and emotion: the case of autobiographical memory. Mem Cognit. 2007;35(7):1659–63. 105. Buck LB. Smell and taste: the chemical senses. In Kandel ER, Schwartz JH, Jessell TM, editors. Principles of Neural Science. 4th edition. New York, NY: McGraw-Hill; 2000, pp. 548–71. 106. Simon SA, de Araujo IE, Gutierrez R, Nicolelis MA. The neural mechanisms of gustation: a distributed processing code. Nat Rev Neurosci. 2006;7(11): 890–901. 107. Jones-Gotman M, Zatorre RJ, Cendes F et al. Contribution of medial versus lateral temporal-lobe structures to human odour identification. Brain 1997;120(Pt 10):1845–56. 108. Ishimaru T, Miwa T, Nomura M, Iwato M, Furukawa M. Reversible hyposmia caused by intracranial tumour. J Laryngol Otol. 1999;113(8):750–3. 109. Wilson RS, Schneider JA, Arnold SE et al. Olfactory identification and incidence of mild cognitive impairment in older age. Arch Gen Psychiatry 2007; 64(7):802–8. 159 Section I: Structural and Functional Neuroanatomy 110. Ponsen MM, Stoffers D, Booij J et al. Idiopathic hyposmia as a preclinical sign of Parkinson’s disease. Ann Neurol. 2004;56(2):173–81. 111. Kopala L, Clark C. Implications of olfactory agnosia for understanding sex differences in schizophrenia. Schizophr Bull. 1990;16(2):255–61. 112. Tabaton M, Monaco S, Cordone MP et al. Prion deposition in olfactory biopsy of sporadic Creutzfeldt–Jakob disease. Ann Neurol. 2004;55(2): 294–6. 113. Attems J, Jellinger KA. Olfactory tau pathology in Alzheimer disease and mild cognitive impairment. Clin Neuropathol. 2006;25(6):265–71. 114. Hawkes C, Shah M, Findley L. Olfactory function in essential tremor: a deficit unrelated to disease duration or severity. Neurology 2003;61(6):871–2; author reply 872. 115. Carter JL. Visual, somatosensory, olfactory, and gustatory hallucinations. Psychiatr Clin North Am. 1992;15(2):347–58. 116. Lee BC, Hwang SH, Rison R, Chang GY. Central pathway of taste: clinical and MRI study. Eur Neurol. 1998;39(4):200–3. 117. Small DM, Bernasconi N, Bernasconi A, Sziklas V, Jones-Gotman M. Gustatory agnosia. Neurology 2005;64(2):311–17. 118. Gottfried JA, Dolan RJ. The nose smells what the eye sees: crossmodal visual facilitation of human olfactory perception. Neuron 2003;39(2):375–86. 160 119. Valenza N, Murray MM, Ptak R, Vuilleumier P. The space of senses: impaired crossmodal interactions in a patient with Balint syndrome after bilateral parietal damage. Neuropsychologia 2004;42(13):1737–48. 120. Delbeuck X, Collette F, Van der Linden M. Is Alzheimer’s disease a disconnection syndrome? Evidence from a crossmodal audio-visual illusory experiment. Neuropsychologia 2007;45(14):3315–23. 121. de Gelder B, Vroomen J, Annen L, Masthof E, Hodiamont P. Audio-visual integration in schizophrenia. Schizophr Res. 2003;59(2–3):211–18. 122. Ro T, Farne A, Johnson RM et al. Feeling sounds after a thalamic lesion. Ann Neurol. 2007;62(5):433–41. 123. Ramachandran VS, Hubbard EM, Butcher PA. Synesthesia, cross-activation and the foundations of neuroepistemology. In Calvert G, Spence C, Stein BE, editors. The Handbook of Multisensory Processes. Cambridge, MA: MIT Press; 2004, pp. 867–83. 124. Farrer C, Franck N, Georgieff N et al. Modulating the experience of agency: a positron emission tomography study. Neuroimage 2003;18(2):324–33. 125. Morin A, Michaud J. Self-awareness and the left inferior frontal gyrus: inner speech use during self-related processing. Brain Res Bull. 2007;74(6): 387–96. 126. Frith C. The neural basis of hallucinations and delusions. C R Biol. 2005;328(2):169–75. 127. Blakemore SJ, Wolpert D, Frith C. Why can’t you tickle yourself? Neuroreport 2000;11(11):R11–16. Section I Structural and Functional Neuroanatomy Chapter Memory 11 Felipe DeBrigard, Kelly S. Giovanello, and Daniel I. Kaufer Human memory consists of several functional systems that collectively support the acquisition, retention, and subsequent retrieval of information. Much of what is known about different memory processes has been gleaned from experiments in non-human primates and other mammals, which allow for direct manipulation of experimental conditions but provide limited information about the human condition. Complementing animal lesion experimental data are individual case studies in a few human subjects uniquely affected with specific lesions that isolate different memory systems. Data generated from these select individuals have widely influenced current theories of memory function by yielding inferences regarding component processes and anatomical substrates based on brain–behavioral correlation. More recently, high-resolution structural and functional imaging methods have greatly facilitated the investigation of structure–function relationships associated with different human memory functions. In particular, functional magnetic resonance imaging (fMRI) allows for assaying neural circuitry in real-time during the performance of various memory tasks. Memory systems are classified according to the temporal duration (short- vs. long-term memory) or the qualitative nature of the information being retained (Figure 11.1). Short-term memory (STM) generally refers to the retention of information over brief periods of time, on the order of seconds. Working memory (WM) is a form of STM that entails a temporary storage buffer for information that undergoes further processing; long-term memory (LTM) involves the acquisition and retention of information over longer intervals of time. Long-term memory can be further subdivided into declarative memory, which Figure 11.1. A framework for understanding memory and its subtypes. refers to the acquisition and retention of knowledge, and non-declarative memory, reflecting experienceinduced changes in performance. In a clinical context, the temporal aspects of learning and memory are parsed into immediate recall (processing and recitation over a period of seconds), recent memory (anterograde learning over a period of minutes), and remote memory (retrograde recall of previously learned information). These terms reflect stages of information processing (encoding, storage, retrieval) that form the basis of standard clinical tests of verbal episodic memory (e.g., word-list or paragraph recall). This chapter will review the clinical context and describe the functional–anatomic architecture of multiple memory systems including WM, declarative memory (i.e., semantic memory and episodic memory), and non-declarative memory (i.e., implicit Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 161 Section I: Structural and Functional Neuroanatomy memory and procedural memory). The account will be based on neuropsychological and functional neuroimaging studies of normal individuals and clinical populations. Clinical overview The anatomical substrates of memory include distributed networks of cortical and subcortical nuclei interconnected by white matter projection pathways. For example, the Papez circuit – comprising the entorhinal complex, hippocampal formation, fornix, mammillary bodies, and anterior/dorsomedial thalamus, and the cingulate gyrus – contributes to learning new information; lesions affecting any of these structures may interfere with this process, resulting in a learning or storage deficit [1]. Although susceptible to injury from a variety of insults, these structures are particularly susceptible to the effects of hypoxicischemic, hypoglycemic, or other metabolic injury (i.e., CA1 region of the hippocampus), increased glucose metabolism in the setting of thiamine deficiency (i.e., mammillary bodies and thalamus), trauma (i.e., hippocampus and fornix), and Alzheimer’s disease (AD) (i.e., entorhinal complex–hippocampal formation). By contrast, injury to frontal–subcortical systems is more often associated with difficulty retrieving recently learned information (producing a retrieval deficit). Accordingly, lesions associated with traumatic brain injury, cerebrovascular disease, multiple sclerosis, HIV/AIDS, and other conditions may have a deleterious effect on frontal-subcortical circuits and are common causes of impaired memory retrieval. Degenerative conditions such as Parkinson’s disease (PD) and Huntington’s disease (HD) that affect basal ganglia and cerebellar structures involved in perceptual-motor processing may cause impairments in WM and procedural memory [2]. From a therapeutic perspective, research on memory functions has focused on remediating “core” cognitive deficits (i.e., short-term episodic memory deficits) in the setting of AD, and WM deficits in the context of attention-deficit disorder (ADD) and schizophrenia. Memory impairment is the most common reason for seeking a cognitive evaluation, and this problem has multiple possible causes. In most cases, memory impairments are comorbid with other cognitive and neurobehavioral problems. Disorders such as herpes encephalitis, which has a predilection for limbic and paralimbic cortical regions, may cause an 162 amnestic syndrome associated with other neurobehavioral features, such as personality change and seizures. Alcohol amnestic disorder (also known as Korsakoff ’s syndrome or Korsakoff ’s psychosis), transient global amnesia (TGA), and amnestic mild cognitive impairment (MCI) are the most common causes of isolated impairment of declarative memory. Although well known to be associated with Korsakoff ’s syndrome, an amnestic syndrome in which a tendency to confabulate features prominently also may develop after rupture of an anterior cerebral artery aneurysm. Secondary memory impairments may develop as complications of electroconvulsive therapy (ECT), epileptic seizures (i.e., complex partial or generalized seizures), or severe alcohol intoxication (“alcoholic blackouts”). A variety of nutritional, metabolic, endocrine, and toxic conditions may impair memory function directly or indirectly via compromise in attentional systems, as with an acute confusional state or delirium. Multiple memory systems Memory is not a unitary function, but instead denotes a large and diverse set of psychological processes and neural systems involved in learning and retrieving information. These processes include, among others, WM, declarative (episodic, semantic) memory, and non-declarative (implicit, procedural) memory, each of which will be considered in the following sections of this chapter. Working memory Working memory refers to the retention of information over brief intervals of time, typically on the order of seconds. It involves the temporary online storage and manipulation of information that can be used for immediate behavior, without being directly available to the senses. It is different from the notion of STM in that it is not merely a relaying stage prior to the storage of information in LTM. Rather, WM encompasses an array of cognitive processes. The amount of information WM can handle is both limited in time (approximately 20 seconds) and in capacity (approximately four to nine items), and is somewhat flexible. By actively rehearsing an informational item, one can keep information in WM for extended periods of time. Similarly, by grouping different items into meaningful chunks of information, WM capacity can increase substantially [3]. The limited capacity of WM allows cognitive scientists to study its nature using a dual-task Chapter 11: Memory methodology. This research strategy is based on the assumption that if two activities are conducted in tandem and neither is impaired, then the processes do not depend on the same system. However, if performance on one task decreases as a function of being carried out along with the other task, then both tasks depend upon the same mnemonic system. This methodology has been widely used by Alan Baddeley in shaping his WM model [4]. The model comprises four components: three material-specific slave systems and one central executive. One slave system is called the “phonological loop.” It mediates the temporal storage and rehearsal of phonemes and sounds. As such, it is essential for language production and comprehension, as well as for the temporary storage of numeric and other symbolic representations. Experimental studies have shown that rehearsal and retrieval of information processed by the phonological loop is sensitive to phono-articulatory characteristics. It has been suggested, for instance, that the phonological loop stores information in a phonetic format, as evidenced by the phonological similarity effect in which recall is poorer for sequences of phonologically similar items [5]. Similarly, it has been suggested that phonological rehearsal involves highlevel activation of speech-motor planning processes [6]. This claim finds support in the word length effect, which states that serial recall accuracy is correlated with length of phonological articulation – the longer and more complex the word, the longer it takes to rehearse. Neuroimaging and neuropsychological studies support dissociation between phonological storage and rehearsal [7–9]. On the one hand, patients with damage to the left supramarginal gyrus of the inferior parietal cortex exhibit poor repetition, produce phonemic paraphasias, and have reduced auditory verbal span, deficits indicative of an impaired phonological store [10, 11]. On the other hand, patients with damage to the left inferior frontal gyrus display output deficits characterized by diminished phrase length and poor articulation, findings indicative of impaired articulatory rehearsal [11, 12]. Importantly, functional neuroimaging studies have shown that storage and maintenance of information involves interactions between posterior buffer regions and anterior rehearsal mechanisms. For example, verbal WM appears to be mediated by the left posterior parietal cortex, which subserves the phonological store, as well as Broca’s area, the left premotor area, and the left supplementary area, which are involved in articulatory rehearsal [13]. Another slave system is the visuospatial sketchpad, which stores and manipulates visual and spatial information. The visuospatial sketchpad is independent of the phonological loop, as it is associated with activity in the right – and not the left – cerebral hemisphere [14]. Additionally, it is selectively disrupted by concurrent activities that do not influence the phonological loop [15]. It is also thought to involve two different components: a visual store that preserves perceptual features of objects, and a spatial or sequential component that may serve a rehearsal function. Neuropsychological findings offer strong support for this dissociation. Patients with occipital and temporal damage exhibit impaired visual storage, but preserved spatial WM [16]. In contrast, patients with parietal deficits show impaired spatial storage, but preserved visual WM [17, 18]. Recent research has also revealed that eye-movements play a key role in the maintenance of spatial, but not object, representations in the visuospatial sketchpad [19]. The final slave system, the episodic buffer, is the most recent addition to the model [20]. The function of this buffer is to represent and integrate inputs from all subcomponents of WM, as well as LTM, in a multimodal neural code. As such, it is thought to process multidimensional information that will later be consolidated or reconsolidated in episodic memory. Moreover, the episodic buffer is thought to link semantic information from the visuospatial sketchpad and the phonological loop in order to integrate this information into complex episodic representations via modulation by the central executive. The observation that amnestic patients can produce coherent episodic narratives despite profound deficits in LTM supports the postulated role of the episodic buffer. Although the precise neural correlates of this episodic buffer remain unspecified, preliminary fMRI evidence suggests that the right frontal lobe may play a key role [21]. Each of the aforementioned slave systems depends on a central executive system. This system, which is also limited in capacity, plays a fundamental role in complex memory span tasks (e.g., random digit generation) and it is closely linked to attentional control. Indeed, Baddeley [22] suggested that the central executive may in fact correspond to Shallice’s [23] supervisory attentional system. It is thought to regulate the flow of information with WM, and the retrieval of material from more permanent LTM into WM. 163 Section I: Structural and Functional Neuroanatomy In addition, the central executive permits attentional shifts between tasks as well as selective attention and inhibition. Neuropsychological evidence suggests that there are two main types of dysexecutive syndrome, each reflecting dysfunction in the central executive system. One type involves marked perseveration, indicating decreased ability to disengage and shift attention, whereas the other is characterized by excessive distractibility, which reflects impairments in attentional inhibition. It has been observed that individuals with AD and frontotemporal dementias are impaired when performing concurrent multiple tasks, indicating that the frontal and prefrontal cortices may be selectively involved in the functioning of the executive system [24]. Furthermore, neuroimaging studies indicate that executive control processes are mediated by the cingulate and dorsolateral prefrontal cortices [25, 26]. Finally, it is worth noting that despite the influence of Baddeley’s WM model, other models have been suggested. Of note is Nelson Cowan’s [27] model, which unlike Baddeley’s model, suggests that WM and LTM process the same types of memory representations. According to Cowan’s model, there are not different kinds of systems operating upon different kinds of WM representations, but rather a unique executive system activating and deactivating memory representations via attentional modulation. Further research is needed to assess the relative virtues of these different models. Declarative memory Declarative memory encompasses the acquisition, long-term retention, and retrieval of events, facts, and concepts [28]. Such knowledge can be retrieved at will and used in a variety of contexts. Declarative memory can be subdivided depending on whether memories are concerned with personally relevant events (i.e., episodic memory) or impersonal information (i.e., semantic memory). Episodic memory Episodic memory enables individuals to recollect conscious experiences from their personal past (e.g., remembering what one had for breakfast this morning). According to Tulving [29], episodic memories are characterized by a sense of subjective awareness of having experienced the remembered events in the past. 164 To tap into this particular feeling or “recollective experience,” Tulving developed the so-called remember/know paradigm. In this paradigm, participants are first presented with stimulus material and then asked to retrieve this information on a memory test. During recall, participants are asked whether they remember the studied event – that is, whether they can picture it in their minds with some detail – or, instead, if they only know that they have studied it (i.e., a sense of knowing something without being able to conjure up additional informational details). This widely implemented paradigm has produced robust results, suggesting two different mnemonic processes: recollection and familiarity. As later suggested by Tulving [30], the hallmark of recollection is a sense of autonoetic (self-awareness) consciousness accompanying the recollective experience; it pertains, therefore, to episodic memory. On the other hand, the absence of autonoetic consciousness during familiarity evidences a different sort of processing, this time related to semantic memory. Although the nature and exact relation between recollection and familiarity is a matter of debate [31] convergent evidence suggests that episodic memory is a distinct memory system. A pervasive deficit in episodic memory is dramatically exemplified in patients with anterograde amnesia, who are unable to acquire and retrieve any events or episodes from their personal life that occurred since the onset of their amnesia. This phenomenon was first documented in patient H.M., a man who, in 1953, underwent surgery for treatment of refractory seizures [32]. The surgery involved bilateral resection of the medial temporal region, which reportedly included removal of the amygdala, anterior two-thirds of the hippocampus, and hippocampal gyrus. Although the surgery was successful in substantially reducing H.M.’s seizures, the procedure produced a pervasive impairment of memory that was termed “global amnesia” [33]. From the time of his surgery at the age of 27 until his death in 2008, H.M. was unable to consciously learn and remember new episodic information. Patients with global amnesia also manifest retrograde amnesia (i.e., the loss of memory for experienced events that occurred prior to brain injury onset). Frequently, remote memories are better preserved than memories for events that occurred shortly before brain injury. This effect, which was described over a century ago by Théodule Ribot [34] and referred subsequently to as Ribot’s law, is only now coming to be understood, Chapter 11: Memory as cognitive neuroscientists investigate how the hippocampus and surrounding medial-temporal structures contribute to the enduring storage of episodic memories. Although there is general agreement that the hippocampus is critical for memory consolidation (i.e., the permanent laying down) of information, memory theorists disagree as to the role the hippocampus plays in the storage of consolidated memories. The traditional view suggests that the medial temporal lobes are not the ultimate repositories for new memories [35, 36]. Rather, storage of new memories requires interaction between medial-temporal and neocortical areas. The hippocampus receives input from distributed neocortical sites about an event to be remembered and forms a compressed representation that binds together the information from different sites that form a complete representation of that event. Partial reinstatement of the activation pattern associated with that event leads to a spreading of activation, whereby the initial pattern of neocortical activation is regenerated. Whenever a neocortical pattern is reinstated, the functional connections between constituent sites are reinforced. Over time, permanent cortico-cortical connections are established, allowing a memory to be retrieved without mediation from the limbic system. As a result, information that is not fully consolidated is vulnerable to partial or complete loss in the setting of hippocampal damage, whereas fully consolidated (i.e., older, representationally stable) memories are able still to be retrieved successfully. More recently, alternative views have been proposed in which the hippocampus plays a more permanent role in the retrieval of episodic memories. One such theory, known as the Multiple Trace Theory [37], suggests that recollection of episodic memories always depends on the hippocampus, and that every time one recollects an episodic memory, a new memory trace is created. Thus, episodic memories that are more frequently remembered have been coded in multiple traces, rendering them less vulnerable to damage. Other views suggest that the hippocampus is always required for recollection, but not for familiarity [38, 39], insofar as it permits the recombination of episodic components into a single memory event [40]. Finally, some views suggest that the hippocampus stores allocentric (i.e., non-self-centered) representations of spatial context, which allow humans and other mammals not only to navigate their immediate surroundings, but also to mentally access the spatio-temporal content of their memories. Further research is needed to fully understand the specific role of the hippocampus and the medial temporal lobes in retrieval. Neuroimaging studies provide additional evidence for the role of the medial temporal lobes in episodic memory. Activation of the medial temporal region is observed during both initial registration of novel events and also during retrieval of recently acquired information [41, 42]. Medial temporal lobe activity is greater during the encoding of experiences that are later remembered versus those that are later forgotten [43, 44]. Episodic memory also depends on frontal lobe function. Although patients with frontal lobe lesions may demonstrate normal performance on tasks of recognition memory, prose recall, and some cued recall tasks, they typically show impairments on free recall, memory for temporal order, and source memory tasks [45]. These latter tasks depend on elaboration of information at encoding, as well as monitoring and decision processes at retrieval – strategic processes proposed to be mediated by frontal regions. On recognition tests, some patients with frontal lesions (primarily in the right hemisphere) make an unusually high number of errors in which items are designated as “old” when they in fact are “new” (i.e., false alarms) [46, 47]. Additionally, Levine and collaborators [48] reported the case of M.L., who after suffering closed head trauma, experienced a severe episodic retrograde amnesia: he was unable to remember any autobiographical experiences prior to the accident. Interestingly, M.L. did not experience anterograde amnesia, as he was still able to encode and further recall events occurring after the accident. M.L.’s pathology was restricted to the right ventral frontal lobe, including the uncinate fasciculus, while his hippocampus was intact. Taken together, these cases indicate that while the hippocampus is essential for encoding episodic information, the frontal lobes are essential for episodic recollection. Finally, the most recent conceptual development in our understanding of episodic memory is the relationship between remembering the past and imagining the future. When studying patient K.C. – an individual whose case offers the clearest known example of a dissociation between episodic memory and semantic memory [49] – researchers noted his inability to remember the past in concert with his inability to envision himself in the future. The capacity to think about 165 Section I: Structural and Functional Neuroanatomy one’s future is related to the capacity to remember previous episodes from one’s life [50]; more specifically, these capacities share the same neural underpinnings [51–54] and are phenomenologically [55] and ontogenetically related [56]. Semantic memory Semantic knowledge encompasses a wide range of information, including facts about the world, the meanings of words and concepts, and the names attached to objects and people. Unlike episodic memories, semantic memories can be retrieved without associated information regarding the context in which they were acquired. By virtue of its diverse nature, not all forms of semantic knowledge share the same properties. Some forms of knowledge can be acquired after a single exposure (e.g., knowledge that Lisbon is the capital of Portugal), whereas other forms may be gradually acquired across multiple repetitions (e.g., understanding the concept “website”). Additionally, semantic information, when first encountered, may vary in the extent to which it is truly novel. For instance, semantic learning may involve establishing new associations between pre-existing representations in memory (e.g., learning that William Shakespeare wrote Romeo and Juliet) or acquiring a new label for information already represented in memory (e.g., foreign-language learning). Finally, a new label and a novel set of properties may be linked to each other (e.g., learning the meaning of the word “microbrew”). Neuropsychological studies of semantic memory have focused on brain lesions that selectively impair different stages of information processing (i.e., acquisition, storage, or retrieval) as well as the organization of knowledge. Evidence for the neural structures subserving the acquisition of new semantic knowledge has come primarily from studying patients with amnesia. Patients with extensive medial temporal lesions, such as amnesic H.M., are unable to acquire the meanings of words that entered the language after the onset of their amnesia [57, 58]. Some findings suggest that the integrity of structures surrounding the hippocampus (subhippocampal cortices) may be critical for new semantic learning. Vargha-Khadem and collaborators [59] reported three young amnestic individuals who sustained severe bilateral hippocampal atrophy as a result of anoxia. Importantly, their deficit appeared to be confined to episodic memory, as they were unable to remember or encode any specific events of their lives, 166 while their capacity to remember and learn new facts was preserved. Inspection of available neuroanatomic data revealed that in all three children, damage was limited to the hippocampus proper with sparing of subhippocampal cortices. These cases suggest that while the hippocampus is necessary for episodic memory, subhippocampal cortices may mediate semantic memory. Consistent with these findings, an adult patient with hippocampal and subhippocampal damage demonstrated profoundly impaired episodic and semantic learning, whereas another adult patient with only hippocampal damage had disproportionately preserved semantic learning [60]. Whereas subhippocampal cortices appear critical for acquiring new semantic information, these areas are not implicated in information storage. Studies of patients with semantic dementia or focal temporal lobe lesions suggest that semantic knowledge is stored/represented in the lateral temporal lobes in a distributed network of information [61, 62]. Memory retrieval requires interaction between retrieval cues and stored representations so as to trigger cortical storage sites to provide memory output. This retrieval process is thought to be mediated by inferolateral frontal and temporopolar regions, as patients with lesions in these areas, especially in the left hemisphere, have significant difficulty retrieving old semantic memories [63]. Neuroimaging studies also have shown activation in these areas when normal subjects make semantic judgments about objects or words [64]. Insight into the organization of semantic memory has come from investigating patients with circumscribed lesions who demonstrate category-specific knowledge deficits. An especially striking dissociation has been observed between knowledge of living and non-living things. Some patients have impaired knowledge of living things (e.g., animals and vegetables), but preserved knowledge of non-living things (e.g., tools and furniture), whereas other patients show the reverse pattern [65, 66]. One interpretation of category-specific deficits is that semantic memory is represented in the brain according to taxonomic categories. Another interpretation is that categories differ in their reliance on knowledge from different sensorimotor modalities, with living things known predominately by their visual attributes and non-living things by their function. Accordingly, category-specific knowledge deficits for living and non-living things may reflect impairments in the Chapter 11: Memory representation of visual and functional knowledge, respectively. Consistent with the neuropsychological literature, pioneering neuroimaging studies showed differential activations for category-specific stimuli. Using positron emission tomography (PET) scans, Martin and collaborators [67] found greater activation in left medial occipital cortex during naming pictures of animals relative to pictures of tools. In contrast, naming pictures of tools revealed greater activation in left premotor and middle temporal cortices. Additionally, several neuroimaging studies showed activation in left prefrontal cortices during semantic retrieval [68]. Perhaps the stronger piece of evidence supporting the observation that left-lateralized damage to the anterior temporal cortex affects semantic rather than episodic memory comes from patients suffering from semantic dementia. One of the best-documented cases of semantic dementia is patient A.M. [69]. Upon examination, A.M. showed severe difficulty remembering the names of things (anomia), even though his speech and prosody remained largely intact. Further testing revealed intact non-verbal episodic retrieval, evidenced by normal performance during tasks such as copying the Rey complex figure. More recently, Davies and collaborators [70], using post-mortem data from a group of seven individuals with semantic dementia cases, discovered that, relative to controls, semantic dementia was associated with anterior temporal atrophy, including parts of the perirhinal cortex, and preservation of adjacent areas in the temporal lobe. The extent to which other brain areas are implicated in semantic memory, as well as the nature of semantic representations in memory, remains an area of active scientific research. Non-declarative memory Non-declarative memory refers to a variety of forms of memory in which learning is expressed as enhanced performance [71]. In this chapter, we focus on two forms of non-declarative memory: implicit memory and procedural memory. Implicit memory Implicit memory describes a type of non-declarative memory in which previous experiences aid task performance without any requirement for conscious awareness of those previous experiences [72]. One well-studied form of implicit memory is repetition priming (referred to as “priming” hereafter). A typical priming task is comprised of study and test phases. During the study phase, participants are exposed to a series of words, pictures, or objects. For example, they might see a word list that contains the word “turnip.” During the test phase, participants perform a seemingly unrelated task. For example, they might have to identify briefly flashed words or generate as many words as possible when cued with the semantic category “vegetable.” Priming is measured as the facilitation in task performance induced by recent exposure to task stimuli (e.g., enhanced accuracy in identifying or generating the word “turnip”), as compared with a baseline condition in which that word had not appeared on the prior study list. Studies in normal participants have identified two types of priming: perceptual priming, which requires analysis of the perceptual attributes of a stimulus (e.g., identification of perceptually degraded stimuli), and conceptual priming, which requires analysis of the meaning of a stimulus (e.g., category exemplar generation) [73]. Importantly, these two types of priming are differentially affected by experimental manipulations that vary the amount of overlap between study and test phases. A change in the perceptual format between study and test reduces perceptual priming, but has no impact on conceptual priming. Alternatively, enhanced conceptual priming occurs with elaborate processing of stimuli at study versus when only shallow processing occurs; this processing manipulation has no effect on perceptual priming. Neuropsychological investigations indicate that globally amnesic patients show intact performance on perceptual and conceptual priming tasks [74]. This finding suggests that the mnemonic operations involved in priming are not dependent on the medial temporal and diencephalic structures implicated in global amnesia. Like globally amnesic patients, AD patients have pathologic changes in limbic structures and show impairments on declarative memory tasks. Unlike amnesic patients, however, those with AD also have extensive neocortical pathology, particularly in frontal, temporal, and parietal association areas [75], and show impaired conceptual priming despite preserved perceptual priming [76, 77]. This pattern of impaired and preserved priming in AD suggests that conceptual priming processes may be localized to frontal, temporal, and parietal association areas that are compromised in AD. In contrast, perceptual priming processes may be localized to early 167 Section I: Structural and Functional Neuroanatomy modality-specific cortices that are relatively spared in AD. Further neuropsychological evidence that perceptual priming is mediated by modality-specific cortices comes from patients with focal occipital lobe lesions who show impaired priming on visual perceptual tasks and preserved priming on conceptual tasks [78, 79]. Taken together, these findings lend strong support to the notion that perceptual and conceptual priming are mediated by separable neural substrates. Neuroimaging studies aimed at localizing priming processes are generally consistent with findings from clinical studies [80]. Visual perceptual priming is mediated by visual association areas, whereas conceptual priming is mediated by more anterior cortices (e.g., superior temporal and anterior frontal regions). Furthermore, these studies have demonstrated that the facilitation resulting from repeated processing of a stimulus is associated with decreased neural activation (also called response suppression) for repeated stimuli relative to new stimuli. Depending on the technique, the reduction in hemodynamic response can be measured as decreased regional cerebral blood flow (using PET) or as decreased blood oxygen-level dependent (BOLD) signal (using fMRI). Using event-related fMRI, Henson, Shallice and Dolan [81] presented a series of familiar faces and familiar symbols while subjects were instructed to search for a target. Simply viewing repeated faces or symbols was associated with decreased neural activation in the fusiform gyrus. Such decreases in neural activity have been interpreted to map onto the behavioral priming effect, as the facilitated, more efficient processing of previously perceived stimuli (see [82] for a thoughtful discussion of this issue). According to one model [83], such decreased activation reflects a neural tuning, or sharpening mechanism, in which only the neurons that respond best to the stimulus are recruited for reprocessing that stimulus at a later time (but see [82, 84] for important caveats with regard to this model). Neural priming is typically evident in areas of stimulus- or concept-specific processing, such as extrastriate cortex of the occipital lobe (for visually perceived stimuli), fusiform cortex (for object or face stimuli), primary auditory cortex in lateral temporal lobe (for aurally perceived stimuli), or inferior frontal gyrus (for priming of semantic information). More recently, Schacter and colleagues [85] reviewed numerous studies reporting reductions in cortical activity during priming. Their review 168 yielded several observations. First, prefrontal regions demonstrate sensitivity to both conceptual- and stimulus-decision mapping components of repetition priming. Robust correlations have been observed between the magnitude of behavioral priming and neural priming in this region, and trancranial magnetic stimulation (TMS) applied to the left prefrontal cortex (PFC) during semantic classification tasks disrupts subsequent behavioral priming. Second, regions in the lateral temporal cortex demonstrate sensitivity to conceptual components of repetition priming and, similar to prefrontal regions, respond amodally. Third, perceptual cortices demonstrate sensitivity to perceptual components of priming and tend not to be correlated with behavior during tasks that encourage conceptual or semantic priming. Neural priming in these regions demonstrates a gradient of stimulus specificity such that the degree of stimulus-specific priming decreases as one proceeds from early (posterior) to late (anterior) regions with the perceptual system, and there is a laterality effect (i.e., less specific in the left than right hemisphere) across later visual regions. Procedural memory Procedural memory is involved in the acquisition of skills and habits, results from repeated practice, and is relatively impervious to the effects of decay or interference. Research studies investigating the acquisition of new perceptual-motor skills have employed simple tasks, such as mirror tracing or rotary pursuit. During mirror tracing, a participant uses a metal stylus to trace a geometric pattern seen in a mirror, while the geometric pattern and the individual’s hand are obscured from view by a board. Learning is measured by the reduction in time to complete tracing of the pattern, as well as the number of errors committed. During rotary pursuit, a participant is given a metal stylus that must be kept in contact with a revolving disk. Learning occurs as the individual becomes more proficient at matching his or her motor movement with the movement of the disk. Early studies of patient H.M. were among the first to establish that globally amnesic patients could acquire and retain new motor skills [33, 86]. Such findings are especially significant given that these patients are frequently unaware of having been previously exposed to the tasks. These results suggest that procedural memory is mediated by neural structures outside the medial temporal-diencephalic region. Chapter 11: Memory In contrast to amnesic patients, other neurological populations, such as PD and HD patients, have poor rotary pursuit learning [87]. Based on these data, it appears that the basal ganglia, which are compromised by these diseases, play a critical role in motor skill learning. However, basal ganglia lesions do not impair all motor skill tasks to the same extent; for example, Gabrieli and colleagues observed that HD patients demonstrate normal mirror tracing despite impaired rotary pursuit learning [88]. In contrast, patients with cerebellar lesions show impaired mirror tracing [89]. These observations suggest that the basal ganglia and cerebellum both contribute to motor skill learning but do so differentially: the basal ganglia are critical for sequence learning whereas the cerebellum is involved in error correction [88]. The perceptual skill that has been studied most extensively in neurological patients is learning to read text that has been geometrically transformed (such as reading mirror-reversed words). Current interest in perceptual skill learning was driven by the classic study of Cohen and Squire [90] in which they examined the performance of amnesic patients on the mirror reading task. The results of the study showed that the amnesic patients were able to learn to read mirrorreversed text as well as age-matched control participants, despite having poor declarative memory for the practice episodes and stimuli. Subsequent studies have replicated these findings in other groups of amnesic patients [91, 92]. Evidence for the role of the basal ganglia in perceptual skill learning comes from patients with HD, who show a mild impairment in mirror reading, despite good declarative memory for the words read [91]. Studies of mirror reading in patients with PD have been mixed, however, with some studies reporting impaired learning [93–95] and other studies reporting intact learning [96, 97]. Perceptual learning studies in healthy adults have used psychophysical tasks such as contrast detection, orientation, and visual search [98, 99]. This research suggests that perceptual learning proceeds in two stages: an initial learning stage, characterized by unskilled and effortful performance, that reflects establishment of task-specific processing routines; and a subsequent stage, ultimately leading to skilled performance, reflecting modification of representations within the processing system [100]. Accordingly, functional imaging studies have demonstrated different neural contributions in the early versus later stages of skill learning. For instance, a study of mirror-reversed reading in normal subjects demonstrated that skill acquisition was accompanied by decreasing activation in regions including both occipital and right superior parietal cortices during initial learning, and increasing activation in regions including the left inferior temporal cortex later on [101]. On the basis of these results, it was proposed that learning to read mirror-reversed text may reflect a transition from right hemisphere visuospatial processing of mirror-reversed stimuli to left hemisphere object recognition areas involved in establishing new representations of mirror-reversed letters [101, 102]. Interestingly, in a follow-up study, Poldrack and Gabrieli [103] found that the caudate was active during initial mirror-reading and showed a significant learning-related increase in activation, consistent with the reported impairment of HD and PD patients in learning the mirror-reading task. In an analogous manner, studies investigating the acquisition of new motor skills have revealed that prefrontal and cerebellar regions are primarily activated early in the course of learning [104, 105]. As task proficiency increases, this activation gives way to a slowly evolving, long-term, experience-dependent reorganization of primary motor cortex [106]. More recent studies have further delineated the roles of prefrontal, cerebellar, and motor cortices during motor skill learning, as well as the parameters under which new motor skill learning occurs. For example, using fMRI and a motor sequence task, Doyon and colleagues [107] found evidence for an experienceinduced shift from the cerebellar cortex to the dentate nucleus during early learning, and from cerebellarcortical to striatal-cortical networks with extended practice; these findings suggest that intrinsic modulation within the cerebellum, together with activation of motor-related cortical activations, serves to establish a procedurally acquired sequence of movements. More recently, Doyon and colleagues [108] investigated the contribution of sleep to consolidation of two motor skills: finger tapping sequence learning (FTSL) and visuomotor adaptation (VMA). They demonstrated that the consolidation processes involved in the FTSL task benefited from sleep (even a short nap) while the simple passage of time was as effective as sleep time for the consolidation of VMA to occur. Such findings point to important task differences in the study of motor skill learning. Finally, Rozanov, Keren and Karni [109] examined the specificity of memory 169 Section I: Structural and Functional Neuroanatomy for a highly trained finger movement sequence. Their results demonstrated that the gains attained in the performance of a well-trained sequence of motor movements can be expressed only when the order of the movements is exactly as practiced. These results may have important implications for the transfer of new motor skills in patient populations, particularly in neurorehabilitation efforts directed at improving motor functions impaired by injury or disease. Conclusion Multiple human memory systems subserve the retention of knowledge, skills, experience, and emotions over a time frame that spans seconds to decades. Neural pathways that encode information within these overlapping systems are being elucidated with structural and functional brain imaging techniques and sophisticated cognitive test paradigms. Such work is enriching our understanding of component memory processes and has great potential for informing clinical diagnostic and therapeutic efforts. Translating research advancements in the cognitive neuroscience of memory into practical clinical assessments and interventions will be greatly facilitated by increased collaboration among basic scientists, clinical investigators, and clinicians. References 1. Arciniegas D, McAllister TW, Kaufer D. Cognitive impairment. In Coffey CE, McAllister TW, Silver JM, editors. Guide to Neuropsychiatric Therapeutics. Philadelphia, PA: Lippincott Williams & Wilkins; 2007, pp. 24–78. 