Psychiatric Disorders: Methods and Protocols [2nd ed.] 978-1-4939-9553-0;978-1-4939-9554-7

Table of contents :
Front Matter ....Pages i-xxiii
Front Matter ....Pages 1-1
Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug Development (Arie Kaffman, Jordon D. White, Lan Wei, Frances K. Johnson, John H. Krystal)....Pages 3-22
Qualitative vs. Quantitative Methods in Psychiatric Research: Updated (A. Benjamin Srivastava, Firas H. Kobiessy, Mark S. Gold)....Pages 23-37
Front Matter ....Pages 39-39
Animal Models of Self-Injurious Behavior: An Update (Darragh P. Devine)....Pages 41-60
Bipolar Disorder: Its Etiology and How to Model in Rodents (Nadja Freund, Georg Juckel)....Pages 61-77
Recent Updates in Modeling Risky Decision Making in Rodents (Caitlin A. Orsini, Shelby L. Blaes, Barry Setlow, Nicholas W. Simon)....Pages 79-92
Front Matter ....Pages 93-93
The Pemoline Model of Self-Injurious Behavior: An Update (Darragh P. Devine)....Pages 95-103
Rodent Models of Adaptive Value Learning and Decision-Making (Alicia Izquierdo, Claudia Aguirre, Evan E. Hart, Alexandra Stolyarova)....Pages 105-119
The Visually Mediated Social Preference Test: A Novel Technique to Measure Social Behavior and Behavioral Disturbances in Zebrafish (William H. J. Norton, Line Manceau, Florian Reichmann)....Pages 121-132
Animal Models of Intoxication by Metal Elements: A Focus on Neurobehavioral Injuries (Abdellatif Abbaoui, Lahcen Tamegart, Halima Gamrani)....Pages 133-142
Animal Models of Early-Life Adversity (Hajar Benmhammed, Samer El Hayek, Inssaf Berkik, Hicham Elmostafi, Rim Bousalham, Abdelhalem Mesfioui et al.)....Pages 143-161
Front Matter ....Pages 163-163
Nicotine Self-Administration as Paradigm for Medication Discovery for Smoking Cessation: Recent Findings in Medications Targeting the Cholinergic System (Jose M. Trigo, Bernard Le Foll)....Pages 165-193
The Human Laboratory and Drug Development in Alcohol Use Disorder: Recent Updates (Chidera C. Chukwueke, Bernard Le Foll)....Pages 195-219
Rodent Models of Methamphetamine Misuse: Mechanisms of Methamphetamine Action and Comparison of Different Rodent Paradigms (Hiba Hasan, Samar Abdelhady, Muhammad Haidar, Christina Fakih, Samer El Hayek, Stefania Mondello et al.)....Pages 221-250
Front Matter ....Pages 251-251
Recent Updates in Animal Models of Nicotine Withdrawal: Intracranial Self-Stimulation and Somatic Signs (Brandon Levin, Isaac Wilks, Sijie Tan, Azin Behnood-Rod, Adriaan Bruijnzeel)....Pages 253-265
Methods for Evaluating the Interaction Between Social Stress and Environmental Enrichment in Animal Models of Nicotine Addiction (Patricia Mesa-Gresa, Aránzazu Duque, Santiago Monleón, Concepción Vinader-Caerols, Rosa Redolat)....Pages 267-280
An Animal Model of Alcohol Binge Drinking: Chronic-Intermittent Ethanol Administration in Rodents (Santiago Monleón, Aránzazu Duque, Patricia Mesa-Gresa, Rosa Redolat, Concepción Vinader-Caerols)....Pages 281-293
Front Matter ....Pages 295-295
Animal Models of Eating Disorders (Maria Scherma, Roberto Collu, Valentina Satta, Elisa Giunti, Paola Fadda)....Pages 297-314
Protocols Using Rodents to Model Eating Disorders in Humans (Neil E. Rowland)....Pages 315-328
Front Matter ....Pages 329-329
Updates in PTSD Animal Models Characterization (Lei Zhang, Xian-Zhang Hu, He Li, Xiaoxia Li, Tianzheng Yu, Jacob Dohl et al.)....Pages 331-344
Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy (Yasser Medlej, Houssein Salah, Lara Wadi, Sarah Saad, Rita Asdikian, Nabil Karnib et al.)....Pages 345-367
Front Matter ....Pages 369-369
Neurological Exam in Rats Following Stroke and Traumatic Brain Injury (Hale Z. Toklu, Zhiui Yang, Mehmet Ersahin, Kevin K. W. Wang)....Pages 371-381
Y-Shaped Maze to Test Spontaneous Object Recognition and Temporal Order Memory After Traumatic Brain Injury (Hala Darwish, Hiba Hasan)....Pages 383-392
An Animal Model to Test Reversal of Cognitive Decline Associated with Beta-Amyloid Pathologies (Farah Deba, Steven Peterson, Ayman K. Hamouda)....Pages 393-412
Methods in Emotional Behavioral Testing in Immature Epilepsy Rodent Models (Houssein Salah, Yasser Medlej, Nabil Karnib, Nora Darwish, Rita Asdikian, Sarah Wehbe et al.)....Pages 413-427
Methods in Electrode Implantation and Wiring for Long-Term Continuous EEG Monitoring in Rodent Models of Epilepsy and Behavioral Disturbances (Yasser Medlej, Houssein Salah, Lara Wadi, Zahraa Atoui, Yasser Fadlallah, Rita Asdikian et al.)....Pages 429-439
Behavior Model for Assessing Decline in Executive Function During Aging and Neurodegenerative Diseases (Brittney Yegla, Thomas C. Foster, Ashok Kumar)....Pages 441-449
Animal Model for Leigh Syndrome (Sara El-Desouky, Yasmeen M. Taalab, Mohamed El-Gamal, Wael Mohamed, Mohamed Salama)....Pages 451-464
Front Matter ....Pages 465-465
Peripheral Biomarkers of Inflammation in Depression: Evidence from Animal Models and Clinical Studies (J. P. Brás, S. Pinto, M. I. Almeida, J. Prata, O. von Doellinger, R. Coelho et al.)....Pages 467-492
The Contribution of Inflammation to Autism Spectrum Disorders: Recent Clinical Evidence (J. Prata, A. S. Machado, O. von Doellinger, M. I. Almeida, M. A. Barbosa, R. Coelho et al.)....Pages 493-510
Interleukin-2 and the Septohippocampal System: An Update on Intrinsic Actions and Autoimmune Processes Relevant to Neuropsychiatric Disorders (Samer El Hayek, Farah Allouch, Luna Geagea, Farid Talih)....Pages 511-530
The Probiotic Mixture VSL#3 Reverses Olanzapine-Induced Metabolic Dysfunction in Mice (Navneet Dhaliwal, Jatinder Dhaliwal, Dhirendra Pratap Singh, Kanthi Kiran Kondepudi, Mahendra Bishnoi, Kanwaljit Chopra)....Pages 531-544
Front Matter ....Pages 545-545
Genetic Studies of Tic Disorders and Tourette Syndrome (Yanjie Qi, Yi Zheng, Zhanjiang Li, Zhisheng Liu, Lan Xiong)....Pages 547-571
MeCP2 Dysfunction in Rett Syndrome and Neuropsychiatric Disorders (Eunice W. M. Chin, Eyleen L. K. Goh)....Pages 573-591
Behavioral Characterization of MeCP2 Dysfunction-Associated Rett Syndrome and Neuropsychiatric Disorders (Eunice W. M. Chin, Eyleen L. K. Goh)....Pages 593-605
Front Matter ....Pages 607-607
The Role of Epigenetics in Addiction: Clinical Overview and Recent Updates (Antoine Beayno, Samer El Hayek, Paul Noufi, Yara Tarabay, Wael Shamseddeen)....Pages 609-631
Chromatin Immunoprecipitation Techniques in Neuropsychiatric Research (Andrew A. Bartlett, Richard G. Hunter)....Pages 633-645
Chromatin Immunoprecipitation Assay for Analyzing Transcription Factor Activity at the Level of Peripheral Myelin Gene Promoters (Joelle Makoukji)....Pages 647-658
Study of Myelin Gene Expression in the Central Nervous System Using Real-Time PCR (Diala El Khoury)....Pages 659-670
Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics (Jaana van Gastel, Jhana O. Hendrickx, Hanne Leysen, Bronwen Martin, Len Veenker, Sophie Beuning et al.)....Pages 671-723
Back Matter ....Pages 725-728

Citation preview

Methods in Molecular Biology 2011

Firas H. Kobeissy Editor

Psychiatric Disorders Methods and Protocols Second Edition

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in Pub Med.

Psychiatric Disorders Methods and Protocols

Edited by

Firas H. Kobeissy Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon

Editor Firas H. Kobeissy Department of Biochemistry and Molecular Genetics Faculty of Medicine American University of Beirut Beirut, Lebanon

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-4939-9553-0 ISBN 978-1-4939-9554-7 (eBook) https://doi.org/10.1007/978-1-4939-9554-7 © Springer Science+Business Media, LLC, part of Springer Nature 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Caption: The Brain: A Curious Tale of Chasing the Enigma “A hand stretched out beyond spiritual world in search for a stable ground. It landed at lab bench; unravelling an, otherwise, enigmatically whispering neurons and genome. Still, the odyssey never reaches destiny.” Cover designed by Dr. Samar Abdelhady, MD. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 233 Spring Street, New York, NY 10013, U.S.A.

Dedication To my mentor and colleague, Professor Julnar Usta, whose sincere and honest devotion to research and teaching has touched many students, researchers, and medical doctors, I dedicate this humble work. I will always learn from her encyclopedic knowledge in Biochemistry. . .

v

Foreword According to the World Health Organization (WHO), an estimated one in six individuals, globally, suffers from neurological diseases, inclusive of mental, traumatic, neurodegenerative, and autoimmune disorders. In light of this and the tumultuous conditions of “conflict zones” and the fallout in terms of psychiatric disorders, drug abuse, poverty, and malnutrition, the second edition of Psychiatric Disorders provides a needed and timely roadmap for research. While much of the neuroscience community has been focused, of late, on neurodegenerative conditions, it is refreshing to see a text that brings together unique expertise and state-of-the-art methodologies that will benefit seasoned researchers, as well as emerging generations of pioneers in the realm of psychiatric illness, mental disorders, and translational scientists. Firas Kobeissy has assembled a panel of experts to act as guides on this journey of discovery. The approach is systematic in introducing relevant preclinical models of pressing psychiatric conditions—from the classics of depression, anxiety, and substance abuse to more recently recognized post-traumatic stress disorder (PTSD), eating disorders, and the ever-elusive search for reliable validated diagnostic and prognostic biomarkers. Despite a focus on laboratory models, the translational relevance to the clinical setting is obvious, particularly for those focused on the development of effective intervention and therapeutic development. Also evident to the reader is the mechanistic quest that underlies each submission.