2. Budson AE. Understanding memory dysfunction. Neurologist 2009;15(2):71–9. 3. Baddeley A, Vallar G, Wilson B. Sentence comprehension and phonological memory: some neuropsychological evidence. In Colheart M, editor. Attention and Performance XII. London: Lawrence Erlbaum Associates; 1987, pp. 509–29. 4. Baddeley A, Hitch GJ. Working memory. In Bower GA, editor. The Psychology of Learning and Motivation. New York, NY: Academic Press; 1975, pp. 47–89. 5. Surprenant AM, Neath I, LeCompte DC. Irrelevant speech, phonological similarity, and presentation modality. Memory 1999;7(4):405–20. 6. Bishop DVM, Robson J. Unimpaired short-term-memory and rhyme judgment in congenitally speechless individuals – implications for the notion of articulatory coding. Q J Exp Psychol–A. 1989;41(1):123–40. 170 7. Chein JM, Fiez JA. Dissociation of verbal working memory system components using a delayed serial recall task. Cereb Cortex 2001;11(11):1003–14. 8. Muller NG, Knight RT. The functional neuroanatomy of working memory: contributions of human brain lesion studies. Neuroscience 2006;139(1):51–8. 9. Vallar G, Papagno C. Neuropsychological impairments of verbal short-term memory. In Baddeley AD, Kopelman MD, Wilson BA, editors. The Handbook of Memory Disorders. 2nd edition. New York, NY: John Wiley and Sons; 2002, pp. 249–70. 10. Vallar G, Papagno C. Neuropsychological impairments of short-term memory. In Baddeley AD, Wilson BA, Watts FN, editors. Handbook of Memory Disorders. Chichester: John Wiley and Sons; 1995, pp. 135–65. 11. Murray LL, Ramage AE, Hopper A. Memory impairments in adults with neurogenic communication disorders. Semin Speech Lang. 2001; 22(2):127–36. 12. Shallice T, Warrington EK. The possible role of selective attention in acquired dyslexia. Neuropsychologia 1977;15(1):31–41. 13. Awh E, Jonides J, Smith EE et al. Dissociation of storage and rehearsal in verbal working memory: evidence from positron emission tomography. Psychol Sci. 1996;7(1):25–31. 14. Gathercole SE. Cognitive approaches to the development of short-term memory. Trends Cogn Sci. 1999;3(11):410–19. 15. Brooks LR. The suppression of visualization by reading. Q J Exp Psychol. 1967;19(4):289–99. 16. Hanley JR, Young AW, Pearson NA. Impairment of the visuo-spatial sketch pad. Q J Exp Psychol A. 1991;43(1): 101–25. 17. Della Sala S, Gray C, Baddeley A, Allamano N, Wilson L. Pattern span: a tool for unwelding visuo-spatial memory. Neuropsychologia 1999;37(10):1189–99. 18. Della Sala S, Logie R. Neuropsychological impairments of visual and spatial working memory. In Baddeley AD, Kopelman MD, Wilson BA, editors. The Handbook of Memory Disorders. 2nd edition. New York, NY: John Wiley and Sons; 2002, pp. 271–92. 19. Postle BR, Ferrarelli F, Hamidi M et al. Repetitive transcranial magnetic stimulation dissociates working memory manipulation from retention functions in the prefrontal, but not posterior parietal, cortex. J Cogn Neurosci. 2006;18(10):1712–22. 20. Baddeley A. The episodic buffer: a new component of working memory? Trends Cogn Sci. 2000;4(11): 417–23. 21. Prabhakaran V, Narayanan K, Zhao Z, Gabrieli JD. Integration of diverse information in working memory Chapter 11: Memory within the frontal lobe. Nature Neuroscience 2000; 3(1):85–90. 22. Baddeley AD. Working Memory. Oxford: Clarendon Press; 1986. 23. Shallice T. Specific impairments of planning. Philos Trans R Soc Lond B Biol Sci. 1982;298(1089):199–209. 24. Baddeley AD, Bressi S, Della Sala S, Logie R, Spinnler H. The decline of working memory in Alzheimer’s disease. A longitudinal study. Brain 1991;114(Pt 6): 2521–42. 25. Smith EE, Jonides J. Storage and executive processes in the frontal lobes. Science 1999;283(5408):1657–61. 26. Kaufer D. Frontal lobe anatomy: dorsolateral and cingulate cortex. In Miller BL, Cummings JL, editors. The Human Frontal Lobes: Functions and Disorders. 2nd edition. New York, NY: Guilford Press; 2007, pp. 44–58. 27. Cowan N. Attention and Memory: an Integrated Framework. New York, NY: Oxford University Press; 1995. 28. Squire LR. Declarative and nondeclarative memory: multiple brain systems supporting learning and memory. In Andersen P et al., editors. Memory Concepts – 1993: Basic and Clinical Aspects. Proceedings of the 7th Novo Nordisk Foundation Symposium “Memory Concepts-1993”, Copenhagen, Denmark, 28–30 June 1993. Amsterdam: Excerpta Medica; 1993, pp. 3–25. 29. Tulving E. Elements of Episodic Memory. Oxford: Clarendon Press; 1983. 30. Tulving E. Episodic memory: from mind to brain. Annu Rev Psychol. 2002;53:1–25. 37. Nadel L, Moscovitch M. Memory consolidation, retrograde amnesia and the hippocampal complex. Curr Opin Neurobiol. 1997;7(2):217–27. 38. Aggleton JP, Brown MW. Episodic memory, amnesia, and the hippocampal-anterior thalamic axis. Behav Brain Sci. 1999;22(3):425–44; discussion 4:44–89. 39. Rugg MD, Yonelinas AP. Human recognition memory: a cognitive neuroscience perspective. Trends Cogn Sci. 2003;7(7):313–19. 40. Cohen NJ, Eichenbaum H. Memory, Amnesia, and the Hippocampal System. Cambridge, MA: MIT Press; 1993. 41. Gabrieli JD, Brewer JB, Desmond JE, Glover GH. Separate neural bases of two fundamental memory processes in the human medial temporal lobe. Science 199;276(5310):264–6. 42. Nyberg L, McIntosh AR, Houle S, Nilsson LG, Tulving E. Activation of medial temporal structures during episodic memory retrieval. Nature 1996;380(6576): 715–17. 43. Brewer JB, Zhao Z, Desmond JE, Glover GH, Gabrieli JD. Making memories: brain activity that predicts how well visual experience will be remembered. Science 1998;281(5380):1185–7. 44. Wagner AD, Schacter DL, Rotte M et al. Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 1998;281(5380):1188–91. 45. Shimamura AP. Memory and frontal lobe function. In Gazzaniga MS, Bizzi E, editors. The Cognitive Neurosciences. Cambridge, MA: MIT Press; 1995, pp. 803–13. 31. Yonelinas AP. The nature of recollection and familiarity: a review of 30 years of research. J Mem Lang. 2002;46(3):441–517. 46. Rapcsak SZ, Reminger SL, Glisky EL, Kaszniak AW, Comer JF. Neuropsychological mechanisms of false facial recognition following frontal lobe damage. Cogn Neuropsych. 1999;16(3–5):267–92. 32. Scoville WB, Milner B. Loss of recent memory after bilateral hippocampal lesions. J Neurol Neurosurg Psychiatry 1957;20(1):11–21. 47. Schacter DL, Curran T, Galluccio L, Milberg WP, Bates JF. False recognition and the right frontal lobe: a case study. Neuropsychologia 1996;34(8):793–808. 33. Milner B, Corkin S, Teuber HL. Further analysis of the hippocampal amnesic syndrome: 14-year follow-up study of H.M. Neuropsychologia 1968;6(3):215–34. 48. Levine B, Black SE, Cabeza R et al. Episodic memory and the self in a case of isolated retrograde amnesia. Brain 1998;121(Pt 10):1951–73. 34. Ribot T. Les maladies de la mémoire. Paris: Libraire Germer Baillere; 1881. 49. Tulving E. Memory and consciousness. Can Psychol. 1985;26(1):1–12. 35. McClelland JL, McNaughton BL, O’Reilly RC. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol Rev. 1995;102(3):419–57. 50. Atance CM, O’Neill DK. The emergence of episodic future thinking in humans. Learn Motiv. 2005;36(2): 126–44. 36. Squire LR, Alvarez P. Retrograde amnesia and memory consolidation: a neurobiological perspective. Curr Opin Neurobiol. 1995;5(2):169–77. 51. Addis DR, Wong AT, Schacter DL. Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration. Neuropsychologia 2007;45(7):1363–77. 52. Klein S, Loftus J, Kihlstrom JF. Memory and temporal experience: the effects of episodic memory loss on an 171 Section I: Structural and Functional Neuroanatomy amnesic patient’s ability to remember the past and imagine the future. Social Cogn. 2002;20(5):353–79. 53. Szpunar KK. Episodic future thought: an emerging concept. Perspect Psychol Sci. 2010;5(2):142–62. 54. Okuda J, Fujii T, Ohtake H et al. Differential involvement of regions of rostral prefrontal cortex (Brodmann area 10) in time- and event-based prospective memory. Int J Psychophysiol. 2007;64(3): 233–46. 55. D’Argembeau A, Van der Linden M. Phenomenal characteristics associated with projecting oneself back into the past and forward into the future: influence of valence and temporal distance. Conscious Cogn. 2004;13(4):844–58. 56. Suddendorf T. Episodic memory versus episodic foresight: Similarities and differences. WIREs Cogn Sci. 2010;1(1):99–107. 57. Gabrieli JD, Cohen NJ, Corkin S. The impaired learning of semantic knowledge following bilateral medial temporal-lobe resection. Brain Cogn. 1988; 7(2):157–77. 58. Verfaellie M, Croce P, Milberg WP. The role of episodic memory in semantic learning: an examination of vocabulary acquisition in a patient with amnesia due to encephalitis. Neurocase 1995;1(4):291–304. 59. Vargha-Khadem F, Gadian DG, Watkins KE et al. Differential effects of early hippocampal pathology on episodic and semantic memory. Science 1997; 277(5324):376–80. 60. Verfaellie M, Koseff P, Alexander MP. Acquisition of novel semantic information in amnesia: effects of lesion location. Neuropsychologia 2000;38(4): 484–92. 67. Martin A, Wiggs CL, Ungerleider LG, Haxby JV. Neural correlates of category-specific knowledge. Nature 1996;379(6566):649–52. 68. Nyberg L, Cabeza R, Tulving E. PET studies of encoding and retrieval: the HERA model. Psychon B Rev. 1996;3(2):135–48. 69. Hodges JR, Graham KS. A reversal of the temporal gradient for famous person knowledge in semantic dementia: implications for the neural organisation of long-term memory. Neuropsychologia 1998;36(8): 803–25. 70. Davies RR, Halliday GM, Xuereb JH, Kril JJ, Hodges JR. The neural basis of semantic memory: evidence from semantic dementia. Neurobiol Aging 2009; 30(12):2043–52. 71. Squire LR. Declarative and nondeclarative memory – multiple brain systems supporting learning and memory. J Cognitive Neurosci. 1992;4(3):232–43. 72. Schacter DL. Implicit memory: history and current status. J Exp Psychol. 1987;13:501–18. 73. Roediger III HL, McDermott KB. Implicit memory in normal human subjects. In Spinnler H, Boller F, editors. Handbook of Neuropsychology. Amsterdam: Elsevier; 1993, pp. 63–130. 74. Schacter DL, Chiu CY, Ochsner KN. Implicit memory: a selective review. Annu Rev Neurosci. 1993; 16:159–82. 75. Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer’s disease. Cereb Cortex 1991;1(1):103–16. 61. Alexander MP. Specific semantic memory loss after hypoxic-ischemic injury. Neurology 1997;48(1): 165–73. 76. Gabrieli JD, Keane MM, Stanger BZ et al. Dissociations among structural-perceptual, lexical-semantic, and event-fact memory systems in Alzheimer, amnesic, and normal subjects. Cortex 1994;30(1):75–103. 62. Kapur N, Ellison D, Parkin AJ et al. Bilateral temporal lobe pathology with sparing of medial temporal lobe structures: lesion profile and pattern of memory disorder. Neuropsychologia 1994;32(1):23–38. 77. Keane MM, Gabrieli JD, Fennema AC, Growdon JH, Corkin S. Evidence for a dissociation between perceptual and conceptual priming in Alzheimer’s disease. Behav Neurosci. 1991;105(2):326–42. 63. Kroll NE, Markowitsch HJ, Knight RT, von Cramon DY. Retrieval of old memories: the temporofrontal hypothesis. Brain 1997;120(Pt 8):1377–99. 78. Gabrieli JDE, Fleischman DA, Keane MM, Reminger SL, Morrell F. Double dissociation between memory-systems underlying explicit and implicit memory in the human brain. Psychol Sci. 1995;6(2): 76–82. 64. Kapur S, Craik FI, Tulving E et al. Neuroanatomical correlates of encoding in episodic memory: levels of processing effect. Proc Natl Acad Sci USA 1994;91(6): 2008–11. 65. Warrington EK, Shallice T. Category specific semantic impairments. Brain 1984;107(Pt 3):829–54. 79. Keane MM, Gabrieli JD, Mapstone HC, Johnson KA, Corkin S. Double dissociation of memory capacities after bilateral occipital-lobe or medial temporal-lobe lesions. Brain 1995;118(Pt 5):1129–48. 66. Warrington EK, McCarthy R. Category specific access dysphasia. Brain 1983;106(Pt 4):859–78. 80. Schacter DL, Buckner RL. Priming and the brain. Neuron 1998;20(2):185–95. 172 Chapter 11: Memory 81. Henson R, Shallice T, Dolan R. Neuroimaging evidence for dissociable forms of repetition priming. Science 2000;287(5456):1269–72. 95. Yamadori A, Yoshida T, Mori E, Yamashita H. Neurological basis of skill learning. Brain Res Cogn Brain Res. 1996;5(1–2):49–54. 82. Henson RN, Rugg MD. Neural response suppression, haemodynamic repetition effects, and behavioural priming. Neuropsychologia 2003;41(3):263–70. 96. Bondi MW, Kaszniak AW. Implicit and explicit memory in Alzheimer’s disease and Parkinson’s disease. J Clin Exp Neuropsychol. 1991;13(2):339–58. 83. Wiggs CL, Martin A. Aging and feature-specific priming of familiar and novel stimuli. Psychol Aging 1994;9(4):578–88. 97. Harrington DL, Haaland KY, Yeo RA, Marder E. Procedural memory in Parkinson’s disease: impaired motor but not visuoperceptual learning. J Clin Exp Neuropsychol. 1990;12(2):323–39. 84. Grill-Spector K, Henson R, Martin A. Repetition and the brain: neural models of stimulus-specific effects. Trends Cogn Sci. 2006;10(1):14–23. 85. Schacter DL, Dobbins IG, Schnyer DM. Specificity of priming: a cognitive neuroscience perspective. Nat Rev Neurosci. 2004;5(11):853–62. 86. Corkin S. Acquisition of motor skill after bilateral medial temporal-lobe excision. Neuropsychologia 1968;6(3):255–65. 87. Heindel WC, Salmon DP, Shults CW, Walicke PA, Butters N. Neuropsychological evidence for multiple implicit memory systems: a comparison of Alzheimer’s, Huntington’s, and Parkinson’s disease patients. J Neurosci. 1989;9(2):582–7. 88. Gabrieli JD, Stebbins GT, Singh J, Willingham DB, Goetz CG. Intact mirror-tracing and impaired rotary-pursuit skill learning in patients with Huntington’s disease: evidence for dissociable memory systems in skill learning. Neuropsychology 1997;11(2): 272–81. 98. Chun MM, Jiang Y. Contextual cueing: implicit learning and memory of visual context guides spatial attention. Cogn Psychol. 1998;36(1):28–71. 99. Karni A, Bertini G. Learning perceptual skills: behavioral probes into adult cortical plasticity. Curr Opin Neurobiol. 1997;7(4):530–5. 100. Karni A, Sagi D. The time course of learning a visual skill. Nature 1993;365(6443):250–2. 101. Poldrack RA, Desmond JE, Glover GH, Gabrieli JD. The neural basis of visual skill learning: an fMRI study of mirror reading. Cereb Cortex 1998;8(1):1–10. 102. Poldrack RA. Neural systems for perceptual skill learning. Behav Cogn Neurosci Rev. 2002;1(1):76–83. 103. Poldrack RA, Gabrieli JD. Characterizing the neural mechanisms of skill learning and repetition priming: evidence from mirror reading. Brain 2001;124(Pt 1): 67–82. 89. Sanes JN, Dimitrov B, Hallett M. Motor learning in patients with cerebellar dysfunction. Brain 1990; 113(Pt 1):103–20. 104. Friston KJ, Frith CD, Passingham RE, Liddle PF, Frackowiak RSJ. Motor practice and neurophysiological adaptation in the cerebellum – a positron tomography study. Proc R Soc Lond Series B. 1992;248(1323):223–8. 90. Cohen NJ, Squire LR. Preserved learning and retention of pattern-analyzing skill in amnesia: dissociation of knowing how and knowing that. Science 1980;210(4466):207–10. 105. Jenkins IH, Brooks DJ, Nixon PD, Frackowiak RS, Passingham RE. Motor sequence learning: a study with positron emission tomography. J Neurosci. 1994; 14(6):3775–90. 91. Martone M, Butters N, Payne M, Becker JT, Sax DS. Dissociations between skill learning and verbal recognition in amnesia and dementia. Arch Neurol. 1984;41(9):965–70. 106. Karni A, Meyer G, Jezzard P et al. Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature 1995;377(6545):155–8. 92. Schmidtke K, Handschu R, Vollmer H. Cognitive procedural learning in amnesia. Brain Cogn. 1996; 32(3):441–67. 107. Doyon J, Song AW, Karni A et al. Experiencedependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci USA 2002;99(2):1017–22. 93. Koenig O, Thomas-Anterion C, Laurent B. Procedural learning in Parkinson’s disease: intact and impaired cognitive components. Neuropsychologia 1999;37(10): 1103–9. 108. Doyon J, Korman M, Morin A et al. Contribution of night and day sleep vs. simple passage of time to the consolidation of motor sequence and visuomotor adaptation learning. Exp Brain Res. 2009;195(1):15–26. 94. Roncacci S, Troisi E, Carlesimo GA, Nocentini U, Caltagirone C. Implicit memory in parkinsonian patients: evidence for deficient skill learning. Eur Neurol. 1996;36(3):154–9. 109. Rozanov S, Keren O, Karni A. The specificity of memory for a highly trained finger movement sequence: change the ending, change all. Brain Res. 2010;1331:80–7. 173 Section I Structural and Functional Neuroanatomy Chapter Language 12 Mario F. Mendez Language is the distinctive human facility for communication through symbols. The expression of language includes all symbolic communication, including speech, reading and writing, sign language, Braille, Morse code, and even musical notation. Language evolved during the last 2.5 million years from a primitive system of individual sounds with concrete referents to strings or sequences of symbols with abstract and generalizable meanings. The main processes of language are the decoding and encoding of sequences of symbols and associating them with concepts and meanings [1]. How is language organized in the brain? Our basic understanding of the neuroanatomy of language and the brain is based on a classical lesion model derived from the study of brain-injured patients in the nineteenth and early twentieth centuries. Clinicians identified the existence of dedicated language centers from the observation of different language impairments after brain lesions in these areas or their interconnecting fiber tracts. Today, we know that the elements of language are not as fixed as defined by the classical lesion model, and that the neuroanatomy of language is complex. Rapid advances in cognitive neuroscience indicate the existence of mental lexicons or dictionaries and processes for phonology, morphology, syntax, semantics, and other features, all interacting in a rapid, large-capacity system. In this neurocomputational system composed of rapid processing streams, there is simultaneous activation at multiple levels and top-down, as well as bottom-up, influences on language production and language comprehension. This chapter describes the neuroanatomy of language beginning with the foundation in the classical lesion model and concludes with an updated view of language-brain organization. Figure 12.1. Brain of Paul Broca’s original patient M. Leborgne, commonly known as “Tan Tan.” Reproduced from Dronkers NF, Plaisant O, Iba-Zizen MT, Cabanis EA. Paul Broca’s historic cases: high resolution MR imaging of the brains of Leborgne and Lelong. Brain 2007;130(Pt 5):1432–41, with permission from Oxford University Press. Classical lesion model of language and the brain Clinical aphasia or language impairments from brain lesions have been the window to localization of language in the brain. In 1861, Paul Broca inaugurated the modern study of language with a description of his patient “Tan Tan” who had lost the ability to speak from a focal brain injury in the inferior frontal region (see Figure 12.1) [2]. Later, Broca reported on the association of impaired verbal fluency with left inferior frontal damage in what came to be known as “Broca’s area” (see Figure 12.2). In 1874, Karl Wernicke described aphasia from loss of language comprehension consequent to left superior temporal injury. He then elaborated a group of specialized and interconnected Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 174 Chapter 12: Language Figure 12.2. Lateral view of the left hemisphere indicating the perisylvian area. The illustration shows Broca’s area in the frontal operculum and Wernicke’s area in the superior temporal gyrus and the corresponding Brodmann’s areas. Reprinted with permission from Mark Dubin, PhD, Department of Molecular, Cellular & Developmental Biology, University of Colorado at Boulder (http://spot.colorado.edu/∼dubin/talks/brodmann/brodmann.html). This figure is presented in color in the color plate section. language regions in the left perisylvian region. The early work of Broca, Wernicke, and others established the dominance of the left hemisphere for language and demonstrated an anterior-posterior dichotomy around the Sylvian fissure, with two hubs, Broca’s area (Brodmann’s area [BA] 44, 45) for language production in the inferior frontal region and Wernicke’s area (BA 22) for language comprehension in the superior temporal gyrus (Figure 12.2). Norman Geschwind subsequently described how disconnection of cortical centers, such as Broca’s and Wernicke’s, can produce distinct language and other syndromes [3, 4]. This localization concept is embodied in the classical lesion or “Wernicke–Geschwind” model of language and the corresponding aphasia syndromes [5] (Figure 12.3). Each classic aphasic syndrome suggests a neuroanatomical association of a specific language function (Table 12.1). Broca’s aphasics are non-fluent and have difficulty encoding language, using grammar, and articulating words and sentences. This profile suggests that Broca’s area processes grammatical structure, including syntactic rules and grammatical morphemes, rather than elements of language that have content or specific meaning. It also suggests that a major role of Broca’s area is the articulation of language. Wernicke’s aphasics, on the other hand, are fluent but have difficulty decoding and understanding language. This suggests that Wernicke’s area selects and uses elements of language with concrete meaning, including content words. Conduction aphasia is a 175 Section I: Structural and Functional Neuroanatomy Table 12.1. The aphasia syndromes and their characteristics. Aphasia syndrome Fluency Auditory comprehension Repetition Naming Reading comprehension Writing Broca’s Abnormal Relatively normal Abnormal Abnormal Normal or abnormal Abnormal Wernicke’s Normal, paraphasic Abnormal Abnormal Abnormal Abnormal Abnormal Global Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal Conduction Normal, paraphasic Relatively normal Abnormal Usually abnormal Relatively normal Abnormal Transcortical motor Abnormal Relatively normal Relatively normal Abnormal Relatively normal Abnormal Transcortical sensory Normal, echolalic Abnormal Relatively normal Abnormal Abnormal Abnormal Mixed transcortical Abnormal, echolalic Abnormal Relatively normal Abnormal Abnormal Abnormal Anomic Normal Relatively normal Normal Abnormal Normal or abnormal Normal or abnormal Transcortical motor aphasia Broca’s aphasia Concepts Motor patterns Figure 12.3. Wernicke–Geschwind model. The diagram illustrates the organization of language and corresponding aphasia syndromes in the left hemisphere. Transcortical sensory aphasia Auditory images Wernicke’s aphasia Conduction aphasia m third perisylvian aphasia, and is due to the disconnection of Broca’s and Wernicke’s areas. This disorder features a prominent disturbance in repetition out of proportion to any other language disturbance. Most cases of conduction aphasia have neuropathology involving the anterior inferior parietal lobe, including the arcuate fasciculus and the supramarginal gyrus [6]. Additional syndromes localize outside of the perisylvian region in the Wernicke–Geschwind model [7]. Transcortical motor and transcortical sensory aphasias have relative preservation of the ability to repeat spoken language in the presence of language impairments otherwise consistent with Broca’s aphasia 176 a or Wernicke’s aphasia, respectively. The neuropathologic lesion underlying transcortical motor aphasia is in the supplementary motor area of the left hemisphere or between that area and Broca’s area. The neuropathologic lesion underlying transcortical sensory aphasia is in the left angular gyrus in the parietal region or in the left posterior superior or middle temporal gyri. Subcortical aphasias can result from lesions in the left basal ganglia, the anterolateral nuclei of the thalamus, or white matter [8]. Language impairment from the basal ganglia may resemble transcortical motor aphasia, and language impairment from the thalamus may resemble transcortical sensory aphasia. With these “subcortical” aphasias, cortical involvement or Chapter 12: Language distal hypometabolism is probably necessary for permanent language changes [9, 10]. Alexias are characterized by the inability to read with or without accompanying agraphia, the inability to write. Alexia with agraphia can result from left angular gyrus lesions, and alexia without agraphia from lesions in the left visual occipital region combined with damage to the splenium of the corpus callosum. Finally, frontal lesions involving motor areas impair speech, or the motor aspects of verbal communication, and can produce dysarthria, apraxia of speech, reiterative speech disorders, and even mutism. Problems with the classical lesion model The classical lesion model does not address a number of critical aspects of language organization and the brain. The model does not formally consider basic linguistic elements such as phonology (the sound pattern of language), morphology (the combination of language’s smallest meaningful units), syntax (the structure of sentences), semantics (the relationship of language to meaning), and pragmatics (intonation, gesture, and other aspects of discourse). In addition, the model does not consider the speed with which children acquire language and the related suggestion by Chomsky that there is a brain region or process that restricts the set of possible human grammars and thereby facilitates language acquisition [11]. Finally, the classical lesion model does not consider brain plasticity or the effects of age, sex, and handedness. The greatest challenge to the classical lesion model, however, comes from advances in neuroimaging and related methodologies. For a long time, information about how the brain processed language could only come from the study of the effects of neurological disease in humans. In the last few decades, many imaging technologies have expanded our understanding of language and the brain, and moved us beyond dependency on the lesion model. These include voxelbased morphometry, positron and single photon emission tomography, functional magnetic resonance imaging, special analyses of electroencephalograms, magnetoencephalography, and transcortical magnetic stimulation. These methodologies have dramatically expanded our understanding of the neuroanatomic relationships and functional connectivity of language organization in the brain [12, 13] (Figure 12.4). Using these methodologies, investigators have challenged the basic assumptions of the classical lesion model. First, damage to more than Broca’s area is needed to cause Broca’s aphasia. Although Broca’s area does participate in syntactic processing and the execution of articulatory movements, lesions confined to Broca’s area may not result in language production deficits [14]. Lesions of the two sections of Broca’s area, the pars triangularis (BA 44) and the pars opercularis (BA 45), are not sufficient or necessary for Broca’s aphasia. In fact, a re-analysis of the preserved brains of Broca’s two original patients confirmed that their lesions extended well beyond BA 44 and 45, deep into the white matter, and involved the insula and the superior longitudinal fasciculus [2]. The anterior insula, which is implicated in the labored articulation and non-fluency, or apraxia of speech, is evident in Broca’s aphasia [15]. This suggests that the complete Broca’s aphasia syndrome is predicated on injury not only to Broca’s area but also to the white matter underlying Broca’s area and to the anterior insula. Second, Wernicke’s aphasia requires more than damage to Wernicke’s area alone. Although Wernicke’s area plays a role in word comprehension, isolated destruction of this area does not necessarily result in decreased auditory comprehension. Involvement of the underlying white matter and adjacent areas, such as the supramarginal and angular gyri in the inferior parietal region, is necessary for a complete disturbance of auditory verbal comprehension [16]. So, neither Broca’s area lesions nor Wernicke’s area lesions are necessary or sufficient for Broca’s aphasia and Wernicke’s aphasia, respectively, challenging the usefulness of the classical lesion model of aphasia. Current views on language and the brain New data support some of the aspects of the classical lesion model of aphasia but dispute or modify others. There is continued support for a left hemispheric specialization for language in the perisylvian region. Recent studies, however, have modified the classic role of Broca’s area. Similarly, recent studies have also modified the classic role of Wernicke’s area. Additionally, more recent characterizations of uncommon language disorders add to our understanding of language and the brain. Finally, lesion re-analysis and functional neuroimaging point to a new organization based on a neurocomputational model of rapid processing streams in which language areas are embedded within complex and highly interconnected networks. 177 Section I: Structural and Functional Neuroanatomy Figure 12.4. Examples of language mapping in the frontal lobe (A) and the combined temporal and parietal lobes (B). Yellow and green circles represent numbered electrocortical stimulation mapping (ESM) language sites and clean ESM sites, respectively. Red boxes represent expression fMR imaging activations. Blue boxes represent comprehension fMR imaging activations. The frontal lobe slices are shown with an ESM radius of 5 mm (determined to produce the highest sensitivity with the least cost to specificity) and temporoparietal lobe slices are shown with an ESM radius of 9 mm. A: For frontal lobe mapping, these brain slices demonstrate that red (expression) activations tend to overlap with, or are adjacent to, essential (yellow) ESM sites, but avoid non-essential (green) ESM sites. Blue activations in the frontal lobe also appear predictive. B: In most cases of temporoparietal lobe mapping, such as the one illustrated here, comprehension fMR imaging 178 Chapter 12: Language Hemispheric specialization The perisylvian area of the language-dominant hemisphere most closely approximates a “language organ” in the human brain. Broca suggested an association between the dominant hand and the dominant hemisphere for language [17], and almost all right-handed persons are left hemisphere dominant for language. Among aphasics, left hemisphere lesions occur in 92% or more of all patients, nearly 100% if they are righthanded, but only about 63% if they are left-handed. Most non-right-handed (left-handed and ambidextrous) persons are also left hemisphere language dominant, although they usually have additional language representation in the right hemisphere. The greater bilaterality of language in most left-handers decreases the clinical value of the classic aphasia syndromes in these patients. Henri Hecaen distinguished familial left-handers with a decreased left hemisphere predominance of language and a greater likelihood of hemispheric symmetry for language compared with righthanders [18]. Neuroanatomic evidence for this organization is the relative absence of hemisphere asymmetry of the planum temporale, which contains Wernicke’s area, in familial left-handers. The planum temporale, an enlargement located on the posterior surface of the temporal lobe, is larger on the left in 65 of 100 post-mortem brains [19]. Most right-handers have a larger planum temporale in the left hemisphere compared with the right, and this asymmetry is evident at about 30 weeks of gestation. In contrast to the left hemisphere, the right hemisphere has a dominant role for emotional features, including determining the emotional prosodic aspects of communication and the emotional state of a speaker from tone of voice [20]. There is increased metabolic activity, cerebral blood flow, and neural activity in perisylvian areas of the left hemisphere when subjects are using language [21]. In comparison, language is temporarily disrupted with sodium amytal injection into the left carotid artery or with electrical charges sent to certain areas of the left hemisphere [22]. Patients who have undergone a “split-brain” procedure with transection of the corpus callosum cannot name objects visible only in the left visual field or held in their left hand [23]. In addition, normal individuals perceive spoken words or phonemes better in the right ear than in the left [24]. Alternative role for Broca’s area Rather than a static seat of grammar or speech articulation, Broca’s area is part of a processing stream of lexical integration that forms sentences. It appears to be a node through which linguistic units are combined and broken down, selected and compared, and also through which integration of the syntactic, semantic, and phonological elements into sentences is accomplished prior to their expression. First, Broca’s area (BA 44, 45) engages other brain areas in which mental lexicon (or dictionary) is represented and facilitates selections of words of interest (lexical selection). Second, it assigns and constructs grammatical structures (“lemmas” or grammatical morphological aspect of words) so as to relate words to each other. This process occurs at the word level, with grammatical morphemes and function words, and at the sentence level with syntax [25]. The words require semantic integration into the context of preceding words in a sentence for the sequences of words to have meaning. Third, Broca’s area constructs a phonological output lexicon. Phonological encoding involves activating word sounds (phonemes, syllables) and monitoring and segmenting them. Through this phonological encoding, Broca’s area plays a role in speech perception, and phoneme, syllable, and word discrimination and identification [14]. Fourth, phonetic encoding precedes actual articulation. This information is ← Figure 12.4. (cont.) activations matched well with ESM language sites (yellow) in the temporoparietal lobes and did not overlap with clean ESM sites (green). Very little expression fMR imaging activations are seen in the temporoparietal lobe region and the reason why such tasks as verbal object naming and word generation do not accurately predict language sites in these regions. C: These maps were obtained in an individual case in which preoperative fMR imaging was the least effective at accurately predicting whether a given cortical area would be involved in language function. In this case, only two of the three essential ESM sites overlapped with fMR imaging activations, and only two of seven of the clean ESM sites completely avoided fMR imaging activations. Reproduced from Pouratian N, Bookheimer SY, Rex DE, Martin NA, Toga AW. Utility of preoperative functional magnetic resonance imaging for identifying language cortices in patients with vascular malformations. J Neurosurg. 2002;97(1):21–32, with permission of Journal of Neurosurgery Publishing Group. This figure is presented in color in the color plate section. 