Approach This volume introduces the novice and savvy researcher to an overview of key concerns in psychiatric disorder research and the state of this broad field before delving into the specifics of models and methodologies. Having fielded complaints from students and researchers on the paucity of translatable details in methods for, well, over 30 years, this volume is a welcome relief in providing much-needed details for validation of methods and serves the reader well in establishing the credibility of the generated data. This credibility is further enhanced by the choice of chapter authors, all of whom are recognized experts in their area. In addition, the editor does not monopolize the volume, which further distinguishes it as a “sincere” effort in publicizing the best of what the field has to offer. Sections of the volume tackle the details of psychiatric illness, substance abuse disorders, and eating disorders. However, its timeliness is underscored by cutting-edge research on biomarkers and “omic” approaches. Particularly attractive to this reader is the transdisciplinary approach that recognizes—indeed invites—collaborative approaches toward sustainable solutions, as well as setting the stage for a “benchtop to bedside” paradigm. Any seasoned researcher readily recognizes the challenges inherent between transitioning from animal models that address basic mechanisms, identify targets of diagnosis and intervention, and the ability to extrapolate to the human condition and clinical field. Most, if not all chapters in the volume, attempt to address such challenges, raising the value of these submissions in terms of relevance.

vii

viii

Foreword

Fig. 1 A cohesive roadmap towards translation and precision health

As one who is keen on promoting “Precision Health,” not just precision or personalized medicine, the intricate interplay between behavior, socioeconomics, gene-environment interaction, and effective diagnostics—possibly theranostics—the availability of a starting point that allows for integration of animal models, molecular mechanisms, and systems biology approach to data analytics holds much promise. The greater challenge, which is met to a great extent in this second edition, is how to not become so verbose as to lose sight of the desired benefit—the equipping of the researcher with the tools to effectively carry out quality applied research and articulate data in a meaningful way (Fig. 1).

Recommendation This volume is an indispensable addition to any serious researcher’s bookshelf, physical or virtual. It is not likely to collect dust, as it provides hands-on know-how and shares that know-how not only to those focused on psychiatric disorders but also for the discerning neuroscientist seeking answers. With contributions on neuroplasticity, neurogenesis, assessment of cognitive decline, dyskinesia, tobacco use, environmental tobacco smoke (ETS), obesity-related behavior, PTSD, and deciphering frontiers in genomics, transcriptomics, and metabolomics, there is food for thought for those who recognized the interconnectedness of environment-neurobehavior and health outcomes. Hassan A. N. El-Fawal Neuroscience, Pharmacology and Toxicology, School of Sciences and Engineering, Institute of Global Health and Human Ecology, The American University in Cairo, Cairo, Egypt

Preface As the of field neuroscience is evolving, newly discoveries at the cellular and molecular levels are revolutionizing the previous classical concepts into new developed understandings, paving the way for the development of new therapies, aiding in the diagnosis, and even proposing novel treatment modalities for psychiatric and neurological disorders. Some—if not most—of these discoveries have been made possible with the advent of experimental animal models. Experimental models that mimic human neuropsychiatric disorders have been described and optimized over the years to assist in understanding the pathogenesis and pathophysiology of psychiatric disorders and for the evaluation of therapies as well. Indeed, several of these models have significantly improved our understanding of the fundamental mechanisms of neuropsychiatric disorders, their development, and cure. Having said that, the need to optimize these experimental models, to correctly reproduce the findings and data generated, cannot be underestimated in order to translate into the clinical settings. Thus, providing researchers and scientists with well-detailed descriptions of the protocols applied in animal models will ultimately help them design their scientific questions and inquiries with ease and efficiency. These described protocols will also serve as a guide for students and postgraduates who are starting their scientific careers seeking learning novel techniques as well as novel models relevant to their research areas. Therefore, this second edition of the Psychiatric Disorders should be useful for graduates, postdoctoral workers, as well as established scientists working in the fields of behavioral and molecular neuropsychiatric research. In this edition, we provide updates on the methods and protocols discussed in the first edition while still providing novel ones. As discussed in the first edition, we have invited top-notch neuroscience and psychiatry experts as well as physician scientists to write integrated chapters on recent updates in the neuropsychiatric research. In this book, authors and colleagues share their invaluable and insightful expertise and opinions. The collection of chapters here reflects the diversity and utility of animal models of psychiatric disorders, their development, establishment, and pathophysiological and molecular profile. In this book, we have included 39 chapters divided into 11 primary parts describing the protocols, techniques, methods, and different models applied in neuropsychiatric disorders. The first part consists of two chapters offering an overview of the experimental modeling of neuropsychiatric studies describing the usefulness and the need of animal models to relate the cellular and molecular changes occurring in human mental illnesses (Kaffman et al.). This is followed by a discussion chapter detailing the dilemma of the qualitative vs. quantitative nature of psychiatric research (Srivastava et al.). The second and third parts (consisting of eight chapters) are dedicated to experimental models of neuropsychiatric illnesses, including self-injurious behavior animal model, bipolar disorder, anxiety, learning, and decision-making testing as well as psychiatric illnesses and neurobehavioral injuries stemming from intoxication by metal elements and early-life adversity. The fourth and fifth parts (consisting of six chapters) discuss animal models of substance abuse. The fourth part discusses experimental models related to nicotine, alcohol, smoking, and methamphetamine abuse paradigms and techniques, while the fifth discusses the methods used to develop these models as well as the techniques to assess their outcome and their

ix

x

Preface

effects. The sixth part (consisting of two chapters) discusses the detailed protocols to model animal models related to maladaptive eating habits and/or behaviors. The seventh and eight parts (consisting of nine chapters) focus on the animal models related mainly to neurodegenerative diseases stemming from natural causes (aging), aberrant genetic background, or those induced by trauma. The chapters review and discuss how to design animal models needed to study neurodegenerative and behavioral consequences of aging, stroke, traumatic brain injury, epilepsy, and Leigh syndrome, while the ninth part (consisting of four chapters) discusses the inflammatory and metabolic alteration profiles relevant to neuropsychiatric disorders including autism spectrum disorders, depression, and other disorders. The last two parts (tenth and eleventh) consist of eight chapters integrating the genetic, epigenetic, and system biology approaches in the field of psychiatric disorders genetics, epigenetics, and systems biology in the field of psychiatric disorders. These chapters outline the novel approaches and molecular techniques needed to decipher and delineate topics in the areas of neuropsychiatric disorders. The tenth chapter focuses on the genetic studies of neuropsychiatric disorders, including Tic disorders and Tourette’s and Rett’s syndrome, providing protocols needed for the development and successful establishment of these genetic animal models. The last chapter focuses on the role of genetics and epigenetics in the development of psychiatric disorders and the use of molecular biology techniques, such as RT-PCR and chromatin immunoprecipitation, and high dimensionality data analytics and acquisition which are crucial for understanding the molecular mechanisms underlying such neuropsychiatric disorders. Finally, we hope that the chapters in this edition will benefit researchers in neuroscience and neuropsychiatric research. It is our hope that this book enables neuroscientists and psychiatrists to achieve success in their scientific endeavors and helps them solve several pending scientific questions with the most creative and insightful approaches. Beirut, Lebanon

Firas H. Kobeissy

Acknowledgments I would like to thank all the book authors and contributors for their valuable work; without their effort, I would have never finished this book. I am very grateful to Dr. Samar Abdelhady, who designed the attractive book art cover and tolerated my endless comments. Thank you.

xi

Contents Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

OVERVIEW OF THE ANIMAL RESEARCH IN PSYCHIATRIC ILLNESS

1 Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arie Kaffman, Jordon D. White, Lan Wei, Frances K. Johnson, and John H. Krystal 2 Qualitative vs. Quantitative Methods in Psychiatric Research: Updated . . . . . . . . A. Benjamin Srivastava, Firas H. Kobiessy, and Mark S. Gold

PART II

3

23

OVERVIEW OF ANIMAL MODELS OF PSYCHIATRIC ILLNESS

3 Animal Models of Self-Injurious Behavior: An Update. . . . . . . . . . . . . . . . . . . . . . . Darragh P. Devine 4 Bipolar Disorder: Its Etiology and How to Model in Rodents . . . . . . . . . . . . . . . . Nadja Freund and Georg Juckel 5 Recent Updates in Modeling Risky Decision Making in Rodents . . . . . . . . . . . . . Caitlin A. Orsini, Shelby L. Blaes, Barry Setlow, and Nicholas W. Simon

PART III

v vii ix xi

41 61 79

METHODS OF ANIMAL MODELS OF PSYCHIATRIC ILLNESS

6 The Pemoline Model of Self-Injurious Behavior: An Update . . . . . . . . . . . . . . . . . Darragh P. Devine 7 Rodent Models of Adaptive Value Learning and Decision-Making . . . . . . . . . . . . Alicia Izquierdo, Claudia Aguirre, Evan E. Hart, and Alexandra Stolyarova 8 The Visually Mediated Social Preference Test: A Novel Technique to Measure Social Behavior and Behavioral Disturbances in Zebrafish . . . . . . . . . William H. J. Norton, Line Manceau, and Florian Reichmann 9 Animal Models of Intoxication by Metal Elements: A Focus on Neurobehavioral Injuries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdellatif Abbaoui, Lahcen Tamegart, and Halima Gamrani 10 Animal Models of Early-Life Adversity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hajar Benmhammed, Samer El Hayek, Inssaf Berkik, Hicham Elmostafi, Rim Bousalham, Abdelhalem Mesfioui, Ali Ouichou, and Aboubaker El Hessni

xiii

95 105

121

133 143

xiv

Contents

PART IV 11

12

13

OVERVIEW OF ANIMAL MODELS OF SUBSTANCE ABUSE

Nicotine Self-Administration as Paradigm for Medication Discovery for Smoking Cessation: Recent Findings in Medications Targeting the Cholinergic System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Jose M. Trigo and Bernard Le Foll The Human Laboratory and Drug Development in Alcohol Use Disorder: Recent Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Chidera C. Chukwueke and Bernard Le Foll Rodent Models of Methamphetamine Misuse: Mechanisms of Methamphetamine Action and Comparison of Different Rodent Paradigms . 221 Hiba Hasan, Samar Abdelhady, Muhammad Haidar, Christina Fakih, Samer El Hayek, Stefania Mondello, Firas H. Kobeissy, and Abdullah Shaito

PART V

METHODS OF ANIMAL MODELS OF SUBSTANCE ABUSE

14

Recent Updates in Animal Models of Nicotine Withdrawal: Intracranial Self-Stimulation and Somatic Signs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Brandon Levin, Isaac Wilks, Sijie Tan, Azin Behnood-Rod, and Adriaan Bruijnzeel 15 Methods for Evaluating the Interaction Between Social Stress and Environmental Enrichment in Animal Models of Nicotine Addiction . . . . . . 267 Patricia Mesa-Gresa, Ara´nzazu Duque, Santiago Monleo n, Concepcio n Vinader-Caerols, and Rosa Redolat 16 An Animal Model of Alcohol Binge Drinking: Chronic-Intermittent Ethanol Administration in Rodents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Santiago Monleon, Ara´nzazu Duque, Patricia Mesa-Gresa, Rosa Redolat, and Concepcion Vinader-Caerols

PART VI 17

18

Animal Models of Eating Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Maria Scherma, Roberto Collu, Valentina Satta, Elisa Giunti, and Paola Fadda Protocols Using Rodents to Model Eating Disorders in Humans . . . . . . . . . . . . . 315 Neil E. Rowland

PART VII 19

METHODS OF ANIMAL MODELS OF EATING DISORDERS

REVIEW OF ANIMAL MODELS OF AGING, NEURODEGENERATIVE DISEASES AND TRAUMATIC BRAIN INJURY

Updates in PTSD Animal Models Characterization . . . . . . . . . . . . . . . . . . . . . . . . . 331 Lei Zhang, Xian-Zhang Hu, He Li, Xiaoxia Li, Tianzheng Yu, Jacob Dohl, and Robert J. Ursano

Contents

20

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Yasser Medlej, Houssein Salah, Lara Wadi, Sarah Saad, Rita Asdikian, Nabil Karnib, Dima Ghazal, Bashir Bashir, Jad Allam, and Makram Obeid