179 Section I: Structural and Functional Neuroanatomy conveyed to motor areas including the anterior insula and supplementary motor area. The cortex just in front of the face part of the motor strip is involved in the control of movements of the mouth, jaw, tongue, palate, larynx and other articulators that are needed for speech, and the anterior insula functions to select and sequence phonemes. Finally, other dorsolateral and ventromedial frontal areas participate in short-term memory for language, retrieval and manipulation of semantic representations, and social elements of discourse such as topic initiation, turn-taking, and personal references. The alternative role of Broca’s area explains many problems seen among persons with Broca’s aphasia. These individuals do not use action words (verbs), function words, and inflections correctly, and they have difficulty connecting morphemes to build words [26]. When comprehension is dependent on understanding the syntactical information in a sentence, individuals with Broca’s aphasia demonstrate comprehension impairments and difficulty integrating words into the context of a sentence [14]. They also demonstrate difficulty discriminating or identifying phonemes or speech syllables and pronouncing new or unfamiliar words, reflecting problems with the phonological output lexicon [27]. Alternative role for Wernicke’s area Wernicke’s area is part of a processing stream that accesses and selects from the mental lexicon, which in turn, activates related concepts or semantics. First, prelexical acoustic or phonetic analysis takes place bilaterally in the left superior temporal gyrus [14]. This region partially segregates different vowel categories suggestive of initial stages of speech sound mapping [28]. These acoustic or phonetic percepts are mapped as a phonological input code in the left midsuperior temporal gyrus. In reading, letters and the orthographic input code are also integrated in the left superior temporal cortex [29]. Second, word recognition requires lexical access and selection from the phonological input lexicon or the best-fitting auditory word forms in the left superior temporal sulcus. The left ventral mid-superior temporal sulcus appears to discriminate speech sounds (words, pseudo-words, reversed speech) from non-speech sounds. Third, word recognition further requires activated word form representations that are subsequently combined with 180 grammatical features (lemma). Fourth, these lexicalphonological elements access a semantic network of contextual and background knowledge. The left midinferior temporal gyrus and anterior temporal pole store and retrieve these semantic word representations. Some individuals with lesions in Wernicke’s area have preserved phoneme discrimination and identification (phonological information) but impaired access to semantics, possibly from damage in the left posterior inferior temporal region (BA 37). Finally, there is a working memory capacity specific to sentence processing that is different for that tapped by span tasks. The alternative role of Wernicke’s area explains many of the language problems experienced by persons with Wernicke’s aphasia. These individuals perceive speech sounds normally but are unable to access the phonological input lexicon and/or auditory word forms. Consequently, they produce erroneous but well-articulated words, paraphasic errors, or neologisms rather than appropriate content words. In Wernicke’s aphasia, content words tend to be absent or replaced by general terms or associations based on context. Contributions of other language disorders Clarification of several uncommon conditions has added to our understanding of language and the brain. Pure word deafness is a pre-language disturbance from bilateral or unilateral lesions undercutting Wernicke’s area and causing impaired comprehension of speech sounds but not of most non-speech sounds. This disorder suggests that there are different systems for speech and non-speech auditory perception [14]. Patients with pure word deafness have deficits at the level of extracting the acoustic cues to speech due to difficulty in perceiving rapid changes in complex pitch patterns. Pure word deafness further indicates that speech perception depends on the ability to perceive rapid temporal changes. Pure anomia or “word-selection” anomia is a disturbance from left inferior, posterior temporal (BA 37) lesions causing confrontation naming difficulty with intact word comprehension. This disorder suggests that this region is crucial for the retrieval of phonological word forms, and that these word forms are dissociable from semantic knowledge [14]. Progressive non-fluent aphasia results from left frontotemporal atrophy and presents with gradual Chapter 12: Language Figure 12.6. Diffusion tensor imaging pathways in conduction aphasia. Reproduced from Catani M, Jones DK, Ffytche DH. Perisylvian language networks of the human brain. Ann Neurol. 2005;57(1):8–16, with permission from John Wiley & Sons, Inc. This figure is presented in color in the color plate section. input lexicon, and associated with posterior temporalinferior parietal dysfunction [34]. Figure 12.5. MRI (T2) images of a patient with semantic aphasia. There is bilateral anterior temporal atrophy disproportionately affecting the left temporal lobe. progression of non-fluency, agrammatism, and articulation difficulty [30, 31]. Recent information indicates that much of the non-fluency may be apraxia of speech from extension of the atrophy to the underlying left anterior insular cortex [15]. Semantic dementia includes semantic aphasia and is associated with atrophy in the anterior, infero-lateral temporal lobe; in this dementia, the aphasia involves impaired understanding of word meaning, often disproportionately affecting specific word categories [32, 33] (Figure 12.5). This profile indicates that the semantic system is organized and coordinated from the anterior temporal poles. On reading, these patients also demonstrate “surface dyslexia,” or the inability to read irregularly spelled words but preserved ability to sound them out, emphasizing the dual route model of reading. Deep dyslexia is a reading disorder from large left hemisphere lesions resulting in residual reading for meaning only. Deep dyslexia, with its access to semantics but difficulty in single-word repetition, supports independent phonological input and output lexicons. Finally, investigators recently described a “logopenic” form of aphasia characterized by difficulty with a phonological store, possibly a part of the phonological Neurocomputational model Language is organized in a complex and highly interconnected network engaged in successive integration of information. Linguistic functions result from distributed groups of connected neurons organized around two processing hubs, Broca’s area and Wernicke’s area, in the perisylvian region of the languagedominant hemisphere. These areas form a central axis of interconnected nodes enabling nearly simultaneous iterative computation of the “best fit” needed for a linguistic task, depending on top-down, as well as bottom-up, influences [35, 36]. An example of processing distributed between hubs rather than localized with specific centers is conduction aphasia: the clinical manifestations of this aphasia vary with the specific inter-hub pathway between Broca’s and Wernicke’s areas that is interrupted [37] (Figure 12.6). Some investigators suggest that, similar to the visual system, there are two main processing streams for language, a ventral stream and a dorsal stream [38]. A bilaterally organized ventral processing stream mediates speech signals for comprehension, and a left-sided dorsal processing stream mediates speech signals for articulation. Language also depends on the production and detection of precise time intervals. Both speech production and detection are unusually fast processes, 181 Section I: Structural and Functional Neuroanatomy and precise timing is needed for both aspects of language. Quick temporal variants characterize language sounds, the processing of which appears to be essential to a correct perception of speech. Speech sounds can be continuously varied, but a subject only hears categories (categorical perception; i.e., no “in between” representations); this auditory “chunking” mechanism facilitates the speed at which such sounds are processed. Fast-changing temporal cues seem to elicit preponderant activation in the auditory system of the dominant hemisphere, and the superior temporal gyrus in that hemisphere is particularly sensitive to the rate of change over time in speech signals. A neuroanatomical correlate of this dominant-hemisphere temporal advantage in processing speed is the greater amount of white matter and myelination in the dominant vs. nondominant temporal lobe [39]. A further important factor for effective speech production is the rapid syntactic organization provided by the left inferior frontal region [40]. Conclusion Current information modifies the classical Wernicke– Geschwind model of language to incorporate the contributions of perisylvian processing hubs that participate in sequential, neurocomputational operations on language-related information. The human brain has the innate capacity to manipulate complex symbol systems such as language, and in childhood the structure of the brain allows for the rapid and efficient acquisition of language. The left perisylvian areas are predisposed for fast timing of strings of symbols organized on a syntactic scaffolding. Broca’s area and Wernicke’s area are critical hubs for rapid processing streams that integrate steps from the extraction of phonetic features to lexical-phonological representations to semantic representations and so on, eventually to articulation and motor speech. Recent and continuing advances in neuroimaging and related technologies offer great promise of clarifying further the structural and functional neuroanatomy of language. References 1. Hauser MD, Chomsky N, Fitch WT. The faculty of language: what is it, who has it, and how did it evolve? Science 2002;298(5598):1569–712. 2. Dronkers NF, Plaisant O, Iba-Zizen MT, Cabanis EA. Paul Broca’s historic cases: high resolution MR 182 imaging of the brains of Leborgne and Lelong. Brain 2007;130(5):1432–41. 3. Geschwind N. Disconnexion syndromes in animals and man. I. Brain 1965;88(2):237–94. 4. Geschwind N. Disconnexion syndromes in animals and man. II. Brain 1965;88(3):585–644. 5. Benson DF, Ardila A. Aphasia: A Clinical Perspective. New York, NY: Oxford University Press; 1996. 6. Damasio H, Damasio AR. The anatomical basis of conduction aphasia. Brain 1980;103(2):337–50. 7. Berthier ML. Poststroke aphasia: epidemiology, pathophysiology and treatment. Drugs Aging 2005; 22(2):163–82. 8. Nadeau SE, Crosson B. Subcortical aphasia. Brain Lang. 1997;58(3):355–402; discussion 18–23. 9. Bhatia KP, Marsden CD. The behavioural and motor consequences of focal lesions of the basal ganglia in man. Brain 1994;117(Pt 4):859–76. 10. Hillis AE, Barker PB, Wityk RJ et al. Variability in subcortical aphasia is due to variable sites of cortical hypoperfusion. Brain Lang. 2004;89(3): 524–30. 11. Chomsky N. Language and Mind. Enl. edition. New York, NY: Harcourt Brace Jovanovich; 1972. 12. Pouratian N, Bookheimer SY, Rex DE, Martin NA, Toga AW. Utility of preoperative functional magnetic resonance imaging for identifying language cortices in patients with vascular malformations. J Neurosurg. 2002;97(1):21–32. 13. Lee A, Kannan V, Hillis AE. The contribution of neuroimaging to the study of language and aphasia. Neuropsychol Rev. 2006;16(4):171–83. 14. Martin RC. Language processing: functional organization and neuroanatomical basis. Annu Rev Psychol. 2003;54:55–89. 15. Peach RK. Acquired apraxia of speech: features, accounts, and treatment. Top Stroke Rehabil. 2004; 11(1):49–58. 16. Binder JR. Neuroanatomy of language processing studied with functional MRI. Clin Neurosci. 1997; 4(2):87–94. 17. Josse G, Tzourio-Mazoyer N. Hemispheric specialization for language. Brain Res Brain Res Rev. 2004;44(1):1–12. 18. Foundas AL, Leonard CM, Hanna-Pladdy B. Variability in the anatomy of the planum temporale and posterior ascending ramus: do right- and left handers differ? Brain Lang. 2002;83(3):403–24. 19. Geschwind N, Levitsky W. Human brain: left-right asymmetries in temporal speech region. Science 1968; 161(837):186–7. Chapter 12: Language 20. Borod JC. Interhemispheric and intrahemispheric control of emotion: a focus on unilateral brain damage. J Consult Clin Psychol. 1992;60(3):339–48. 21. Cardebat D, Demonet JF, De Boissezon X et al. Behavioral and neurofunctional changes over time in healthy and aphasic subjects: a PET Language Activation Study. Stroke 2003;34(12):2900–6. 22. Wyllie E, Luders H, Murphy D et al. Intracarotid amobarbital (Wada) test for language dominance: correlation with results of cortical stimulation. Epilepsia 1990;31(2):156–61. 23. Gazzaniga MS. The role of language for conscious experience: observations from split-brain man. Prog Brain Res. 1980;54:689–96. 24. Manning WH, Louko LJ, DiSalvo VS. A right-ear effect for auditory feedback control of children’s newly acquired phonemes. J Speech Hear Res. 1978;21(3): 580–8. 25. Dapretto M, Bookheimer SY. Form and content: dissociating syntax and semantics in sentence comprehension. Neuron 1999;24(2):427–32. 26. Piras F, Marangolo P. Noun-verb naming in aphasia: a voxel-based lesion-symptom mapping study. Neuroreport 2007;18(14):1455–8. 27. Baldo JV, Schwartz S, Wilkins D, Dronkers NF. Role of frontal versus temporal cortex in verbal fluency as revealed by voxel-based lesion symptom mapping. J Int Neuropsychol Soc. 2006;12(6):896–900. 28. Obleser J, Boecker H, Drzezga A et al. Vowel sound extraction in anterior superior temporal cortex. Hum Brain Mapp. 2006;27(7):562–71. 29. van Atteveldt N, Formisano E, Goebel R, Blomert L. Integration of letters and speech sounds in the human brain. Neuron 2004;43(2):271–82. 30. Clark DG, Charuvastra A, Miller BL, Shapira JS, Mendez MF. Fluent versus nonfluent primary progressive aphasia: a comparison of clinical and functional neuroimaging features. Brain Lang. 2005; 94(1):54–60. 31. Westbury C, Bub D. Primary progressive aphasia: a review of 112 cases. Brain Lang. 1997;60(3): 381–406. 32. Gorno-Tempini ML, Dronkers NF, Rankin KP et al. Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol. 2004;55(3): 335–46. 33. Hodges JR, Patterson K, Oxbury S, Funnell E. Semantic dementia. Progressive fluent aphasia with temporal lobe atrophy. Brain 1992;115(Pt 6):1783–806. 34. Gorno-Tempini ML, Brambati SM, Ginex V et al. The logopenic/phonological variant of primary progressive aphasia. Neurology 2008;71(16):1227–34. 35. Hillis AE, Boatman D, Hart J, Gordon B. Making sense out of jargon: a neurolinguistic and computational account of jargon aphasia. Neurology 1999;53(8): 1813–24. 36. Mesulam M. Imaging connectivity in the human cerebral cortex: the next frontier? Ann Neurol. 2005; 57(1):5–7. 37. Catani M, Jones DK, ffytche DH. Perisylvian language networks of the human brain. Ann Neurol. 2005;57(1): 8–16. 38. Hickok G, Poeppel D. The cortical organization of speech processing. Nat Rev Neurosci. 2007;8(5): 393–402. 39. Zatorre RJ, Belin P, Penhune VB. Structure and function of auditory cortex: music and speech. Trends Cogn Sci. 2002;6(1):37–46. 40. Rodd JM, Longe OA, Randall B, Tyler LK. The functional organisation of the fronto-temporal language system: evidence from syntactic and semantic ambiguity. Neuropsychologia 2010;48(5):1324–35. 183 Section I Structural and Functional Neuroanatomy Chapter Affective prosody 13 Elliott D. Ross In humans, vocal–acoustic communication is a highly evolved behavior. Although lower animals have wellestablished systems of vocal–acoustic communication, humans possess a communication system of complexity and flexibility sufficient to qualify as language: this system encompasses the ability to store and cognitively modulate vast numbers of verbal-semantic representations, to produce intricate patterns of articulation, and to generate syntactical relationships with nearly infinite flexibility [1–9]. It has been assumed therefore, that human language is an evolutionary adaptation related to the marked expansion of prefrontal and temporoparietooccipital heteromodal neocortex [10– 17]. This gradual encephalization leads, in turn, to the corticalization and resultant cognitization [18–23] of communication systems that in lower animals are heavily represented in non-neocortical areas, including the basal ganglia, thalamus, limbic system, paralimbic mesocortex, brainstem, and cerebellum [24–34]. Based on the fundamental discoveries of Broca [35, 36], Wernicke [37], and Lichtheim [38] in the late 1800s that focal lesions in the left but not right hemisphere (in strongly right-handed individuals) may produce deficits in the verbal-linguistic aspects of language resulting in various aphasic syndromes [39], language is viewed as a dominant and highly lateralized function of the left hemisphere [9]. Because language is a distinguishing feature of humans, the right hemisphere has been relegated inappropriately to the role of the non-dominant or “minor” hemisphere [40]. Nevertheless, the very first functional imaging study of language [41], which was expected to show predominantly left-sided activation, showed instead that both hemispheres were activated in a relatively homologous pattern that included the posterior and anterior opercular regions and the medial surfaces of the frontal lobes. In fact, all subsequent functional imaging research that has probed various aspects of language, including verbal-linguistic and paralinguistic functions has shown robust bilateral activations on initial data acquisition that, at minimum, involve the posterior perisylvian regions [9, 42–49]. Thus, modern functional imaging data overwhelmingly support the concept that human language is, in fact, a distributed process that actively engages both hemispheres [9]. This chapter considers the topic of affective prosody as an important aspect of language that appears to be lateralized to the right hemisphere. Constituents of language and communication Human language is composed of various basic elements that are classified as being either linguistic or paralinguistic [2, 50]. The linguistic or propositional elements are characterized by words (vocabulary, lexicon) and syntactical relationships (grammar) that convey meaning based on word sequences. The basic acoustical features underlying word production (phonology) and comprehension (phonetics) are called segments, and these have distinctive acoustical signatures associated with various consonants, vowels, and syllables. It is these word-related constituents of language that are primarily disrupted by focal lefthemisphere injury that causes aphasic syndromes [9, 39]. In contrast, paralinguistic aspects of language are accessory to words, and include prosody and kinesics. The acoustical features underlying prosody include pitch, intonation (variation of pitch over time), melody, cadence, loudness, timbre (voice quality), Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 184 Chapter 13: Affective prosody stress, and pauses. As will be described in subsequent sections of this chapter, there are various levels of prosodic communication that convey linguistic, affective (attitudinal and emotional), dialectical, and idiosyncratic information [50]. Kinesics refers to facial, limb, and body movements associated with language and communications [51]. Movements that are used for standard, well-defined communication (semiotics), such as the thumbs down sign for displeasure or the “V” for “victory” sign, are classified as pantomime whereas movements used to color, emphasize, and embellish communication are classified as gestures. Most spontaneous kinesic activity often blends gestures and pantomime into a single movement. Prosody Monrad-Krohn [52] initiated the modern clinical study of prosody after caring for a native Norwegian woman during WWII who sustained a shrapnel wound to the left frontal area that precipitated acute Broca’s aphasia. The woman made an excellent recovery except that she acquired a foreign accent. The acquired accent caused her considerable emotional distress during the Nazi occupation of Norway because she was mistaken for being a German and, consequently, socially ostracized. Although her speech had preserved melody, as evidenced by her ability to sing, intone, and emote, she had inappropriate application of stresses and pauses to her speech giving it the patina of a foreign accent. Based on this patient and others, Monrad-Krohn [53] divided prosody into four major components: intrinsic (or linguistic), intellectual (or attitudinal), emotional, and inarticulate. Intrinsic (or linguistic) prosody enhances and clarifies the linguistic aspects of a language through judicious use of stress pauses and intonation without altering either words or word order. For example, raising the intonation at the end of a statement by a halfoctave transforms a statement into a question. Altering word stress and timing may clarify word meaning, i.e., “the Redcoats are coming” (British regulars) versus “the red-coats are coming” (red-colored coats) or changing the stress on certain words and altering the pausal structure of the sentence may clarify potentially ambiguous syntax, i.e., “The man . . . and woman dressed in black . . . came to visit” (only the woman was dressed in black) versus “The man and woman dressed in black . . . came to visit” (both were dressed in black) [54, 55]. Intellectual (or attitudinal) prosody imparts attitudinal information to discourse that may drastically alter meaning. For example, if the sentence “He is smart” is spoken with emphatic stress on “is,” then it becomes a resounding acknowledgment of the person’s ability. If, instead, the emphasis resides on “smart” with a terminal rise in intonation, then sarcasm is communicated. Emotional prosody infuses speech with primary types of emotions such as happiness, sadness, fear, and anger. In modern terminology, the term affective prosody refers to the combination of Monrad-Krohn’s intellectual and emotional prosody [50]. Inarticulate prosody is the use of certain paralinguistic non-verbal elements, such as grunts and sighs, to embellish discourse. In addition, there is also dialectical (or regional) prosody that belies a speaker’s origin and idiosyncratic prosody that gives rise to voice patterns and qualities that are unique to an individual. Both these subclassifications can be considered as part of intrinsic prosody. Monrad-Krohn [53] also described various clinical disorders of prosody caused by brain injury or disease. Dysprosody is a change in voice quality that may result in a foreign accent syndrome [56–58]. It is encountered primarily in patients with reasonably good recovery from non-fluent aphasias due to left hemisphere lesions that alter the patient’s dialectical and idiosyncratic aspects of prosody. Aprosody is the general lack of attitudinal and emotional prosody observed, for example, in patients with Parkinson’s disease as part of their bradykinesia, masked facies, and softly monotonous voice. Hyperprosody refers to the excessive use of prosody that occurs in acutely manic or psychotic patients, or in Broca’s or global aphasics who have very few words at their disposal but use them vehemently to convey their emotional states, an observation first made by Hughlings Jackson in the nineteenth century [59]. Although Monrad-Krohn did not attribute disorders of prosody specifically to focal right brain damage, he did predict that, in addition to aprosodic speech, disturbed prosodic comprehension should also be encountered after brain damage. Indeed, publications over the last three decades, discussed below, establish that focal lesions of the right hemisphere lead to fundamental disturbances in the production and/or comprehension of affective prosody. The resulting syndromes have been termed aprosodias, and research has shown that functional-anatomic correlations of 185 Section I: Structural and Functional Neuroanatomy the aprosodias in the right hemisphere are similar to those of the aphasias in the left hemisphere [60– 62]. Lastly, the syndrome of phonagnosia has been described [63, 64] in which patients with lesions of the right parietal region lose the ability to identify familiar individuals by their voice characteristics alone, similar to the syndrome of prosopagnosia [65] (the inability to identify familiar individuals by facial features alone). Kinesics Disturbances in pantomime, involving both production and comprehension, have been linked to left brain damage [66–72]. Goodglass and Kaplan [66] proposed that disorders of pantomime in aphasics with significant comprehension deficits may be related to their inability to comprehend symbols whereas disorders of pantomime in aphasics without significant comprehension deficits are associated with ideomotor apraxia. Although other investigators have not shown such tight correlation of a specific pantomimal disturbance with a specific linguistic disturbance [68, 69, 71, 72], disorders of pantomime are almost always associated with left hemisphere damage that usually results in aphasia, unless corpus callosum injury is the responsible lesion [73]. Gestural kinesics, however, is often preserved in aphasic patients [51, 59]. In 1979, Ross and Mesulam [74] reported two right-handed patients with ischemic infarctions of the right frontoparietal operculum who, in addition to affectively flat speech, had a general loss of spontaneous gestural activity that involved the non-paralyzed right face and limbs. Neither patient had disturbances in ideomotor praxis. Based on these patients, it was suggested that gestural as opposed to pantomimal behavior was a dominant and lateralized function of the right hemisphere. Subsequent studies lend further support to this hypothesis by showing that the right hemisphere is not only specialized for producing gestures but also for comprehending their meaning [75–86]. In summary, affective prosody, gestures, and other paralinguistic features of language impart vitality to human discourse that is essential for overall communication competency [54, 86–90]. These features are also important for enabling the experience of satisfactory psychosocial interactions, and for overall emotional well-being in both children and adults [91–95]. In fact, all the index cases that provided insights into 186 the potential role of the right hemisphere in dominantly modulating the affective aspects of communication, which in turn initiated contemporary programs of research, presented clinically because of psychosocial discord [96–98]. Although affective prosody is considered a paralinguistic or subservient feature of language, it is crucial to emphasize that if the affectiveprosodic message of a statement is at variance with its verbal-linguistic message, such as encountered in irony or sarcasm, adults and most children will overwhelmingly believe the affective-prosodic intent [55, 87, 90, 99, 100]. The role of the right hemisphere in language and communication The first clinician to suggest a lateralized role for the right hemisphere in communication was Hughlings Jackson in his 1879–1880 publication [59]. He observed that patients with dense aphasias were often still able to communicate intent through gestures and vocalizations using the few words available to them. Thus, he suggested that the emotional aspects of language and communication might be dominant functions of the right hemisphere. Clinicians, however, did not formally investigate his observations, until the 1970s when Heilman and colleagues [100–105] and Ross and colleagues [60–62, 74, 106] published a series of papers describing loss of the affective-prosodic aspects of language following focal brain damage. In 1975, Heilman and colleagues [101] tested 12 right-handed patients with temporoparietal lesions on their ability to comprehend affective prosody by presenting verbally neutral sentences with various emotional intonations. Six had left brain damage (LBD) and six had right brain damage (RBD). The patients with LBD had minimal aphasic deficits that did not interfere with testing. Patients with RBD scored near chance in their ability to detect emotions compared to controls and LBD patients. In a follow-up study [102], LBD and RBD patients with temporoparietal injuries were tested for their ability to repeat verbally neutral sentences with various emotional intonations. Once again, the RBD patients were severely impaired on the task compared with controls and LBD patients. In 1979, Ross and Mesulam [74] published a report describing two patients with computed tomography (CT)-verified ischemic lesions of the right frontoparietal operculum. Both of these patients had left hemiplegia. They displayed markedly flattened affect Chapter 13: Affective prosody Table 13.1. The aprosodias. Note: (∗ ) Indicates types of aprosodias with functional–anatomic correlations in the right hemisphere that are similar to the aphasias resulting from left hemisphere lesions that are predominantly cortical in location (9, 39, 60–62, 110–113, 138, 186). Agesic (aprosodia) (187) is similar to anomic aphasia (188). Spontaneous Comprehension Repetition of affective of affective Type of aprosodia communication affective prosody prosody Comprehension Lesion location in of gestures right hemisphere Motor ∗ Poor Poor Good Good Frontoparietal operculum Sensory ∗ Good Poor Poor Poor Temporoparietal operculum Conduction Good Poor Good Good (insufficient cases) Global ∗ Poor Poor Poor Poor Perisylvian Transcortical Motor ∗ Poor Good Good Good Medial frontal Transcortical Sensory ∗ Good Good Poor Poor Temporal parasylvian Mixed Transcortical Poor Good Poor Poor (insufficient cases) Agesic Good Good Good Poor (insufficient cases) because they could not project emotions into their speech or kinesic behaviors, yet retained the ability to comprehend affect in the speech of others. Both patients were right-handed and neither demonstrated aphasic or apraxic deficits. Based on these cases and those published by Heilman and colleagues [101, 102], it was hypothesized that the affective aspects of communication, as originally proposed by Hughlings Jackson [59], were dominant and lateralized functions of the right hemisphere, and that the functional–anatomic organization of affective prosody in the right hemisphere was similar to that of propositional language in the left hemisphere. An issue not resolved in the paper, however, was whether all prosodic aspects of language were impaired by RBD. Subsequent studies [93, 103, 107–109] have carefully examined this issue, and the composite data indicate that the linguistic features of prosody may be impaired by either LBD or RBD. Based on the hypothesized organization of affective prosody in the right hemisphere [74], Ross [60] studied ten patients with focal RBD due to stroke. The patients were tested within four weeks of their event, except for one patient tested at 10 months after stroke, and the lesions were localized by CT. The patients underwent a bedside assessment of their ability to modulate affective prosody and gestures in a manner similar to methods utilized for assessing propositional language. The patients were examined qualitatively for their ability to: (1) spontaneously project affect into their speech (affective prosody) and display kinesic behaviors (gestures) during discourse; (2) repeat verbally neutral sentences with different emotional intonations, i.e., happiness, sadness, surprise, disinterest, and anger; (3) comprehend affective prosody auditorily; and (4) comprehend gestures visually. When analyzed for functional–anatomic relationships, all patients with opercular lesions bordering the right Sylvian fissure had some disorder of affective communication with loss of affective repetition. Transcortical syndromes were also observed in which patients retained the ability to repeat with affective variation but had flattened affect and/or loss of the ability to comprehend affective prosody and gestures. The specific combinations of affective-prosodic deficits following localized lesions in the right hemisphere appeared to be reasonably analogous to the functional–anatomic relationships of aphasic deficits observed after focal left brain damage. Thus, the syndromes associated with RBD were called “aprosodias,” and the same modifiers used for classifying the aphasias were adopted for descriptive purposes (Table 13.1). In a follow-up study, using blinded evaluations of affective communication in patients with ischemic infarctions localized by CT, Gorelick and Ross [61] corroborated the proposed aprosodia classifications and their functional–anatomic relationships in the right hemisphere. They also reported that the prevalence of aprosodic syndromes following RBD was equal to the prevalence of aphasic syndromes following LBD, underscoring that the aprosodias are common syndromes. 187 Section I: Structural and Functional Neuroanatomy Using quantitative testing methods, including acoustical analysis of voice, and magnetic resonance imaging (MRI) to localize lesions in patients with ischemic infarctions, Ross and Monnot [62] confirmed robustly that RBD patients with loss of the ability to produce affective prosody in their spontaneous speech had, at minimum, lesions that involved the right posterior frontal operculum or homolog of Broca’s area, whereas RBD patients with loss of the ability to comprehend affective prosody had, at minimum, lesions that involved the right posterior temporal operculum or homolog of Wernicke’s area. These localizations have also been confirmed by other investigators [110– 113]. However, these functional–anatomic relationships only hold for acute brain damage for the time period immediately post-injury because of long-term recovery of function due to neural re-organization [9, 62]. Reports that have not confirmed these functional– anatomic relationships have either studied patients many months to years after brain injury or used patients with mixed etiologies of brain injury [62, 114–119]. In addition, patients with slowly progressive lesions such as brain tumors may not show expected aprosodic deficits, as noted by Lebrun and colleagues [120]; as the tumor gradually destroys cerebral tissue, the brain has time to re-organize itself and functional– anatomic relationships change accordingly [9, 62]. This phenomenon was first noted and conceptualized by Hughlings Jackson ([59] see pp. 343–5) as the “Momentum of Lesions” [121]. Similar to the aphasias, strictly subcortical lesions that involve right basal ganglia structures and thalamus have also been reported to cause aprosodias [9, 60–62, 117, 122–125]. There has also been a case report of crossed aprosodia, in which a strongly righthanded patient becomes aprosodic but not aphasic following a left hemisphere stroke, similar to cases of crossed aphasia, in which a strongly right-handed patient becomes aphasic following a right hemisphere stroke [126]. When functional imaging studies are performed probing the affective-prosodic aspects of language, bilateral activations are always observed that, at minimum, involve the posterior perisylvian regions, analogous to the results of functional imaging studies probing the semantic, phonetic, or phonologic aspects of language [9, 41–49]. However, if the stimuli are manipulated by incrementally reducing the articulatory– verbal content, asymmetries in the intensity of the 188 bilateral perisylvian activations occur with a relative shift to the right hemisphere [49, 127–129], supporting the concept that affective prosody is a lateralized and dominant function of the right hemisphere. Nevertheless, aprosodic deficits have been described following LBD [111, 119] bringing into question the concept that affective prosody is always a lateralized and dominant function of the right hemisphere [104]. Hemispheric lateralization of affective prosody The terms “dominant” and “lateralized” are used interchangeably in the literature even though they have different neurological implications regarding brain functions. If a unilateral lesion produces a behavioral deficit that affects both sides of space, then the function is considered dominant [9, 106], a criterion met easily by the various aphasic and aprosodic syndromes. If a behavioral function is also strongly lateralized, then it must be shown that the behavioral deficit does not occur following lesions of the opposite hemisphere, as exemplified by the aphasias. A behavioral function may be lateralized but not necessarily dominant, as exemplified by forebrain lesions that cause contralateral but not ipsilateral paralysis. Unlike the aphasias, the lateralization of affective prosody has been more difficult to resolve because various publications have documented affective-prosodic disturbances among patients with left hemisphere damage with and without aphasic deficits [106, 111, 119]. In patients with dense aphasias, the ability to comprehend affective prosody correlates positively with the severity of aphasic deficits [130, 131], suggesting that the presence of moderate to severe propositional language impairments may interfere with the ability of examiners to assess affective prosody [106]. However, patients with left hemisphere damage may develop affective-prosodic impairments that are not aphasic [106, 111, 118, 119]. Thus, some investigators call into question whether affective prosody is truly a lateralized dominant function of the right hemisphere [111, 119]. There is, however, another possible explanation for these findings [106]. If one assumes that affective prosody is a dominant and lateralized function of the right hemisphere and propositional language is a dominant and lateralized function of the left hemisphere, then considerable inter-hemispheric interaction must occur to ensure that the articulatory-verbal and affective-prosodic Chapter 13: Affective prosody elements of speech are temporally and behaviorally coherent [106, 122, 132, 133]. For example, if a speaker wishes to express anger, the right hemisphere must be apprised by the left hemisphere of what words will be articulated and their temporal pace, so that the right hemisphere can properly time the insertion of affective-prosodic information into the spoken sentence or phrase. Conversely, in order for the right hemisphere to insert attitudinal information such as sarcasm or irony into a sentence, certain syllables may need to be prolonged to obtain the correct temporal pace [87, 134–136]. Thus, left hemisphere damage could alter the inter-hemispheric coordination of language functions, causing an indirect disruption of affective prosody. This possibility was explored by Ross and colleagues [62, 106] in a series of patients with RBD and LBD due to ischemic infarction localized by MRI. The patients were tested within 6 weeks of their stroke using the Aprosodia Battery, in which the verbalarticulatory demands are reduced incrementally when assessing comprehension and production of affective prosody; that is, stimuli were presented with varying affects carried either by a fully articulated sentence (“I am going to the other movies”), a repeated monosyllabic utterance (“ba ba ba ba ba ba”), or an asyllabic utterance (“aaaaaaaaaah”). Among LBD patients, reducing the verbal articulatory demands caused a robust incremental improvement in their ability to comprehend and repeat affective prosody compared with controls, whereas the similar maneuver among RBD patients led to either no improvement or worsening of performance compared with controls. In addition, the affective prosodic deficits of LBD patients were not correlated to the presence, severity, or type of aphasic deficits or the cortical location of lesions [62, 106]. However, lesions involving the paracallosal white matter located below the supplementary motor area and cingulate gyrus best predicted loss of spontaneous affective prosody and affective prosodic repetition in LBD patients. In contrast, affective prosodic deficits were highly correlated with the cortical location of lesions among patients with RBD [62]. These findings suggested that the predominant mechanism underlying affective-prosodic deficits following LBD is loss of interhemispheric integration of the dominant and lateralized language functions represented in each hemisphere (the LBD/Callosal profile). In contrast, the predominant mechanism underlying affective prosodic deficits following RBD is loss of the ability to directly modulate affective prosody (the RBD/Aprosodic profile), a finding supporting the hypothesis of Blonder and colleagues [104] and Bowers and colleagues [105] that RBD causes loss of affective-communicative representations as the theoretical basis for the aprosodias, similar to LBD causing loss of semantic-syntactic representations as the theoretical basis for the aphasias. Bedside assessment of affective communication Although quantitative acoustical and neuropsychological test batteries are available for assessing affective prosody such as the Aprosodia Battery [62, 106] and the Florida Affective Battery [137, 138], clinicians can readily incorporate an examination of aprosodic deficits into their bedside neurological examination. This procedure is similar to the assessment commonly used for aphasic deficits [39, 50]. Spontaneous affective prosody and gesturing When interviewing patients, the examiner should observe whether or not they gesture or impart affect into their spontaneous conversation. Also, patients should be asked questions that are emotionally laden, such as those probing their reactions to the current illness or past emotional experiences. Attention should also be devoted to how well patients insert appropriate affective prosody and gestures into their discourse. Repetition of affective prosody This process involves patients imitating the examiner immediately after being presented with an emotionally neutral sentence or a repeated monosyllable using different affects, such as happy, sad, angry, surprised, disinterested, or neutral. The key observation is how well patients insert the appropriate affective prosody and gestures into their imitation. In an attempt to imitate surprise (terminal rise in intonation of ∼2 octaves), some patients may slightly raise their voice at the end of the repetition (terminal rise in intonation of ∼1/2 octave). This is not a correct response because they are attempting to imitate surprise using linguistic prosody by converting the repetition from a statement into a question. 