PART VIII 21 22

23

24

25

26

27

METHODS OF ANIMAL MODELS OF AGING, NEURODEGENERATIVE DISEASES AND TRAUMATIC BRAIN INJURY

Neurological Exam in Rats Following Stroke and Traumatic Brain Injury . . . . . . Hale Z. Toklu, Zhiui Yang, Mehmet Ersahin, and Kevin K. W. Wang Y-Shaped Maze to Test Spontaneous Object Recognition and Temporal Order Memory After Traumatic Brain Injury . . . . . . . . . . . . . . . . . . Hala Darwish and Hiba Hasan An Animal Model to Test Reversal of Cognitive Decline Associated with Beta-Amyloid Pathologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Farah Deba, Steven Peterson, and Ayman K. Hamouda Methods in Emotional Behavioral Testing in Immature Epilepsy Rodent Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Houssein Salah, Yasser Medlej, Nabil Karnib, Nora Darwish, Rita Asdikian, Sarah Wehbe, Ghadir Makki, and Makram Obeid Methods in Electrode Implantation and Wiring for Long-Term Continuous EEG Monitoring in Rodent Models of Epilepsy and Behavioral Disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasser Medlej, Houssein Salah, Lara Wadi, Zahraa Atoui, Yasser Fadlallah, Rita Asdikian, Rana Bou Khalil, Rabih Hashash, and Makram Obeid Behavior Model for Assessing Decline in Executive Function During Aging and Neurodegenerative Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brittney Yegla, Thomas C. Foster, and Ashok Kumar Animal Model for Leigh Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara El-Desouky, Yasmeen M. Taalab, Mohamed El-Gamal, Wael Mohamed, and Mohamed Salama

PART IX 28

xv

371

383

393

413

429

441 451

INFLAMMATORY AND METABOLIC PROFILES RELEVANT TO NEUROPSYCHIATRIC DISORDERS

Peripheral Biomarkers of Inflammation in Depression: Evidence from Animal Models and Clinical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 J. P. Bra´s, S. Pinto, M. I. Almeida, J. Prata, O. von Doellinger, R. Coelho, M. A. Barbosa, and S. G. Santos 29 The Contribution of Inflammation to Autism Spectrum Disorders: Recent Clinical Evidence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 J. Prata, A. S. Machado, O. von Doellinger, M. I. Almeida, M. A. Barbosa, R. Coelho, and S. G. Santos 30 Interleukin-2 and the Septohippocampal System: An Update on Intrinsic Actions and Autoimmune Processes Relevant to Neuropsychiatric Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Samer El Hayek, Farah Allouch, Luna Geagea, and Farid Talih

xvi

31

Contents

The Probiotic Mixture VSL#3 Reverses Olanzapine-Induced Metabolic Dysfunction in Mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Navneet Dhaliwal, Jatinder Dhaliwal, Dhirendra Pratap Singh, Kanthi Kiran Kondepudi, Mahendra Bishnoi, and Kanwaljit Chopra

PART X

OVERVIEW AND METHODS OF GENETIC ANIMAL MODELS OF NEUROPSYCHIATRIC DISORDERS

32

Genetic Studies of Tic Disorders and Tourette Syndrome . . . . . . . . . . . . . . . . . . . . 547 Yanjie Qi, Yi Zheng, Zhanjiang Li, Zhisheng Liu, and Lan Xiong 33 MeCP2 Dysfunction in Rett Syndrome and Neuropsychiatric Disorders . . . . . . . 573 Eunice W. M. Chin and Eyleen L. K. Goh 34 Behavioral Characterization of MeCP2 Dysfunction-Associated Rett Syndrome and Neuropsychiatric Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Eunice W. M. Chin and Eyleen L. K. Goh

PART XI 35

36

37

38

39

ROLE OF GENETICS, MOLECULAR TECHNIQUES, AND EPIGENETICS IN NEUROPSYCHIATRIC DISORDERS

The Role of Epigenetics in Addiction: Clinical Overview and Recent Updates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antoine Beayno, Samer El Hayek, Paul Noufi, Yara Tarabay, and Wael Shamseddeen Chromatin Immunoprecipitation Techniques in Neuropsychiatric Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew A. Bartlett and Richard G. Hunter Chromatin Immunoprecipitation Assay for Analyzing Transcription Factor Activity at the Level of Peripheral Myelin Gene Promoters. . . . . . . . . . . . . Joelle Makoukji Study of Myelin Gene Expression in the Central Nervous System Using Real-Time PCR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diala El Khoury Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics . . . . . . . . . . . . . . . Jaana van Gastel, Jhana O. Hendrickx, Hanne Leysen, Bronwen Martin, Len Veenker, Sophie Beuning, Violette Coppens, Manuel Morrens, and Stuart Maudsley

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

609

633

647

659

671

725

Contributors ABDELLATIF ABBAOUI  Neurosciences, Pharmacology and Environment Unit, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, Morocco SAMAR ABDELHADY  Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon CLAUDIA AGUIRRE  Department of Psychology, University of California at Los Angeles, Los Angeles, CA, USA JAD ALLAM  Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon FARAH ALLOUCH  Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon M. I. ALMEIDA  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; INEB-Instituto de Engenharia Biome´dica, University of Porto, Porto, Portugal RITA ASDIKIAN  Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon ZAHRAA ATOUI  Faculty of Medicine, American University of Beirut, Beirut, Lebanon M. A. BARBOSA  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; INEB-Instituto de Engenharia Biome´dica, University of Porto, Porto, Portugal; ICBAS-Instituto de Cieˆncias Biome´dicas Abel Salazar, University of Porto, Porto, Portugal ANDREW A. BARTLETT  Department of Psychology, University of Massachusetts Boston, Boston, MA, USA BASHIR BASHIR  Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon ANTOINE BEAYNO  Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon AZIN BEHNOOD-ROD  Department of Psychiatry, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, USA HAJAR BENMHAMMED  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco INSSAF BERKIK  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco SOPHIE BEUNING  Collaborative Antwerp Psychiatric Research Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium MAHENDRA BISHNOI  National Agri-food Biotechnology Institute (NABI), Mohali, Punjab, India SHELBY L. BLAES  Department of Psychiatry, University of Florida, Gainesville, FL, USA RIM BOUSALHAM  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco J. P. BRA´S  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; INEB-Instituto de Engenharia Biome´dica, University of Porto, Porto, Portugal; ICBAS-Instituto de Cieˆncias Biome´dicas Abel Salazar, University of Porto, Porto, Portugal ADRIAAN BRUIJNZEEL  Department of Psychiatry, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, USA

xvii

xviii

Contributors

EUNICE W. M. CHIN  Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, Singapore KANWALJIT CHOPRA  Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Punjab University, Chandigarh, India CHIDERA C. CHUKWUEKE  Department of Pharmacology and Toxicology, King’s College Circle, University of Toronto, Toronto, ON, Canada R. COELHO  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; FMUP-Faculty of Medicine, University of Porto, Porto, Portugal; Department of Clinical Neurosciences and Mental Health, Centro Hospitalar Universita´rio Sa˜o Joa˜o, Porto, Portugal ROBERTO COLLU  Department of Biomedical Sciences, Division of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy VIOLETTE COPPENS  Collaborative Antwerp Psychiatric Research Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium NORA DARWISH  Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon HALA DARWISH  Hariri School of Nursing, Abu-Haidar Neuroscience Institute, American University of Beirut, Beirut, Lebanon; Department of Biochemistry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon FARAH DEBA  Department of Pharmaceutical Science, Fisch College of Pharmacy, University of Texas at Tyler, Tyler, TX, USA DARRAGH P. DEVINE  Department of Psychology, Behavioral and Cognitive Neuroscience Program, University of Florida, Gainesville, FL, USA JATINDER DHALIWAL  Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Punjab University, Chandigarh, India NAVNEET DHALIWAL  Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Punjab University, Chandigarh, India JACOB DOHL  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA; Department of Military and Emergency Medicine, Consortium for Health and Military Performance, Uniformed Services University of the Health Sciences, Bethesda, MD, USA ARA´NZAZU DUQUE  Universidad Internacional de Valencia, Valencia, Spain SARA EL-DESOUKY  Medical Experimental Research Center (MERC), Faculty of Medicine, Mansoura University, Mansoura, Egypt MOHAMED EL-GAMAL  Medical Experimental Research Center (MERC), Faculty of Medicine, Mansoura University, Mansoura, Egypt; Toxicology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt; IUF—Leibniz Research Institute for Environmental Medicine, Du¨sseldorf, Germany SAMER EL HAYEK  Department of Psychiatry, Faculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon; Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon ABOUBAKER EL HESSNI  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco DIALA EL KHOURY  Department of Biology Louaize Lebanon, NDU Natural and Applied Sciences, Notre Dame University, Zouk Mosbeh, Lebanon HICHAM ELMOSTAFI  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

Contributors

xix

MEHMET ERSAHIN  Department of Neurosurgery, Istanbul Medeniyet University, Istanbul, Turkey PAOLA FADDA  Department of Biomedical Sciences, Division of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy; Centre of Excellence “Neurobiology of Addiction”, University of Cagliari, Cagliari, Italy; CNR Institute of Neuroscience— Cagliari, National Research Council, Cagliari, Italy; National Neuroscience Institute, Cagliari, Italy YASSER FADLALLAH  Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon CHRISTINA FAKIH  Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon THOMAS C. FOSTER  McKnight Brain Institute, University of Florida, Gainesville, FL, USA NADJA FREUND  Division of Experimental and Molecular Psychiatry, Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, RuhrUniversity, Bochum, Germany HALIMA GAMRANI  Neurosciences, Pharmacology and Environment Unit, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, Morocco LUNA GEAGEA  Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon DIMA GHAZAL  Faculty of Sciences, Lebanese University, Beirut, Lebanon ELISA GIUNTI  Department of Biomedical Sciences, Division of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy EYLEEN L. K. GOH  Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, Singapore; Department of Research, National Neuroscience Institute, Singapore, Singapore; Neuroscience Academic Clinical Programme, Singhealth Duke-NUS Academic Medical Center, Singapore, Singapore MARK S. GOLD  Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA MUHAMMAD HAIDAR  Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon AYMAN K. HAMOUDA  Department of Pharmaceutical Science, Fisch College of Pharmacy, University of Texas at Tyler, Tyler, TX, USA EVAN E. HART  Department of Psychology, University of California at Los Angeles, Los Angeles, CA, USA HIBA HASAN  Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon RABIH HASHASH  Animal Care Facility, American University of Beirut, Beirut, Lebanon JHANA O. HENDRICKX  Receptor Biology Lab, Department of Biomedical Research, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Sciences, University of Antwerp, Antwerp, Belgium XIAN-ZHANG HU  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA RICHARD G. HUNTER  Department of Psychology, University of Massachusetts Boston, Boston, MA, USA; Laboratory of Neuroendocrinology, The Rockefeller University, New York, NY, USA ALICIA IZQUIERDO  Department of Psychology, University of California at Los Angeles, Los Angeles, CA, USA; The Brain Research Institute, University of California at Los Angeles, Los Angeles, CA, USA; Integrative Center for Learning and Memory, University of

xx

Contributors

California at Los Angeles, Los Angeles, CA, USA; Integrative Center for Addictions, University of California at Los Angeles, Los Angeles, CA, USA FRANCES K. JOHNSON  Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA GEORG JUCKEL  Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, Bochum, Germany ARIE KAFFMAN  Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA NABIL KARNIB  Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Byblos, Lebanon RANA BOU KHALIL  Animal Care Facility, American University of Beirut, Beirut, Lebanon FIRAS H. KOBEISSY  Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon KANTHI KIRAN KONDEPUDI  National Agri-food Biotechnology Institute (NABI), Mohali, Punjab, India JOHN H. KRYSTAL  Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA ASHOK KUMAR  McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA BERNARD LE FOLL  Translational Addiction Research Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Addictions Division, CAMH, Toronto, ON, Canada; Department of Pharmacology and Toxicology, King’s College Circle, University of Toronto, Toronto, ON, Canada BRANDON LEVIN  Department of Psychiatry, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, USA HANNE LEYSEN  Receptor Biology Lab, Department of Biomedical Research, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Sciences, University of Antwerp, Antwerp, Belgium HE LI  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA XIAOXIA LI  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA ZHANJIANG LI  Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Center of Schizophrenia, Beijing Institute for Brain Disorders, Beijing, China ZHISHENG LIU  Department of Pediatric Neurology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China A. S. MACHADO  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; FMUP-Faculty of Medicine, University of Porto, Porto, Portugal; Department of Clinical Neurosciences and Mental Health, Centro Hospitalar ˜ rio Sa˜o Joa˜o, Porto, Portugal UniversitA GHADIR MAKKI  Faculty of Sciences, Lebanese University, Beirut, Lebanon JOELLE MAKOUKJI  Neurogenetics Program, Division of Pediatric Neurology, Department of Pediatrics and Adolescent Medicine, AUBMC Special Kids Clinic, American University of