189 Section I: Structural and Functional Neuroanatomy Comprehension of affective prosody After patients are presented with an emotionally neutral sentence or a repeated monosyllable using different vocal affects, such as happy, sad, angry, surprised, disinterested, or neutral, they are immediately asked to identify the affect verbally. If needed, multiple choices can be presented. Standing behind patients during this assessment avoids giving them visual clues to the intended affect. Comprehension of gestures The examiner stands in front of patients and mimes a particular emotion using only gestural activity involving the face and limbs. As with affective-prosodic comprehension, patients are requested to verbally identify the mimed emotion. Again, if needed, multiple choices can be presented. Affective prosody in other clinical settings Congenital or early childhood lesions involving the right hemisphere have been associated with aberrant psychosocial development, including excessive shyness, poor eye contact, and deficient modulation of affective prosody and gestures. These deficits have been conceptualized as right hemisphere learning disability [91–93], similar to dyslexia being considered a left hemisphere learning disability. Recent studies by Monnot and colleagues [139, 140] using the Aprosodia Battery have examined the effects of ethanol exposure and abuse on comprehension of affective prosody. A highly robust relationship with age of ethanol exposure was found (r = 0.88 and r2 = 0.78, explaining 78% of the data variance), such that the earlier the exposure to ethanol, the more devastating the deficit. Individuals who were exposed to ethanol in utero, even if they were not alcohol abusers themselves, had mean comprehension score of −5.0 standard deviations below controls, essentially performing at chance. Yet these individuals did not meet criteria for either fetal alcohol syndrome or fetal alcohol effects, and had no other cognitive or language deficits [139]. In contrast, patients whose exposure and ethanol abuse began as adults (⬎24 years of age) did not have impaired comprehension of affective prosody. From these data, it was postulated that early exposure to alcohol may cause a developmental disorder 190 that preferentially affects affective rather than propositional communication systems. Affective prosody has also been studied in normal elderly individuals. Comprehension appears to deteriorate with advancing age [141–144]. In patients with Alzheimer’s disease, affective prosodic comprehension is impaired well before the onset of anomicaphasic deficits, a finding thought to reflect an aging effect [145, 146]. Orbelo and colleagues [143, 144] used the Aprosodia Battery in normal elders and observed that the pattern of comprehension deficit fits the RBD/Aprosodic profile, but is less severe and not explainable by hearing loss. Thus the findings lend support to the hypothesis that cognitive aging is due primarily or, at least initially, to right hemisphere decline [147–149]. In addition, aging substantially impairs the comprehension of attitudinal prosody [143]. In this regard, a common occurrence in dementia clinics is that elderly patients and their spouses, whether cognitively impaired or not, often fail to appreciate jokes, sarcasm, double entendres, witticisms, or irony (unpublished observation). These individuals may react negatively to comments meant to put them at ease because they tend to interpret conversations literally. This clinical observation and the documented age-associated loss of the ability to comprehend attitudinal prosody [143] suggest that interaction with elderly patients should strive to employ literal communication whenever possible. One of the cardinal aspects of schizophrenia is flattening of affect [150–152], which includes loss of the ability to modulate affective prosody [153– 155]. Using the Aprosodia Battery, Ross and colleagues [156] examined 45 patients with medically stable schizophrenia to ascertain if the pattern of deficit suggested left, right, or mixed hemispheric dysfunction. The results showed that 84% of the patients had an aprosodic syndrome with 58% having significant deficits in comprehension. The pattern of deficits for both comprehension and repetition was statistically identical to the RBD/Aprosodic pattern, but not related statistically to other cardinal symptom clusters associated with schizophrenia. This suggests that loss of affective prosodic comprehension could be a core deficit contributing to the psychosocial impoverishment characteristic of the disease. Deficits in spontaneous affective prosody, however, were associated with avolition, apathy, and asociality. Most recently, Freeman and colleagues [157] used the comprehension portion of the Aprosodia Battery Chapter 13: Affective prosody to assess patients with well-documented chronic posttraumatic stress disorder (PTSD) [158]. This research was initiated because some of the symptoms of PTSD suggest right hemisphere dysfunction; that is, patients often have a restricted range of affect and family members often report difficulty in reading their emotions. In addition, an intriguing case report documented that nightmares and intrusive recollections of combat experience associated with PTSD, were ameliorated after a right frontal stroke [159], and there is preliminary evidence that low-frequency repetitive right transcranial magnetic stimulation, causing cortical inhibition, improves PTSD symptoms [160, 161]. Eleven patients with PTSD were evaluated [157]. Every patient had deficient comprehension of affective prosody compared with age-matched controls. As in schizophrenia [156], the performance was identical statistically to the RBD/Aprosodic profile found in patients with RBD. Ethanol was not found to contribute to the findings because, if ethanol abuse occurred, it began, with one exception, after age 23. The authors surmised that without evidence for RBD it was unlikely that an affective-prosodic comprehension deficit could be acquired as a result of PTSD. Rather, the individuals may have had a pre-existing developmental deficit in processing affective prosody, making them vulnerable to chronic PTSD when exposed to combat. Affective prosody, gestures, and the neurology of emotions The aprosodias are disturbances in the modulation of graded emotional behaviors associated with language and communication that are organized predominantly, but not exclusively, in the neocortex [9, 62]. As such, the aprosodias represent disruptions of cognitive functions that can be manipulated for psychosocial purposes and ulterior motives as part of the social emotional system [162–164]. Other aspects of emotions, such as the experience of an emotion or the display of coarse, all or none, emotional behaviors, such as laughing, crying, and anger, may be unaffected by lesions causing aprosodias because they are organized predominantly by the limbic system and related descending pathways via the hypothalamus and brainstem [163–166]. Because of this neuroanatomic arrangement, paradoxical behaviors may occur during clinical interactions that may interfere with arriving at a correct diagnosis. A dramatic example involves the challenge of diagnosing depression in patients with focal brain lesions [167]. For example, patients with motor types of aprosodia will exhibit a flat affect even when discussing highly emotional issues, such as depression or suicide [167, 168]. Consequently, clinicians may discount their verbal reports of emotional distress and not diagnose or treat their depression. Patients with RBD, in addition to being aprosodic, may have neglect and denial of illness causing them to verbally deny that they are depressed even though they have appropriate behavioral, vegetative, and neuroendocrine indicators of melancholic depression that will respond to antidepressant medications. Patients with motor or global aprosodia may be able to display appropriate emotional behaviors during very sad, happy, or angry situations even though they have an otherwise flat affect during normal discourse [60, 61]. However, if the emotional displays are excessive and inappropriate to the psychosocial situation, similar to patients with pathologic regulation of affect due to pseudobulbar palsy [163, 164, 169, 170], then the patient may have an underlying depression [167, 168]. The pathological regulation of affect responds very quickly to antidepressant medications, in contrast to other symptoms associated with depression, especially mood [168]. Other contributions of the right hemisphere to language and communication Although this chapter has focused on affective prosody and disorders of affective communication, the right hemisphere makes other contributions to language and communication that are not well appreciated by clinicians [9]. Some functions contribute to higherorder semantic-linguistic competency and include: (1) the comprehension of connotative, non-standard (third and fourth order) word meaning as opposed to denotative or standard word meaning; (2) comprehension of metaphor; (3) thematic comprehension, i.e., understanding communicative intent conveyed by paragraphs and chapters as opposed to words and sentences; and (4) comprehension of complex linguistic relationships [171–181]. In addition, the right hemisphere is involved in the comprehension of non-literal, idiomatic, types of expressions that are not semantically or literally feasible [182–184], e.g., “he is walking 191 Section I: Structural and Functional Neuroanatomy on clouds,” and the modulation of semantically emotional words and curses [59, 76, 185]. Conclusion This chapter considered the topic of the right hemisphere’s contribution to language and communication, with special emphasis on affective prosody and gestures. The various levels of prosodic communication that convey linguistic, dialectical, and idiosyncratic information were also reviewed. Formal methods for assessing disturbances of affective prosody, and a brief description of the bedside assessment of affective prosody were provided in order to enhance the clinical skill set of BN&NP subspecialists. Finally, the clinical conditions in which disturbances of affective prosody occur commonly were reviewed, thereby illuminating the contexts in which it may be productive for clinicians to undertake assessment for aprosodic defects [91–93, 139–146, 156, 157, 167, 168]. Acknowledgments This work was supported, in part, by grants from the Merit Review Board, Medical Research Service, Department of Veterans Affairs, Washington, DC. I am indebted to Marilee Monnot, PhD, for suggestions to improve the manuscript. References 7. Schoenemann PT. Syntax as an emergent characteristic of the evolution of semantic complexity. Minds Machines 1999;9(3):309–46. 8. Bass AH, Gilland EH, Baker R. Evolutionary origins for social vocalization in a vertebrate hindbrain-spinal compartment. Science 2008;321(5887):417–21. 9. Ross ED. Cerebral localization of functions and the neurology of language: fact versus fiction or is it something else? Neuroscientist 2010;16(3):222–43. 10. Geschwind N. The development of the brain and the evolution of language. In Stuart CIJM, editor. Monograph Series on Language and Linguistics. Washington, DC: Georgetown University Press; 1964, pp. 155–69. 11. Geschwind N. Disconnexion syndromes in animals and man. I. Brain 1965;88(2):237–94. 12. Geschwind N. Disconnexion syndromes in animals and man. II. Brain 1965;88(3):585–644. 13. Penfield W. Speech, perception and the uncommitted cortex. In Eccles JC, editor. Brain and Conscious Experience Study Week, September 28–October 4, 1964, of the Pontificia Academia Scientiarum [papers and discussions]. New York, NY: Springer-Verlag; 1966, pp. 217–37. 14. Passingham RE. Anatomical differences between the neocortex of man and other primates. Brain Behav Evol. 1973;7(5):337–59. 15. Killackey HP. Neocortical expansion: an attempt toward relating phylogeny and ontogeny. J Cogn Neurosci. 1990;2(1):1–17. 1. Thorpe WH. Animal vocalization and communication. In Millikan CH, Darley FL, editors. Brain Mechanisms Underlying Speech and Language; Proceedings. New York, NY: Grune and Stratton; 1967, pp. 2–12. 16. Killackey HP. Evolution of the human brain: a neuroanatomical perspective. In Gazzaniga MS, Bizzi E, editors. The Cognitive Neurosciences. Cambridge, MA: MIT Press; 1995, pp. 1243–53. 2. Fromkin V, Rodman R. An Introduction to Language. 2nd edition. New York, NY: Holt, Rinehart and Winston; 1978. 17. Mesulam MM. Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann Neurol. 1990;28(5):597–613. 3. Wilson EO. Animal communication. In Wang WSY, editor. The Emergence of Language: Development and Evolution: Readings from Scientific American Magazine. New York, NY: W.H. Freeman; 1991, pp. 3–15. 18. Jackson JH. The Croonian Lectures on evolution and dissolution of the nervous system. Br Med J. 1884;1(1213, 1214, 1215):591–3, 660–3, 703–7. 4. Savage-Rumbaugh ES, Murphy J, Sevcik RA et al. Language comprehension in ape and child. Monogr Soc Res Child Dev. 1993;58(3–4):1–222. 5. Fisher SE, Marcus GF. The eloquent ape: genes, brains and the evolution of language. Nat Rev Genet. 2006; 7(1):9–20. 192 6. Stumpner A, von Helversen D. Evolution and function of auditory systems in insects. Naturwissenschaften 2001;88(4):159–70. 19. Jackson JH. Remarks on evolution and dissolution of the nervous system. In Tuke DH, Savage GH, editors. The Journal of Mental Science. London: Association of Medical Officers of Asylums and Hospitals for the Insane (London England), Medico-psychological Association of Great Britain and Ireland, Royal Medico-psychological Association, Longman, Green, Longman & Roberts; 1887, pp. 25–48. Chapter 13: Affective prosody 20. Harlow HF. The neuro-physiological correlates of learning and intelligence. Psychol Bull. 1936;33(7): 479–525. 37. Eggert GH, Wernicke C. Wernicke’s Works on Aphasia: A Sourcebook and Review. The Hague: Mouton; 1977. 21. Jerison HJ. Animal intelligence as encephalization. Philos Trans R Soc Lond B Biol Sci. 1985;308(1135): 21–35. 38. Lichtheim L. On aphasia. In Bucknill JC, Crichton-Browne J, Ferrier D, Jackson JH, de Watteville A, editors. Brain: A Journal of Neurology. London: Macmillan & Co.; 1885, pp. 433–84. 22. Mesulam MM. From sensation to cognition. Brain 1998;121(Pt 6):1013–52. 23. Deacon TW. Evolutionary perspectives on language and brain plasticity. J Commun Disord. 2000;33(4): 273–90; quiz 90–1. 24. Schmahmann JD, Pandya DN. Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems. Cortex 2008;44(8):1037–66. 25. Myers RE. Comparative neurology of vocalization and speech: proof of a dichotomy. Ann N Y Acad Sci. 1976;280:745–60. 26. Nottebohm F. Reassessing the mechanisms and origins of vocal learning in birds. Trends Neurosci. 1991;14(5): 206–11. 27. Jurgens U. The role of the periaqueductal grey in vocal behaviour. Behav Brain Res. 1994;62(2):107–17. 28. Jurgens U. Neural pathways underlying vocal control. Neurosci Biobehav Rev. 2002;26(2):235–58. 29. MacLean PD, Newman JD. Role of midline frontolimbic cortex in production of the isolation call of squirrel monkeys. Brain Res. 1988;450(1–2):111–23. 30. Davis PJ, Zhang SP, Winkworth A, Bandler R. Neural control of vocalization: respiratory and emotional influences. J Voice 1996;10(1):23–38. 31. Lieberman P. On the nature and evolution of the neural bases of human language. Am J Phys Anthropol. 2002;Suppl. 35:36–62. 32. Reiner A, Perkel DJ, Mello CV, Jarvis ED. Songbirds and the revised avian brain nomenclature. Ann N Y Acad Sci. 2004;1016:77–108. 33. Kittelberger JM, Land BR, Bass AH. Midbrain periaqueductal gray and vocal patterning in a teleost fish. J Neurophysiol. 2006;96(1):71–85. 34. Ackermann H, Mathiak K, Riecker A. The contribution of the cerebellum to speech production and speech perception: clinical and functional imaging data. Cerebellum 2007;6(3):202–13. 35. Broca P. Rermarques sue le siége de la faculté du langage articulé, suivies d’une observation d’aphémie. Bull Société Anatomique 1861;6:330–57, 98–407. 36. Berker EA, Berker AH, Smith A. Translation of Broca’s 1865 report: localization of speech in the third left frontal convolution. Arch Neurol. 1986;43(10): 1065–72. 39. Benson DF. Aphasia, Alexia, and Agraphia. New York, NY: Churchill Livingstone; 1979. 40. Benton AL. The ‘minor’ hemisphere. J Hist Med Allied Sci. 1972;27(1):5–14. 41. Larsen B, Skinhoj E, Lassen NA. Variations in regional cortical blood flow in the right and left hemispheres during automatic speech. Brain 1978;101(2): 193–209. 42. Petersen SE, Fox PT, Posner MI, Mintun M, Raichle ME. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 1988;331(6157):585–9. 43. Binder JR, Frost JA, Hammeke TA et al. Human brain language areas identified by functional magnetic resonance imaging. J Neurosci. 1997;17(1): 353–62. 44. Buchanan TW, Lutz K, Mirzazade S et al. Recognition of emotional prosody and verbal components of spoken language: an fMRI study. Brain Res Cogn Brain Res. 2000;9(3):227–38. 45. Wildgruber D, Pihan H, Ackermann H, Erb M, Grodd W. Dynamic brain activation during processing of emotional intonation: influence of acoustic parameters, emotional valence, and sex. Neuroimage 2002;15(4):856–69. 46. Demonet JF, Thierry G, Cardebat D. Renewal of the neurophysiology of language: functional neuroimaging. Physiol Rev. 2005;85(1):49–95. 47. Van Lancker Sidtis D. Does functional neuroimaging solve the questions of neurolinguistics? Brain Lang. 2006;98(3):276–90. 48. Sidtis JJ. Some problems for representations of brain organization based on activation in functional imaging. Brain Lang. 2007;102(2):130–40. 49. Mitchell RL, Ross ED. fMRI evidence for the effect of verbal complexity on lateralisation of the neural response associated with decoding prosodic emotion. Neuropsychologia 2008;46(12):2880–7. 50. Ross E. Affective prosody and the aprosodias. In Mesulam MM, editor. Principles of Behavioral and Cognitive Neurology. 2nd edition. Oxford: Oxford University Press; 2000, pp. 316–31. 51. Critchley M. The Language of Gesture. London: E. Arnold & Co.; 1939. 193 Section I: Structural and Functional Neuroanatomy 52. Monrad-Krohn GH. Dysprosody or altered melody of language. Brain 1947;70(Pt 4):405–15. 53. Monrad-Krohn GH. The third element of speech: prosody and its disorders. In Halpern L, editor. Problems in Dynamic Neurology. Jerusalem: Hebrew University Press; 1963, pp. 101–18. 69. Seron X, van der Kaa MA, Remitz A, van der Linden M. Pantomime interpretation and aphasia. Neuropsychologia 1979;17(6):661–8. 54. Crystal D. The English Tone of Voice: Essays in Intonation, Prosody and Paralanguage. London: Edward Arnold; 1975. 70. De Renzi E, Motti F, Nichelli P. Imitating gestures. A quantitative approach to ideomotor apraxia. Arch Neurol. 1980;37(1):6–10. 55. Van Lancker D, Canter GJ, Terbeek D. Disambiguation of ditropic sentences: acoustic and phonetic cues. J Speech Hear Res. 1981;24(3):330–5. 71. Feyereisen P, Seron X. Nonverbal communication and aphasia: a review. I. Comprehension. Brain Lang. 1982;16(2):191–212. 56. Aronson AE. Clinical Voice Disorders: An Interdisciplinary Approach. New York, NY: B.C. Decker; 1980. 72. Feyereisen P, Seron X. Nonverbal communication and aphasia: a review. II. Expression. Brain Lang. 1982; 16(2):213–36. 57. Berthier ML, Ruiz A, Massone MI, Starkstein SE, Leiguarda RC. Foreign accent syndrome – behavioral and anatomical findings in recovered and non-recovered patients. Aphasiology 1991;5(2):129–47. 73. Heilman KM, Watson RT. The disconnection apraxias. Cortex 2008;44(8):975–82. 58. Edwards RJ, Patel NK, Pople IK. Foreign accent following brain injury: syndrome or epiphenomenon? Eur Neurol. 2005;53(2):87–91. 59. Jackson JH. On affections of speech from diseases of the brain In Bucknill JC, Crichton-Browne J, Ferrier D, Jackson JH, De Watteville A, editors. Brain: A Journal of Neurology. London: Macmillan & Co; 1879–1880, pp. 304–30; 203–22; 323–56. 60. Ross ED. The aprosodias. Functional-anatomic organization of the affective components of language in the right hemisphere. Arch Neurol. 1981;38(9): 561–9. 74. Ross ED, Mesulam MM. Dominant language functions of the right hemisphere? Prosody and emotional gesturing. Arch Neurol. 1979;36(3):144–8. 75. Buck R, Duffy RJ. Nonverbal communication of affect in brain-damaged patients. Cortex 1980;16(3):351–62. 76. Cicone M, Wapner W, Gardner H. Sensitivity to emotional expressions and situations in organic patients. Cortex 1980;16(1):145–58. 77. DeKosky ST, Heilman KM, Bowers D, Valenstein E. Recognition and discrimination of emotional faces and pictures. Brain Lang. 1980;9(2):206–14. 78. Benowitz LI, Bear DM, Rosenthal R et al. Hemispheric specialization in nonverbal communication. Cortex 1983;19(1):5–11. 61. Gorelick PB, Ross ED. The aprosodias: further functional-anatomical evidence for the organisation of affective language in the right hemisphere. J Neurol Neurosurg Psychiatry 1987;50(5):553–60. 79. Borod JC, Koff E, Lorch MP, Nicholas M. Channels of emotional expression in patients with unilateral brain damage. Arch Neurol. 1985;42(4):345–8. 62. Ross ED, Monnot M. Neurology of affective prosody and its functional-anatomic organization in right hemisphere. Brain Lang. 2008;104(1):51–74. 80. Borod JC, Koff E, Perlman Lorch M, Nicholas M. The expression and perception of facial emotion in brain-damaged patients. Neuropsychologia 1986; 24(2):169–80. 63. Van Lancker DR, Cummings JL, Kreiman J, Dobkin BH. Phonagnosia: a dissociation between familiar and unfamiliar voices. Cortex 1988;24(2):195–209. 64. Van Lancker DR, Kreiman J, Cummings J. Voice perception deficits: neuroanatomical correlates of phonagnosia. J Clin Exp Neuropsychol. 1989;11(5): 665–74. 65. Meadows JC. The anatomical basis of prosopagnosia. J Neurol Neurosurg Psychiatry 1974;37(5):489–501. 194 68. Cicone M, Wapner W, Foldi N, Zurif E, Gardner H. The relation between gesture and language in aphasic communication. Brain Lang. 1979;8(3):324–49. 81. Borod JC, Koff E, Lorch MP, Nicholas M, Welkowitz J. Emotional and non-emotional facial behaviour in patients with unilateral brain damage. J Neurol Neurosurg Psychiatry 1988;51(6):826–32. 82. Mammucari A, Caltagirone C, Ekman P et al. Spontaneous facial expression of emotions in brain-damaged patients. Cortex 1988;24(4):521–33. 66. Goodglass H, Kaplan E. Disturbance of gesture and pantomime in aphasia. Brain 1963;86:703–20. 83. Blonder LX, Burns AF, Bowers D, Moore RW, Heilman KM. Right hemisphere facial expressivity during natural conversation. Brain Cogn. 1993; 21(1):44–56. 67. Gainotti G, Lemmo MS. Comprehension of symbolic gestures in aphasia. Brain Lang. 1976;3(3):451–60. 84. Borod JC, Bloom RL, Brickman AM, Nakhutina L, Curko EA. Emotional processing deficits in Chapter 13: Affective prosody individuals with unilateral brain damage. Appl Neuropsychol. 2002;9(1):23–36. 85. Charbonneau S, Scherzer BP, Aspirot D, Cohen H. Perception and production of facial and prosodic emotions by chronic CVA patients. Neuropsychologia 2003;41(5):605–13. 86. Blonder LX, Heilman KM, Ketterson T et al. Affective facial and lexical expression in aprosodic versus aphasic stroke patients. J Int Neuropsychol Soc. 2005; 11(6):677–85. 87. Bolinger DLM. Language, the Loaded Weapon: The Use and Abuse of Language Today. London: Longman; 1980. 88. Plutchik R. The nature of emotions: clinical implications. In Clynes M, Panksepp J, editors. Emotions and Psychopathology. New York, NY: Plenum Press; 1988. p. xiii. 89. Arndt H, Janney RW. Verbal, prosodic, and kinesic emotive contrasts in speech. J Pragmatics 1991; 15(6):521–49. 100. Bowers D, Coslett HB, Bauer RM, Speedie LJ, Heilman KM. Comprehension of emotional prosody following unilateral hemispheric lesions: processing defect versus distraction defect. Neuropsychologia 1987;25(2):317–28. 101. Heilman KM, Scholes R, Watson RT. Auditory affective agnosia. Disturbed comprehension of affective speech. J Neurol Neurosurg Psychiatry 1975;38(1):69–72. 102. Tucker DM, Watson RT, Heilman KM. Discrimination and evocation of affectively intoned speech in patients with right parietal disease. Neurology 1977;27(10): 947–50. 103. Heilman KM, Bowers D, Speedie L, Coslett HB. Comprehension of affective and nonaffective prosody. Neurology 1984;34(7):917–21. 104. Blonder LX, Bowers D, Heilman KM. The role of the right-hemisphere in emotional communication. Brain 1991;114:1115–27. 90. Mehrabian A. Nonverbal Communication. New Brunswick, NJ: Aldine Transaction; 2007. 105. Bowers D, Bauer RM, Heilman KM. The nonverbal affect lexicon: theoretical perspectives from neuropsychological studies of affect perception. Neuropsychology 1993;7(4):433–44. 91. Weintraub S, Mesulam MM. Developmental learning disabilities of the right hemisphere. Emotional, interpersonal, and cognitive components. Arch Neurol. 1983;40(8):463–8. 106. Ross ED, Thompson RD, Yenkosky J. Lateralization of affective prosody in brain and the callosal integration of hemispheric language functions. Brain Lang. 1997;56(1):27–54. 92. Voeller KK. Clinical neurologic aspects of the right-hemisphere deficit syndrome. J Child Neurol. 1995;10(Suppl. 1):S16–22. 107. Danly M, Shapiro B. Speech prosody in Broca’s aphasia. Brain Lang. 1982;16(2):171–90. 93. Trauner DA, Ballantyne A, Friedland S, Chase C. Disorders of affective and linguistic prosody in children after early unilateral brain damage. Ann Neurol. 1996;39(3):361–7. 94. Carton JS, Kessler EA, Pape CL. Nonverbal decoding skills and relationship well-being in adults. J Nonverbal Behav. 1999;23(1):91–100. 95. Wymer JH, Lindman LS, Booksh RL. A neuropsychological perspective of aprosody: features, function, assessment, and treatment. Appl Neuropsychol. 2002;9(1):37–47. 96. Ross ED. The divided self. Sciences 1982;22(2):8–12. 97. Ross E. Right hemisphere syndromes and the neurology of emotions. In Schachter SC, Devinsky O, editors. Behavioral Neurology and the Legacy of Norman Geschwind. Philadelphia, PA: Lippincott-Raven; 1997, pp. 183–4. 108. Danly M, Cooper WE, Shapiro B. Fundamental frequency, language processing, and linguistic structure in Wernicke’s aphasia. Brain Lang. 1983; 19(1):1–24. 109. Weintraub S, Mesulam MM, Kramer L. Disturbances in prosody. A right-hemisphere contribution to language. Arch Neurol. 1981;38(12):742–4. 110. Denes G, Caldognetto EM, Semenza C, Vagges K, Zettin M. Discrimination and identification of emotions in human voice by brain-damaged subjects. Acta Neurol Scand. 1984;69(3):154–62. 111. Cancelliere AE, Kertesz A. Lesion localization in acquired deficits of emotional expression and comprehension. Brain Cogn. 1990;13(2):133–47. 112. Darby DG. Sensory aprosodia: a clinical clue to lesions of the inferior division of the right middle cerebral artery? Neurology 1993;43(3 Pt 1):567–72. 98. Heilman KM. Matter of Mind: A Neurologist’s View of Brain–Behavior Relationships. Oxford: Oxford University Press; 2002. 113. Starkstein SE, Federoff JP, Price TR, Leiguarda RC, Robinson RG. Neuropsychological and neuroradiologic correlates of emotional prosody comprehension. Neurology 1994;44(3 Pt 1):515–22. 99. De Groot AW. Structural linguistics and syntactic laws. Word 1949;5:1–12. 114. Ehlers L, Dalby M. Appreciation of emotional expressions in the visual and auditory modality in 195 Section I: Structural and Functional Neuroanatomy normal and brain-damaged patients. Acta Neurol Scand. 1987;76(4):251–6. prosody, as revealed by functional magnetic resonance imaging. Neuropsychologia 2003;41(10):1410–21. 115. Bradvik B, Dravins C, Holtas S et al. Disturbances of speech prosody following right hemisphere infarcts. Acta Neurol Scand. 1991;84(2):114–26. 129. Wildgruber D, Riecker A, Hertrich I et al. Identification of emotional intonation evaluated by fMRI. Neuroimage 2005;24(4):1233–41. 116. Pell MD, Baum SR. The ability to perceive and comprehend intonation in linguistic and affective contexts by brain-damaged adults. Brain Lang. 1997;57(1):80–99. 130. Schlanger BB, Schlanger P, Gerstman LJ. The perception of emotionally toned sentences by right hemisphere-damaged and aphasic subjects. Brain Lang. 1976;3(3):396–403. 117. Breitenstein C, Daum I, Ackermann H. Emotional processing following cortical and subcortical brain damage: contribution of the fronto-striatal circuitry. Behav Neurol. 1998;11(1):29–42. 131. Seron X, Van der Kaa MA, Vanderlinden M, Remits A, Feyereisen P. Decoding paralinguistic signals: effect of semantic and prosodic cues on aphasics’ comprehension. J Commun Disord. 1982;15(3):223–31. 118. Wertz RT, Henschel CR, Auther LL, Ashford JR, Kirshner HS. Affective prosodic disturbance subsequent to right hemisphere stroke: a clinical application. J Neurolinguistics 1998;11(1–2):89–102. 132. Speedie LJ, Coslett HB, Heilman KM. Repetition of affective prosody in mixed transcortical aphasia. Arch Neurol. 1984;41(3):268–70. 119. Adolphs R, Damasio H, Tranel D. Neural systems for recognition of emotional prosody: a 3-D lesion study. Emotion 2002;2(1):23–51. 120. Lebrun Y, Lessinnes A, De Vresse L, Leleux C. Dysprosody and the non-dominant hemisphere. Lang Sci. 1985;7(1):41–52. 121. Riese W. Aphasia in brain tumors; its appearance in relation to the natural history of the lesion. Confin Neurol. 1949;9(1–2):64–79. 122. Ross ED, Harney JH, deLacoste-Utamsing C, Purdy PD. How the brain integrates affective and propositional language into a unified behavioral function. Hypothesis based on clinicoanatomic evidence. Arch Neurol. 1981;38(12):745–8. 123. Wolfe GI, Ross ED. Sensory aprosodia with left hemiparesis from subcortical infarction. Right hemisphere analogue of sensory-type aphasia with right hemiparesis? Arch Neurol. 1987;44(6):668–71. 124. Cohen MJ, Riccio CA, Flannery AM. Expressive aprosodia following stroke to the right basal ganglia: a case report. Neuropsychology 1994;8(2):242–5. 125. Van Lancker Sidtis D, Pachana N, Cummings JL, Sidtis JJ. Dysprosodic speech following basal ganglia insult: toward a conceptual framework for the study of the cerebral representation of prosody. Brain Lang. 2006;97(2):135–53. 126. Ross ED, Anderson B, Morgan-Fisher A. Crossed aprosodia in strongly dextral patients. Arch Neurol. 1989;46(2):206–9. 127. Meyer M, Alter K, Friederici AD, Lohmann G, von Cramon DY. FMRI reveals brain regions mediating slow prosodic modulations in spoken sentences. Hum Brain Mapp. 2002;17(2):73–88. 128. Mitchell RL, Elliott R, Barry M, Cruttenden A, Woodruff PW. The neural response to emotional 196 133. Klouda GV, Robin DA, Graff-Radford NR, Cooper WE. The role of callosal connections in speech prosody. Brain Lang. 1988;35(1):154–71. 134. Haverkate H. A speech act analysis of irony. J Pragmat. 1990;14(1):77–109. 135. Anolli L, Ciceri R, Infantino MG. Irony as a game of implicitness: acoustic profiles of ironic communication. J Psycholinguist Res. 2000;29(3): 275–311. 136. Rockwell P. Lower, slower, louder: vocal cues of sarcasm. J Psycholinguist Res. 2000;29(5):483–95. 137. Bowers D, Blonder LX, Heilman KM. Florida Affective Battery. Gainsville, FL: University of Florida, Center for Neuropsychological Studies, Cognitive Neuroscience Laboratory; 1999. Available from: http://www.neurology.ufl.edu/forms/fab manual.pdf. 138. Heilman KM, Leon SA, Rosenbek JC. Affective aprosodia from a medial frontal stroke. Brain Lang. 2004;89(3):411–16. 139. Monnot M, Nixon S, Lovallo W, Ross E. Altered emotional perception in alcoholics: deficits in affective prosody comprehension. Alcohol Clin Exp Res. 2001; 25(3):362–9. 140. Monnot M, Lovallo WR, Nixon SJ, Ross E. Neurological basis of deficits in affective prosody comprehension among alcoholics and fetal alcohol-exposed adults. J Neuropsychiatry Clin Neurosci. 2002;14(3):321–8. 141. Cohen ES, Brosgole L. Visual and auditory affect recognition in senile and normal elderly persons. Int J Neurosci. 1988;43(1–2):89–101. 142. Allen R, Brosgole L. Facial and auditory affect recognition in senile geriatrics, the normal elderly and young adults. Int J Neurosci. 1993;68(1–2):33–42. 143. Orbelo DM, Testa JA, Ross ED. Age-related impairments in comprehending affective prosody with Chapter 13: Affective prosody comparison to brain-damaged subjects. J Geriatr Psychiatry Neurol. 2003;16(1):44–52. with chronic posttraumatic stress disorder. J Neuropsychiatry Clin Neurosci. 2009;21(1):52–8. 144. Orbelo DM, Grim MA, Talbott RE, Ross ED. Impaired comprehension of affective prosody in elderly subjects is not predicted by age-related hearing loss or age-related cognitive decline. J Geriatr Psychiatry Neurol. 2005;18(1):25–32. 158. Freeman T, Powell M, Kimbrell T. Measuring symptom exaggeration in veterans with chronic posttraumatic stress disorder. Psychiatry Res. 2008;158(3):374–80. 145. Roberts VJ, Ingram SM, Lamar M, Green RC. Prosody impairment and associated affective and behavioral disturbances in Alzheimer’s disease. Neurology 1996;47(6):1482–8. 146. Testa JA, Beatty WW, Gleason AC, Orbelo DM, Ross ED. Impaired affective prosody in AD: relationship to aphasic deficits and emotional behaviors. Neurology 2001;57(8):1474–81. 147. Botwink J. Intellectual abilities. In Birren JE, Schaie KW, editors. Handbook of the Psychology of Aging. New York, NY: Van Nostrand Reinhold; 1977. 148. Hochnadel G, Kaplan E. Neuropsychology of normal aging. In: Albert ML, editor. Clinical Neurology of Aging. New York, NY: Oxford University Press; 1984, pp. 231–44. 149. Prodan CI, Orbelo DM, Ross ED. Processing of facial blends of emotion: support for right hemisphere cognitive aging. Cortex 2007;43(2):196–206. 150. Abrams R, Taylor MA. A rating scale for emotional blunting. Am J Psychiatry 1978;135(2):226–9. 151. Andreasen NC. Affective flattening and the criteria for schizophrenia. Am J Psychiatry 1979;136(7):944–7. 152. Alpert M, Rosen A, Welkowitz J, Sobin C, Borod JC. Vocal acoustic correlates of flat affect in schizophrenia. Similarity to Parkinson’s disease and right hemisphere disease and contrast with depression. Br J Psychiatry Suppl. 1989;(4):51–6. 153. Fricchione G, Sedler MJ, Shukla S. Aprosodia in eight schizophrenic patients. Am J Psychiatry 1986;143(11): 1457–9. 154. Borod JC, Alpert M, Brozgold A et al. A preliminary comparison of flat affect schizophrenics and brain-damaged patients on measures of affective processing. J Commun Disord. 1989;22(2):93–104. 159. Freeman TW, Kimbrell T. A “cure” for chronic combat-related posttraumatic stress disorder secondary to a right frontal lobe infarct: a case report. J Neuropsychiatry Clin Neurosci. 2001;13(1): 106–9. 160. McCann UD, Kimbrell TA, Morgan CM et al. Repetitive transcranial magnetic stimulation for posttraumatic stress disorder. Arch Gen Psychiatry 1998;55(3):276–9. 161. Cohen H, Kaplan Z, Kotler M et al. Repetitive transcranial magnetic stimulation of the right dorsolateral prefrontal cortex in posttraumatic stress disorder: a double-blind, placebo-controlled study. Am J Psychiatry 2004;161(3):515–24. 162. Ross ED, Homan RW, Buck R. Differential hemispheric lateralization of primary and social emotions – implications for developing a comprehensive neurology for emotions, repression, and the subconscious. Neuropsychiatry Neuropsychol Behav Neurol. 1994;7(1):1–19. 163. Ross E. Cortical representation of the emotions. In Trimble MR, Cummings JL, editors. Contemporary Behavioral Neurology. Boston, MA: Butterworth-Heinemann; 1997, pp. 107–26. 164. Ross ED, Prodan CI, Monnot M. Human facial expressions are organized functionally across the upper-lower facial axis. Neuroscientist 2007;13(5): 433–46. 165. Ross ED. Hemispheric specialization for emotions, affective aspects of language and communication and the cognitive control of display behaviors in humans. Prog Brain Res. 1996;107:583–94. 166. Ross ED. Sensory-specific amnesia and hypoemotionality in humans and monkeys: gateway for developing a hodology of memory. Cortex 2008; 44(8):1010–22. 155. Murphy D, Cutting J. Prosodic comprehension and expression in schizophrenia. J Neurol Neurosurg Psychiatry 1990;53(9):727–30. 167. Ross ED, Rush AJ. Diagnosis and neuroanatomical correlates of depression in brain-damaged patients. Implications for a neurology of depression. Arch Gen Psychiatry 1981;38(12):1344–54. 156. Ross ED, Orbelo DM, Cartwright J et al. Affective-prosodic deficits in schizophrenia: comparison to patients with brain damage and relation to schizophrenic symptoms [corrected]. J Neurol Neurosurg Psychiatry 2001;70(5):597–604. 168. Ross ED, Stewart RS. Pathological display of affect in patients with depression and right frontal brain damage. An alternative mechanism. J Nerv Ment Dis. 1987;175(3):165–72. 157. Freeman TW, Hart J, Kimbrell T, Ross ED. Comprehension of affective prosody in veterans 169. Kinnier Wilson SA. Original papers: some problems in neurology – No. II. – Pathological laughing and crying. J Neurol Psychopathol. 1924;4(16):299–333. 197 Section I: Structural and Functional Neuroanatomy 170. Lieberman A, Benson DF. Control of emotional expression in pseudobulbar palsy. A personal experience. Arch Neurol. 1977;34(11):717–19. 178. Rehak A, Kaplan JA, Weylman ST et al. Story processing in right-hemisphere brain-damaged patients. Brain Lang. 1992;42(3):320–36. 171. Winner E, Gardner H. The comprehension of metaphor in brain-damaged patients. Brain 1977; 100(4):717–29. 179. Schneiderman EI, Murasugi KG, Saddy JD. Story arrangement ability in right brain-damaged patients. Brain Lang. 1992;43(1):107–20. 172. Caramazza A, Berndt RS, Basili AG, Koller JJ. Syntactic processing deficits in aphasia. Cortex 1981;17(3):333–48. 180. Marini A, Carlomagno S, Caltagirone C, Nocentini U. The role played by the right hemisphere in the organization of complex textual structures. Brain Lang. 2005;93(1):46–54. 173. Delis DC, Wapner W, Gardner H, Moses JA, Jr. The contribution of the right hemisphere to the organization of paragraphs. Cortex 1983;19(1): 43–50. 174. Brownell HH, Potter HH, Michelow D, Gardner H. Sensitivity to lexical denotation and connotation in brain-damaged patients: a double dissociation? Brain Lang. 1984;22(2):253–65. 175. Brownell HH, Potter HH, Bihrle AM, Gardner H. Inference deficits in right brain-damaged patients. Brain Lang. 1986;27(2):310–21. 176. Foldi NS. Appreciation of pragmatic interpretations of indirect commands: comparison of right and left hemisphere brain-damaged patients. Brain Lang. 1987;31(1):88–108. 177. Hough MS. Narrative comprehension in adults with right and left hemisphere brain-damage: theme organization. Brain Lang. 1990;38(2): 253–77. 198 181. Mitchell RL, Crow TJ. Right hemisphere language functions and schizophrenia: the forgotten hemisphere? Brain 2005;128(Pt 5):963–78. 182. Van Lancker DR, Kempler D. Comprehension of familiar phrases by left- but not by right-hemisphere damaged patients. Brain Lang. 1987;32(2):265–77. 183. Van Lancker D. The neurology of proverbs. Behav Neurol. 1990;3:169–87. 184. Mashal N, Faust M, Hendler T, Jung-Beeman M. Hemispheric differences in processing the literal interpretation of idioms: converging evidence from behavioral and fMRI studies. Cortex 2008;44(7): 848–60. 185. Borod JC, Andelman F, Obler LK, Tweedy JR, Welkowitz J. Right hemisphere specialization for the identification of emotional words and sentences: evidence from stroke patients. Neuropsychologia 1992;30(9):827–44. Section I Structural and Functional Neuroanatomy Chapter Praxis 14 Kenneth M. Heilman There are two major cognitive control systems that help the human program movements. One system helps determine when to move and the other how to move. Akinesia, or hypokinesia, is a disorder in the “when” or “action-intentional system.” Some of the other disorders of the action-intentional system include motor impersistence, defective response inhibition, and motor perseveration. Although these disorders are caused by brain damage and are terribly disabling, this chapter will focus on the disorders of the “how” system. The term apraxia was originally used by Steinthal in 1871 [1] to describe a misuse of tools and objects, a disorder of the “how” system. Although the term apraxia, derived from Greek, literally means “without action,” the word akinesia is used currently to describe the failure to initiate an action in the absence of weakness. The term apraxia has been used for a variety of how-movement disorders including disorders of gait, speech and eye control, but this chapter will focus on apraxia of the upper limbs. Apraxia of the forelimbs can be task specific or general. Task-specific apraxias include dressing apraxia and constructional apraxia; these specific forms of apraxia will not be discussed in this chapter, which instead will focus on the general forms of apraxia. As noted by Geschwind [2, 3], who helped to re-establish interest in apraxia, these disorders are in part defined by what they are not. For example, there are many sensory, motor and cognitive disorders that might impair a person’s ability to correctly make skilled purposeful movements, including: weakness, sensory loss, abnormal and involuntary movements such as tremors, dystonia, chorea, ballismus, athetosis, myoclonus, loss of coordination such as ataxia, as well as seizures. Patients with severe cognitive, memory and attentional disorders may also have difficulty performing skilled acts, because they do not understand, forget, or get distracted. The presence of these more elemental motor, sensory and cognitive disorders do not, however, preclude that a patient can also have limb apraxia. Unfortunately, the presence of these disorders might prevent the examiner from adequately testing for apraxia. Clinical relevance Apraxia often goes unrecognized by patients as well as physicians and there are several possible reasons for its poor recognition. In regard to the patient, apraxia is often associated with left hemispheric injury such as that produced by diseases such as stroke. Since many of these patients have a hemiparesis of their preferred right arm and hand, when these patients attempt to perform skilled acts with their non-preferred left arm and learn that they are impaired, they may attribute their poor performance to pre-morbid clumsiness of their non-dominant left forelimb. However, many apraxic patients who do not have a right hemiplegia but who are impaired when attempting to perform learned skilled movement with their preferred right upper limb still fail to recognize their deficits; this unawareness of deficits appears to be a form of anosognosia. Not only do patients often fail to complain about the deficits associated with apraxia, but many health professionals also fail to test for limb apraxia. Limb apraxia has been noted to be a heterogeneous group of disorders with a variety of different clinical presentations; unfortunately, there is a paucity of standardized clinical tests that can be used to assess for these disorders. Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 199 Section I: Structural and Functional Neuroanatomy For many years neurologists, neuropsychologists, speech pathologists, and other clinicians thought that limb apraxia was a rare disorder seen only in the laboratory by a handful of academicians, and that these disorders did not interfere with performing activities of daily living or instrumental activities. However, it is now widely recognized that apraxia is a major cause of disability which can interfere with a patient’s ability to perform both basic and instrumental activities of daily living. Additionally, apraxia can occur with a variety of diseases that cause damage to the brain, the most common perhaps being stroke and degenerative dementias, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and tauopathies, such as corticobasal degeneration. The apraxias Hugo Liepmann, a student of Carl Wernicke, made important contributions to the understanding of apraxia that parallel the paradigm shifts made by Wernicke in the study of aphasia. In a series of papers written between 1900 and 1920, Liepmann described three major forms of apraxia: ideomotor apraxia, which was also called ideo-kinetic apraxia in Liepmann’s terminology [4]; ideational apraxia; and limbkinetic apraxia, also referred to by some investigators as melokinetic or innervatory apraxia. Studies in our laboratory introduced two other forms: conceptual apraxia and dissociation apraxia. We have discussed each of these forms of apraxia elsewhere [5], discussing the means of their testing, the abnormal behaviors that characterize them, and their pathophysiology. In this chapter, we also discuss the signs and pathophysiology of these apraxic disorders but use a processing approach. Like many other cognitive activities, performing skilled purposeful movements requires the sequential and/or parallel activation of distributed modular networks. Degradation of the different modules or disconnections between modules in these networks induces specific forms of apraxia, and these forms of apraxia will be discussed in the following sections. Conceptual apraxia Definition and description Human forelimbs perform several functions of major importance, interacting and altering the environment as well as helping to take care of others and oneself. In 200 order to be successful at these motor-action endeavors, a person must have motives and goal-oriented behaviors. Apathy, abulia, and akinesia are major causes of disability but, as mentioned earlier, these actionintentional system (i.e., “when”) disorders will not be discussed here. When attempting to alter the environment, one needs to decide what needs to be altered and “how” it will be altered. Thus, a person has to know if he or she will need a tool-implement to help accomplish their goals, the type of tool that would be needed, and how this tool will be moved by the person to accomplish this goal. In regard to tool knowledge, patients with conceptual apraxia might not know the mechanical advantage afforded by specific tools and implements. For example, an individual may be shown a nut on a bolt that is not fully tightened; when that individual next is shown a hammer, handsaw, chisel, pliers, and wrench and asked to select the correct tool to complete the job, he or she might incorrectly select the handsaw. Some patients with a milder form of conceptual apraxia might be able to select the correct tool (e.g., wrench); however, if this tool is not present in their tool chests and they therefore must use another tool, they might then select the incorrect tool. For example, an individual might be presented with a wooden board in which a nail has been driven partially and with a tool chest in which there is no hammer; instead of selecting pliers or wrench to pound in this nail, the individual might select the handsaw. In general, the highest level of tool conceptual knowledge allows individuals to create a new tool to solve a mechanical problem; conceptual apraxia is characterized in some patients by the loss of this ability [6, 7]. After a person is given, correctly selects, or devises a tool to use for a task, he or she then must select and perform the action that is associated with that tool. For example, if a person wants to insert a nail into a board then that person has to make pounding motions, not slicing or sawing movements; if, instead, a person wants to cut something then he or she must make slicing rather than pounding movements. As will be discussed below, patients with ideomotor apraxia make movement errors but the clinician can usually detect that the intent of the movement is correct. In contrast, patients with conceptual apraxia make content-movement errors, such that when asked to pantomime a specific transitive movement and shown a partially driven-in nail, the patient pantomimes the Chapter 14: Praxis movements associated with using another tool, such as slicing. Some patients with severe conceptual apraxia will make errors even when given the tool; for example, even when shown a hammer or even when holding the hammer the patient makes slicing movements. Testing Unfortunately, there is a paucity of standardized tests that are available to assess for conceptual apraxia. The Florida Action Recall Test (FLART) [8], developed to assess conceptual apraxia, consists of 45 line drawings of objects or scenes for which an action with a tool is required. First, the patient must decide what in the scene needs to be altered. Next, the subject must imagine the proper tool with which to make these alterations and then pantomime its use. There are several types of errors that may be made by patients. They may fail to know which elements of the scene need to be altered, which tool allows a person to make those alterations, and/or the correct actions associated with the use of a tool. For example, a patient shown a partially driven-in nail might make no movement and state “I do not know what to do” or instead make slicing movement rather than pounding movements. Both of these errors are conceptual errors. However, a patient might make pounding movements but either hold his hand in an incorrect posture or fail to make flexion-extension or radial-ulnar movements at the wrist. These latter errors are considered evidence for an ideomotor apraxia (discussed below) and not a sign of conceptual apraxia. In today’s “bean counter” controlled practice of medicine, physicians and neuropsychologists often cannot afford to observe patients when they are performing basic or instrumental activities of daily living; however, observations of these activities often best allow the clinician to detect the presence of conceptual apraxia. For example, we observed a patient with a hemispheric stroke after he was brought a meal on a tray that was placed next to his bed [9]. Before the patient got his tray, we placed some extra implements, such as a toothbrush, on his tray along with the silverware. The first thing this patient did was to take a sugar packet, tear it open, and pour it into his iced tea. Although there was a long teaspoon on his tray, he picked up a knife and placed it in his glass of tea. Rather than stirring the sugar by moving the knife he instead rotated the glass. While unorthodox, these actions did allow him to accomplish his mission. On his dinner plate there was creamed corn; when he attempted to eat his creamed corn, he picked up the toothbrush, rather than a fork, and was unsuccessful, leading him to use his fingers to get this item into his mouth. In order to make certain that this patient did not have a visual agnosia (which could have interfered with his object and food item recognition), we asked him to name all the utensils on his tray – he had no difficulty doing so. Pathophysiology Conceptual apraxia is reported among persons with degenerative dementias such as Alzheimer’s disease (AD) [6] and in the semantic dementia subtype of frontotemporal lobar degeneration [10]. In addition, a study of patients with right and left hemisphere strokes revealed that right-handed patients with left hemisphere strokes may demonstrate conceptual apraxia [7]. Mechanical knowledge, at least in part, is learned throughout life and thus is likely to be a form of memory. Warrington and Shallice [11] reported that whereas some patients had degradation of semantic memories of living things, others had degradation of semantic memories of non-living things. In our study of patients with conceptual apraxia due to stroke, many of the participants with left hemisphere lesions who demonstrate conceptual apraxia had intact verbal comprehension, an observation suggesting that tool-action semantic representations are independent of lexical-semantics. Unfortunately, when we attempted to localize the region of the left hemisphere that when damaged was most likely to lead to conceptual apraxia, we were unable to localize it [7]. Some of the patients with conceptual apraxia following left hemisphere strokes had temporoparietal injury, others had frontal injury, and still others a combination of frontal and temporoparietal injury. Failure to find a significant specific locus for conceptual apraxia suggests that either we had an insufficient number of subjects or that mechanical knowledge is widely distributed neuroanatomically. Support for the latter possibility comes from a functional magnetic resonance imaging study of conceptual mechanical knowledge in normal subjects [12], in which investigators observed activation in multiple areas of the left hemisphere, including the parietal-temporal-occipital junction, inferior frontal and anterior dorsal premotor areas. 201 Section I: Structural and Functional Neuroanatomy If mechanical knowledge is stored in the left hemisphere of right-handed people, then disconnection of the cerebral hemispheres due to a callosal lesion in a right-handed person might produce signs of conceptual apraxia when that individual attempts to use his left hand to solve mechanical problems. Liepmann and Maas reported patient Ochs [13], an individual who appeared to have a callosal disconnection and whose many left upper extremity errors appeared to be content errors. Unfortunately, Ochs had a right hemiparesis from a brainstem lesion and thus the performance of his right and left hand could not be compared. The patient reported by Watson and Heilman [14], who sustained an infarction of her corpus callosum, initially made content errors with her left but not her right hand and arm. If the content errors performed by this woman’s left forelimb were only in response to verbal commands it might have suggested that language speech commands could not get access to the right hemisphere; however, this woman also made content errors when holding the actual tools or implements in her left hand. Her right hand performance, in these same conditions, however, was flawless. Ideational apraxia Definition and description The term ideational apraxia has been used to describe many different forms of apraxic disorders. For example, De Renzi and Lucchelli [15] defined ideational apraxia as failure to correctly use actual tools and implements. In their article entitled “Ideational Apraxia,” they noted that patients with lesions of their left inferior parietal lobe made spatial errors when attempting to use actual tools and implements. However, Poizner et al. [16] demonstrated that patients with ideomotor apraxia are often impaired when using tools and objects. Unfortunately, when Heilman first described dissociation apraxia he called it ideational apraxia; conceptual apraxia also has been called ideational apraxia [9, 12]. Despite the overlapping use of these terms, these are presently recognized as distinct types of apraxia. The latter type of apraxia was described in the preceding section of this chapter and dissociation apraxia will be discussed later in this chapter. The remainder of this section focuses specifically on ideational apraxia. 202 Many goal-oriented behaviors require that a person perform a series of acts to accomplish a goal. For example, a person making a ham and cheese sandwich to bring to work for lunch ordinarily follows a series of steps when doing so. First, two slices of bread are cut from the loaf. Mustard and/or mayonnaise are then put on the slices of bread. Next, the ham, cheese, and lettuce are placed on one slice of this bread, and then covered with the other piece of bread. Finally, the sandwich is cut in half and put into a vinyl sandwich bag. According to Liepmann’s definition [17], a person with ideation apraxia demonstrates problems properly sequencing these acts; for example, cutting the bread before placing the mustard, ham, and cheese on it. Pathophysiology Patients with frontal lobe injuries often have trouble with sequencing; however, De Renzi and co-workers [18] noted that this sequencing disorder was more commonly associated with left inferior parietal than frontal injury. After degeneration of the medial temporal lobes, AD involves degeneration of the inferior parietal lobes; consistent with the findings of De Renzi and colleagues [18], Crutch et al. [19] observed that patients with AD often had ideational apraxia. Ideomotor apraxia Definition and description After a person decides what tool or implement they need to use to accomplish each act in a goal, the next step is to use this tool or implement to accomplish the desired task. In order to correctly perform the required actions, an individual needs to know: (1) how to hold the tool; (2) how to direct the tool to the location where the action of the tool is required; (3) how to move their forelimb through space so that the tool-implement can correctly move on the target upon which it is acting; (4) how fast to move the tool; and (5) how much force with which to use the tool. After having selected the correct tool for the job and upon knowing the type of action needed to use the tool effectively to accomplish the goal, a patient with ideomotor apraxia might: (1) fail to hold her hand correctly (i.e., make a postural error); (2) fail to move her forelimb to the target of the tool’s action or fail to maintain the correct spatial orientation; (3) move the incorrect joints of the limb involved or not properly Chapter 14: Praxis coordinate multiple joint movements; and (4) use the incorrect speed or force. Testing When testing patients for ideomotor apraxia, if possible, both the right and the left forelimbs should be tested. In general, there are two types of gestures or movements that a person can make with their forelimb, transitive and intransitive. Transitive movements are those that are used with tools and implements. Intransitive movements are those usually used to communicate with others. For example, when standing at the side of the road, if a person makes a fist and points his thumb in the direction to which he wants to travel then that is understood as a sign that he is requesting someone to stop and give him a ride (hitchhiking). This is an intransitive gesture. When the policeman puts his open hand up in the air with his palm facing a driver it means that he wants the driver to stop his car. This is another intransitive gesture. In contrast, asking a person to pretend she is using a pair of scissors to cut in half a piece of paper she will be pantomiming a transitive act. In general, when assessing patients for ideomotor apraxia, testing patients by asking them to pantomime transitive acts is much more likely to detect ideomotor apraxia then testing them performing intransitive acts. While having patients pantomime transitive acts is the most sensitive means of assessing for ideomotor apraxia, patients should be asked to imitate the examiner performing both meaningful and meaningless gestures. Additionally, being allowed to see pictures of tools or even to hold actual tools or objects and to demonstrate how to use these tools and implements is valuable. It also is valuable to determine whether a patient can name transitive pantomimes made by the examiner and is able to discriminate between well- and poorly performed transitive pantomimes made by the examiner. Patients with ideomotor apraxia may produce several types of movement errors, including postural, allocentric orientation, and egocentric movement errors. Each of these is considered further here. can be observed in these patients: postural or internal configuration errors, spatial movement errors, and errors of spatial orientation. When using a tool or implement, the hand has to be held in a certain posture. Goodglass and Kaplan [20] noted that when patients with ideomotor apraxia are asked to perform a pantomime of a transitive act they often use a body part, such as the finger or the hand, as the tool. For example, when patients with ideomotor apraxia are asked to show the examiner how they would use a screwdriver they might use their forefinger as the shaft of the screwdriver; when asked to pantomime using a pair of scissors, they may use their fingers as if they were the blades. These movements are emblems of the tool and are not the correct pantomime. When given the command to pretend that they are using a pair of scissors, many normal people make similar “body part as tool” errors. Thus, it is important that patients be instructed not to use a body part as a tool. We have found that, unlike normal subjects who improve with these instructions, patients with ideomotor apraxia often continue using their body parts as tools [21]. However, when patients can be convinced not to use their body parts as tools, patients with ideomotor apraxia often fail to correctly position their arms, hands, and fingers. Allocentric orientation errors When asked to pantomime the use of the tool (e.g., “show me how you would cut a piece of paper in half by using a pair of scissors”) normal individuals orient the pantomimed tool to an imaginary target of the tool’s action. Patients with ideomotor apraxia often fail to correctly orient their forelimbs to an imaginary target. For example, when pantomiming the use of scissors to cut a piece of paper in half, most normal subjects will keep the blades of the scissor oriented in a sagittal plane; in contrast, patients with ideomotor apraxia may orient the scissors laterally so that they are in the coronal plane or even fail to maintain any consistent plane of movement [22]. Egocentric movement errors Postural errors When attempting to perform skilled transitive acts, patients with ideomotor apraxia primarily make spatial errors. There are three types of spatial errors that A tool must be moved in certain directions with respect to the object upon which this tool is acting in order for this tool to perform its intended action. When attempting to pantomime or even use actual 203 Section I: Structural and Functional Neuroanatomy tools, patients with ideomotor apraxia often incorrectly move the tool through space. The core concept of the movement may be correct, but other, more refined, elements are incorrect. For example, when patients with ideomotor apraxia are asked to pantomime hammering a nail into a board, these patients will often demonstrate pounding-like movements by flexing and extending their arm at the elbow and shoulder. However, correctly using a hammer to pound a nail into a board requires when extending the arm at the elbow to also flex or ulnar-deviate the wrist; failing to move the wrist results in the head of the hammer not coming down upon the nail [22, 23]. Some egocentric movement errors associated with ideomotor apraxia are the result of incorrectly stabilizing some joints and moving the incorrect joints. For example, by trying to rotate a screwdriver on axis by using only the wrist joint, the screwdriver will not rotate on axis and instead makes circular movements. However, many transitive movements require the coordinated movements of multiple joints (e.g., slicing) and patients with ideomotor apraxia often have difficulty coordinating these multiple joint movements. Pathophysiology Callosal disconnection One of the first people to intensively investigate the pathophysiology of ideomotor apraxia was Hugo Liepmann. In one of his most important reports, Liepmann and Maas [13] described a 70-year-old man whose post-mortem examination revealed an infarction in the distribution of the left anterior cerebral artery which damaged the anterior two-thirds of the corpus callosum. Unfortunately, this patient also had a lesion of his left pons that caused a right hemiparesis. Prior to this patient’s death, Liepmann and Maas noted that when attempting to write or perform skilled movements to command with his left hand this patient demonstrated an inability to correctly perform skilled movements. Liepmann was a student of Carl Wernicke and was aware that the left hemisphere mediated language. Thus, injury to the corpus callosum, the major interhemispheric connecting system, could have isolated the left hemisphere language systems from the motor systems in the right hemisphere that control the left hand and arm. Liepmann and Maas, however, rejected this language disconnection hypothesis 204 because this patient was unable not only to correctly perform skilled purposeful gestures to command but also to use real implements/tools with his left forelimb. Based on these observations, Liepmann and Maas posited that the left hemisphere of right-handed people not only mediates language-speech but also contains movement formulae, the spatial-temporal movement representations that are important for programming purposeful learned skilled movements. They therefore suggested that the callosal injury sustained by their patient prevented movement representations in his left hemisphere from gaining access to the motor systems in his right hemisphere. Liepmann and Maas’ callosal disconnection study played an important role in developing an understanding of the role of the corpus callosum in interhemispheric communication as well as the role of the left hemisphere in the control of movements of the ipsilateral left forelimb. Unfortunately the case they described was less than optimal for this purpose. Their patient’s stroke, which appeared to be in the distribution of the anterior cerebral artery, not only damaged the anterior portion of the corpus callosum but also damaged the superior and medial portions of the left frontal lobe. As we will discuss below, damage to the left medial frontal lobe, including the supplementary motor area, can induce an ideomotor apraxia of both the right and left hands [24]. To demonstrate that the patient reported by Liepmann and Maas had a ideomotor apraxia of his left hand because he had a callosal disconnection, Liepmann and Maas needed to demonstrate that their patient did not have an ideomotor apraxia of his right forelimb; unfortunately, this patient also had a brainstem infarction that caused a right hemiparesis and thus he could not be tested for this finding. Another major problem with Liepmann and Maas’ report is that their patient (who had a callosal lesion) often made what appeared to be content errors; as discussed above, content errors suggest a conceptual apraxia rather than an ideomotor apraxia. Another patient with apraxia from a callosal disconnection was described by Geschwind and Kaplan [25]. Their patient had a left hemisphere glioblastoma and in the peri-operative period he developed an infarction in the distribution of the anterior cerebral artery that injured the anterior four-fifths of his corpus callosum. This patient was able to use his right hand flawlessly, but could not correctly use his left hand in Chapter 14: Praxis response to verbal commands. He was, however, able to imitate and use actual objects normally with his left forelimb; thus, he could not be classified as having an ideomotor apraxia but instead appeared to have a verbal dissociation apraxia. The signs and mechanism of dissociation apraxia are discussed in a subsequent section. Gazzaniga and his co-workers [26] studied several epileptic patients who had surgical disconnection of their corpus callosum. Like Geschwind and Kaplan’s patient, these patients were able to imitate and use actual implements with their left forelimb. Thus, these investigators concluded that callosal damage induced a language disconnection. Gazzaniga et al. [26] suggested that if patients had impaired imitation and trouble using actual objects this apraxia would be the result of extracallosal damage. Watson and Heilman [14] reported a woman with a subarachnoid hemorrhage who appeared to contradict the assertion of Gazzaniga et al. [26] that extracallosal damage induces ideomotor apraxia with impairment of imitation and actual object use. This woman’s subarachnoid hemorrhage caused a spasm of the anterior cerebral artery, which caused an infarction of the body of her corpus callosum. Magnetic resonance imaging of this patient’s brain revealed no evidence of any extracallosal damage [27]. Unlike Geschwind and Kaplan’s [25] patient, who was only impaired in pantomiming commands with his left forelimb, this woman was impaired in performing transitive pantomimes and intransitive movements with her left upper limb to command, but she also had trouble imitating the examiner and using actual objects with her left forelimb. In addition, her spatial movement errors were typical of those observed with ideomotor apraxia. In contrast to the apraxia observed when she was using her left forelimb, her ability to pantomime transitive movements and to imitate and use actual objects with her right forelimb was flawless. Graff-Radford et al. [28] and Lausberg et al. [29] have reported patients that replicated the finding of Watson and Heilman [14, 27]. These patients with callosal disconnection also demonstrated left, but not right, ideomotor apraxia making spatial errors with pantomimes, imitation, and actual object use. The patients with callosal disconnection reported by Watson and Heilman [14, 27], Graff-Radford et al. [28], and Lausberg et al. [29] might have differed from those reported by Geschwind and Kaplan [25] and Gazzaniga et al. [26] because these two groups of patients had differences in their brain organizationlaterality. Liepmann [17] reported that left hemisphere lesions are most likely to cause ideomotor apraxia in right-handed individuals. However, Liepmann also noted that whereas about 95% of patients with large left hemisphere lesions are aphasic, only about 50% have ideomotor apraxia [17]. The fact that there are a lower percentage of left hemisphere-damaged patients who have ideomotor apraxia than have aphasia suggests that a substantial percentage of right-handed people either have movement representations stored in both hemispheres or instead have movement representations stored in the right hemisphere. Apraxia in righthanded patients from right hemisphere injury has been reported [30]; however, this is rare. The majority of people who do not have apraxia from a large left hemisphere lesion therefore probably have movement representations stored in both hemispheres. When a patient has movement representations in both hemispheres and they have an injury to the anterior portions of their corpus callosum, he or she should have the clinical picture described by Geschwind and Kaplan [25]: when using the left forelimb to pantomime to command, the movement is impaired as a result of disconnection of the language-dominant left hemisphere from the left hand-controlling right hemisphere. However, the left hand is able to be used by such individuals to perform imitation and to use actual objects because these actions can be performed without language and because the movement representations necessary for their performance are stored in their right hemispheres. In contrast, the patients reported by Watson and Heilman [14, 27], Graff-Radford et al. [28], and Lausberg et al. [29] appear to have their movement representation restricted to their left hemisphere; thus, callosal disconnection prevented both language and movement formulae from reaching the right hemisphere. These patients therefore made errors when pantomiming to command, imitating, and using actual objects, i.e., they demonstrated unilateral ideomotor apraxia. Intrahemispheric disconnection As mentioned above, Liepmann [17] noted that about 50% of patients with large left hemisphere damage will have an ideomotor apraxia. Unlike patients with 205 Section I: Structural and Functional Neuroanatomy callosal lesions (who have this apraxia restricted to their left forelimb), ideomotor apraxia can be observed in both hands among patients without a hemiparesis. Both Liepmann [17] and Geschwind [2, 3] noted that it was primarily lesions in the region of the left supramarginal gyrus that induce ideomotor apraxia. To account for the development of ideomotor apraxia following injury to this region, Geschwind [2, 3] proposed a disconnection hypothesis. In the subcortical white matter of the supramarginal gyrus is the arcuate fasciculus. This fasciculus is the white matter pathway that connects Wernicke’s area, important in language comprehension, to the left hemisphere’s convexity premotor areas. The premotor cortex of the left hemisphere projects to the right hemisphere’s premotor cortex and both premotor cortices project to the motor cortex on the same side. According to Geschwind’s [2, 3] disconnection model, injury to the region of the supramarginal gyrus interrupts the arcuate fasciculus; thus, when a person with injury to this region hears a command, he is unable to transfer this information to the premotor cortices and hence unable to correctly carry out the command. Inferior parietal lobe Geschwind’s [2, 3] verbal disconnection hypothesis cannot explain why patients with lesions in the region of the supramarginal gyrus cannot correctly imitate or correctly use actual objects. An alternative explanation is that the region of the inferior parietal lobe contains the multimodal (visuo-kinesthetic) movement representations (spatial-temporal movement representations) and that degradation of these representations of skilled movements induces apraxia [31, 32]. Unlike Geschwind’s [2, 3] disconnection hypothesis, this representational hypothesis can account for why injury to this area would also impair imitation and the use of actual objects. If a patient has a degradation of a movement representation, then – in addition to being unable to produce the behavior that this representation programs – such a patient also should have trouble discriminating correctly versus incorrectly performed pantomimes. Therefore, we tested this representation hypothesis of ideomotor apraxia by assessing the ability of apraxic patients, who suffered with anterior versus posterior left hemisphere strokes, to discriminate correctly from incorrectly performed pantomimes of transitive movements. We found that patients with anterior left 206 hemisphere lesions could correctly discriminate between pantomimes of these types whereas those with posterior lesions were impaired, thereby supporting the representational hypothesis. Recently, functional imaging studies have also provided converging evidence that movement representations are stored in the inferior parietal lobe [33]. Lissauer [34] noted that patients with ventral temporal-occipital lesions had trouble identifying objects (visual object agnosia) and Balint [35] noted that patients with dorsal parietal-occipital lesions have trouble determining the position of objects in relation to the body (optic ataxia and psychic paralysis of gaze). Ungerleider and Haxby [36] called these the “what” and “where” visual systems. Watson et al. [37] suggested and provided support for the postulate that in humans the integration of the areas important in stimulus localization (e.g., the dorsal visual [“where”] system) and stimulus identification (the ventral [“what”] system) takes place in the inferior parietal lobe. The synthesis of these systems would appear to be critical for developing the correct postures and body-centered (egocentric) joint movements when pantomiming the use of a tool or implements as well as for directing the subject’s hand and arm to the location where the action is directed. Injury to the left inferior parietal cortex would impair these functions and patients with inferior parietal injuries do demonstrate both postural and orientation errors [22]. Premotor cortex The movement representations we discussed above that are stored in the inferior parietal lobe are stored in a polymodal (visual-kinesthetic) three-dimensional spatial-temporal code [5]. In some respects, this code could be considered similar to sheet music. Sheet music provides the piano player with a program that denotes the sequences of spatial movements required to be made by the pianist’s fingers and the duration of each movement as well as the duration between movements. The corticospinal neurons, found primarily in Brodmann’s area 4 (primary motor cortex), might be thought of as being similar to the piano keys. These corticospinal neurons activate the motor nerves in the spinal cord, which in the metaphor we have been using might be the felt hammers in the piano. The felt hammer hits the wires that make the sound and the motor nerves when active make specific muscle groups contract. Chapter 14: Praxis At one time it was thought that each site of the motor cortex activated one muscle that moved a joint in a specific direction, but more recent studies show the primary motor cortex can influence the speed or movement as well as its direction through space [38]. Other investigators have suggested that it might be joint angle that is coded and programmed in the primary motor cortex [39]. For the purpose of our discussion of ideomotor apraxia, the functional organization of the corticospinal system is not critical. However, in order for the corticospinal system to provide the motor units with the correct firing patterns, this system must get instructions from another portion of the brain. Thus, the spatial-temporal knowledge (sheet music) stored in the parietal lobe that guides purposeful voluntary movements have to be transformed into motor programs (the piano player) and these motor programs have to activate the motor cortex using specific firing patterns. The premotor cortex, which is anterior to the motor cortex, can be divided into two major divisions: a lateral portion and a medial portion. Each of these major subdivisions of the premotor area can also be further divided into subunits. These subunits include rostral and caudal subregions. Medially, the premotor cortex is divided into the caudally situated supplementary motor area (SMA) and the rostrally located pre-SMA. The lateral portions of the premotor cortex are divided into ventral and dorsal subregions, and each of these dorsal and ventral subregions is divided further into caudal and rostral portions. Picard and Strick [40] noted that the more rostral portions do not have strong direct connections with the motor cortex and appear to be heavily connected with regions of the prefrontal cortex. According to these investigators, the role of the rostral premotor area is primarily executive-planning functions rather than motor control. In contrast, the more caudal portions (such as SMA and caudal lateral premotor cortex) appear to play a critical role in mediating and programming skilled movements. Whereas electrical stimulation of the primary motor cortex induces simple movements such as flexion of the thumb, stimulation of the supplementary motor area induces complex movements that may include the entire forelimb. The role of the lateral versus medial caudal premotor cortex (SMA) in controlling purposeful skilled movements has still not been entirely elucidated. There is, however, evidence for the hypothesis that SMA primarily programs the aspects of skilled movement that are entirely internally initiated (self-directed actions) whereas the lateral premotor cortex is more involved with movements that are directed by external sensory (e.g., visual) stimuli [41]. Some investigators find that the lateral premotor cortex also might activate these movements [42]. Thus, the medial premotor cortex (SMA) may be more important in programming the egocentric aspects of skilled movements and the lateral premotor cortex the allocentric (target) aspects. The connectivity of the SMA also places it in a good anatomic position to act as the piano player. It receives projections from parietal neurons, which contain the spatial temporal movement representations (the sheet music) and project to corticospinal motor neurons (the piano keys). Physiological studies have revealed that the neurons in the SMA discharge before neurons in the primary motor cortex [43]. Functional imaging studies (measuring alterations of blood flow, which indirectly reflects synaptic activity), revealed that single simple repetitive movements such as finger flexion increase activation of the primary motor cortex contralateral to the finger that is flexing. In contrast, blood flow increases in both the contralateral motor cortex and in the supplementary motor area when subjects make complex purposeful movements. Finally, when normal subjects think about/plan making complex movements but do not make actual movements, blood flow increases in the SMA but not the primary motor cortex [44, 45]. This series of studies provide evidence that in normal subjects the SMA is critical in programming complex purposeful voluntary movements – in the metaphor begun earlier in this section, SMA is the pianist. In regard to ideomotor apraxia, Watson et al. [24] reported several patients with left-sided medial frontal lesions involving the SMA who demonstrated an ideomotor apraxia when tested with either arm. Unlike patients with parietal lesions, these patients with SMA injury could discriminate between well and incorrectly performed pantomimes. Patients with corticobasal degeneration often suffer with an ideomotor apraxia and Leiguarda et al. [46] suggested that these patients’ deficits may also be related to dysfunction of the supplementary motor area. Imaging studies appear to support this hypothesis [47]. We observe that most of our patients with corticobasal degeneration are able to discriminate wellperformed from incorrectly performed pantomimes of 207 Section I: Structural and Functional Neuroanatomy transitive movements, supporting the hypothesis that these patients’ movement representations are intact and that their disability might be related to dysfunction of the supplementary or convexity premotor areas. Returning to our metaphor, it appears that information from the sheet music gets to the pianist by means of vision; how then does the spatial-temporal movement information needed to perform transitive acts get from the parietal lobe to the premotor areas? Additionally, can a disconnection between these two regions also induce an ideomotor apraxia? Based on the work of Schmahmann et al. [48], the pathway that connects the supramarginal gyrus and premotor cortex is the superior longitudinal fasciculus. Although Schmahmann et al. [48] described this white matter pathway in rhesus monkeys, studies in humans using diffusion tensor imaging tractography [49] also appear to demonstrate a similar white matter pathway. If the white matter connections between these two critical areas are interrupted then these patients should also have an ideomotor apraxia. Pramstaller and Marsden [50] reviewed the reports of 82 patients with apraxia from subcortical lesions. These investigators found that the majority of these patients sustained large lesions with damage to the basal ganglia and/or thalamus together with the white matter. The injured portions of white matter included the internal capsule as well as the periventricular and peristriatal white matter, interrupting association fibers in the superior longitudinal fasciculus. Discrimination between correctly and incorrectly performed pantomimes might be performed by a visuo-spatial analysis that does not require motor programming. Thus, a disconnection between the stored spatial-temporal movement representations in the parietal lobe and the regions that translate these representations to movement programs in the premotor cortex should induce an ideomotor apraxia with intact discrimination. Hanna-Pladdy et al. [51] compared the performance on praxis performance and discrimination tasks in patients with left hemisphere cortical and subcortical lesions. The patients with cortical lesions demonstrated production deficits, as well as impaired gesture discrimination. Whereas the patients with subcortical injuries also exhibited apraxic production deficits they had normal discrimination. Unfortunately, at the time the Hanna-Pladdy et al. study was performed the exact white matter pathways that induced these deficits could not be 208 determined, but now with diffusion tensor imaging the white matter pathways that when damaged induce ideomotor apraxia can be examined. Dissociation apraxia Definition, description, and testing In some circumstances, a person might be required to perform a learned action in response to a command, or by seeing a tool, or by observing another person perform an action. There are patients who are unable to correctly pantomime transitive movements to command using either forelimb, but who are able to correctly pantomime when seeing the tool or implement, to imitate normally, and to use actual objects normally. As mentioned, this disorder is now called verbal dissociation apraxia [5]. De Renzi et al. [52] also reported similar patients, but also described patients who were unable to correctly pantomime transitive gestures in response to seeing tools but who were able to do so in response to commands. Heilman et al. [5] suggested that verbal dissociation apraxia was induced by dissociation between lexical-semantic language representations and movement representations and that visual-object dissociation apraxia reported by De Renzi et al. [52] was caused by a functional dissociation between object recognition units and movement representations. However, in both disorders the movement representations and their connections to premotor and motor cortex were