Contributors

xxi

Beirut Medical Center, Beirut, Lebanon; Department of Biochemistry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon LINE MANCEAU  Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK BRONWEN MARTIN  Faculty of Pharmacy, Biomedical and Veterinary Sciences, University of Antwerp, Antwerp, Belgium STUART MAUDSLEY  Receptor Biology Lab, Department of Biomedical Research, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Sciences, University of Antwerp, Antwerp, Belgium YASSER MEDLEJ  Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon PATRICIA MESA-GRESA  Department of Psychobiology, University of Valencia, Valencia, Spain ABDELHALEM MESFIOUI  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco WAEL MOHAMED  Clinical Pharmacology Department, Faculty of Medicine, Menoufia University, Al Minufya, Egypt; Department of Basic Medical Science, Kulliyyah of Medicine, International Islamic University, Kuantan, Pahang, Malaysia STEFANIA MONDELLO  Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; “Oasi” Institute for Research on Mental Retardation and Brain Aging (I.R.C.C.S.), Troina, EN, Italy SANTIAGO MONLEO´N  Department of Psychobiology, University of Valencia, Valencia, Spain MANUEL MORRENS  Collaborative Antwerp Psychiatric Research Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Department of Psychiatry, University of Antwerp, Duffel, Belgium WILLIAM H. J. NORTON  Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK PAUL NOUFI  Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon MAKRAM OBEID  Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; Division of Child Neurology, Department of Pediatric and Adolescent Medicine, American University of Beirut Medical Center, Beirut, Lebanon CAITLIN A. ORSINI  Department of Psychiatry, University of Florida, Gainesville, FL, USA ALI OUICHOU  Laboratory of Genetics, Neuroendocrinology, and Biotechnology, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco STEVEN PETERSON  Department of Pharmaceutical Sciences, Irma Lerma Rangel College of Pharmacy, Texas A&M University, Kingsville, TX, USA S. PINTO  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; FMUP-Faculty of Medicine, University of Porto, Porto, Portugal; Department of Clinical Neurosciences and Mental Health, Centro Hospitalar Universita´rio Sa˜o Joa˜o, Porto, Portugal J. PRATA  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; FMUP-Faculty of Medicine, University of Porto, Porto, Portugal; Department of Psychiatry and Mental Health, Centro Hospitalar Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal YANJIE QI  Laboratoire de Neuroge´ne´tique, Centre de Recherche, Institut Universitaire en Sante´ Mentale de Montre´al, Montreal, QC, Canada; Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China

xxii

Contributors

ROSA REDOLAT  Department of Psychobiology, University of Valencia, Valencia, Spain FLORIAN REICHMANN  Otto Loewi Research Centre, Medical University of Graz, Graz, Austria NEIL E. ROWLAND  Department of Psychology, University of Florida, Gainesville, FL, USA SARAH SAAD  Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon HOUSSEIN SALAH  Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon MOHAMED SALAMA  Faculty of Medicine, Medical Experimental Research Center (MERC), Mansoura University, Mansoura, Egypt; Toxicology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt; Atlantic Fellow for Global Brain Health Institute (GBHI), Trinity College Dublin (TCD), Dublin, Ireland S. G. SANTOS  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; INEB-Instituto de Engenharia Biome´dica, University of Porto, Porto, Portugal; ICBAS-Instituto de Cieˆncias Biome´dicas Abel Salazar, University of Porto, Porto, Portugal VALENTINA SATTA  Department of Biomedical Sciences, Division of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy MARIA SCHERMA  Division of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy BARRY SETLOW  Department of Neuroscience, University of Florida, Gainesville, FL, USA; Center for Addiction Research and Education, University of Florida, Gainesville, FL, USA; Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL, USA ABDULLAH SHAITO  Department of Biological and Chemical Sciences, Faculty of Arts and Sciences, Lebanese International University, Beirut, Lebanon WAEL SHAMSEDDEEN  Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA NICHOLAS W. SIMON  Department of Psychology, University of Memphis, Memphis, TN, USA DHIRENDRA PRATAP SINGH  Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Punjab University, Chandigarh, India; National Agri-food Biotechnology Institute (NABI), Mohali, Punjab, India A. BENJAMIN SRIVASTAVA  Division on Substance Use Disorder, Department of Psychiatry, Columbia University Medical Center, New York State Psychiatric Institute, New York, NY, USA ALEXANDRA STOLYAROVA  Department of Psychology, University of California at Los Angeles, Los Angeles, CA, USA YASMEEN M. TAALAB  Toxicology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt; German Institute of Disaster Medicine and Emergency Medicine, Tubingen, Germany FARID TALIH  Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; Psychiatry Department, American University of Beirut Medical Center, Beirut, Lebanon LAHCEN TAMEGART  Neurosciences, Pharmacology and Environment Unit, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, Morocco SIJIE TAN  Department of Psychiatry, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, USA YARA TARABAY  Faculty of Pedagogy, Lebanese University, New Rawda, Lebanon; Faculty of Natural and Applied Sciences, Notre Dame University, Louaize, Lebanon

Contributors

xxiii

HALE Z. TOKLU  Department of Clinical Sciences, University of Central Florida College of Medicine, Gainesville, FL, USA; HCA North Florida Division, Graduate Medical Education, Tallahassee, FL, USA JOSE M. TRIGO  Translational Addiction Research Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada ROBERT J. URSANO  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA JAANA VAN GASTEL  Receptor Biology Lab, Department of Biomedical Research, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Sciences, University of Antwerp, Antwerp, Belgium LEN VEENKER  Faculty of Medicine and Health Sciences, Collaborative Antwerp Psychiatric Research Institute, University of Antwerp, Antwerp, Belgium CONCEPCIO´N VINADER-CAEROLS  Department of Psychobiology, University of Valencia, Valencia, Spain O. VON DOELLINGER  i3S-Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, Porto, Portugal; FMUP-Faculty of Medicine, University of Porto, Porto, Portugal; Department of Psychiatry and Mental Health, Centro Hospitalar do Taˆmega e Sousa, Penafiel, Portugal LARA WADI  Faculty of Medicine, American University of Beirut, Beirut, Lebanon KEVIN K. W. WANG  Department of Emergency Medicine, University of Florida, Gainesville, FL, USA LAN WEI  Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA SARAH WEHBE  Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon JORDON D. WHITE  Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA ISAAC WILKS  Department of Psychiatry, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, USA LAN XIONG  Laboratoire de Neuroge´ne´tique, Centre de Recherche, Institut Universitaire en Sante´ Mentale de Montre´al, Montreal, QC, Canada; De´partement de Psychiatrie, Faculte´ de Me´decine, Universite´ de Montre´al, Montreal, QC, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada ZHIUI YANG  Department of Emergency Medicine, University of Florida, Gainesville, FL, USA BRITTNEY YEGLA  McKnight Brain Institute, University of Florida, Gainesville, FL, USA TIANZHENG YU  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA; Department of Military and Emergency Medicine, Consortium for Health and Military Performance, Uniformed Services University of the Health Sciences, Bethesda, MD, USA LEI ZHANG  Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA YI ZHENG  Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Center of Schizophrenia, Beijing Institute for Brain Disorders, Beijing, China

Part I Overview of the Animal Research in Psychiatric Illness

Chapter 1 Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug Development Arie Kaffman, Jordon D. White, Lan Wei, Frances K. Johnson, and John H. Krystal Abstract Most large pharmaceutical companies have downscaled or closed their clinical neuroscience research programs in response to the low clinical success rate for drugs that showed tremendous promise in animal experiments intended to model psychiatric pathophysiology. These failures have raised serious concerns about the role of preclinical research in the identification and evaluation of new pharmacotherapies for psychiatry. In the absence of a comprehensive understanding of the neurobiology of psychiatric disorders, the task of developing “animal models” seems elusive. The purpose of this review is to highlight emerging strategies to enhance the utility of preclinical research in the drug development process. We address this issue by reviewing how advances in neuroscience, coupled with new conceptual approaches, have recently revolutionized the way we can diagnose and treat common psychiatric conditions. We discuss the implications of these new tools for modeling psychiatric conditions in animals and advocate for the use of systematic reviews of preclinical work as a prerequisite for conducting psychiatric clinical trials. We believe that work in animals is essential for elucidating human psychopathology and that improving the predictive validity of animal models is necessary for developing more effective interventions for mental illness. Key words Animal models, Predictive validity, Psychiatry, Systematic reviews, CRF

1

Introduction The recent withdrawal of big pharmaceutical companies from clinical neuroscience research, the dwindling pipeline of innovative treatments, and the large number of clinical failures have raised serious concerns about the future of psychiatric research [1, 2]. These trends have emerged due to poor understanding of the underlying psychopathology of common psychiatric conditions and the use of a classification system that lumps together heterogeneous etiologies that may require different treatment modalities [3–5]. Additional factors include the lack of objective markers to diagnose and monitor treatment response and frequent clinical failure of pharmacological treatments that showed initial promise

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_1, © Springer Science+Business Media, LLC, part of Springer Nature 2019

3

4

Arie Kaffman et al.

in animal models [1, 6–9]. The ability of animal models to predict clinical outcomes in humans is referred to as predictive validity, a term closely related to construct validity, which is the ability of the model to recapitulate key aspects of the pathology [10]. In contrast to the slow progress in the development of new psychiatric treatments, the field of neuroscience has seen a rapid expansion of novel tools and approaches that allow us to address some of the above challenges in ways that were not feasible before. The primary goal of this chapter is to discuss how these advances can improve the predictive validity of animal models in psychiatry. Subheading 2 of this review examines the main obstacles and challenges that are responsible for the slow progress in generating new treatments for common psychiatric and neurological conditions. In Subheading 3, we discuss how key technological and conceptual advances have helped to overcome these traditional obstacles. Subheading 4 uses the translational failure of CRFR1 antagonists in clinical trials as a case study to examine common pitfalls and lessons learned about improving predictive validity of animal work.

2

Challenges and Obstacles Most of the commonly used pharmacological treatments for psychiatric conditions were discovered serendipitously [1, 7, 11], and many of the drugs that were developed using animal models have failed to show efficacy in clinical trials [1, 6, 9, 12–15]. A major obstacle in drug development is the complexity of the human brain, which is comprised of 160 billion neurons, each of which connects to roughly 10,000 other neurons, establishing an overwhelming grid of roughly 1000 trillion synaptic connections that are dynamically monitored and maintained by a host of non-neuronal cells [16, 17]. This complexity coupled with the inaccessible and delicate nature of the human brain are responsible for our rudimentary understanding of how the brain retains and process information and how it generates emotions [4, 17, 18]. Unlike some neurodegenerative diseases such as Alzheimer and Parkinsonism where pathognomonic abnormalities provide possible clues for the underlying pathology, the gross morphology of common psychiatric conditions appears normal [4, 7]. In addition, the underlying microscopic abnormalities of psychiatric conditions are caused by complex interactions between environmental factors and abnormal function of many genes [18], making the search for a biological underpinning daunting. In the absence of biological markers and a poor understanding of the underlying pathology, the American Psychiatric Association and the World Health Organization developed two classification systems for mental illness known as the Diagnostic and Statistical

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

5

Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), respectively. The original goal of these classification systems was the development of a common language to facilitate diagnostic consistency from clinician to clinician [3]. It was assumed that better inter-rater reliability would group individuals with similar pathology and promote the identification of underlying causes [3]. To qualify for a specific diagnosis, a patient has to endorse a certain number of complaints from a list of possible symptoms. For example, to diagnose major depression, a patient needs to endorse the presence of at least five out of the nine possible symptoms lasting for at least 2 weeks. Depressed mood or anhedonia is required to be present among the five core symptoms, with changes in appetite, sleep, energy, and concentration, a sense of worthlessness, or suicidal thoughts accounting for the rest of the symptoms needed to meet the full criteria for major depression. Importantly, an increase or decrease in either appetite, sleep, or energy level can be used to make a diagnosis of major depression. Thus, two individuals that share no common symptoms can be diagnosed with major depression [3, 19]. The DSM/ICD manuals managed to achieve adequate interrater reliability [19–22], but in doing so created heterogeneous classification systems that have hindered the development of effective new treatments in three major ways. First, the underlying heterogeneity has made it more difficult to identify a unique underlying pathology. Second, it made it more challenging to develop effective treatments that work across different pathologies. Finally, the somewhat arbitrary and subjective nature of the DSM/ICD classification systems has made the development of suitable animal models an arguably impossible task [4, 7].

3

The Opportunity: New Tools and Approaches The development of new tools and conceptual approaches in the field of neuroscience has revolutionized our ability to study how the brain functions and has helped identify objective and measurable biomarkers of common psychiatric conditions. These advances are briefly reviewed and are discussed in terms of their implications for improving the predictive validity of animal models in psychiatric research.

3.1

Imaging

One of the most clinically significant advances in imaging has been the development of positron emission technology (PET) ligands to quantify amyloid burden in Alzheimer’s patients [23, 24]. This technique can distinguish between Alzheimer’s disease and other forms of dementia and has demonstrated the accumulation of amyloid plaques years before individuals show any signs of cognitive decline. This observation provides an important opportunity

6

Arie Kaffman et al.

for early diagnosis and interventions that were not previously available [23, 24]. Novel PET ligands have also been used to diagnose neuroinflammation in live subjects providing novel insights into the role that neuroinflammation plays in the development of schizophrenia [25] and depression [26]. Animal models have played a critical role in the development and the refinement of these PET ligands [27–31] and will continue to guide new advances in this area [32, 33]. For example, reduced levels of the glutamate transporter, GLT-1, are a central biomarker of addiction in several animal models [14, 34], and the development of a GLT-1 PET ligand will help to clarify the construct validity of this finding in human addiction. Resting state functional magnetic resonance imaging (rsfMRI) is another example of an imaging technique that has shown a great potential to improve our ability to diagnose, treat, and study psychiatric conditions [5]. rsfMRI provides information about the strength of the connectivity between different brain regions. Furthermore, it can generate connectivity maps that are stable over time and can be used as objective biomarkers of psychopathology. For example, Drysdale et al. [5] have used rsfMRI to define four different types of connectivity maps, or biotypes, in a large cohort of depressed individuals. Each of the four biotypes clustered in a different quadrant of an X and Y axis system in which frontostriatalthalamic connectivity correlated with anhedonia and psychomotor retardation was represented on the X axis and fronto-limbic connectivity which is highly predictive of anxiety was aligned across the Y axis [5]. These biotypes were stable overtime, unaffected by age or medication use, and were used to successfully diagnose depression in 82% of the cases of an independent cohort [5]. Eighty-two percent of individuals that were characterized as biotype 1 responded to a 5-week treatment with repeated transcranial magnetic stimulation (rTMS) compared to only 25% and 29% response rates for biotypes 2 and 4, respectively. Connectivity pattern was a better predictor of response to rTMS compared to clinical presentation prior to treatment. Finally, 60% of patients diagnosed with generalized anxiety disorder were also classified as biotype 4, despite the fact that they did not meet criteria for major depression. In contrast, only 10% of patients diagnosed with schizophrenia showed connectivity patterns consistent with any of the four biotypes [5]. These findings demonstrate that rsfMRI can be used to partition individuals diagnosed based on the DSM classification system into four stable biotypes that seem to respond differently to treatment. Some of these biotypes are not specific for depression, as they are commonly seen in individuals with anxiety but no depression. This work highlights the utility of rsfMRI to better diagnose and treat individuals with psychiatric conditions. Moreover, recent advances have allowed the use of rsfMRI to generate connectivity maps in rodents and nonhuman primates

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

7

[35, 36], providing an important translational tool to examine the mechanism by which these connectivity maps are established and how they contribute to anhedonia and anxiety-like behavior. For example, work from our group has recently shown that adult mice exposed to early life stress have increased fronto-limbic connectivity that is highly correlated with anxiety-like behavior [37], allowing for more mechanistic understanding of parallel findings in humans [38–44]. 3.2 Additional New Technological Advances

Optogenetics and pharmacogenetics are novel technologies that use light or specific ligands to turn “on” and “off” specific population of cells or axonal terminals in behaving animals [45, 46]. These tools allow researchers to examine the contribution that a specific cell population of interest makes to complex behaviors such as anxiety, anhedonia, feeding, and drug-seeking behavior [46]. Although most of the work has been done in rodents, recent research has extended this field to nonhuman primates [46, 47]. Elegant optogenetic work in mice has recently demonstrated that activation of the corticotrophin-releasing factor receptor 2 (CRFR2) in the lateral septum is necessary and sufficient for promoting anxiety and hypothalamic-pituitary axis (HPA) activation in response to restraints [48]. These findings challenged previous assertion that activation of CRFR1, but not CRFR2, is responsible for inducing anxiety, providing a possible explanation as to why CRFR1 antagonists have failed to reduce anxiety in clinical trials (see also Subheading 4). The availability of rapid and relatively inexpensive RNA sequencing and proteomics platforms allow for unbiased characterization of gene and protein expression in ways that were not available before. These tools, coupled with advances in viral vectors and novel molecular tools for editing the genome, allow for rigorous characterization of the role that specific transcripts play in modifying network function and complex behaviors. A good example of the utility of these approaches is the recent discovery that transient reduction in the expression of the orthodenticle homeobox 2 (Otx2) transcription factor in the ventral tegmental area of mice exposed to early life stress is responsible for the increased sensitivity of these mice to additional stress in adulthood [49]. Genetic work has also identified numerous genes such as Shank3, TSC1/2, DISC1, NLNG3, and CNTNAP2 that are implicated in the development of schizophrenia and autism spectrum disorders [4, 50, 51]. Manipulations in the expression of these genes in mice elucidated the role that these genes play in synaptic development and complex behavior [50, 51]. Advances in epigenetics and stem cell technology have allowed somatic cells, such as fibroblasts, to be reprogrammed directly into inducible neurons [52] or pluripotent cells (iPS) that can then be differentiated, in vitro, into a variety of cell types including neurons and glial

8

Arie Kaffman et al.

cells [53, 54]. This technology allows for detailed characterization of neurons derived from individuals diagnosed with schizophrenia [55, 56], bipolar disorder [56], Alzheimer’s disease [57], and many other psychiatric conditions [54] in a dish. This approach circumvents the challenge of characterizing molecular changes in the brain of living humans and will likely improve the construct validity of animal models by allowing for a direct comparison of findings obtained with human iPS and animal models [58]. Advances in artificial intelligence and machine learning have generated machines with a capacity to learn, process information, perceive, and strategize that resemble and even surpass the human brain [17]. The basic premise of this approach is that “the best way to understand a complex system is to build it from the ground up” and it does so by combining multiple disciplines including cognitive neuroscience, computational modeling, and statistics [17]. A fascinating example is the recent work by Testolin et al. showing that an unsupervised hierarchical generative network that was initially trained to recognize simple natural images can be efficiently trained to recognize letters in a highly sophisticated and accurate way [59]. Such a network could potentially be used to model different types of dyslexia in humans, bypassing the difficulty of studying this issue in animals. 3.3 New Conceptual Categorization Systems of Mental Illness

Concerns about the utility of the DSM/ICD classification systems in uncovering underlying pathology and guiding the development of new treatments have inspired the NIMH to develop an alternative approach known as the Research Domain Criteria (RDoC) [3, 60]. The RDoC differs from the DSM/ICD approach in several important ways. For example, the RDoC system places brain circuit dysfunction, rather than groups of symptoms, as the organizing principle for defining pathology. One example is the role of the prefrontal cortex, hippocampus, and amygdala in regulating fear conditioning and extinction [61, 62]. Pathology is also defined using quantifiable and continuous variables that are related to a circuit output (e.g., fronto-limbic connectivity assessed by rsfMRI as a proxy of anxiety). The notion that psychopathology is better represented as a continuous scale is conceptually different than the discrete diagnostic categories used by the DSM/ICD system [3, 63]. A third approach, recently advocated by the current NIMH director, Dr. Josh Gordon, is the use of unsupervised and unbiased bottom-up large data approaches to characterizing pathology [64]. His argument is that the DSM and the RDoC approaches make certain assumptions about pathology that are guided by partial knowledge and biases that can be avoided by allowing the data to sort itself into distinct patterns that are related to pathology. The work by Drysdale et al. provides a good example of the utility of this unsupervised approach [5]. As discussed above, these authors obtained rsfMRI connectivity maps from a large

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

9

number of individuals diagnosed with major depression and then used unsupervised clustering system to divide this population into four biotypes [5]. Individuals that are characterized as biotype 4 may present with generalized anxiety or major depression suggesting that a single pathology may manifest itself with different symptoms yet respond similarly to treatment. This is consistent with the observations that depression and anxiety respond to similar treatments and that other common conditions, such as diabetes, can present differently (e.g., retinopathy, neuropathy, renal failure) despite having a common underlying pathology and response to treatment. Moreover, “biotype 4 depression” is likely to respond differently to treatment compared to the “depression” of biotype 1 patients [5]. These findings demonstrate how the RDoC and unsupervised clustering systems can be used to refine the DSM/ICD diagnoses system in a manner that improves clinical outcomes. Identifying similar biotypes in rodents and nonhuman primates will likely improve the predictive and construct validity of animal models for the treatment of anxiety and depression. 3.4 Rating the Strength of the Preclinical Evidence

“Best practice guidelines” are a set of recommendations that help clinicians choose among treatment options based on the strongest available data. These clinical guidelines are based on systematic reviews of the literature followed by analysis that rates the quality of the evidence based on formalized criteria [65]. This evidencebased approach has improved clinical outcomes by addressing issues such as the placebo effect, different forms of biases in scientific research, and underpowered studies [65]. In a landmark publication, Sandercock and Roberts argued that a similar approach is needed to evaluate the strength of the preclinical data and should be a prerequisite for conducting clinical trials in humans [66]. As an example, they pointed to systematic reviews of clinical trials involving close to 7000 stroke patients that found no evidence to support the clinical use of nimodipine in reducing neurological sequelae of acute focal stroke [67]. Although these clinical trials were inspired by animal work, a systematic review of the preclinical data found no convincing evidence that nimodipine improved clinical outcomes in animals [68]. These findings support the notion that systemic reviews can improve the predictive validity of animal models, avoid unnecessary testing in humans, and reduce the costs associated with unjustified clinical trials [68] (for a more skeptical view on this issue, see ref. 9). Over the past two decades, several tools have been developed to formalize the evaluation process of preclinical studies [69]. Moreover, a recent systemic review of animal studies was instrumental for designing clinical trials that confirmed the utility of hypothermia in the management of acute ischemic stroke [70]. We were able to find only one example in which systematic review was used to assess preclinical data of psychiatric research [71]

10

Arie Kaffman et al.