intact. These are not common disorders, and the loci of lesion that induces these forms of intrahemispheric dissociation apraxias are not known. Patients with ideomotor apraxia typically are more impaired when performing to command than when imitating. When a patient has degraded movement representations, observing an examiner make movements provides patients with movement information that is not available from their own degraded movement representations. However, there are several reports of patients who were more impaired when imitating an examiner perform a gesture than when performing to command or actually using tools and implements [53–56]. Whereas some patients demonstrated that the imitation of novel gestures was more impaired than previously learned gestures [53], other patients were more impaired when imitating meaningful gestures than meaningless gestures [57]. Some aphasic patients have more problems with imitation Chapter 14: Praxis than spontaneous speech, and this disorder has been called conduction aphasia; since patients with this form of apraxia have more problems with visual imitation of gesture than performing gestures to command, this disorder has been called conduction apraxia [56]. However, another term that describes these modality-specific dissociation disorders would be “visual-imitative dissociation apraxia.” The mechanism of this visual-motor imitative disorder is not known. When patients with this disorder perform to commands their pantomimes are relatively intact, suggesting that there is no degradation of their movement representations and that their premotor areas do have the ability to translate these visualkinesthetic movement representations into motor programs and thereby to innovate the motor cortex. Thus, the inability to imitate viewed gestures might suggest a failure of the visual perceptual systems to successfully access the areas of the brain that program movements. Support for this postulate comes from the report of Merians et al. [58], who wrote about a patient with a left occipital and inferior temporal lobe lesion that spared the inferior parietal cortex. This patient performed well to verbal command but was impaired when imitating. In contrast, three patients with ideomotor apraxia whose lesions included the left parietal cortex had the most severe deficits when pantomiming to verbal command and improved pantomime performance with imitation. The observation that there is, in some patients, dissociation between imitation of meaningful and meaningless gestures suggest that there are at least two routes for imitation. Meaningful and previously learned movement might be imitated by accessing movement representations and meaningless movements might directly access the premotor areas that alter this visual input into motor programs. As mentioned, Geschwind and Kaplan [25] reported a patient with a corpus callosum disconnection who, when performing pantomimes to verbal command with his left forelimb, was apraxic but was able to perform normally with the right forelimb. This patient could imitate and use actual objects normally with both hands. Although this patient’s behavior is similar to the patients with the intrahemispheric verbal dissociation apraxia described above, unlike the patients with intrahemispheric verbal dissociation apraxia that involves both forelimbs, the callosal verbal dissociation apraxia seen in this patient only involved the left forelimb. Limb-kinetic apraxia Definition, description, and testing Limb-kinetic apraxia is characterized by a loss of dexterity or deftness such that patients with this disorder are impaired at making precise, independent but coordinated finger movements. One of the simplest and most sensitive means of testing patients for limbkinetic apraxia is to have the patients attempt to rotate a coin, such as a nickel, between the forefinger, middle finger and thumb, as rapidly as possible. Many patients with ideomotor apraxia will be unable to rotate the coin, others will drop it frequently, and still others perform this movement slowly [59]. Again using the metaphor begun earlier in this chapter, after the piano player reads the music and hits the proper piano keys in the proper sequence and duration, the piano key must move the hammers that hit the strings. If the sheet music is the movement program stored in the parietal lobe, the piano player is the motor program performed by the supplementarypremotor area, the hammer is the motor neurons, and the strings are the muscles, then the piano keys, which activate the hammers (lower motor neurons), is the corticospinal system. Pathophysiology Lawrence and Kuypers [60] demonstrated that destruction of the pyramidal neurons in the brainstem (corticospinal tract) in monkeys did not cause paralysis but did induce a loss of deftness, in this case independent and precise finger movement –which are the signs of a limb-kinetic apraxia. Other investigators report that damage to the primary motor area or its projections to the motor neurons in the spinal cord does not cause a paralysis, and instead results in a specific deficit in fine manual actions –what Liepmann [17] termed limb-kinetic apraxia [61–64]. Hannah-Pladdy and co-workers [59] found that right-handed patients with left hemisphere lesions not only demonstrated a limb-kinetic apraxia of their right (contralateral) hand but also frequently exhibit a loss of deftness of their ipsilateral (left) hand. In contrast, patients with right hemisphere lesions may have a limb-kinetic apraxia of their left hand but usually perform normally with their right (ipsi-lesional) hand. Thus, it appears that in right-handed people the left hemisphere corticospinal system influences the deftness of the left hand more than vice versa, but the 209 Section I: Structural and Functional Neuroanatomy means by which this asymmetry of control is mediated is not fully known. It is possible that the left hemisphere’s motor cortex influences the right hemisphere by transferring information by way of the corpus callosum or influences the left hand by means of ipsilateral pathways. Support for the callosal hypothesis comes from the report of a right-handed man who infarcted the anterior and middle parts of his corpus callosum [65]. This man lost agility and deftness in his left hand, including the ability to make fast and nimble finger movements of his left hand, and these investigators thought that this patient had a melokinetic (limb-kinetic) apraxia induced by a hemispheric disconnection. Treatment Recently, a consensus conference was held to review the treatment for limb apraxia [66]. The investigators and clinicians participating in this conference concluded, “despite evidence that several types of limb apraxia significantly impact functional abilities, surprisingly few studies have focused on development of treatment paradigms.” The successful therapies for ideomotor apraxia as reviewed by Buxbaum et al. [66] used a variety of different strategies including: multiple cues, error correction, conductive education, where treatment is focused on a task analysis of the movements with articulationverbalization of the task elements, the use of compensatory strategies to help perform activities of daily living, and imitation with errorless learning. In regard to limb-kinetic apraxia, Quencer and coworkers [67] demonstrated that patient with Parkinson’s disease (PD) often demonstrate limb-kinetic apraxia. Gebhardt et al. [68] demonstrated that whereas dopaminergic agents improve the bradykinesia associated with PD, the limb-kinetic apraxia Figure 14.1. Cartoon of the praxis system and the injuries that can induce the different forms of apraxia. The right hemisphere is on the top half and the left hemisphere on the bottom half of this cartoon of the brain. O-R-U = Object recognition units. These visual representations are stored in the ventral occipital-temporal cortex. L-S = The lexical-semantic network. The other modular networks are labeled. The arrows represent the pathways that connect these modular networks. The lines with letters attached represent either damage to a network or disconnection between the networks. Damage to the different networks and their connections induce different forms of apraxia, including: A = ideomotor apraxia with impaired pantomime to command, imitation, tool-implement use and impaired discrimination; B = intrahemispheric disconnection ideomotor apraxia, with intact discrimination; C = premotor ideomotor apraxia with same symptoms as B; D = callosal (disconnection) interhemispheric ideomotor apraxia and/or callosal interhemispheric conceptual apraxia; E = intrahemispheric limb-kinetic apraxia; F = interhemispheric disconnection limb-kinetic apraxia; G = visual intrahemispheric dissociation apraxia; H = verbal intrahemispheric dissociation apraxia; I = conceptual apraxia. Some patients have movement representations in both hemispheres (right hemisphere movement representations are illustrated in this cartoon as a cloud). In these patients a callosal disconnection, J = verbal dissociation apraxia of the left, but not right, hand. 210 Chapter 14: Praxis does not improve with this treatment. A search of PubMed did not reveal any other studies where investigators attempted to treat limb-kinetic apraxia. In addition, there are at present no studies demonstrating treatments of either conceptual or ideational apraxia. Conclusion In this chapter, several types of limb apraxia were discussed. The cartoon in Figure 14.1 displays the systems that mediate purposeful, skilled movements and the injuries that can induce the different forms of limb apraxia discussed in this review. Patients with conceptual apraxia have lost the knowledge of the mechanical advantage offered by tools and might make errors such as selecting the wrong tool for a specific task, using the incorrect action with a tool, and be unable to correctly use alternative tools, as well as being unable to create tools. Patients with ideational apraxia are impaired at sequencing a series of independent acts that lead to a goal (e.g., making a sandwich). Patients with ideomotor apraxia primarily make spatial errors. The errors may be postural, or may consist of egocentric and allocentric movement errors. There are several forms of dissociation apraxia in which patients cannot perform correct purposeful movements in response to a stimulus in a specific modality (speech commands), but can correctly perform these movements in response to a stimulus in another modality (e.g., seeing the object with which a tool interacts). Finally, limbkinetic (melokinetic, innervatory) apraxia is characterized by a loss of hand–wrist deftness. Within each of these five major categories of limb apraxia there are subtypes. For example, there are patients with ideomotor apraxia who cannot discriminate well-performed movements from incorrectly performed movements and there are other patients who can normally discriminate. Some of these forms and subtypes of apraxia can be caused by disconnection and others by degradation or destruction of critical brain representations. References 1. Steinthal H. Abriss der Sprachwissenschaft. Berlin: F. Dümmlers; 1871. 2. Geschwind N. Disconnexion syndromes in animals and man. I. Brain. 1965;88(2):237–94. 3. Geschwind N. Disconnexion syndromes in animals and man. II. Brain. 1965;88(3):585–644. 4. Goldenberg G. Apraxia and beyond: life and work of Hugo Liepmann. Cortex 2003;39(3):509–24. 5. Heilman KM, Rothi LG. Apraxia. In Heilman KM, Valenstein E, editors. Clinical Neuropsychology. 4th edition. Oxford: Oxford University Press; 2003, pp. 215–35. 6. Ochipa C, Rothi LJ, Heilman KM. Conceptual apraxia in Alzheimer’s disease. Brain 1992;115(4): 1061–71. 7. Heilman KM, Maher LM, Greenwald ML, Rothi LJ. Conceptual apraxia from lateralized lesions. Neurology 1997;49(2):457–64. 8. Schwartz RL, Adair JC, Raymer AM et al. Conceptual apraxia in probable Alzheimer’s disease as demonstrated by the Florida Action Recall Test. J Int Neuropsychol Soc. 2000;6(3):265–70. 9. Ochipa C, Rothi LJ, Heilman KM. Ideational apraxia: a deficit in tool selection and use. Ann Neurol. 1989; 25(2):190–3. 10. Hodges JR, Bozeat S, Lambon Ralph MA, Patterson K, Spatt J. The role of conceptual knowledge in object use evidence from semantic dementia. Brain 2000;123(9): 1913–25. 11. Warrington EK, Shallice T. Category specific semantic impairments. Brain 1984;107(3):829–54. 12. Ebisch SJ, Babiloni C, Del Gratta C et al. Human neural systems for conceptual knowledge of proper object use: a functional magnetic resonance imaging study. Cereb Cortex 2007;17(11):2744–51. 13. Liepmann H, Maas O. Fall von linksseitiger agraphie und apraxie bei rechsseitiger lahmung. Zeitschrift Psychol Neurol. 1907;10:214–27. 14. Watson RT, Heilman KM. Callosal apraxia. Brain 1983;106(2):391–403. 15. De Renzi E, Lucchelli F. Ideational apraxia. Brain 1988;111(5):1173–85. 16. Poizner H, Clark MA, Merians AS et al. Joint coordination deficits in limb apraxia. Brain 1995; 118(1):227–42. 17. Liepmann H. Apraxia. Erbgn der ges Med. 1920;1: 516–43. 18. De Renzi E, Faglioni P, Lodesani M, Vecchi A. Performance of left brain-damaged patients on imitation of single movements and motor sequences. Frontal and parietal-injured patients compared. Cortex 1983;19(3):333–43. 19. Crutch SJ, Rossor MN, Warrington EK. The quantitative assessment of apraxic deficits in Alzheimer’s disease. Cortex 2007;43(7): 976–86. 20. Goodglass H, Kaplan E. Disturbance of gesture and pantomime in aphasia. Brain 1963;86:703–20. 211 Section I: Structural and Functional Neuroanatomy 21. Raymer AM, Maher LM, Foundas AL, Heilman KM, Rothi LJ. The significance of body part as tool errors in limb apraxia. Brain Cogn. 1997;34(2): 287–92. 22. Rothi LJG, Mack L, Verfaellie M, Brown P, Heilman KM. Ideomotor apraxia – error pattern-analysis. Aphasiology 1988;2(3–4):381–7. 23. Poizner H, Mack L, Verfaellie M, Rothi LJ, Heilman KM. Three-dimensional computer graphic analysis of apraxia. Neural representations of learned movement. Brain 1990;113(1):85–101. 24. Watson RT, Fleet WS, Gonzalez-Rothi L, Heilman KM. Apraxia and the supplementary motor area. Arch Neurol. 1986;43(8):787–92. 37. Watson RT, Valenstein E, Day A, Heilman KM. Posterior neocortical systems subserving awareness and neglect. Neglect associated with superior temporal sulcus but not area 7 lesions. Arch Neurol. 1994;51(10): 1014–21. 38. Georgopoulos AP, Lurito JT, Petrides M, Schwartz AB, Massey JT. Mental rotation of the neuronal population vector. Science 1989;243(4888):234–6. 39. Scott SH, Kalaska JF. Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. J Neurophysiol. 1997;77(2):826–52. 40. Picard N, Strick PL. Imaging the premotor areas. Curr Opin Neurobiol. 2001;11(6):663–72. 25. Geschwind N, Kaplan E. A human cerebral deconnection syndrome. A preliminary report. Neurology 1962;12:675–85. 41. Larsson J, Gulyas B, Roland PE. Cortical representation of self-paced finger movement. Neuroreport 1996;7(2):463–8. 26. Gazzaniga MS, Bogen JE, Sperry RW. Dyspraxia following division of the cerebral commissures. Arch Neurol. 1967;16(6):606–12. 42. Weeks RA, Honda M, Catalan MJ, Hallett M. Comparison of auditory, somatosensory, and visually instructed and internally generated finger movements: a PET study. Neuroimage 2001;14(1 Pt 1):219–30. 27. Watson RT, Heilman KM, Bowers D. Magnetic resonance imaging (MRI, NMR) scan in a case of callosal apraxia and pseudoneglect. Brain 1985;108(2): 535–6. 28. Graff-Radford NR, Welsh K, Godersky J. Callosal apraxia. Neurology 1987;37(1):100–5. 43. Deecke L, Lang W, Uhl F et al. Movement-related potentials and magnetic fields: new evidence for SMA activation leading MI activation prior to voluntary movement. Electroencephalogr Clin Neurophysiol Suppl. 1999;50:386–401. 29. Lausberg H, Cruz RF, Kita S, Zaidel E, Ptito A. Pantomime to visual presentation of objects: left hand dyspraxia in patients with complete callosotomy. Brain 2003;126(2):343–60. 44. Babiloni C, Carducci F, Del Gratta C et al. Hemispherical asymmetry in human SMA during voluntary simple unilateral movements. An fMRI study. Cortex 2003;39(2):293–305. 30. Rapcsak SZ, Gonzalez Rothi LJ, Heilman KM. Apraxia in a patient with atypical cerebral dominance. Brain Cogn. 1987;6(4):450–63. 45. Thobois S, Dominey PF, Decety J et al. Motor imagery in normal subjects and in asymmetrical Parkinson’s disease: a PET study. Neurology 2000;55(7):996–1002. 31. Heilman KM, Rothi LJ, Valenstein E. Two forms of ideomotor apraxia. Neurology 1982;32(4):342–6. 32. Rothi LJ, Heilman KM, Watson RT. Pantomime comprehension and ideomotor apraxia. J Neurol Neurosurg Psychiatry 1985;48(3):207–10. 33. Hermsdorfer J, Terlinden G, Muhlau M, Goldenberg G, Wohlschlager AM. Neural representations of pantomimed and actual tool use: evidence from an event-related fMRI study. Neuroimage 2007; 36(Suppl. 2):T109–18. 34. Lissauer H. Ein Fall von Seelenblindheit nebst einem Beitrage zur Theorie derselben. Eur Arch Psychiatry Clin Neurosci. 1890;21(2):222–70. 35. Balint R. Seelenlahmung des “Schuens” optische Ataxie, raumliche Storung der Aufmerksamkeit. Mschr Psychiat Neurol. 1909;25:51–66. 36. Ungerleider LG, Haxby JV. ‘What’ and ‘where’ in the human brain. Curr Opin Neurobiol. 1994;4(2): 157–65. 212 46. Leiguarda R, Lees AJ, Merello M, Starkstein S, Marsden CD. The nature of apraxia in corticobasal degeneration. J Neurol Neurosurg Psychiatry 1994; 57(4):455–9. 47. Peigneux P, Salmon E, Garraux G et al. Neural and cognitive bases of upper limb apraxia in corticobasal degeneration. Neurology 2001;57(7):1259–68. 48. Schmahmann JD, Pandya DN, Wang R et al. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 2007;130(Pt 3):630–53. 49. Catani M, ffytche DH. The rises and falls of disconnection syndromes. Brain 2005;128(Pt 10): 2224–39. 50. Pramstaller PP, Marsden CD. The basal ganglia and apraxia. Brain 1996;119(Pt 1):319–40. 51. Hanna-Pladdy B, Heilman KM, Foundas AL. Cortical and subcortical contributions to ideomotor apraxia: Chapter 14: Praxis analysis of task demands and error types. Brain 2001; 124(Pt 12):2513–27. 52. De Renzi E, Faglioni P, Sorgato P. Modality-specific and supramodal mechanisms of apraxia. Brain 1982; 105(Pt 2):301–12. 53. Goldenberg G, Hagmann S. The meaning of meaningless gestures: a study of visuo-imitative apraxia. Neuropsychologia 1997;35(3):333–41. 54. Peigneux P, Van Der Linden M, Andres-Benito P et al. [A neuropsychological and functional brain imaging study of visuo-imitative apraxia]. Rev Neurol. (Paris) 2000;156(5):459–72. 55. Mehler M. Visuo-imitative apraxia. Neurology 1987; 37(Suppl. 1):129. 56. Ochipa C, Rothi LJ, Heilman KM. Conduction apraxia. J Neurol Neurosurg Psychiatry 1994;57(10):1241–4. 57. Tessari A, Canessa N, Ukmar M, Rumiati RI. Neuropsychological evidence for a strategic control of multiple routes in imitation. Brain 2007;130(Pt 4): 1111–26. 58. Merians AS, Clark M, Poizner H et al. Visual-imitative dissociation apraxia. Neuropsychologia 1997;35(11): 1483–90. 59. Hanna-Pladdy B, Mendoza JE, Apostolos GT, Heilman KM. Lateralised motor control: hemispheric damage and the loss of deftness. J Neurol Neurosurg Psychiatry 2002;73(5):574–7. 60. Lawrence DG, Kuypers HG. The functional organization of the motor system in the monkey. II. The effects of lesions of the descending brain-stem pathways. Brain 1968;91(1):15–36. 61. Denny-Brown D, Botterell EH. The motor functions of the agranular frontal cortex. Res Publ Assoc Res Nerv Ment Dis. 1948;27:235–345. 62. Travis AM. Neurological deficiencies after ablation of the precentral motor area in Macaca mulatta. Brain 1955;78(2):155–73. 63. Kermadi I, Liu Y, Tempini A, Rouiller EM. Effects of reversible inactivation of the supplementary motor area (SMA) on unimanual grasp and bimanual pull and grasp performance in monkeys. Somatosens Mot Res. 1997;14(4):268–80. 64. Rouiller E, Yu X, Moret V et al. Dexterity in adult monkeys following early lesion of the motor cortical hand area: the role of cortex adjacent to the lesion. Eur J Neurosci. 1998;10(2):729–40. 65. Verstichel P, Meyrignac C. [Left unilateral melokinetic apraxia and left dynamic apraxia following partial callosal infarction]. Rev Neurol. (Paris) 2000;156(3): 274–7. 66. Buxbaum LJ, Haaland KY, Hallett M et al. Treatment of limb apraxia: moving forward to improved action. Am J Phys Med Rehabil. 2008;87(2): 149–61. 67. Quencer K, Okun MS, Crucian G et al. Limb-kinetic apraxia in Parkinson disease. Neurology 2007; 68(2):150–1. 68. Gebhardt A, Vanbellingen T, Baronti F, Kersten B, Bohlhalter S. Poor dopaminergic response of impaired dexterity in Parkinson’s disease: bradykinesia or limb kinetic apraxia? Mov Disord. 2008;23(12): 1701–6. 213 Section I Structural and Functional Neuroanatomy Chapter Visuospatial function 15 Doron Merims and Morris Freedman In this chapter, we discuss the neuroanatomy underlying visuospatial function. Our focus will include the anatomical organization of visuospatial processing, visuospatial syndromes relevant to clinical disorders, as well as specific neurologic diseases affecting visuospatial function. Visuospatial processing The organization of visual processing in the brain involves two general components. The first of these transmits information from the retina to the striate cortex, and the second includes neurons that leave the striate cortex to project to various regions in the interconnected hierarchical network of extrastriate cortical areas [1]. The first component of the visual system starts with the optic nerves, which consist mainly of axons originating in the ganglion layer of the retina. The optic nerves converge to form the optic chiasm. The fibers lying medially in the optic nerves cross the chiasm and continue in the optic tract on the contralateral side of the brain, while the lateral fibers do not cross and continue in the optic tract on the ipsilateral side. The optic tract ends in the lateral geniculate body of the thalamus. Visual information continues along the geniculocalcarine tract (optic radiation) to the striate cortex [2]. The primary visual area, also called the striate cortex, corresponds to Brodmann’s area (BA) 17 and is located in the depths of the calcarine fissure, mainly on the medial surface of the occipital lobe. The primary visual area contains well-developed outer and internal granular layers (II and IV). The internal granular layer of the primary visual area receives fibers from the lateral geniculate body via the optic radiation [2]. The unimodal visual association areas in the human brain occupy peristriate cortex that corresponds to BA 18 and 19 and parts of the fusiform, inferior temporal and middle temporal gyri (BA 37, 20 and 21) [3]. Lesions in the pathway to the striate cortex result in a contralateral visual field defect that affects all aspects of vision. The deficits are thus specific for location but not for modality. Lesions in the areas that receive projections from the striate cortex may cause impairment in different modalities of the visual system and are less specific for location [1]. Schneider [4] described two different types of visual impairment in the hamster due to cortical and tectal ablations. Ablating the superior colliculus abolished the ability to orient toward an object, but not the ability to identify it, while ablation of the visual cortical areas (BA 17 and 18) had the opposite effect. Ungerleider and Mishkin [5] distinguished between two different visual pathways originating in the occipital cortex, the ventral and dorsal streams. The superior longitudinal fasciculus follows a dorsal path, traversing the posterior parietal region in its course to the frontal lobes. The dorsal stream is responsible for spatial localization (i.e., object location) and is alternatively known as the “where” stream. The inferior longitudinal fasciculus projects to the inferior temporal area; this ventral stream has a role in visual object recognition, and is known as the “what” stream. In their theory of perception and action, Goodale and Milner [6] proposed that the ventral stream plays a major role in perceptual identification of objects, whereas the dorsal stream mediates the required sensorimotor transformations for visually guided actions directed at these objects. The role of right and left hemispheres in visuospatial function was investigated by Delis and colleagues [7]. They compared the performance of patients with left hemisphere damage to patients with right Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 214 Chapter 15: Visuospatial function hemisphere lesions and normal controls. Patients and normal controls were asked to remember visual hierarchical stimuli consisting of larger forms constructed of smaller forms. An example of a stimulus was a large letter (“M”) created out of many copies of a small letter (“z”). The major difference between the groups was their ability to remember the two levels of hierarchical stimuli on a forced-choice recognition test. Patients with right hemisphere damage made significantly more errors than control subjects in remembering the larger forms relative to the smaller forms. In contrast, patients with left hemisphere damage made significantly more errors than control subjects in remembering the smaller forms relative to the larger forms. Ng and colleagues [8] supported the concept that both hemispheres participate in visuospatial processing in a study using functional magnetic resonance imaging (fMRI) in ten right-handed normal volunteers, as well as lesion analyses in patients with focal parietal damage. The fMRI was done while the normal volunteers were performing a spatial processing task and showed robust bilateral cortical activation in both superior parietal lobes. However, the right parietal lobe showed earlier and stronger signal change. Lesion analysis of 17 patients indicated that damage to either the right or left parietal lobe led to impaired judgment of line orientation, but relatively greater difficulty was found in the right parietal lesion sample. These studies support the conclusion that the right cerebral hemisphere, and in particular the parietal lobe, plays a special role in visuospatial function. Visuospatial clinical syndromes Neglect Neglect is a failure to report, respond or orient to novel or meaningful stimuli presented to the side opposite a brain lesion, when this failure cannot be attributed to sensory or motor dysfunction [9]. Neglect reflects a lateralized disruption of spatial attention. The left neglect syndrome is characterized by a reduction of neural resources that can be mobilized by sensory events located on the left and by motor plans directed towards the left. When neglect is severe, the patient may behave as if half the environment has ceased to exist [10]. Neglect can be either sensory or motor. Sensory neglect manifests by a deficit in awareness of stimuli contralateral to a lesion that does not involve sensory projection systems or the primary cortical sensory areas to which they project. Motor neglect is characterized by failure to respond to a stimulus even though the patient is aware of the stimulus and has the strength to respond [9]. Patients with neglect may be slower to initiate a motor response to targets appearing in left hemispace (directional hypokinesia). Patients with directional hypokinesia were found to have a lesion involving the ventral lateral putamen, the claustrum, and the white matter subjacent to the frontal lobe [11]. Ghacibeh and colleagues [12] examined the effect of repetitive transcranial magnetic stimulation (rTMS) on the right frontal and right parietal areas during a line bisection task in healthy volunteers. They used a task that dissociated motor-intentional from sensory-attentional neglect. Right frontal rTMS caused motor-intentional neglect, whereas right parietal rTMS caused sensory-attentional neglect. Lesions in other brain areas also may cause neglect. The area that is most commonly involved is the right posterior parietal lobe, especially the inferior parietal area. The most common cause is stroke [13, 14] and thus a major portion of the data regarding the neuroanatomy of neglect is derived from patients who have had focal cerebral infarcts. The anatomical correlates of visual neglect were investigated in 110 right-handed stroke patients with lesions confined to the right hemisphere. Neglect was found to be associated more frequently with lesions posterior to the Rolandic fissure, as compared with frontal lesions. When the cerebral lesion is confined to deep structures, neglect occurs more frequently when nuclei such as the thalamus and the basal ganglia are damaged. Conversely, lesions limited to the subcortical white matter are rarely associated with neglect [13]. Using intra-operative direct electrical stimulation during tumor resection, Thiebaut de Schotten and colleagues [15] described two patients who underwent surgical resection of a low-grade glioma. In one patient, the glioma was centered on the caudal part of the right temporal lobe. In the second patient, the glioma was centered on the right inferior parietal lobule. Both patients showed a rightward deviation on line bisection upon stimulation of two cortical sites: the supramarginal gyrus (SMG) and the caudal portion of the superior temporal gyrus (cSTG). In addition, the second patient showed a maximal rightward deviation upon stimulation of a restricted subcortical region on the floor of the surgical cavity that corresponded to a portion of the superior occipitofrontal fasciculus 215 Section I: Structural and Functional Neuroanatomy connecting the parietal to the frontal lobe. The findings suggest that the SMG, the cSTG, and a poorly known parietal-frontal pathway, the superior occipitofrontal fasciculus, but not the rostral superior temporal gyrus, are critical to the symmetrical processing of the visual scene in humans. Watson and Heilman [16] described three patients with right thalamic hemorrhage, contralateral motor neglect, and limb akinesia. These patients also had anosognosia and emotional flattening. Ischemic stroke to the right medial thalamus may also cause hemispatial neglect [17]. Mort and colleagues [18] used high-resolution MRI protocols to map the lesions of 35 patients with neglect and either right middle cerebral artery (MCA) or right posterior cerebral artery (PCA) stroke. For patients with MCA stroke, the critical area involved in all patients with neglect was the angular gyrus on the lateral surface of the inferior parietal lobe. The superior temporal gyrus was involved only in half of these patients. Eight MCA neglect patients had frontal involvement, with lesions overlapping in the inferior frontal gyrus, but the same overlap region was also involved in four MCA patients without neglect. Park and colleagues [19] examined 45 patients with right or left PCA territory infarctions. The overall frequency of hemispatial neglect was 42.2% and did not significantly differ between right (48.0%) and left (35.0%) PCA groups, but the severity of neglect was significantly greater in those with right PCA territory lesions. Isolated occipital lesions did not cause neglect; however, injury to both the occipital lobe and the splenium of the corpus callosum did produce this syndrome. Bird and colleagues [20] showed that, in patients with neglect after right PCA territory infarction, the region maximally involved was the white matter of the occipital lobe, but there also was a high degree of overlap extending anteriorly into the ventral medial temporal lobe. Using MRI, Committeri and colleagues [21] demonstrated the anatomical segregation of personal and extrapersonal neglect. They found that awareness of extrapersonal space is based on the integrity of right frontal and superior temporal regions whereas the right inferior parietal regions (supramarginal gyrus, post-central gyrus, and especially the white matter medial to them) are crucial areas for the awareness of personal space. Common but less crucial regions for both personal and extrapersonal awareness were located in the temporo-perisylvian cortex. 216 The presence of visual neglect in the studies cited above was identified primarily by patient performance on “pencil and paper” tests such as line bisection [22] and cancellation tasks [3]. On the line bisection task, the patient is asked to divide a line by placing a mark at its central point. Patients with neglect displace the mark ipsilateral to their lesion. On cancellation tests, patients are asked to cross out letters, numbers, or symbols, such as stars. The severity of neglect can be quantified by the number of target objects that are missed [22, 23]. The presence of neglect can also be determined by performance on different drawing tasks such as drawing a clock or a flower. On these tasks, patients with neglect ignore the left side of the image. On clock drawing, for example, left hemiinattention often results in omission of numbers on the left [24]. Simultanagnosia Simultanagnosia is a disorder characterized by the inability to see more than one object at a time [25]. Patients have difficulty describing a complex scene and may report only one or a few components of the visual array. Simultanagnosia is one of the three components of Balint’s syndrome, which also includes optic ataxia (deficient visually guided reaching), and oculomotor apraxia (impaired visual scanning) [26]. Simultanagnosia typically occurs following bilateral lesions in the superior occipitoparietal regions. Rizzo and Hurtig [27] described three patients with simultanagnosia caused by bilateral superior occipital lobe strokes. These patients had no abnormalities in visual acuity or visual fields to explain their impairment. A striking example of this syndrome was recently reported by Smith and colleagues [28] in an 87-yearold artist who had a presumed top of the basilar stroke secondary to atrial fibrillation. During the first 4 weeks following her stroke, there was a change in her drawings, with selective attention to the left lower quadrant and important aspects of the whole image missing. Subsequently, she improved to the point where there was no major difference from her style of painting compared with before the stroke. The authors interpreted her changed paintings post-stroke as showing selective inattention to the whole of the object. This transient inability to perceive the overall figure or scene was thought to represent simultanagnosia. The localization of her lesion was not determined by neuroimaging. Chapter 15: Visuospatial function Transient Balint’s syndrome was reported in a 74-year-old woman who received non-ionic contrast media during renal angiography. The contrast material was noted in the bilateral parietooccipital cortices on the initial computed tomography (CT) scan and disappeared after clinical resolution of the symptoms [29]. Coslett and Lie [25] described a patient with profound simultanagnosia and optic ataxia in the presence of bilateral posterior parietal infarcts and white matter hyperintensities. The patient could not report more than one attribute of an object; for example, he could not name the color of the ink in which words were written despite being able to name the word correctly. These investigators suggested that, in patients with simultanagnosia, the inability to offer explicit information about object identity, attributes, or location reflects a failure to link spatial representations computed in the parietal lobe to information about object identity and perceptual attributes stored in the temporal lobe. They suggested the presence of at least two subtypes of dorsal simultanagnosia: one characterized by an early visual attention impairment that may be related to lesions involving the superior parietooccipital junction bilaterally, and another reflecting impairments in the binding of information computed in the dorsal and ventral visual streams. Patients with this “binding deficit” simultanagnosia may have lesions involving the inferior parietal lobes bilaterally. Another group of patients with simultanagnosia appear to have a lesion restricted to the left occipitotemporal junction [30]. Simultanagnosia may also be a feature of degenerative brain disorders such as posterior cortical atrophy [31, 32], corticobasal degeneration [33], and Alzheimer’s disease (AD) [34]. In addition to simultanagnosia following ischemic stroke [28, 35], the syndrome may appear after brain hemorrhage [36], with brain tumors [37], and with infections including progressive multifocal leukoencephalopathy associated with acquired immunodeficiency syndrome (AIDS) and subacute sclerosing panencephalitis [38]. Visuospatial memory Studies on visuospatial memory have distinguished between visuospatial working memory and memory for visuospatial information. With regard to working memory, an fMRI study in 11 healthy subjects suggested that an area in the superior frontal sulcus plays a predominant role in visuospatial working memory [39]. Other studies, using positron emission tomography (PET) in humans, suggest that the neural systems involved in working memory for spatial location and object identification are functionally segregated, with a dorsal frontal region being important for spatial location and a more ventral region (involving the middle, inferior, and orbital frontal areas) being critical for object identification [40]. A widely known study of memory for visuospatial information is that of Maguire and colleagues [41]. These investigators examined 16 London taxi drivers, who, through their daily work, acquired a substantial amount of large-scale spatial information (as evidenced by passing the licensing examinations). These taxi drivers were compared with a group of comparison subjects who did not drive taxis. The volume of the posterior hippocampus (as assessed using quantitative MRI) was larger among the taxi drivers whereas anterior hippocampal volume was larger among the comparison subjects. Additionally, posterior hippocampal volume correlated with the amount of time spent as a taxi driver. In another study, Iaria and colleagues [42] used a virtual radial maze task and fMRI to investigate the modulation of brain activity while healthy subjects spontaneously adopted different navigational strategies. Subjects were categorized as using a non-spatial strategy if they associated the arms of the maze with numbers or letters, or they counted the arms clockwise or counterclockwise from a single starting point. They were categorized as using a spatial memory strategy if they used at least two landmarks and did not mention a non-spatial strategy. Subjects using a spatial memory strategy showed activation of the right hippocampus in the early phase of performance, whereas those using a non-spatial strategy showed a sustained increase in activity within the caudate nucleus in the later stages and no increase in hippocampal activity. In a subsequent study, Bohbot and colleagues [43] used voxel-based morphometry to identify brain regions co-varying with the navigational strategies used by healthy subjects. They used the MRI scans of subjects tested previously in the study by Iaria and colleagues [42] and found that those who adopted a spatial memory strategy had significantly more gray matter in the hippocampus and less gray matter in the caudate nucleus compared with those who used a non-spatial strategy. In addition, the gray matter in the hippocampus was negatively correlated with the gray matter of the caudate nucleus. The 217 Section I: Structural and Functional Neuroanatomy authors suggested that a competitive interaction exists between the hippocampus and the caudate, one or the other being optimal for different tasks. Gray matter regions anatomically connected to the hippocampus, such as the amygdala, parahippocampal, perirhinal, entorhinal, and orbitofrontal cortices were found to co-vary with gray matter in the hippocampus. Bohbot and colleagues [43] suggested that the correspondence between navigational strategies and gray matter density found in their study has implications for clinical intervention aimed at recovering function in patients with hippocampal or caudate dysfunction. Another dissociation, supported by lesion studies, divides visuospatial memory into two categories: allocentric and egocentric. The allocentric system refers to the spatial location of objects in relation to each other. It has been suggested that the hippocampus is involved in allocentric spatial memory, and functions to consolidate allocentric information into long-term memory [44]. The egocentric system, which refers to the location of an object in relation to the observer, is thought to be mediated by the posterior parietal cortex [45]. The amygdala also may be involved in visuospatial memory. In one study, quantitative MRI measurement of hippocampal sclerosis in patients suffering from temporal lobe epilepsy failed to find a correlation between the right hippocampus and visuospatial memory. Instead, a correlation was found between the volume of the right amygdala and visuospatial memory [46]. Depth perception and stereopsis Depth perception can be achieved, to a certain degree, with monocular vision by use of depth cues such as motion parallax (a depth cue that results from one’s motion) and the obscuration of distant objects by nearer ones. However, for maximal three-dimensional visuospatial processing, the brain uses the difference between both eyes (disparity) as a cue for evaluation of depth [47]. Stereopsis is the perception of depth arising from these small differences between the images in the two eyes. Tsao and colleagues [48] used fMRI to show that a cluster of areas at the parietooccipital junction is specialized for stereopsis. Similarly, Merboldt and colleagues [49] showed with fMRI that stereoscopic depth perception relies on the recruitment of neuronal populations in higher-order visual areas of the parietal cortex rather than in primary visual areas. The finding that 218 transient activity caused by the recognition of a minor change (view angle) of the visual percept is localized to the