but suspect that this approach will improve the predictive validity of animal work. Such reviews should not only list all available research in order to avoid publication bias but also evaluate the strength of the evidence as it relates to clinical outcomes using a clearly defined set of assumptions. For example, reduced anxiety-like behavior after acute administration of a new anxiolytic drug using normal animals should not have the same predictive validity compared to studies showing that the drug is able to fully reverse stable anxietylike behavior seen in animals exposed to early life stress (ELS). This is because ELS leads to robust increase in anxiety-like behavior across diverse mammalian species including rodents, nonhuman primates, and humans [4, 72]. Similarly, when assessing the anxiolytic potential of CRFR1 antagonists in adult humans, reduced anxiety in constitutive CRFR1 knockout mice should have less predictive value compared to mice in which the CRFR1 was eliminated in adulthood (for more detailed discussion on this issue, see Subheading 4.7). Similar to the case with nimodipine [68], we suspect that thoughtful and systematic evaluation of preclinical work will raise questions about the rationale for conducting many of the failed clinical trials in psychiatric research and predict that this approach will help improve clinical outcomes in psychiatric research. 3.5

Success Stories

There are a few examples where animal work has played an important role in clarifying human psychopathology and has led to the development of new psychiatric medications. One of the most compelling examples is the discovery that abnormal expression of the orexin receptor is responsible for narcolepsy in a canine model of the disease [73]. This work, and preclinical work by others [74], uncovered an important role for the orexin system in regulating sleep-awake cycle and has led to the development of suvorexant, an orexin receptor antagonist that is now available to treat insomnia [75]. Elegant work in nonhuman primates has identified the alpha2A receptor in pyramidal neurons of the prefrontal cortex as a critical target for regulating working memory and attention [76, 77]. These findings paved the way for clinical trials showing that long-acting guanfacine, an alpha2A agonist, is an effective non-stimulant alternative for the treatment of ADHD in children [78]. Furthermore, the administration of myelin oligodendrocyte glycoprotein (MOG) to animals as a model of multiple sclerosis has highlighted important contribution of B cells and autoimmune antibodies to the demyelination process. This discovery led to the development of monoclonal antibodies to CD20 that depleted a specific population of B cells [79–81] and the recent approval of Ocrevus, a CD20 monoclonal antibody, for the treatment of primary progressive multiple sclerosis [82, 83]. These “success stories” demonstrate that animal work can advance psychiatric treatment and raise the question as to why these examples are

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

11

relatively rare [1, 2, 12]. We chose the CRFR1 antagonists as an example of the challenges associated with this type of translational work and discuss possible ways to address these issues in the following sections.

4 CRFR1 Antagonists as a Case Study for Improving Predictive Validity of Animal Models in Psychiatry 4.1 The CorticotrophinReleasing Factor (CRF) System

The CRF system is a complex set of four ligands, two receptors, and one modifier protein that coordinate endocrine, autonomic, immunological, and behavioral responses to stress in mammals [84, 85]. The four neuropeptide ligands include CRF and three urocortins (UNC1, UNC2, UNC3). These peptides are expressed in different brain regions and bind with different affinities to two highly homologous G-protein-coupled receptors, CRFR1 and CRFR2 [84–86]. The distribution of these two receptors is somewhat different, with CRFR1 highly expressed in the anterior pituitary, hippocampus, cortex, and amygdala, while CRFR2 is expressed in dorsal raphe nucleus (DRN), lateral septum (LS), periaqueductal gray (PAG), and choroid plexus. CRF levels increase in several brain regions including the paraventricular nucleus (PVN) of the hypothalamus, amygdala, bed nucleus of the stria terminalis (BNST), and LC in response to threat [84, 86, 87]. The release of CRF in these brain areas promotes and coordinates the immediate “fight-or-flight” response as well as long-term adaptations to chronic stress [86–88]. Until recently, the prevailing dogma in the field has been that activation of the CRFR1 promotes a fight-or-flight response that includes the activation of the hypothalamic-pituitary-adrenal (HPA) axis, reduced appetitive behaviors, and increased heart rate, arousal, and anxiety [84, 86, 87]. In contrast, activation of the CRFR2 was thought to be important for terminating the acute stress response and to restore homeostasis [84–86]. This model is supported by a large body of work showing that CRFR1 knockout mice have blunted HPA reactivity and reduced anxiety-like behavior [6, 85]. Similarly, CRFR1 agonists promote anxiety, while CRFR1 antagonists reduce fight-or-flight responses [84, 85]. CRFR2 knockout mice show exaggerated HPA reactivity in response to stress and are more anxious compared to wild-type littermates, suggesting an important role for terminating the stress response [84–86]. Secretion of CRF from cells located in the PVN activate CRF1positive cells in the anterior pituitary causing the release of adrenocorticotropic hormone (ACTH) into the blood circulation followed by the secretion of glucocorticoids (corticosterone in rodents and cortisol in humans) from the adrenal gland [85, 88]. Elevated levels of glucocorticoids in turn activate the glucocorticoid receptor in a variety of tissues to induce metabolic, cognitive, and inflammatory changes that help the animal cope with

12

Arie Kaffman et al.

threat [88–91]. Prolonged exposure to glucocorticoids causes multiple metabolic, immunological, and behavioral abnormalities. Therefore, multiple mechanisms have evolved to ensure efficient termination of this response [88–91]. Another important hub of the CRF response to stress is the central nucleus of the amygdala (CeA) [87]. CRF-positive cells in the CeA are activated in response to multiple types of threats to stimulate a broad network of autonomic, cognitive, and behavioral responses that is independent of CRF activation of the HPA [87, 88, 92, 93]. Under low levels of stress, CRFR1 stimulation depresses glutamatergic transmission, but activation of CRFR1 enhances glutamatergic transmission in the CeA under high levels of stress [86]. These findings explain why blockade of CRFR1 in the CeA had no effect on basal anxiety levels but attenuated anxiety under stressful conditions [86]. Stress-reactive CRF-positive cells located at the BNST and the CeA innervate dopaminergic neurons in the nucleus accumbens (NAc) where they modulate appetitive behaviors such as social interaction and exploration of a novel object [87, 94]. Dopaminergic neurons in the NAc express both CRFR1 and CRFR2, and incubation of NAc slices with CRF increased the release of dopamine in a dose-dependent manner that requires the co-activation of CRFR1 and CRFR2. Intra-NAc administration of CRF promoted conditioned place preference and enhanced exploration of a novel object, while administration of nonselective CRF receptor antagonist into the NAc blocked novel object exploration. Together these findings reveal an important role for CRF in driving appetitive/ rewarding behaviors. Interestingly, exposure to repeated swimming induced helpless behavior and blocked the natural tendency to explore a novel object. This stress-mediated anhedonia persisted 90 days after the initial exposure to stress and caused abnormal dopaminergic response to CRF. For example, in animals that were exposed to repeated swimming, CRF was no longer able to induce dopamine release and triggered an aversive response in the conditioned place preference [94]. These findings show that CRF causes appetitive response in naive animals and aversive responses in animals exposed to severe stress. Similarly, rats that were exposed to early life stress show reduced sucrose consumption and low levels of social play later in life. These anhedonia-like behaviors were associated with abnormal activation of CRF-positive cells in CeA and were reversed by viral-mediated CRF knockdown in the CeA [93]. 4.2 CRF Activation in Early Life Causes Long-Term Changes in Stress Reactivity, Cognition, and Appetitive Behaviors

The number of CRF-positive cells in the hippocampus reaches a developmental peak at around postnatal day 18 (P18) after which the number of these cells declines significantly to levels seen in adulthood [95]. Adult rats that were exposed to stress during the postnatal period have increased number of CRF-positive cells in the hippocampus [96]. Chronic elevation of CRF in the hippocampus of ELS animals has been shown to reduce spine density and to

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

13

simplify dendritic arborization in the hippocampus. These structural abnormalities appear to be responsible for the poor hippocampal-dependent memory seen in adult animals that were exposed to ELS [97]. These assertions are supported by work showing that exposure of organotypic slices to CRF causes simplification of dendritic arborization and the retraction of postsynaptic spines, changes that resemble those seen in animals exposed to ELS [98]. Transient expression of CRF in forebrain neurons during the first 3 weeks of life, using the doxycycline Tet-Off system, causes increased anxiety- and depression-like behaviors in adulthood [99], and intracerebroventricular injection of CRF to newborn pups leads to cognitive deficits in adulthood [100]. In addition, administration of CRFR1 antagonist early in life reversed the dendritic abnormalities and the cognitive deficits seen in rats exposed to early stress [96] with similar findings reported in mice in which CRFR1 was knocked out in glutamatergic forebrain neurons [101, 102]. Importantly, administration of CRFR1 antagonists in adulthood appears to be less effective in reversing the cognitive deficits associated with early life stress [97]. Thus, CRF activation early in life leads to changes in synaptic connectivity, increased anxiety, and anhedonia that persist into adulthood [97]. 4.3 Back to the Drawing Board: Lessons Learned from Clinical Failures

The observations that stress increases CRF levels in circuits that regulate anxiety and appetitive behaviors and that stress-related behavioral changes could be blocked by CRFR1 antagonists suggested a central role for CRFR1 activation in both the acute and long-term consequences of stress. This assertion was further supported by clinical studies showing increased CRF protein levels in the cerebrospinal fluid of depressed patients and individuals with severe PTSD [84, 86]. Increased CRF mRNA levels were also reported in the PVN and LC of postmortem tissue obtained from depressed patients and cortical areas of suicide victims [84]. Additionally, single nucleotide polymorphism (SNP) within the CRF gene and the CRF1 receptor was associated with increased risk for depression and PTSD [84, 88]. These findings inspired the development of several CRFR1 antagonists that were then tested in randomized control trials for the treatment of major depression, generalized anxiety, social anxiety, PTSD, irritable-bowl syndrome, and alcohol dependence. The results were consistently negative across all clinical trials [13, 84]. These disappointing outcomes raised the question as to why CRFR1 antagonists appeared so promising in preclinical studies but failed to show efficacy in clinical trials (for additional commentaries on this issue, see refs. 6, 13, 84). We address this question by highlighting common pitfalls associated with preclinical work and suggest experimental approaches to address these challenges.

14

Arie Kaffman et al.

4.4 Underutilization of Animal Models with Robust and Stable Anxiety- or Depression-Like Phenotypes

A closer look at the preclinical work indicates that the vast majority of the studies focused on the ability of acute administration of CRFR1 antagonist to modify anxiety- or depression-like behavior in normal male rodents or nonhuman primates [6]. This approach has little in common with clinical trials in which chronic administration of CRFR1 antagonist is used to reverse a stable and highly entrenched psychopathology such as anxiety, PTSD, depression, or substance abuse. Conspicuously missing from the preclinical work are studies examining the ability of chronic and systemic administration of CRFR1 antagonists to reverse stable and clinically relevant anxiety- or depression-like phenotypes in animals. For example, repeated exposure to forced swimming leads to longterm deficits in appetitive behaviors that are associated with abnormal dopaminergic response to CRF [94]. It would be important to know whether chronic administration of CRFR1 antagonist after exposure to repeated swimming can reverse the appetitive deficits (i.e., novel object exploration, helpless behavior, and reduction in CRF-mediated dopamine release). Similarly, exposure to ELS is a significant risk factor for the development of anxiety and anhedonia across a broad range of mammalian species, including rodents, nonhuman primates, and humans [72, 103]. As discussed above, ELS induces a stable anhedonic state in male rats that can be reversed by CRF knockdown in the CeA [93]. Therefore, it would be informative to know whether chronic treatment with CRFR1 antagonist could reverse the deficits in sucrose preference and social exploration seen in adult rats that were exposed to ELS. Note that systemic administration of CRFR1 antagonist may not recapitulate the behavioral outcomes seen with localized knockdown of CRF for several reasons. First, CRF activates both CRFR1 and CRFR2, and therefore blocking CRFR1 alone may not be sufficient to reverse the anhedonia. Moreover, deletion of CRFR1 in dopaminergic neurons increases anxiety, while deletion of CRFR1 in glutamatergic neurons reduces anxiety [104], suggesting that CRFR1 antagonists have opposing effects depending on the cell population they target. Similarly, CRFR1 activation in the amygdala increases anxiety, while CRFR1 activation in the globus pallidus is anxiolytic [86]. These findings highlight the complexity by which the CRF system modifies anxiety and depression and the challenges of using systemic and chronic CRFR1 blockade to reverse stable anxiety- and depression-like phenotypes in animals. In summary, the overreliance on behavioral outcomes seen after acute administration in animals with normal levels of anxiety or anhedonia is an important reason for the “translational failure” of CRFR1 antagonists and many other pharmacological interventions. Similar shortcomings are seen in animal models of addiction in which the efficacy of new compounds to block drug-seeking behavior is tested in “normal animals” [34, 105–107] and not in

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

15

a subpopulation of animals that show compulsive drug use [105, 108]. We therefore advocate testing the efficacy of new compounds in animal models with robust and clinically relevant abnormalities and consider this type of work as stronger evidence when conducting systematic reviews of preclinical work. 4.5 The Need to Assess Outcomes Using Chronic Administration

As stated above, most of the animal work with CRFR1 antagonists tested the effects of acute administration of the drugs on stress reactivity. Consistent with the poor outcomes found in human clinical trials, the few examples in which chronic and systemic treatment with CRFR1 antagonists were used to reverse stable or semi-stable anxiety-like phenotype have not been particularly encouraging. For example, exposure to repeated social defeat leads to long-lasting changes in anxiety and appetitive behaviors that are reversed by chronic administration of antidepressants [109]. However, chronic and systemic administration of the CRFR1 antagonist, GSK876008, was not effective in reversing deficits in sucrose preference nor in reversing helpless behavior in the forced swim test [110]. Moreover, chronic treatment with the CRFR1 antagonist antalarmin failed to reverse most of the acute and chronic anxiety-like behaviors induced by 14-day social separation in male rhesus macaque monkeys [111]. In contrast, an acute oral dose of antalarmin was able to reduce HPA activation and anxiety-like behavior in male rhesus macaques exposed to social intruder stress [112]. The different outcomes seen following chronic versus acute administration suggest that chronic administration of CRFR1 antagonist may lead to compensatory changes that reduce the efficacy of this intervention. Assessing the ability of chronic blockade of CRFR1 to reverse stable anxiety-like phenotype in animals will likely improve the predictive validity of this approach in clinical trials. Additional work is also needed to test whether injecting CRFR1 antagonists immediately after a traumatic event, such as social defeat, can prevent the development of PTSDlike symptoms or anhedonia in animals. In other words, animal studies can help clarify whether CRFR1 antagonists are more effective in preventing stress-induced psychopathology versus reversing it.

4.6 Unanticipated Complexity

Another reason for the translational failure of CRFR1 antagonists is the oversimplified assumption that over-activation of CRFR1 in adulthood is critical for inducing mood and anxiety symptoms. This is not likely to be the case for several reasons. For example, systemic administration of CRFR1 antagonist inhibited lightmediated startle but slightly enhanced fear-mediated startle response [113]. These opposing outcomes are likely due to the different circuits by which light and shock induce startle [113]. Recent work has shown that CRFR2 stimulation also plays a role in increasing anxiety-like behavior and HPA activation [48],

16

Arie Kaffman et al.

suggesting that blockade of both CRFR1 and CRFR2 might be necessary for anxiolytic effects. In addition, higher levels of CRFR2 are found in the amygdala of humans and nonhuman primates compared to rodents underscoring the importance of using nonhuman primates to study this issue [111]. Additional work is therefore needed to test whether nonselective CRF antagonists can reverse stable anxiety- and depression-like phenotypes in rodents and nonhuman primates. 4.7 Lack of Developmental Consideration

5

Much of the translational work has been based on the observation that CRFR1 knockout mice show a robust and reproducible reduction in anxiety [6]. It is unclear, however, whether deleting CRFR1 early in development and/or in adulthood is responsible for the anxiolytic phenotype. In fact, in several cases where this issue was examined [114, 115], it was found that deletion during development, and not in adulthood, is responsible for the anxiety-like phenotype. As discussed in Subheading 4.2, transient alterations in the levels of CRF early in the development causes long-term changes in cognition-, anxiety-, and depression-like behavior [99, 100], and blocking this response early in life with CRFR1 antagonists or CRFR1 deletion reverses the long-term effects of ELS on anxiety and cognition [96, 102]. Finally, CRFR1 antagonists used early in life appear to be more effective in reversing the cognitive deficits associated with ELS compared to interventions that block CRFR1 in adulthood [97]. These preclinical findings raise the possibility that CRFR1 antagonists might be more effective in treating childhood anxiety as opposed to adult anxiety.

Conclusions Significant progress in neuroscience research has provided new tools to characterize and investigate the mammalian brain in ways that were not available two decades ago. These rapid changes coupled with new conceptual approaches to diagnose mental illness and the use of systematic reviews to evaluate the strength of the preclinical data should improve the predictive validity of animal work in psychiatric research.

Acknowledgments This work was supported by NARSAD Independent Investigator Award 2016, NIMH grant R01 MH-100078, and the Clinical Neuroscience Division of the VA National Center for PTSD.

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . .

17

References 1. O’Brien PL, Thomas CP, Hodgkin D, Levit KR, Mark TL (2014) The diminished pipeline for medications to treat mental health and substance use disorders. Psychiatr Serv 65:1433–1438 2. Miller G (2010) Is pharma running out of brainy ideas? Science 329:502–504 3. Hyman SE (2010) The diagnosis of mental disorders: the problem of reification. Annu Rev Clin Psychol 6:155–179 4. Kaffman A, Krystal JH (2012) New frontiers in animal research of psychiatric illness. Methods Mol Biol 829:3–30 5. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:28–38 6. Griebel G, Holmes A (2013) 50 years of hurdles and hope in anxiolytic drug discovery. Nat Rev Drug Discov 12:667–687 7. Nestler EJ, Hyman SE (2010) Animal models of neuropsychiatric disorders. Nat Neurosci 13:1161–1169 8. Cryan JF, Markou A, Lucki I (2002) Assessing antidepressant activity in rodents: recent developments and future needs. Trends Pharmacol Sci 23:238–245 9. Greek R, Menache A (2013) Systematic reviews of animal models: methodology versus epistemology. Int J Med Sci 10:206–221 10. Willner P (1984) The validity of animal models of depression. Psychopharmacology 83:1–16 11. Ban TA (2006) The role of serendipity in drug discovery. Dialogues Clin Neurosci 8:335–344 12. Wong EHF, Yocca F, Smith MA, Lee CM (2010) Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters’ perspective. Int J Neuropsychopharmacol 13:1269–1284 13. Spierling SR, Zorrilla EP (2017) Don’t stress about CRF: assessing the translational failures of CRF1 antagonists. Psychopharmacology 234:1467–1481 14. Spencer S, Kalivas PW (2017) Glutamate transport: a new bench to bedside mechanism for treating drug abuse. Int J Neuropsychopharmacol 20:797–812

15. Berry-Kravis E, Des Portes V, Hagerman R, Jacquemont S, Charles P, Visootsak J, Brinkman M, Rerat K, Koumaras B, Zhu L, Barth GM, Jaecklin T, Apostol G, von Raison F (2016) Mavoglurant in fragile X syndrome: results of two randomized, double-blind, placebo-controlled trials. Sci Transl Med 8:321ra325 16. Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Jacob Filho W, Lent R, Herculano-Houzel S (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 513:532–541 17. van Gerven M (2017) Computational foundations of natural intelligence. Front Comput Neurosci 11:112 18. Hyman SE (2008) A glimmer of light for neuropsychiatric disorders. Nature 455:890–893 19. Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ, Kuhl EA, Kupfer DJ (2013) DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. Am J Psychiatry 170:59–70 20. Helzer JE, Clayton PJ, Pambakian R, Reich T, Woodruff RA Jr, Reveley MA (1977) Reliability of psychiatric diagnosis. II. The test/retest reliability of diagnostic classification. Arch Gen Psychiatry 34:136–141 21. Helzer JE, Robins LN, Taibleson M, Woodruff RA Jr, Reich T, Wish ED (1977) Reliability of psychiatric diagnosis. I. A methodological review. Arch Gen Psychiatry 34:129–133 22. Pies R (2007) How “objective” are psychiatric diagnoses?: (guess again). Psychiatry (Edgmont) 4:18–22 23. Martinez G, Vernooij RW, Fuentes Padilla P, Zamora J, Bonfill Cosp X, Flicker L (2017) 18F PET with florbetapir for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev (11):CD012216 24. Villemagne VL, Dore V, Burnham SC, Masters CL, Rowe CC (2018) Imaging tau and amyloid-beta proteinopathies in Alzheimer disease and other conditions. Nat Rev Neurol 14(4):225–236 25. Bloomfield PS, Selvaraj S, Veronese M, Rizzo G, Bertoldo A, Owen DR, Bloomfield MA, Bonoldi I, Kalk N, Turkheimer F,

18

Arie Kaffman et al.

McGuire P, de Paola V, Howes OD (2016) Microglial activity in people at ultra high risk of psychosis and in schizophrenia: an [(11)C] PBR28 PET brain imaging study. Am J Psychiatry 173:44–52 26. Setiawan E, Wilson AA, Mizrahi R, Rusjan PM, Miler L, Rajkowska G, Suridjan I, Kennedy JL, Rekkas PV, Houle S, Meyer JH (2015) Role of translocator protein density, a marker of neuroinflammation, in the brain during major depressive episodes. JAMA Psychiat 72:268–275 27. Klunk WE, Engler H, Nordberg A, Wang YM, Blomqvist G, Holt DP, Bergstrom M, Savitcheva I, Huang GF, Estrada S, Ausen B, Debnath ML, Barletta J, Price JC, Sandell J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Langstrom B (2004) Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 55:306–319 28. Mathis CA, Bacskai BJ, Kajdasz ST, McLellan ME, Frosch MP, Hyman BT, Holt DP, Wang YM, Huang GF, Debnath ML, Klunk WE (2002) A lipophilic thioflavin-T derivative for positron emission tomography (PET) imaging of amyloid in brain. Bioorg Med Chem Lett 12:295–298 29. Bacskai BJ, Hickey GA, Skoch J, Kajdasz ST, Wang Y, Huang GF, Mathis CA, Klunk WE, Hyman BT (2003) Four-dimensional multiphoton imaging of brain entry, amyloid binding, and clearance of an amyloid-beta ligand in transgenic mice. Proc Natl Acad Sci U S A 100:12462–12467 30. Karlstetter M, Nothdurfter C, Aslanidis A, Moeller K, Horn F, Scholz R, Neumann H, Weber BH, Rupprecht R, Langmann T (2014) Translocator protein (18 kDa) (TSPO) is expressed in reactive retinal microglia and modulates microglial inflammation and phagocytosis. J Neuroinflammation 11:3 31. Banati RB (2002) Visualising microglial activation in vivo. Glia 40:206–217 32. Johnson FK, Kaffman A (2017) Early life stress perturbs the function of microglia in the developing rodent brain: new insights and future challenges. Brain Behav Immun 69:18–27 33. Sehlin D, Fang XTT, Cato L, Antoni G, Lannfelt L, Syvanen S (2016) Antibodybased PET imaging of amyloid beta in mouse models of Alzheimer’s disease. Nat Commun 7:10759 34. Mulholland PJ, Chandler LJ, Kalivas PW (2016) Signals from the fourth dimension regulate drug relapse. Trends Neurosci 39:472–485