intraparietal sulcus suggests that this process represents an early search for binocular disparity, which precedes or even initiates depth perception. Cerebral akinetopsia Akinetopsia is the inability to perceive visual motion [50] and like many visuospatial deficits can be functionally disturbing. Blanke and colleagues [51] reported a 41-year-old patient with akinetopsia accompanied by dog phobia. MRI showed bilateral parietal lobe strokes, more evident on the left and with extension toward the occipital lobe. The patient could recognize a dog, but would constantly misperceive its position and direction of motion. However, the patient also had difficulty walking on or crossing a crowded street even without dogs present, and his functional impairment could not be solely attributed to dog phobia. The lesion was largely restricted to the parietal lobes, a component of the dorsal stream of the visual system that is responsible for spatial processing. Using focal electrical stimulation in a patient undergoing invasive monitoring for epilepsy, Blanke and colleagues [52] studied whether extrastriate cortex was involved in the unidirectional motion blindness. Findings suggested that unilateral electrical stimulation in the posterior temporal lobes, more specifically the middle temporal gyrus (area hMT+, also referred to as V5), may diminish the capacity to discriminate motion in the contralateral visual field. Beckers and Zeki [53] studied the effect of deactivation of areas V1 and V5 in normal subjects using transcranial magnetic stimulation (TMS). Stimulation of V5 induced severe impairment in perception of the direction of motion. TMS of V1 also showed impairment in motion perception but to a lesser degree than was apparent in V5. Neurological conditions and visuospatial dysfunction This section will consider visuospatial impairment in common neurologic diseases such as AD and Parkinson’s disease (PD). Less common disorders such as Huntington’s disease (HD) and Williams syndrome (WMS) will also be described to emphasize the variety of etiologies that may affect the visuospatial system. Chapter 15: Visuospatial function Alzheimer’s disease Basal ganglia lesions Although memory loss is the most prominent feature of AD, visuospatial function is also impaired, even in the early stages of the disease [54]. Using PET and tests to assess spatial vision and object recognition, Fujimori and colleagues [55] showed that visuospatial disturbances were significantly correlated with lower metabolic rate of both inferior parietal lobules in patients with mild to moderate AD. More specifically, object recognition deficits were significantly correlated with lower metabolic rate in the right middle temporal gyrus and the right inferior parietal lobule. Grossi and colleagues [56] performed a longitudinal study of a patient with selective, progressive impairment of topographical orientation. Six years after the onset of this disturbance, and three years after the first evaluation, the patient developed typical cognitive impairments of AD with PET findings of bilateral hypoperfusion in parietotemporal areas. Although not an autopsy-proven case of AD, this patient may represent a highly focal variant of AD, with visuospatial impairment as the dominant and presenting symptom. Su and colleagues [61] studied 37 patients with basal ganglia hemorrhage and found that visuospatial function and memory were the most affected cognitive domains. The explanation for visuospatial dysfunction associated with basal ganglia disorders may be disruption of the cortical-striatal circuits involving parietal and temporal cortices that may lead to impairment in visuospatial analysis [62]. In addition, striatal damage has been found to selectively impair performance based on egocentric (body-centered) rather than allocentric (object-centered) spatial cues [63]. Lineweaver and colleagues [64] compared the visuospatial performance of patients with HD, a disease that primarily affects the basal ganglia, to performance in patients with AD. Speed, but not accuracy, of mental rotation decreased with increasing angle of orientation in patients with HD. In contrast, accuracy but not speed of rotation decreased with increasing angle of orientation in patients with AD. The slowing exhibited by HD patients may be mediated by damage to the basal ganglia, whereas the spatial manipulation deficit of AD patients may reflect pathology in parietal and temporal lobe cortices important for visuospatial processing [64]. Lawrence and colleagues [65] showed that patients with HD exhibited deficits on tests of spatial recognition and impaired reaction times on visual search. HD patients were also impaired on spatial but not visual object working memory, and showed impaired pattern–location associative learning. On two visuospatial recognition memory tasks, one assessing memory for hand positions (egocentric) and the other assessing memory for spatial locations (allocentric), HD patients were impaired relative to matched healthy controls on both. Correlation analyses indicated that performance of HD patients on the Hand Position Memory task, but not the Spatial Location Memory task, was associated with global cognitive impairment [66]. Posterior cortical atrophy Posterior cortical atrophy (PCA) is a clinically homogeneous but pathologically heterogeneous syndrome in which the onset of a progressive dementia is characterized by visual deficits. AD is the most common pathological correlate of PCA [57], although this syndrome is also associated with progressive subcortical gliosis and Creutzfeldt–Jakob disease [58]. McMonagle and colleagues [59] compared cognitive performance of patients with PCA with that of patients with probable AD and normal controls. Patients were included in the PCA group if they showed gradual onset of progressive cognitive impairment with prominent visuospatial dysfunction. The PCA patients had marked impairment on visuospatial tasks with poor reading and writing in the presence of relatively preserved memory, in striking contrast to the patients with AD. The most common visual symptoms among PCA patients were simultanagnosia and optic ataxia; complete Balint’s syndrome was detected in five individuals. Unilateral neglect and visual field defects were found less commonly. PET studies in patients with PCA have shown hypometabolism in the occipitoparietal cortices, more prominent on the right [60]. Dementia with Lewy bodies and Parkinson’s disease with dementia Dementia with Lewy bodies (DLB) and Parkinson’s disease with dementia (PDD) can be considered to lie on the same spectrum of neurodegeneration, and their cognitive impairments have much in common. In particular, visual perception impairments are similar in 219 Section I: Structural and Functional Neuroanatomy patients with DLB and PDD [67]. In both diseases, visual perception is more impaired if visual hallucinations are present [68]. Dementia with Lewy bodies In contrast to their relatively mild episodic memory deficits, the visuospatial abilities of patients with DLB are profoundly impaired, and significantly worse than those of patients with AD [69, 70]. Tiraboschi and colleagues [71] collected first-visit data of 23 pathologically proven DLB and 94 AD cases. Whereas visual hallucinations at presentation were more specific for DLB (99%), visuospatial impairment was observed in 74% of the DLB cases and found to be the most sensitive symptom. Visual hallucinations at presentation were the best positive predictor of DLB at autopsy, and lack of visuospatial impairment was the best negative predictor. The best model for differentiating DLB from AD in the earliest stages of disease was found to include visual hallucinations and visuospatial dysfunction but not extrapyramidal symptoms. Lobotesis and colleagues [72] compared regional cerebral blood flow (rCBF) using single-photon emission computed tomography (SPECT) in DLB patients, AD patients, and normal age-matched control subjects. Both DLB and AD subjects had significantly reduced rCBF in parietal and temporal regions compared with control subjects. A significant difference between DLB and AD was in the occipital regions, with the DLB patients showing a greater rCBF deficit. Harding and colleagues [73] studied the pathology of 63 cases with Lewy body (LB) pathology and described consistent association between the presence of visual hallucinations and cortical pathology. The overall LB burden was significantly greater in the parahippocampal and inferior temporal cortices in cases with early hallucinations. Higher LB densities were found in the inferior temporal cortex in cases with DLB than in cases with PDD. Cases with hallucinations had significantly more LB/field on average than those without hallucinations in the parahippocampus and amygdala, but not in the frontal, anterior cingulate or inferior temporal cortices. Abnormalities in the occipital and temporoparietal areas in patients with DLB may explain the prominent visuospatial deficits in these patients. Parkinson’s disease with dementia Visuospatial deficits in PD are clearly related to dementia and progression of the disease [74]. Witt and 220 colleagues [75] evaluated the influence of the subthalamic nucleus (STN) in extrapersonal space orientation using STN deep brain stimulation (DBS) in PD as a reversible model of functional ablation. They examined 12 patients with PD one year after implantation of DBS electrodes in the STN after overnight withdrawal of L-dopa. Patients were tested with both stimulators turned on, the right only, the left only, or none at all. No asymmetries in spatial orientation were found when both stimulators were off, when both stimulators were on, and when only the right stimulator was on. When only the left subthalamic stimulation was switched on, the reaction times of both hands to visual stimuli in left extra-personal hemispace increased significantly, and the line bisection test showed a significant shift to the right. These results lead to the conclusion that the STN and its cortical projections may influence the network involved in visuospatial orientation. Williams syndrome WMS is a rare genetic condition characterized by mild to moderate cognitive and behavioral abnormalities including unusually heightened drive toward social interaction. In addition, there are distinctive facial features, growth delay, cardiovascular anomalies, and occasional hypercalcemia. The etiology relates to microdeletions on chromosome 7q11.23 [76, 77]. The main visuospatial deficit in WMS is in spatial localization [78, 79]. Paul and colleagues [78] showed that patients with WMS perform markedly worse than healthy controls on a location-matching task. However, their performance on a face-matching task was similar to controls. These investigators suggested that patients with WMS might have selective involvement of the dorsal stream of visual processing. Chiang and colleagues [80] compared 3D T1-weighted brain MRI scans of patients with WMS with healthy controls and found marked volume reductions of the parietal and occipital lobes, thalamus, basal ganglia, and midbrain in WMS. Using fMRI in WMS patients, Meyer-Lindenberg and colleagues [81] found isolated hypoactivation in the parietal portion of the dorsal visual stream with normal activation in the ventral visual stream. Using high-resolution structural MRI, they also found symmetrical reduction in gray matter; one such area, the parietooccipital region/intraparietal sulcus, is immediately adjacent to the dorsal visual stream. The authors suggested that the Chapter 15: Visuospatial function visuoconstructive deficit in WMS could be attributed to impaired input from this structurally altered region. Mitochondrial myopathies Mitochondrial myopathies are clinically heterogeneous disorders that can affect multiple organ systems. The Kearns–Sayre syndrome (KSS) is the bestknown example of a mitochondrial myopathy, and is characterized by onset before age 15, progressive external ophthalmoplegia, and pigmentary degeneration of the retina, with other systemic manifestations [82]. Chronic progressive external ophthalmoplegia (CPEO) is a closely related disorder. KSS and CPEO patients were compared with healthy control subjects matched for age, sex, and education with a neuropsychological test battery covering verbal skills, verbal and visual memory, visuospatial perception, visual construction, attention, abstraction, and flexibility [83]. The patients with KSS or CPEO did not have dementia, but there was evidence of neuropsychological dysfunction, suggesting selective impairment of visuospatial perception and executive function. A specific pattern of cognitive deficits implicating parietooccipital and prefrontal dysfunction was suggested [83]. In an earlier study by Turconi and colleagues [84], 16 patients with mitochondrial encephalomyopathy showed no global cognitive impairment but did score lower on non-verbal versus verbal tasks. Visuospatial skills and short-term memory were selectively impaired. SPECT scans were carried out on 12 of the patients, and the most frequent finding was hypoperfusion in both temporal lobes [84]. Conclusion Impairment of visuospatial function is common with focal brain lesions, as well as more widespread disorders such as neurodegenerative diseases. Knowledge of the neuroanatomy underlying deficits in visuospatial function is critical for understanding the mechanisms leading to the disability experienced by patients with visuospatial dysfunction. This understanding may in turn provide insights for better assessment and management strategies of affected patients. Acknowledgments During the preparation of this work, M. Freedman held a research grant from the Ontario Mental Health Foundation and was supported by the Saul A. Silverman Family Foundation, Toronto, Ontario, Canada, as part of a Canada International Scientific Exchange Program (CISEPO) project. References 1. Barton JS, Rizzo M, Eslinger PJ. Visual dysfunction. In Rizzo M, Eslinger PJ, editors. Principles and Practice of Behavioral Neurology and Neuropsychology. Philadelphia, PA: W.B. Saunders; 2004, pp. 267–84. 2. Clemente DC. Development and gross anatomy of the central nervous system. In Gray H, Clemente CD, editors. Anatomy of the Human Body. 30th American edition. Philadelphia, PA: Lea & Febiger; 1985, pp. 933–1148. 3. Mesulam MM. Principles of Behavioral and Cognitive Neurology. 2nd edition. Oxford: Oxford University Press; 2000. 4. Schneider GE. Two visual systems. Science 1969; 163(870):895–902. 5. Ungerleider L, Mishkin M. Two cortical visual systems. In Ingle D, Goodale MA, Mansfield RJW, editors. Analysis of Visual Behavior. Cambridge, MA: MIT Press; 1982, p. 834. 6. Goodale MA, Milner AD. Separate visual pathways for perception and action. Trends Neurosci. 1992;15(1): 20–5. 7. Delis DC, Robertson LC, Efron R. Hemispheric specialization of memory for visual hierarchical stimuli. Neuropsychologia 1986;24(2):205–14. 8. Ng VW, Eslinger PJ, Williams SC et al. Hemispheric preference in visuospatial processing: a complementary approach with fMRI and lesion studies. Hum Brain Mapp. 2000;10(2):80–6. 9. Heilman KM, Watson RT, Valenstein E. Neglect and related disorders. In Heilman KM, Valenstein E, editors. Clinical Neuropsychology. 4th edition. Oxford: Oxford University Press; 2003, pp. 296–346. 10. Mesulam MM. Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci. 1999;354(1387):1325–46. 11. Sapir A, Kaplan JB, He BJ, Corbetta M. Anatomical correlates of directional hypokinesia in patients with hemispatial neglect. J Neurosci. 2007;27(15): 4045–51. 12. Ghacibeh GA, Shenker JI, Winter KH, Triggs WJ, Heilman KM. Dissociation of neglect subtypes with transcranial magnetic stimulation. Neurology 2007; 69(11):1122–7. 221 Section I: Structural and Functional Neuroanatomy 13. Vallar G, Perani D. The anatomy of unilateral neglect after right-hemisphere stroke lesions. A clinical/ CT-scan correlation study in man. Neuropsychologia 1986;24(5):609–22. 30. Bauer RM, Demery JA. Agnosia. In Heilman KM, Valenstein E, editors. Clinical Neuropsychology. 4th edition. Oxford: Oxford University Press; 2003, pp. 236–95. 14. Vallar G. Extrapersonal visual unilateral spatial neglect and its neuroanatomy. Neuroimage 2001;14(1 Pt 2):S52–8. 31. Graff-Radford NR, Bolling JP, Earnest FT et al. Simultanagnosia as the initial sign of degenerative dementia. Mayo Clin Proc. 1993;68(10):955–64. 15. Thiebaut de Schotten M, Urbanski M, Duffau H et al. Direct evidence for a parietal-frontal pathway subserving spatial awareness in humans. Science 2005;309(5744):2226–8. 32. Yoon SJ, Park JM, Na DL. Simultanagnosia in posterior cortical atrophy. J Neurol Neurosurg Psychiatry 2002; 72(2):269. 16. Watson RT, Heilman KM. Thalamic neglect. Neurology 1979;29(5):690–4. 33. Mendez MF. Corticobasal ganglionic degeneration with Balint’s syndrome. J Neuropsychiatry Clin Neurosci. 2000;12(2):273–5. 17. Watson RT, Valenstein E, Heilman KM. Thalamic neglect. Possible role of the medial thalamus and nucleus reticularis in behavior. Arch Neurol. 1981; 38(8):501–6. 34. Mendez MF, Turner J, Gilmore GC, Remler B, Tomsak RL. Balint’s syndrome in Alzheimer’s disease: visuospatial functions. Int J Neurosci. 1990;54(3–4): 339–46. 18. Mort DJ, Malhotra P, Mannan SK et al. The anatomy of visual neglect. Brain 2003;126(Pt 9):1986–97. 35. Kanwisher N. Neural events and perceptual awareness. Cognition 2001;79(1–2):89–113. 19. Park KC, Lee BH, Kim EJ et al. Deafferentationdisconnection neglect induced by posterior cerebral artery infarction. Neurology 2006;66(1):56–61. 36. Gillen JA, Dutton GN. Balint’s syndrome in a 10-yearold male. Dev Med Child Neurol. 2003;45(5):349–52. 20. Bird CM, Malhotra P, Parton A et al. Visual neglect after right posterior cerebral artery infarction. J Neurol Neurosurg Psychiatry 2006;77(9):1008–12. 21. Committeri G, Pitzalis S, Galati G et al. Neural bases of personal and extrapersonal neglect in humans. Brain 2007;130(Pt 2):431–41. 22. Halligan P, Wilson B, Cockburn J. A short screening test for visual neglect in stroke patients. Int Disabil Stud. 1990;12(3):95–9. 23. Ruff RM, Evans RW, Light RH. Automatic detection vs controlled search: a paper-and-pencil approach. Percept Mot Skills. 1986;62(2):407–16. 24. Freedman M, Leach L, Kaplan E et al. Focal brain damage. In Freedman M, editor. Clock Drawing: A Neuropsychological Analysis. New York, NY: Oxford University Press; 1994, pp. 98–127. 25. Coslett HB, Lie G. Simultanagnosia: when a rose is not red. J Cogn Neurosci. 2008;20(1):36–48. 26. Moreaud O. Balint syndrome. Arch Neurol. 2003;60(9): 1329–31. 27. Rizzo M, Hurtig R. Looking but not seeing: attention, perception, and eye movements in simultanagnosia. Neurology 1987;37(10):1642–8. 28. Smith WS, Mindelzun RE, Miller B. Simultanagnosia through the eyes of an artist. Neurology 2003;60(11): 1832–4. 29. Merchut MP, Richie B. Transient visuospatial disorder from angiographic contrast. Arch Neurol. 2002;59(5): 851–4. 222 37. Naccache L, Slachevsky A, Levy R, Dubois B. Simultanagnosia in a patient with right brain lesions. J Neurol. 2000;247(8):650–1. 38. Yapici Z. Subacute sclerosing panencephalitis presenting with Balint’s syndrome. Brain Dev. 2006; 28(6):398–400. 39. Courtney SM, Petit L, Maisog JM, Ungerleider LG, Haxby JV. An area specialized for spatial working memory in human frontal cortex. Science 1998; 279(5355):1347–51. 40. Courtney SM, Ungerleider LG, Keil K, Haxby JV. Object and spatial visual working memory activate separate neural systems in human cortex. Cereb Cortex 1996;6(1):39–49. 41. Maguire EA, Gadian DG, Johnsrude IS et al. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci USA 2000;97(8):4398–403. 42. Iaria G, Petrides M, Dagher A, Pike B, Bohbot VD. Cognitive strategies dependent on the hippocampus and caudate nucleus in human navigation: variability and change with practice. J Neurosci. 2003;23(13): 5945–52. 43. Bohbot VD, Lerch J, Thorndycraft B, Iaria G, Zijdenbos AP. Gray matter differences correlate with spontaneous strategies in a human virtual navigation task. J Neurosci. 2007;27(38):10,078–83. 44. Holdstock JS, Mayes AR, Cezayirli E et al. A comparison of egocentric and allocentric spatial memory in a patient with selective hippocampal damage. Neuropsychologia 2000;38(4):410–25. Chapter 15: Visuospatial function 45. Vallar G, Lobel E, Galati G et al. A fronto-parietal system for computing the egocentric spatial frame of reference in humans. Exp Brain Res. 1999;124(3): 281–6. 46. Pegna AJ, Caldara-Schnetzer AS, Perrig SH et al. Is the right amygdala involved in visuospatial memory? Evidence from MRI volumetric measures. Eur Neurol. 2002;47(3):148–55. 47. Barlow HB, Blakemore C, Pettigrew JD. The neural mechanism of binocular depth discrimination. J Physiol. 1967;193(2):327–42. 48. Tsao DY, Vanduffel W, Sasaki Y et al. Stereopsis activates V3A and caudal intraparietal areas in macaques and humans. Neuron 2003;39(3): 555–68. 49. Merboldt KD, Baudewig J, Treue S, Frahm J. Functional MRI of self-controlled stereoscopic depth perception. Neuroreport 2002;13(14):1721–5. 50. Zihl J, von Cramon D, Mai N. Selective disturbance of movement vision after bilateral brain damage. Brain 1983;106(Pt 2):313–40. 51. Blanke O, Vaclavik V, Landis T, Safran AB. Dog phobia in a motion-blind patient. Cogn Neuropsychiatry 2003;8(3):211–21. 52. Blanke O, Landis T, Safran AB, Seeck M. Direction-specific motion blindness induced by focal stimulation of human extrastriate cortex. Eur J Neurosci. 2002;15(12):2043–8. 53. Beckers G, Zeki S. The consequences of inactivating areas V1 and V5 on visual motion perception. Brain 1995;118(Pt 1):49–60. 54. Kaskie B, Storandt M. Visuospatial deficit in dementia of the Alzheimer type. Arch Neurol. 1995;52(4):422–5. 55. Fujimori M, Imamura T, Hirono N et al. Disturbances of spatial vision and object vision correlate differently with regional cerebral glucose metabolism in Alzheimer’s disease. Neuropsychologia 2000;38(10): 1356–61. 56. Grossi D, Fasanaro AM, Cecere R, Salzano S, Trojano L. Progressive topographical disorientation: a case of focal Alzheimer’s disease. Neurol Sci. 2007;28(2): 107–10. 57. Tang-Wai DF, Graff-Radford NR, Boeve BF et al. Clinical, genetic, and neuropathologic characteristics of posterior cortical atrophy. Neurology 2004;63(7): 1168–74. 58. Victoroff J, Ross GW, Benson DF, Verity MA, Vinters HV. Posterior cortical atrophy. Neuropathologic correlations. Arch Neurol. 1994;51(3):269–74. 59. McMonagle P, Deering F, Berliner Y, Kertesz A. The cognitive profile of posterior cortical atrophy. Neurology 2006;66(3):331–8. 60. Nestor PJ, Caine D, Fryer TD, Clarke J, Hodges JR. The topography of metabolic deficits in posterior cortical atrophy (the visual variant of Alzheimer’s disease) with FDG-PET. J Neurol Neurosurg Psychiatry 2003;74(11):1521–9. 61. Su CY, Chen HM, Kwan AL, Lin YH, Guo NW. Neuropsychological impairment after hemorrhagic stroke in basal ganglia. Arch Clin Neuropsychol. 2007;22(4):465–74. 62. Amick MM, Schendan HE, Ganis G, Cronin-Golomb A. Frontostriatal circuits are necessary for visuomotor transformation: mental rotation in Parkinson’s disease. Neuropsychologia 2006;44(3):339–49. 63. Mijovic-Prelec D, Bentley P, Caviness VS, Jr. Selective rotation of egocentric spatial representation following right putaminal hemorrhage. Neuropsychologia 2004;42(13):1827–37. 64. Lineweaver TT, Salmon DP, Bondi MW, Corey-Bloom J. Differential effects of Alzheimer’s disease and Huntington’s disease on the performance of mental rotation. J Int Neuropsychol Soc. 2005;11(1):30–9. 65. Lawrence AD, Watkins LH, Sahakian BJ, Hodges JR, Robbins TW. Visual object and visuospatial cognition in Huntington’s disease: implications for information processing in corticostriatal circuits. Brain 2000;123(Pt 7):1349–64. 66. Davis JD, Filoteo JV, Kesner RP, Roberts JW. Recognition memory for hand positions and spatial locations in patients with Huntington’s disease: differential visuospatial memory impairment? Cortex 2003;39(2):239–53. 67. Mosimann UP, Mather G, Wesnes KA et al. Visual perception in Parkinson disease dementia and dementia with Lewy bodies. Neurology 2004; 63(11):2091–6. 68. Cormack F, Aarsland D, Ballard C, Tovee MJ. Pentagon drawing and neuropsychological performance in Dementia with Lewy bodies, Alzheimer’s disease, Parkinson’s disease and Parkinson’s disease with dementia. Int J Geriatr Psychiatry 2004;19(4):371–7. 69. Salmon DP, Galasko D, Hansen LA et al. Neuropsychological deficits associated with diffuse Lewy body disease. Brain Cogn. 1996;31(2):148–65. 70. Calderon J, Perry RJ, Erzinclioglu SW et al. Perception, attention, and working memory are disproportionately impaired in dementia with Lewy bodies compared with Alzheimer’s disease. J Neurol Neurosurg Psychiatry 2001;70(2):157–64. 71. Tiraboschi P, Salmon DP, Hansen LA et al. What best differentiates Lewy body from Alzheimer’s disease in early-stage dementia? Brain 2006;129(Pt 3): 729–35. 223 Section I: Structural and Functional Neuroanatomy 72. Lobotesis K, Fenwick JD, Phipps A et al. Occipital hypoperfusion on SPECT in dementia with Lewy bodies but not AD. Neurology 2001;56(5):643–9. 73. Harding AJ, Broe GA, Halliday GM. Visual hallucinations in Lewy body disease relate to Lewy bodies in the temporal lobe. Brain 2002;125(Pt 2): 391–403. 74. Levin BE, Llabre MM, Reisman S et al. Visuospatial impairment in Parkinson’s disease. Neurology 1991;41(3):365–9. 75. Witt K, Kopper F, Deuschl G, Krack P. Subthalamic nucleus influences spatial orientation in extrapersonal space. Mov Disord. 2006;21(3):354–61. 76. Amenta S, Sofocleous C, Kolialexi A et al. Clinical manifestations and molecular investigation of 50 patients with Williams syndrome in the Greek population. Pediatr Res. 2005;57(6):789–95. 79. Nakamura M, Watanabe K, Matsumoto A et al. Williams syndrome and deficiency in visuospatial recognition. Dev Med Child Neurol. 2001;43(9): 617–21. 80. Chiang MC, Reiss AL, Lee AD et al. 3D pattern of brain abnormalities in Williams syndrome visualized using tensor-based morphometry. Neuroimage 2007;36(4):1096–109. 81. Meyer-Lindenberg A, Kohn P, Mervis CB et al. Neural basis of genetically determined visuospatial construction deficit in Williams syndrome. Neuron 2004;43(5):623–31. 82. DiMauro S, Bonilla E, Zeviani M, Nakagawa M, DeVivo DC. Mitochondrial myopathies. Ann Neurol. 1985;17(6):521–38. 77. Hirota H, Matsuoka R, Kimura M et al. Molecular cytogenetic diagnosis of Williams syndrome. Am J Med Genet. 1996;64(3):473–7. 83. Bosbach S, Kornblum C, Schroder R, Wagner M. Executive and visuospatial deficits in patients with chronic progressive external ophthalmoplegia and Kearns–Sayre syndrome. Brain 2003;126(Pt 5): 1231–40. 78. Paul BM, Stiles J, Passarotti A, Bavar N, Bellugi U. Face and place processing in Williams syndrome: evidence for a dorsal-ventral dissociation. Neuroreport 2002; 13(9):1115–9. 84. Turconi AC, Benti R, Castelli E et al. Focal cognitive impairment in mitochondrial encephalomyopathies: a neuropsychological and neuroimaging study. J Neurol Sci. 1999;170(1):57–63. 224 (A) (B) (C) (D) (E) Figure 2.1. General structure of the brain (A). Major areas considered in this chapter include the brainstem and cerebellum (B), diencephalon (C), limbic and paralimbic structures (D), basal ganglia (see Figure 2.5), and cerebral cortex (E). Corpus callosum Cingulate gyrus Orbital and medial prefrontal cortex Cut edge of midbrain Parahippocampal gyrus Temporal lobe Mammillothalamic tract Anterior nucleus of the thalamus Fornix Medial dorsal nucleus of the thalamus Anterior commissure Ventral basal ganglia Hypothalamus Optic chiasm Amygdala Mammillary body Hippocampus Figure 2.6. Limbic and paralimbic areas (green shading) viewed parasagittally. The top panel depicts these areas as if viewed through the left hemisphere. The bottom panel illustrates these areas in greater detail. Reprinted from Purves D, Augustine GJ, Fitzpatrick D et al. (editors), Neuroscience, 2nd edition; 2001, with permission from Sinauer Associates. Figure 2.8. Lobar divisions of the cerebral hemispheres (left lateral view). Figure 2.9. Brodmann’s areas in the human brain. Reprinted with permission from Mark Dubin, PhD, Department of Molecular, Cellular & Developmental Biology, University of Colorado at Boulder (http://spot.colorado.edu/∼dubin/talks/brodmann/brodmann.html). Figure 2.11. Orthogonal frontal projection of the cerebral and cerebellar arteries in situ, together with some bony landmarks and the lateral ventricles. Labeled structures include: 1 – Calvaria (inner border). 2 – Medial occipital artery, parieto-occipital branch. 3 – Trunk of the corpus callosum. 4 – Lateral ventricle. 5 – Insula. 6 – Medial occipital artery. 7 – Superior cerebellar artery, medial branch. 8 – Lateral occipital artery. 9 – Free margin of the lesser wing of the sphenoid bone. 10 – Middle meningeal artery, intraosseous part (in-constant). 11 – Middle meningeal artery, frontal branch. 12 – Middle meningeal artery, parietal branch. 13 – Superior margin of petrous part of the temporal bone. 14 – Superior cerebellar artery, lateral branch. 15 – Posterior cerebral artery. 16 – Superior cerebellar artery. 17 – Basilar artery. 18 – Anterior inferior cerebellar artery. 19 – Posterior inferior cerebellar artery, medial branch. 20 – Posterior inferior cerebellar artery, lateral branch. 21 – Posterior inferior cerebellar artery. 22 – Vertebral artery, intracranial part. 23 – Maxillary artery, pterygoid part. 24 – Middle meningeal artery. 25 – Superficial temporal artery. 26 – Maxillary artery, manidibular part. 27 – Vertebral artery, atlantal part. 28 – External carotid artery. 29 – Facial artery. 30 – Vertebral artery, cervical part. 31 – Paracentral artery. 32 – Pericallosal artery. 33 – Callosomarginal artery. 34 – Middle cerebral artery, terminal part. 35 – Middle cerebral artery, insular part. 36 – Anterior cerebral artery, post-communicating part. 37 – Anterior communicating artery. 38 – Anterior cerebral artery, pre-communicating part. 39 – Middle cerebral artery, sphenoid part. 40 – Internal carotid artery, cavernous part. 41 – Internal carotid artery, petrous part. 42 – Internal carotid artery, cervical part. 43 – Common carotid artery. Reprinted from Nieuwenhuys R, Voogd J, Huijzen CV. The Human Central Nervous System. 4th edition. New York, NY: Springer; 2008, with permission from Springer Science+Business Media. Figure 2.12. Orthogonal lateral projection of the cerebral and cerebellar arteries, together with external and bony landmarks. Some neural structures are illustrated in outline; in the center, two lines tangential to the anterior and posterior commisures (AC and PC, respectively) are seen: the one passing above the AC and beneath the PC is part of the bicommissural line of Talairach (BC); the other tangent is part of the upper horizontal line of Krönlein (CH). Additional abbreviations include: CM – canthus-meatus line; FH – horizontal line of Frankfurt; GI – glabella-inion line; VCA – vertical tangent to anterior commissure; and VCP – vertical tangent to posterior commissure. Labeled structures include: 1 – Central sulcus. 2 – Pericallosal artery. 3 – Callosomarginal artery. 4 – Corpus callosum. 5 – Outline of ventricles. 6 – Outline of insula. 7 – Anterior cerebral artery. 8 – Middle cerebral artery, frontal trunk. 9 – Anterior commissure. 10 – Middle cerebral artery, parietal trunk. 11 – Middle cerebral artery, temporal trunk. 12 – Posterior commissure. 13 – Medial occipital artery. 14 – Lateral occipital artery. 15 – Superior cerebellar artery, medial branch. 16 – Superior cerebellar artery, lateral branch. 17 – Superior cerebellar artery. 18 – Posterior cerebral artery. 19 – Posterior communicating artery. 20 – Internal carotid artery, cerebral part. 21 – Internal carotid artery, cavernous part. 22 – Siphon point. 23 – Middle cerebral artery, sphenoid part. 24– Ektocanthion (Canthus externus). 25 – Glabella. 26 – Orbital (on infraorbital margin). 27 – Internal carotid artery, petrous part. 28 – Basilar artery. 29 – Superior margin of petrous part of the temporal bone. 30 – Anterior inferior cerebellar artery. 31 – Porion (on supramental margin). 32 – Fourth ventricle. 33– Posterior inferior cerebellar artery, medial branch. 34 – Posterior inferior cerebellar artery, lateral branch. 35 – Posterior inferior cerebellar artery. 36 – Vertebral artery, intracranial part. 37 – Vertebral artery, atlantal part. 38 – Internal carotid artery, cervical part. 39 – Maxillary artery. 40 – Middle meningeal artery. 41 – External carotid artery. 42 – Vertebral artery, cervical part. 43 – Common carotid artery. 44 – Spinal cord. 45 – Inion (external occipital protuberance). Reprinted from Nieuwenhuys R, Voogd J, Huijzen CV. The Human Central Nervous System. 4th edition. New York, NY: Springer; 2008, with permission from Springer Science+Business Media. Figure 2.13. The ventricular system, including the lateral ventricles (dark blue, rostral), third ventricle (purple), cerebral aqueduct (green), fourth ventricle (light blue, caudal), and choroid plexus (red). Adapted from 3D Brain from G2C Online (www.g2conline.org), produced by the Dolan DNA Learning Center, Cold Spring Harbor Laboratory. (A) (B) (C) (F) (D) (G) (E) (H) Figure 3.1. A, Posterior, and B, right lateral surface reconstructions of the human cerebellum derived from MRI images. The named fissures are demarcated in color, and the fissures and lobules are identified. C, Surface reconstruction of the cerebellum seen from the oblique posterior view, with lobules demarcated. Parasagittal images of human cerebellum on MRI 2 mm lateral to midline in D, and 18 mm lateral to midline in E. Fissures are color coded according to the convention used in A and B, and the lobules are designated. F, Superior (SCP), middle (MCP), and inferior (ICP) cerebellar peduncles in human identified with diffusion spectrum imaging, overlaid on diffusion-weighted image of cerebellum and brainstem. G, Cryosection image of post-mortem human cerebellum in the coronal plane 52 mm behind the anterior commissure – posterior commissure (AC-PC), with deep cerebellar nuclei identified: D – dentate nucleus, E – emboliform nucleus, F – fastigial nucleus, G – globose nucleus. H, Diagram of a single cerebellar folium is shown sectioned in its longitudinal axis (diagram right) and transversely (left) to depict the histology of the cerebellar cortex. Purkinje cells are red; superficial and deep stellate, basket, and Golgi cells are black; granule cells and ascending axons and parallel fibers are yellow; mossy and climbing fibers are blue. Also shown are the glomeruli with mossy fiber rosettes, claw-like dendrites of granule cells, and Golgi axons. (A, B, D, E, G reproduced from Schmahmann JD, Doyon J, Toga A, Evans A, Petrides M. MRI Atlas of the Human Cerebellum. San Diego, CA: Academic Press; Copyright Elsevier, 2000, with permission from Elsevier; C from Makris N, Schlerf JE, Hodge SM et al. MRI-based surface-assisted parcellation of human cerebellar cortex: an anatomically specified method with estimate of reliability. Neuroimage 2005;25(4):1146–60, with permission from Academic Press; F reproduced from Granziera C, Schmahmann JD, Hadjikhani N et al. Diffusion spectrum imaging shows the structural basis of functional cerebellar circuits in the human cerebellum in vivo. PLoS One 2009;4(4):e5101, with permission; H reproduced from Williams PL, Bannister LH, Berry MM et al. (editors). Gray’s Anatomy. 38th edition. New York, NY: Churchill Livingstone; 1995; Copyright Elsevier, 1995, with permission from Elsevier. Redrawn from Eccles JC, Ito M, Szentágothai J. The Cerebellum as a Neuronal Machine. Berlin: Springer-Verlag; Copyright Elsevier, 1967. (A) (D) (B) (C) Figure 3.2. Activation Likelihood Estimation (ALE) activation maps for the domains of A, spatial cognition, B, motor tapping with the right hand, and C, language tasks drawn from a meta-analysis of functional imaging studies [26]. The right cerebellum is depicted on the right. The results are overlaid upon an image of the cerebellum in the coronal plane at y = −70 from the MRI Atlas of the Human Cerebellum [7], and the cerebellar fissures and lobules at this level are identified in D. Reproduced from Schmahmann JD, Doyon J, Toga A, Evans A, Petrides M. MRI Atlas of the Human Cerebellum. San Diego, CA: Academic Press; Copyright Elsevier, 2000, with permission of Elsevier; and with permission of Academic Press from Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 2009;44(2):489–501. Figure 3.3. Representative rostral (y = −44) to caudal (y = −76) coronal sections through a human cerebellum showing activation patterns in a functional magnetic resonance imaging experiment in a single subject [28]. Tasks investigated sensorimotor function (finger tapping, red), language (verb generation, blue), spatial cognition (mental rotation, green), working memory (n-back task, purple), and emotional processing (viewing images from the International Affective Picture System, yellow). Lobules V, VI, Crus I (Cr I), Crus II (Cr II), VIIB and VIII are labeled. The right and left cerebellar hemispheres are as indicated. Reproduced with permission of Masson Spa from Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 2010;48(7):831–844. Figure 4.2. Diffusion tensor image showing the arcuate fasciculus. Note the additional fascicle extending to Geschwind’s territory in the inferior parietal cortex, not recognized by traditional neuroanatomic investigation. Reproduced from Catani M, Jones DK, Ffytche DH. Perisylvian language networks of the human brain. Ann Neurol. 2005;57(1):8–16, with permission from John Wiley & Sons, Inc. IPs/SPL FEF VFC (IFg/MFg) TPJ (IPL/STG) Top-down control L FEF R FEF Novelty R VFC L VFC Circuit breaker Stimulusresponse selection L TPJ R IPs L IPs R TPJ Behavioral valence Visual areas Stimulus-driven control Figure 8.10. Illustration of a neuroanatomic model of attentional control. Top: diagram indicating brain regions involved in the control of attention. Bottom: Schematic of the mechanisms of a model of attentional control [43]. The dorsal network (IPs-FEF), indicated by the black arrows, is involved in the top-down, or “goal-directed,” control of attention. The ventral network (TPJ-VFC), indicated by the gray arrows, is involved in bottom-up, or “stimulus-driven,” control of attention. The dorsal system is also modulated by bottom-up information, with the TPJ communicating with the IPs and acting as a “circuit breaker” allowing salient bottom-up information to interrupt voluntary, top-down orienting, in turn reorienting attention to salient aspects of the environment. Abbreviations: IPs: intraparietal sulcus; SPL: superior parietal lobule; FEF: frontal eye field; TPJ: temporoparietal junction; IPL: inferior parietal lobule; STG: superior temporal gyrus; VFC: ventral frontal cortex; IFg: inferior frontal gyrus: MFg: middle frontal gyrus; L: left; R: right. Adapted from Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201–15, with permission from Macmillan Publishers Ltd. Figure 12.2. Lateral view of the left hemisphere indicating the perisylvian area. The illustration shows Broca’s area in the frontal operculum and Wernicke’s area in the superior temporal gyrus and the corresponding Brodmann’s areas. Reprinted with permission from Mark Dubin, PhD, Department of Molecular, Cellular & Developmental Biology, University of Colorado at Boulder (http://spot.colorado.edu/∼dubin/talks/ brodmann/brodmann.html). (A) (B) (C) Figure 12.4. Examples of language mapping in the frontal lobe (A) and the combined temporal and parietal lobes (B). Yellow and green circles represent numbered electrocortical stimulation mapping (ESM) language sites and clean ESM sites, respectively. Red boxes represent expression fMR imaging activations. Blue boxes represent comprehension fMR imaging activations. The frontal lobe slices are shown with an ESM radius of 5 mm (determined to produce the highest sensitivity with the least cost to specificity) and temporoparietal lobe slices are shown with an ESM radius of 9 mm. A: For frontal lobe mapping, these brain slices demonstrate that red (expression) activations tend to overlap with, or are adjacent to, essential (yellow) ESM sites, but avoid non-essential (green) ESM sites. Blue activations in the frontal lobe also appear predictive. B: In most cases of temporoparietal lobe mapping, such as the one illustrated here, comprehension fMR imaging activations matched well with ESM language sites (yellow) in the temporoparietal lobes and did not overlap with clean ESM sites (green). Very little expression fMR imaging activations are seen in the temporoparietal lobe region and the reason why such tasks as verbal object naming and word generation do not accurately predict language sites in these regions. C: These maps were obtained in an individual case in which preoperative fMR imaging was the least effective at accurately predicting whether a given cortical area would be involved in language function. In this case, only two of the three essential ESM sites overlapped with fMR imaging activations, and only two of seven of the clean ESM sites completely avoided fMR imaging activations. Reproduced from Pouratian N, Bookheimer SY, Rex DE, Martin NA, Toga AW. Utility of preoperative functional magnetic resonance imaging for identifying language cortices in patients with vascular malformations. J Neurosurg. 2002;97(1):21–32, with permission of Journal of Neurosurgery Publishing Group. Figure 12.6. Diffusion tensor imaging pathways in conduction aphasia. Reproduced from Catani M, Jones DK, Ffytche DH. Perisylvian language networks of the human brain. Ann Neurol. 2005;57(1):8–16, with permission from John Wiley & Sons, Inc. (a) (b) Figure 16.5. White matter tractography may be used to label brain voxels according to the white matter structure of which they form a part. (a) Image tractograms of the projection (green), association (red) and callosal (blue) fibers are mapped into the brain space, indicating their position with respect to other brain regions, and include the anterior region of corona radiata (acr), external capsule (ec), internal capsule (ic), and posterior region of corona radiata (pcr). (b) A similar procedure has been used to label various white matter tracts, including the corpus callosum (purple), superior longitudinal fasciculus (yellow), cingulum (green), uncinate fasciculus (dark red), inferior occipito-frontal fasciculus (orange), inferior longitudinal fasciculus (brown), corticobulbar tract (light blue), corticospinal tract (white), fornix and stria terminalis (light yellow). Tract positions are shown in several sagittal and axial slices. Reprinted from Lazar M. Mapping brain anatomical connectivity using white matter tractography. NMR Biomed 2010;23:821–35, with permission of John Wiley & Sons Ltd. (A) (B) (C) (D) (E) (F) Figure 18.1. Graphic illustrations of relationships between mood and affect. In each panel, the Y-axis represents the valence of emotion and emotional feeling and the X-axis represents time (days to weeks or longer). Mood is represented by a thick solid line and affect is represented by a thin dotted line. The amplitude of each line reflects the intensity of emotion and emotional feeling. (A) Mood is euthymic (from Greek eu “normal” + thymia “state of mind”) – that is, the emotional climate is temperate and stable over days to weeks. Affect varies around that mood, with clearly identifiable shifts between positive (e.g., happiness) and negative (e.g., irritation, frustration, anger) of modest intensity occurring during any given day. (B) Mood is dysphoric (from Greek dusphoros “hard to bear”), i.e., persistently sad or sad/irritable most of the day nearly every day for several weeks. Affect continues to vary around that mood, but it is restricted to emotions and emotional feelings that are predominantly negative and whose amplitudes are attenuated. (C) Mood varies from persistently and excessively positive (i.e., euphoric and expansive) for a week or longer to excessively negative (i.e., sad). Affect is labile and intense when superimposed on persistently and excessively positive mood and its valence and amplitude are more restricted when superimposed on persistently and excessively negative mood. (D) Moderate affective lability superimposed on euthymic mood. (E) Severely pathological affect (i.e., pathological laughing and crying) superimposed on euthymic mood. (F) Severely pathological affect (i.e., pathological laughing and crying) superimposed on dysphoric mood (i.e., major depressive episode). Figure 18.2. Graphic illustrations of several types of mood and affect. The Y-axis represents the valence of emotion and emotional feeling and the X-axis represents time (days to weeks or longer). Moods are represented by thick solid lines and affects are represented by thin dotted lines, the intensity of which are reflected by their amplitudes. This graphic offers a visual representation of the concepts of mood and affect: mood is a slow-frequency phenomenon (background, emotional “climate”) and affect is a fast frequency phenomenon (foreground, emotional “weather”). Figure 18.5. A schematic representation of many of the structures and circuits supporting emotional generation, expression, experience, and control and their functional relationships. Glutamatergic, presumed excitatory projections are shown in green, GABAergic projections are shown in orange, and modulatory projections in blue. In the model proposed here, dysfunction in the amygdala and/or the medial prefrontal network results in dysregulation of transmission throughout an extended brain circuit that stretches from the cortex to the brainstem, generating emotion and its expression through motoric, visceral, autonomic, endocrine, and neurochemical effectors. Intra-amygdaloid connections link the basal and lateral amygdaloid nuclei to the central and medial nuclei of the amygdala. Parallel and convergent efferent projections from the amygdala and the medial prefrontal network to the hypothalamus, periaqueductal gray, nucleus basalis, locus coeruleus, dorsal raphe, and medullary vagal nuclei organize neuroendocrine, autonomic, neurotransmitter and behavioral responses to stressors and emotional stimuli. Structures of the default system (or network) support emotional experience. The amygdala and medial prefrontal network interact with the cortico-striatal-pallidal-thalamic loop, through prominent connections both with the accumbens nucleus and medial caudate, and with the mediodorsal and paraventricular thalamic nuclei, to control and limit responses. Abbreviations: 5-HT – serotonin; ACh – acetylcholine; BNST – bed nucleus of the stria terminalis; Cort. – corticosteroid; CRH – corticotrophin releasing hormone; Ctx – cortex; NorAdr – norepinephrine; PAG – periaqueductal gray; PVH – paraventricular nucleus of the hypothalamus; PVZ – periventricular zone of hypothalamus; VTA – ventral tegmental area. Reprinted from Price JL, Drevets WC. Neurocircuitry of mood disorders. Neuropsychopharmacology 2010;35(1):192–216, with permission. (A) (B) Figure 26.3. An example of diffusion tensor imaging (DTI) as applied to the study of traumatic brain injury (TBI) (axial T1-weighted image with DTI overlaid). White matter tractography using TBI is used here to visualize the anterior forceps of the corpus callosum in a male who experienced multiple concussions due to blast forces. This case is provided courtesy and with the permission of Dr. Rajendra Morey, Duke University and Mid-Atlantic Mental Illness Research, Education and Clinical Center, Veterans Integrated Service Network 6, Durham, North Carolina. Figure 18.6. Results from a study examining the effects on brain activation and emotion of systematic variations in the goal and content of reappraisal strategies. A: Regardless of whether the goal is to increase or decrease emotion, lateral prefrontal and anterior cingulate cortices are activated. B: When the goal is to decrease emotion, right dorsolateral and ventrolateral prefrontal as well as right orbitofrontal cortex is more active than are left-hemispheric structures (left panel). By contrast, when the goal of control is to increase emotion, left lateral and dorsomedial prefrontal cortical regions are differentially recruited when imagining worsening experiences and outcomes (right panel). Abbreviations: LPFC, lateral prefrontal cortex; MPFC, medial prefrontal cortex; ACC, anterior cingulate cortex; OFC, orbitofrontal cortex. Adapted from Ochsner KN, Gross JJ. The cognitive control of emotion. Trends Cogn Sci. 2005;9(5):242–9, with permission. Figure 27.1. Functional magnetic resonance imaging (fMRI) using a light pain stimulation paradigm, which produced bilateral thalamic activation. (A) (B) Figure 27.2. Magnetic resonance imaging (MRI) of a man with remote traumatic brain injury (TBI). High-resolution T2-weighted imaging (A), diffusion tensor imaging (DTI) with color-coded fractional anisotropy (FA) mapping (B), and fiber tracking (C) based on the identification of the region of interest (boxed area in A). (C) Figure 27.4. Functional magnetic resonance imaging (fMRI) of a visual task. In this example, a 40-year-old subject was asked to fixate on a small fixation cross while concentric circles expanded from this central point at a rate of 8 Hz for 20 seconds. This 20-second period was followed by a rest condition when the subject was presented the same stimulus with eyes closed. This cycle was repeated three times. (A) (B) (C) (E) (F) (G) (D) Figure 27.6. Examples of various diffusion tensor images generated using data acquired from a 30-year-old healthy adult. Figure 27.7. Typical patterns of cerebral metabolism as imaged using fluorodeoxyglucose positron emission tomography (FDG-PET) among persons with mild, moderate, and severe Alzheimer’s disease (AD). FDG-PET scans are displayed as three-dimensional stereotactic surface projection (SSP) maps normalized to pons generated with the software program Neurostat. Maps are shown with relative cerebral metabolism or statistical significance increasing on the color scale from the lowest values shown in blue to the highest values in red and white. For orientation, a reference brain is shown in row A with regions of interest in dementia evaluations in color; orange areas usually hypometabolic in AD, blue and purple areas typically hypometabolic in frontotemporal dementia. Row B shows the pattern of metabolism in 27 normal elderly subjects. This is used for statistical comparisons with metabolism in individual patients (rows D, F, and H). There are increasing severity and extent of cerebral glucose hypometabolism as AD progresses from mild (rows C and D) and moderate (rows E and F) to severe (rows G and H). Reproduced from Foster NL, Wang AY, Tasdizen T et al. Realizing the potential of positron emission tomography with 18F-fluorodeoxyglucose to improve the treatment of Alzheimer’s disease. Alzheimers Dement. 2008;4(1 Suppl. 1):S29–36, with permission from Elsevier. Figure 27.8. Statistical parametric mapping of metabolic activity using fluorodeoxyglucose positron emission tomography (FDG-PET) in patients with relatively common neurodegenerative disorders. Statistical parametric mapping of regions of decreased metabolic activity (relative to the global mean, thresholded at p ⬍ 0.001 with cluster cut-off of 20 voxels) are overlaid onto a single subject T1 magnetic resonance image. Abbreviations: PD – Parkinson’s disease; MSA – multisystem atrophy; PSP – progressive supranuclear palsy; CBD – corticobasal degeneration; DLB – dementia with Lewy bodies; AD – Alzheimer’s disease; FTD – frontotemporal dementia. Reproduced from Teune LK, Bartels AL, de Jong BM et al. Typical cerebral metabolic patterns in neurodegenerative brain diseases. Mov Disord. 2010;25(14):2395–404, with permission from John Wiley and Sons. Figure 28.4. Sleep architecture presented in a bipolar montage. The stages of sleep are identified by their typical EEG findings and cycle throughout the night among lighter (Stages 1 and 2) and deeper (Stages 3, 4, and REM) stages of sleep. Stage 1 sleep (top left) is characterized by loss of the posterior dominant rhythm, further diminishment of movement and muscle artifact and increasing diffuse slowing of background activity. Stage 2 sleep (top right) is characterized by the appearance of sleep spindles and K-complexes. Stages 3 and 4 sleep (bottom left) are characterized by various percentages of higher amplitude delta range activity. REM sleep (bottom right) is characterized by a low-voltage, irregular background with multiple rapid eye movements. Muscle artifact is virtually absent during normal REM sleep. Figure 28.5. Normal posterior dominant rhythm with attenuation upon eye opening (black arrow) and reappearance with eye closure (purple arrow). Figure 28.7. Widespread, or diffuse, beta activity. Figure 28.14. Photoparoxysmal response. Figure 29.4. Scalp topography of posterior-dominant alpha. A flat projection of the top of the head is shown and the circles indicate approximate electrode locations (nose is up in figure). The power distribution across electrodes exhibits a typical posterior dominance for alpha, mapped at 10 Hz. Figure 29.8. Time, frequency, and time–frequency representations. The signal in the left column is a mixture of two pure tones (20 and 80 Hz). The signal in the right column is the same complex tone as the left, but amplitude modulated at 5 Hz. Top panels: Time domain; Bottom panels: Frequency domain (FFT); Middle panels: Time–frequency domain (Short-Time Fourier Transform). The reds in the color scale indicate greater spectral power. Note that for the stationary signal on the left, a simple frequency representation (middle panel) conveys as much information as the time–frequency representation (bottom). For the non-stationary signal on the right, however, the amplitude modulation effect is lost in the FFT (middle panel), but captured effectively in the time–frequency representation (bottom panel). Figure 29.9. Examples of time–frequency transformations. Top panel: Original signal in the time domain. The signal is linearly decreasing in frequency across time (i.e., it is a chirp signal). Middle panel: Short-time Fourier Transform of chirp. Bottom panel: Pseudo-Wigner Distribution of chirp. Note the increased frequency resolution of the Pseudo-Wigner Distribution. Reds in the color scale indicate greater spectral power. Figure 29.10. Magnetic response to visually cued index finger flexions. Top panel: Averaged evoked field (EF) (101 trials) from a single channel over the right hemisphere. Time zero indicates movement onset. The evoked responses seen in the immediate post-movement period are known as motor evoked fields (MEF, or MEP for EEG recordings) and reflect the somatosensory feedback from motor cortex and from the peripheral sensation of movement. Bottom panel: Wavelet-based time-frequency transformation of same trials in same channel used to create averaged EF (color scale: blues indicate event-related de-synchronization (ERD) and reds indicate event-related synchronization (ERS)). Note the prominent beta-band changes in the panel, including ERD in the pre- and peri-movement period and then ERS following the ERD. The power increase from the evoked response in the top panel is evident immediately following movement onset at the lower frequencies (⬍10 Hz). The ERD/ERS in the beta-band is difficult to visualize in the averaged evoked response, although a small change in amplitude may be discerned. Figure 29.11. Subdividing the spectral response into evoked and induced components. The data illustrated in Figure 29.10 are shown here, focusing more clearly on the relevant time and frequency windows. Top panel: evoked power, relative to pre-movement baseline period (−3 to −2 s); Middle panel: relative induced power; Bottom panel: phase-locking factor. Color scales: reds indicates greater power (top two panels) or phase-locking (bottom panel) – blues indicate lower power or phase-locking. Figure 29.12. Distributed source analysis of the visual evoked magnetic field and electric potential at 100 ms (M/P100) produced by averaging EEG and MEG data to repeated presentations of a central visual fixation crosshair. A cortically constrained minimum norm solution was used for source reconstruction in this case. Figure 29.14. Independent component analysis of a 248-channel MEG dataset from a child with medial temporal lobe epilepsy. The first 20 (of 248) ICA components are illustrated (top: waveforms; bottom: topography). Component 14 shows a spike at approximately 232 seconds from the initiation of recording. The spike shows a dipole-like phase-reversal over the left temporal cortex in the topography. Follow-on source analysis revealed a hippocampal origin. Also of interest are component 16, which reflects eyeblinks, and component 19, a magnetocardiographic artifact (note the broad phase-reversal over the left and right sensors reflecting a very distant source). A significant amount of slowing in the MEG record (mixed delta/theta) is also seen in the first 10 components. Color scale: reds are positive and blues negative component values. (A) (B) (C) (D) Figure 31.1. Lymphomatosis cerebri. A: Coronal brain autopsy sections showed no mass lesions or hemorrhages, and no obvious abnormalities of the white matter except for very subtle discoloration best seen in the left inferior frontal gyrus (arrow). B: Whole mount sections stained with hematoxylin and eosin (H&E) (left) and CD45 for lymphocytes (right) showed that the densest lymphoma cell infiltrates were found in this same abnormal left inferior frontal gyrus, where they filled the subgyral white matter, with relative sparing of the overlying cortical gray matter. C: High-power photomicrograph of the white matter showing clusters of lymphoma cells that permeated, splayed, and disrupted the myelinated fibers, seen as elongate linear strands (Luxol fast blue-periodic acid Schiff (LFB-PAS)). D: High-power photomicrograph of the white matter from the same area as seen in Figure 31.1C, showing better preservation of axons. Anti-neurofilament immunostaining with light hematoxylin counterstain. (A) (B) (C) (D) (E) (F) Figure 31.2. Lymphomatosis cerebri. A: Low-power photomicrograph illustrating that less-dense lymphomatous infiltrates could also be found in the cerebellar white matter (WM), where they again favored white matter over the cerebellar gray matter (GM) or molecular layer (ML). Meningeal involvement was focally identified (arrow). Hematoxylin and eosin (H&E) stain. B: Low power photomicrograph showing that in the subcortical gray matter areas the preference of the cells for white matter over gray matter still existed. Note extensive involvement of the white matter, with virtual stoppage cells where the gray matter of the putamen (P) meets the adjacent white matter (arrows); H&E stain used in this photomicrograph. C: High-power photomicrograph illustrates that while some degree of perivascular accumulation of lymphoma cells was found, individual cell permeation by the cytologically malignant cells (D: arrows) was more typical. E: Lymphoma cells were immunoreactive for CD20, a B-cell marker; CD20 immunostaining with light hematoxylin counterstain. F: Only a few accompanying non-neoplastic T-cells were identified; CD3 immunostaining with light hematoxylin counterstain. (A) (B) (C) (D) (E) (F) (G) (H) Figure 31.3. Mycosis fungoides. A: Low-power photomicrograph of the skin from the lower extremities demonstrated increased numbers of lymphocytes within the upper dermis that were cytologically atypical, features diagnostic for mycosis fungoides. Hematoxylin and eosin (H&E) stain. B: At the same magnification and in the same region, the atypical lymphocytes are easily highlighted by their strongly positive reaction for CD8, an immunomarker for suppressor T-cells. Cases of mycosis fungoides with CD8+ predominance are far fewer than those with CD4+ strong staining. CD8 immunostaining with light hematoxylin counterstain. C: Same region of skin immunostained with CD4 shows that CD4-positive helper T-cells are far less frequent. CD4 immunostaining with light hematoxylin counterstain. D: Low power photomicrograph taken from the white matter of the cerebral hemispheres shows cytologically atypical lymphocytes that percolate through the white matter, yielding only subtle hypercellularity and only focal angiocentric arrangement (arrow). H&E stain. E: High-power magnification revealed the individual neoplastic T-cells of mycosis fungoides with classic “cerebriform,” folded and grooved nuclei (arrow). H&E stain. F: Immunohistochemical staining for CD8 highlighted these individual tumor cells far better in the white matter than did routine H&E staining. G: High-power photomicrograph of one of the mycosis fungoides cells in white matter immunostained for CD8. H: Leptomeningeal involvement could also be discerned in some areas. H&E stain. (A) (B) (C) (D) (E) (F) Figure 31.4. Intravascular lymphoma. A: Magnetic resonance imaging (MRI) performed soon after diagnosis reveals multiple bilateral areas of increased T2 signal in the cerebral white matter (illustrated) as well as in the pons and cerebellar hemispheres. B: Fluid-attenuated inversion recovery (FLAIR) imaging similarly demonstrates multiple foci with ill-defined borders scattered throughout the supratentorial region. The lesions principally occupy the white matter, but some cortical involvement is also present. Probable restrictive diffusion was noted, with definitive enhancement (not shown). C: T2-weighted MRI scan performed hours prior to demise shows even more extensive white matter lesions; these proved to be white matter infarctions devoid of intravascular tumor cells at the time of autopsy. D: FLAIR (pictured), diffusion, and post-contrast images prior to demise are similar in appearance to the MRI performed soon after diagnosis, despite the absence of tumor cells at autopsy. E and F: Coronal brain autopsy sections at the level of the mamillary bodies (E) and occipital horns (F) showed no mass lesions or hemorrhages, although obvious abnormalities of the white matter were seen bilaterally in the cerebral white matter as grayish areas of partial cavitation, indicative of multifocal white matter infarctions (arrows). (A) (B) (C) (D) (E) (F) (G) (H) Figure 31.5. Intravascular lymphoma. A: Low-power photomicrograph of the pre-mortem brain biopsy revealed that vessels of all sizes were packed with lymphoma cells; lymphoma was confined to the intralumenal space. Hematoxylin and eosin (H&E) stain. B: Medium-power photomicrograph shows that even individual small capillaries contained single lymphoma cells; no infarction of surrounding brain was found on the small biopsy sections (H&E stain). C: High-power photomicrograph illustrates the extreme cytological atypia of the individual lymphoma cells within blood vessels (H&E stain). D: These cells, including those in capillaries, were highlighted by their strong immunoreactivity for CD20 (CD20 immunostaining for B-cell lymphocytes, with light hematoxylin counterstain). E: Whole-mount section of the cerebral white matter discloses the discrete white matter areas of pallor and infarction (H&E stain). F: Whole-mount section of the lower spinal cord revealed multifocal areas of infarction, correlating with the patient’s lower extremity weakness noted in life (luxol fast blue-periodic acid Schiff stain for myelin). G: On lower power microscopic sections, infarcted white matter lesions could be recognized by their vacuolization, partial cavitation, and pallor (upper left in photograph; H&E stain). H: On high-power magnification, nearby blood vessels (seen at low power in block G) contained no residual intralumenal lymphoma cells after the patient’s chemotherapy treatment. Only reactive, perivascular non-neoplastic lymphocytes remained at autopsy near the infarcts. Section I Structural and Functional Neuroanatomy Chapter Executive function 16 David B. Arciniegas Executive function refers to a complex set of processes that manage and control other, relatively basic, cognitive functions [1, 2] and that support purposeful goaldirected behaviors [2–5]. These processes are engaged most fully when confronting novel problems or situations for which no previously established routines exist [5, 6]. By facilitating pattern identification, strategy development, and problem solving, executive function enables an individual to respond flexibly and adaptively to the environment, to develop goals and anticipate their consequences, and to direct cognition, emotion, and behavior in the service of goal attainment [7, 8]. The broad anatomic areas contributing to the networks upon which executive functions are predicated render them vulnerable to disruption by many conditions affecting the brain [2, 9–17]. When the function of these networks is compromised, executive dysfunction ensues. This dysfunction may manifest as limited conceptualization and abstraction, cognitive inflexibility, impaired control of attention, working memory, declarative memory, language, praxis, calculation, and visuospatial function and other cognitive abilities, impaired decision-making and judgment (including impulsivity, disinhibition, and carelessness), difficulty setting goals, organizing, maintaining, and shifting plans, and difficulty with error detection and correction [5]. Deficits in executive function compromise an individual’s ability to meet the demands of everyday life in a flexible and adaptive manner, even when basic cognitive functions are relatively preserved [18–20]. Accordingly, an understanding of the phenomenology and neuropathophysiology of executive function and dysfunctions is required of subspecialists in Behavioral Neurology & Neuropsychiatry (BN&NP) [21]. Toward that end, this chapter reviews conceptual issues and definitions of executive function. It is argued that executive function is a multidimensional construct and it is suggested that subspecialists in BN&NP regard executive function principally as a cognitive domain. The neuroanatomy and neurochemistry of executive function is reviewed, beginning with early formulations of frontal lobe functioning and ending with a description of the distributed neural networks supporting executive function. Finally, neuropsychological tests and bedside assessments of executive function are identified and discussed briefly. Conceptual issues and definitions The referents of executive function offered in the medical and psychology literatures are diverse [5, 22] and consensus is lacking on a definition of this cognitive domain [2, 5, 22]. Royall et al. (2002) [2], writing on behalf of the American Neuropsychiatric Association Committee on Research (ANPA CoR), suggest that the historical foundations for the concept of executive function derive from the application of systems engineering descriptions of the 1960s to the study of human cognitive processes [23] and the seminal analyses of higher cortical function performed by Luria [24, 25]. Among the earliest and clearest descriptions of this area of cognition is Luria’s (1962) [24] characterization of the prefrontal cortices as regions of the brain responsible for integrating and attaching informative or regulatory significance to selected elements of incoming stimuli (i.e., establishing “the provisional basis of action”). He suggested that this information is processed “with the intimate participation of the frontal lobes” in a manner that fosters “complex Behavioral Neurology & Neuropsychiatry, eds. David B. Arciniegas, C. Alan Anderson, and Christopher M. Filley. C Cambridge University Press 2013. Published by Cambridge University Press. 225 Section I: Structural and Functional Neuroanatomy programs of behavior; the constant monitoring of the performance of these programs and the checking of behavior with comparison of actions performed and the original plans; [and] the provision of a system of “feedback” on the basis of which complex forms of behavior are regulated” (translated in [26], p. 248). This system was conceptualized as supporting the “general organization of behavior” [26] through programming, regulation, and verification [25]. Over the following decades, general descriptions of executive functions arose in the context of theories describing the psychological mechanisms and cerebral loci of control over specific cognitive processes. Norman and Shallice (1986) [6, 27] described a “supervisory system” for attentional control (i.e., executive control of attention) that is used in situations in which routine or automatic processes are inadequate (contention scheduling). Building upon the filter model proposed by Broadbent (1958) [28], it was suggested that the supervisory attentional system facilitates target selection from amongst multiple competing inputs, monitors for and corrects errors, and resolves response conflicts. Posner and Rothbart (1998, 2009) [29, 30], reviewing the literature on attention and self-regulation from a developmental perspective, extended this view to include effortful control of mental events (i.e., a category of mental events that is contrasted with those that are automatically, or nonvolitionally, activated by internal or external stimuli). The concept of a supervisory (executive) system subsequently expanded to encompass cognitive processes responsible for supplanting automatic responses with strategies necessary for volitional goal-directed behavior, especially in novel or complex circumstances, and for monitoring their effectiveness [5]. Baddeley (1986) [31] posited a role for a “central executive” that regulates and manipulates information held in working memory (i.e., the phonological loop, the visuospatial “sketchpad,” and the episodic buffer that integrates short- and long-term memory). Mesulam (2000) [32] suggests that the role of the central executive applies most directly to the volitional manipulation of information held in working memory, reflecting the “top-down” influence (i.e., supramodal role) of lateral prefrontal cortices on the orchestration of working memory in all domains of information processing. The referent of the central executive was subsequently clarified by Stuss and Alexander. (2007) [33] as several separate processes: facilitation (energizing) of neural systems involved in decision-making 226 (contention scheduling), response initiation, monitoring of task activity and timing (e.g., error detection), and adjustment of behavior. In the context of developing a conceptual model of attention-deficit hyperactivity disorder, Barkley (1997) [34] integrated definitions of executive function offered by others [35–38]. He suggests that executive function comprises self-directed (cognitive) actions, the organization of behavioral contingencies across time, the use of self-directed speech, rules, or plans, deferred gratification, and goal-directed, futureoriented, purposive or intentional actions. These domain- or condition-specific control functions form the background for broader, albeit still cognitively focused, models of executive function. Zelazo et al. (1997) [39] describe executive function as a macro-construct that spans four phases of problem solving: representation, planning, execution, and evaluation. Similarly, the American Psychiatric Association (1994, 2000) [40, 41] describes executive function as involving conceptualization (i.e., thinking abstractly) and the ability to plan, initiate, sequence, monitor, and stop complex behavior. Miyake et al. (2000) [42] describe executive function as the processes involved in the continuous monitoring and modification of information in working memory (updating), the capacity to supersede automatic responses (inhibition), and the capacity to flexibly alternate between different tasks or mental states (shifting). Baron (2004) [8] suggests that executive functions are metacognitive capacities that facilitate stimulus perception, adaptive responding, flexibility (i.e., changing responses), anticipating future goals and consequences integrating responses rationally, and using such capacities to achieve goals. More recently, Banich (2009) [43] described executive function as a multidimensional construct encompassing the set of abilities required to guide effortful behavior toward a goal in non-routine situations. These abilities include prioritizing and sequencing behavior, inhibiting familiar or stereotyped behaviors, generating and maintaining an attentional set focused on contextually relevant information, providing resistance to distracting or task-irrelevant information, switching between goals, using information in the service of decision-making, categorization and abstraction, and managing novel information and/or situations. These executive functions are organized into a “cascade-of-control” model involving four distinct and Chapter 16: Executive function interactive information-processing steps mediated by discrete prefrontal cortices: establishing a bias toward task-relevant processes, biasing to task-relevant representations, selecting information that guides responding, and evaluating responses. The concept of executive function, or at least the language used to describe it, sometimes extends beyond cognition to include volition [4], initiation [2, 44], emotional evaluation and regulation [45–47], insight [48–53] and theory of mind (i.e., insight into thoughts and feelings of other people) [47, 54, 55]. For example, Lezak (1995, 2004) [4, 56] suggests that there are at least four general components of executive function: volition, planning, purposive active, and effective performance. In this view, executive function explains not “what” or “how much” an individual does something (e.g., a cognitive task, a behavior) but instead “whether” and “how” it is done. Royall et al. (2002) [2] suggest that this simple dichotomy usefully divides the broad array of cognitive, emotional, and behavioral functions in which prefrontal systems participate into executive and non-executive types. A more recent multi-perspective approach undertaken by Packwood et al. (2011) [22] reviewed 60 studies on executive function. Although all of the studies reviewed purported to study “executive function,” the authors identified 68 sub-components (i.e., specific processes) referred to by this term. Latent semantic analysis (to account for semantic overlap) and hierarchical cluster analysis (to account for psychometric overlap) reduced the referents of executive function to 18 clusters of clinically identifiable processes and abilities (Table 16.1). They suggested that this relatively large number of abilities comprising executive function reflects a tendency in the clinical neuropsychological literature to rest definitions of executive functions on behaviors (or deficits) revealed by test performances. Even if these 18 clusters can be organized into a smaller number of core abilities, such as those described by Lezak [4, 56], this analysis makes clear that executive function is not a unidimensional construct – i.e., there is no single “central executive” or simple homunculus-like supervisory system [22, 33, 46, 57]. Defining executive function for clinical practice The cognitive neuroscience literature, from which much of the preceding review is drawn, takes a broad Table 16.1. Clusters of executive functions and their constituents as described by Packwood et al. (2011) [22]. Cluster Elements 1 Working memory, efficient retrieval of works from memory, temporal coding, concentration, performing a sequence of actions 2 Strategy generation, conceptualization, attentional set formation, set maintenance 3 Executive memory (as demonstrated by verbal and non-verbal fluency tasks) 4 Goal-setting, “central executive” function 5 Impulsivity 6 Initiation 7 Inhibition, interference control, response control, mental control, visual search, sustained attention (vigilance) 8 Freedom from/resistance to distraction 9 Problem solving, controlling actions 10 Planning/goal management, developing a plan, executing a plan 11 Organization 12 Strategy use, self-generative behavior, self-monitoring, selective attention, set shifting, attentional control (shifting attention), cue-directed attention 13 Cognitive flexibility 14 Concept formation 15 Abstraction 16 Spontaneous verbal formation, fluency, response generation, response control (i.e., response suppression, response modulation), verbal efficiency 17 Information processing, sequencing 18 Perseveration, reasoning view of executive function. By many of these accounts [2, 4, 22, 29, 30, 34, 44–47], initiation, emotional regulation, comportment, and behavioral control all fall within the category of executive function. For academic purposes (i.e., studying information processing and control systems in theory and in the human brain), such a broad view of executive function is useful to the extent that it facilitates identification of general mechanisms of control over information processing in the human brain. Clinicians, especially subspecialists in BN&NP, also recognize that cognitive control processes contribute to emotional and behavioral regulation. However, regulation of non-cognitive functions is 227 Section I: Structural and Functional Neuroanatomy conceptualized [58–64] and assessed separately from cognition [65–69]. Similarly, cognitive impairments commonly co-occur with disturbances of emotion and behavior, but disturbances of emotion and behavior (e.g., disorders of motivation, disorders of mood and affect, impaired comportment, impulsivity, agitation, aggression) are diagnosed separately from cognitive disorders, including the dysexecutive syndrome [14, 41, 70, 71]. In clinical practice, executive function is regarded as a cognitive domain, understood to be a multidimensional (i.e., non-unitary) construct, and assessed separately from emotion and behavior [41, 72–75] (see Chapter 23). Within the domain of executive function are complex sets of cognitive processes involved in the management and control of other, relatively basic, aspects of information processing. These processes and their outcomes support purposeful goal-directed behaviors, especially those required to address novel problems or situations for which no previously established routines exist, and interact with the processes and outcomes of systems involved in the regulation of emotion and behavior. The merits of “splitting” control processes into cognitive (i.e., executive) and non-cognitive (i.e., emotional and behavioral) types are debatable and are subjects of disagreement within and between researchers and clinicians. Additional theoretical and experimental research is needed to determine whether a unified account of cognitive, emotional, and behavioral control process in the human brain is tenable [2, 22, 43, 75]. Since a widely accepted, theoretically comprehensive, and experimentally supported account that integrates control processes across cognition, emotion, and behavior is not available presently, executive function is considered in this chapter and volume to be a domain of cognition. Neuroanatomy of executive function There is a longstanding tradition amongst clinicians and neuroscientists of attributing executive function to the frontal lobes, describing them as “frontal lobe functions,” and referring to their disturbances as “frontal lobe syndromes” or “frontal lobe disorders” [2, 9, 20, 24, 26, 32, 43, 75, 76]. Neuroanatomic and neuroimaging studies performed over the last several decades suggest that this tradition requires reconsideration. Executive function requires the integrated actions of the frontal-subcortical circuits, open-loop 228 connections to other neocortical areas, limbic and paralimbic structures, thalamic nuclei, pontocerebellar networks, modulatory neurochemical projections from mesencephalic and ventral forebrain structures, and the white matter connections within and between all of these areas [2, 32, 75, 77–80] (see also Chapter 5). As such, executive dysfunction is more accurately understood as dysfunction within or across these networks [81]. Understanding this revised view of the neuroanatomy of executive function necessitates reviewing briefly the history of ideas on this subject and findings from modern neuroanatomic studies. Early observations on frontal function In his review of disturbances associated with lesions of the frontal region, Luria (1962, 1980) [24, 26] credits Gratiolet (1861) [82] with first describing the frontal lobes as the site of the “regulating mind.” He then describes decades of research in the late 1800s and early 1900s involving stimulation and extirpation of the frontal lobes in animals, and identifies Jackson [83], Bianchi [84, 85], Bekheterev [86], and Pavlov [87] as key contributors to the foundation upon which modern views of the neuroanatomy of executive function rests. Jackson (1884) [83] identified the prefrontal regions as the highest and most complex motor centers that indirectly represent movements of all parts of the body. Bianchi (1895, 1920) [84, 85], through ablation studies of animals, identified the role of the frontal lobes in coordinating motor and sensory elements, using the products of the sensory zones to create mental syntheses (i.e., integrative function), and relate to sensorimotor zones in the same manner that motor systems relate to subcortical nuclei. He observed that bilateral extirpation (i.e., lesion or resection) of the frontal lobes results in behavioral disorganization and lack of adaptation to new conditions. Bekhterev (1907) [86], summarizing observations begun in the 1880s, observed disintegration of goaldirected behavior following extirpation of the frontal lobes, including loss of the regulatory activity required to correctly evaluate external impressions and purposively direct movements in accordance with the evaluation. Pavlov (1949) [87], through studies involving extirpation of the frontal lobes in dogs, regarded the prefrontal cortices as integrators of goal-directed movement. He introduced the concept of the “motor Chapter 16: Executive function analyzer,” referring to the cortical motor areas that served as an afferent analyzing apparatus of impulses received from all other parts of the brain, especially the kinesthetic signals providing information on the course of a movement and its effects. He regarded the frontal lobes as an essential component, and the most complex aspect, of the cortical division of the motor analyzer and hypothesized that they facilitate the selection of goal-directed movements. Pavlov’s concept of a “motor analyzer” informed subsequent works (summarized in [24, 26]) on the regulatory functions served by the prefrontal cortices. These works identified the prefrontal cortices as subserving complex forms of motor operations and the evaluating of the effects of action, and suggested that their destruction precludes selective goal-directed behavior. The works of Gratiolet (1861) [82], Jackson (1884) [83], Bianchi (1895, 1920) [84, 85], Bekhterev (1907) [86], Pavlov (1949) [87] and other investigators [24, 26] established the importance of the prefrontal cortices in the integration, coordination, and inhibitory functions as well as evaluation (i.e., selfobservation) of movements and bodily activities. Prefrontal cortices Luria (1962, 1980) [24, 26] emphasized connections between prefrontal cortices and other areas as illuminating their role in the functional organization of the cerebral hemispheres. He anchored his review of the functional anatomy of the prefrontal cortices to the work of Brodmann (1909, 1914) [88, 89] (Figure 16.1). He recognized that the prefrontal divisions of the cortex differ in a number of respects from the motor and premotor areas, including the structure of the second and their association layers, in which giant pyramidal Betz cells are absent (Figure 16.2), as well as the system of vertical connections between the prefrontal divisions of the cortex and the thalamus. He identified Brodmann’s areas (BA) 9, 10, 11, 24, 44, 45, and 46 as subdivisions of the prefrontal cortices possessing specific afferent-efferent conn