35. Jonckers E, Shah D, Hamaide J, Verhoye M, Van der Linden A (2015) The power of using functional fMRI on small rodents to study brain pharmacology and disease. Front Pharmacol 6:231 36. Wu D, Zhang J (2016) Recent progress in magnetic resonance imaging of the embryonic and neonatal mouse brain. Front Neuroanat 10:18 37. Johnson FK, Delpech JC, Thompson GJ, Wei L, Hao J, Herman P, Hyder F, Kaffman A (2018) Amygdala hyper-connectivity in a mouse model of unpredictable early life stress. Transl Psychiatry 8:49 38. van der Werff SJ, Pannekoek JN, Veer IM, van Tol MJ, Aleman A, Veltman DJ, Zitman FG, Rombouts SA, Elzinga BM, van der Wee NJ (2013) Resting-state functional connectivity in adults with childhood emotional maltreatment. Psychol Med 43:1825–1836 39. Birn RM, Patriat R, Phillips ML, Germain A, Herringa RJ (2014) Childhood maltreatment and combat posttraumatic stress differentially predict fear-related fronto-subcortical connectivity. Depress Anxiety 31:880–892 40. Herringa RJ, Phillips ML, Fournier JC, Kronhaus DM, Germain A (2013) Childhood and adult trauma both correlate with dorsal anterior cingulate activation to threat in combat veterans. Psychol Med 43:1533–1542 41. Wang L, Dai Z, Peng H, Tan L, Ding Y, He Z, Zhang Y, Xia M, Li Z, Li W, Cai Y, Lu S, Liao M, Zhang L, Wu W, He Y, Li L (2014) Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Hum Brain Mapp 35:1154–1166 42. Cisler JM, James GA, Tripathi S, Mletzko T, Heim C, Hu XP, Mayberg HS, Nemeroff CB, Kilts CD (2013) Differential functional connectivity within an emotion regulation neural network among individuals resilient and susceptible to the depressogenic effects of early life stress. Psychol Med 43:507–518 43. Dean AC, Kohno M, Hellemann G, London ED (2014) Childhood maltreatment and amygdala connectivity in methamphetamine dependence: a pilot study. Brain Behav 4:867–876 44. Philip NS, Sweet LH, Tyrka AR, Price LH, Bloom RF, Carpenter LL (2013) Decreased default network connectivity is associated with early life stress in medication-free healthy adults. Eur Neuropsychopharmacol 23:24–32 45. Wiegert JS, Mahn M, Prigge M, Printz Y, Yizhar O (2017) Silencing neurons: tools,

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . . applications, and experimental constraints. Neuron 95:504–529 46. Jiang J, Cui H, Rahmouni K (2017) Optogenetics and pharmacogenetics: principles and applications. Am J Physiol Regul Integr Comp Physiol 313:R633–R645 47. Galvan A, Stauffer WR, Acker L, El-Shamayleh Y, Inoue KI, Ohayon S, Schmid MC (2017) Nonhuman primate optogenetics: recent advances and future directions. J Neurosci 37:10894–10903 48. Anthony TE, Dee N, Bernard A, Lerchner W, Heintz N, Anderson DJ (2014) Control of stress-induced persistent anxiety by an extraamygdala septohypothalamic circuit. Cell 156:522–536 49. Pena CJ, Kronman HG, Walker DM, Cates HM, Bagot RC, Purushothaman I, Issler O, Loh YE, Leong T, Kiraly DD, Goodman E, Neve RL, Shen L, Nestler EJ (2017) Early life stress confers lifelong stress susceptibility in mice via ventral tegmental area OTX2. Science 356:1185–1188 50. Varghese M, Keshav N, Jacot-Descombes S, Warda T, Wicinski B, Dickstein DL, HaronyNicolas H, De Rubeis S, Drapeau E, Buxbaum JD, Hof PR (2017) Autism spectrum disorder: neuropathology and animal models. Acta Neuropathol 134:537–566 51. Monteiro P, Feng G (2017) SHANK proteins: roles at the synapse and in autism spectrum disorder. Nat Rev Neurosci 18:147–157 52. Wapinski OL, Vierbuchen T, Qu K, Lee QY, Chanda S, Fuentes DR, Giresi PG, Ng YH, Marro S, Neff NF, Drechsel D, Martynoga B, Castro DS, Webb AE, Sudhof TC, Brunet A, Guillemot F, Chang HY, Wernig M (2013) Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell 155:621–635 53. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, Tomoda K, Yamanaka S (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131:861–872 54. Parr CJC, Yamanaka S, Saito H (2017) An update on stem cell biology and engineering for brain development. Mol Psychiatry 22:808–819 55. Brennand K, Savas JN, Kim Y, Tran N, Simone A, Hashimoto-Torii K, Beaumont KG, Kim HJ, Topol A, Ladran I, Abdelrahim M, Matikainen-Ankney B, Chao SH, Mrksich M, Rakic P, Fang G, Zhang B, Yates JR III, Gage FH (2015) Phenotypic differences in hiPSC NPCs derived from

19

patients with schizophrenia. Mol Psychiatry 20:361–368 56. Madison JM, Zhou F, Nigam A, Hussain A, Barker DD, Nehme R, van der Ven K, Hsu J, Wolf P, Fleishman M, O’Dushlaine C, Rose S, Chambert K, Lau FH, Ahfeldt T, Rueckert EH, Sheridan SD, Fass DM, Nemesh J, Mullen TE, Daheron L, McCarroll S, Sklar P, Perlis RH, Haggarty SJ (2015) Characterization of bipolar disorder patient-specific induced pluripotent stem cells from a family reveals neurodevelopmental and mRNA expression abnormalities. Mol Psychiatry 20:703–717 57. Israel MA, Yuan SH, Bardy C, Reyna SM, Mu Y, Herrera C, Hefferan MP, Van Gorp S, Nazor KL, Boscolo FS, Carson CT, Laurent LC, Marsala M, Gage FH, Remes AM, Koo EH, Goldstein LS (2012) Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature 482:216–220 58. Yi F, Danko T, Botelho SC, Patzke C, Pak C, Wernig M, Sudhof TC (2016) Autismassociated SHANK3 haploinsufficiency causes Ih channelopathy in human neurons. Science 352:aaf2669 59. Testolin A, Stoianov I, Zorzi M (2017) Letter perception emerges from unsupervised deep learning and recycling of natural image features. Nat Hum Behav 1:843 60. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P (2010) Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 167:748–751 61. Delgado MR, Nearing KI, Ledoux JE, Phelps EA (2008) Neural circuitry underlying the regulation of conditioned fear and its relation to extinction. Neuron 59:829–838 62. LeDoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci 23:155–184 63. Widiger TA, Clark LA (2000) Toward DSM-V and the classification of psychopathology. Psychol Bull 126:946–963 64. Gordon J (2018) The future of RDoC. National Institute of Mental Health, Bethesda, MD 65. Lohr KN (2004) Rating the strength of scientific evidence: relevance for quality improvement programs. Int J Qual Health Care 16:9–18 66. Sandercock P, Roberts I (2002) Systematic reviews of animal experiments. Lancet 360:586

20

Arie Kaffman et al.

67. Horn J, Limburg M (2001) Calcium antagonists for ischemic stroke: a systematic review. Stroke 32:570–576 68. Horn J, de Haan RJ, Vermeulen M, Luiten PG, Limburg M (2001) Nimodipine in animal model experiments of focal cerebral ischemia: a systematic review. Stroke 32:2433–2438 69. Hooijmans CR, Ritskes-Hoitinga M (2013) Progress in using systematic reviews of animal studies to improve translational research. PLoS Med 10:e1001482 70. van der Worp HB, Macleod MR, Kollmar R, European Stroke Research Network for, H (2010) Therapeutic hypothermia for acute ischemic stroke: ready to start large randomized trials? J Cereb Blood Flow Metab 30:1079–1093 71. Khan MS, Boileau I, Kolla N, Mizrahi R (2018) A systematic review of the role of the nociceptin receptor system in stress, cognition, and reward: relevance to schizophrenia. Transl Psychiatry 8:38 72. Kaffman A, Meaney MJ (2007) Neurodevelopmental sequelae of postnatal maternal care in rodents: clinical and research implications of molecular insights. J Child Psychol Psychiatry 48:224–244 73. Mignot EJ (2014) History of narcolepsy at Stanford University. Immunol Res 58:315–339 74. Scammell TE (2003) The neurobiology, diagnosis, and treatment of narcolepsy. Ann Neurol 53:154–166 75. Winrow CJ, Renger JJ (2014) Discovery and development of orexin receptor antagonists as therapeutics for insomnia. Br J Pharmacol 171:283–293 76. Wang M, Ramos BP, Paspalas CD, Shu Y, Simen A, Duque A, Vijayraghavan S, Brennan A, Dudley A, Nou E, Mazer JA, McCormick DA, Arnsten AF (2007) Alpha2A-adrenoceptors strengthen working memory networks by inhibiting cAMP-HCN channel signaling in prefrontal cortex. Cell 129:397–410 77. Arnsten AF, Wang M (2016) Targeting prefrontal cortical systems for drug development: potential therapies for cognitive disorders. Annu Rev Pharmacol Toxicol 56:339–360 78. Connor DF, Arnsten AFT, Pearson GS, Greco GF (2014) Guanfacine extended release for the treatment of attention-deficit/ hyperactivity disorder in children and adolescents. Expert Opin Pharmacol 15:1601–1610 79. von Budingen HC, Hauser SL, Ouallet JC, Tanuma N, Menge T, Genain CP (2004)

Epitope recognition on the myelin/oligodendrocyte glycoprotein differentially influences disease phenotype and antibody effector functions in autoimmune demyelination. Eur J Immunol 34:2072–2083 80. von Budingen HC, Tanuma N, Villoslada P, Ouallet JC, Hauser SL, Genain CP (2001) Immune responses against the myelin/oligodendrocyte glycoprotein in experimental autoimmune demyelination. J Clin Immunol 21:155–170 81. von Budingen HC, Hauser SL, Fuhrmann A, Nabavi CB, Lee JI, Genain CP (2002) Molecular characterization of antibody specificities against myelin/oligodendrocyte glycoprotein in autoimmune demyelination. Proc Natl Acad Sci U S A 99:8207–8212 82. Dolgin E (2016) Therapies: progressive steps. Nature 540:S7–S9 83. Gelfand JM, Cree BAC, Hauser SL (2017) Ocrelizumab and other CD20+ B-cell-depleting therapies in multiple sclerosis. Neurotherapeutics 14(4):835–841 84. Sanders J, Nemeroff C (2016) The CRF system as a therapeutic target for neuropsychiatric disorders. Trends Pharmacol Sci 37:1045–1054 85. Bale TL, Vale WW (2004) CRF and CRF receptors: role in stress responsivity and other behaviors. Annu Rev Pharmacol Toxicol 44:525–557 86. Henckens MJ, Deussing JM, Chen A (2016) Region-specific roles of the corticotropinreleasing factor-urocortin system in stress. Nat Rev Neurosci 17:636–651 87. Gray TS (1993) Amygdaloid CRF pathways. Role in autonomic, neuroendocrine, and behavioral responses to stress. Ann N Y Acad Sci 697:53–60 88. Arborelius L, Owens MJ, Plotsky PM, Nemeroff CB (1999) The role of corticotropin-releasing factor in depression and anxiety disorders. J Endocrinol 160:1–12 89. Seckl JR (2008) Glucocorticoids, developmental ‘programming’ and the risk of affective dysfunction. Prog Brain Res 167:17–34 90. Rhen T, Cidlowski JA (2005) Antiinflammatory action of glucocorticoids—new mechanisms for old drugs. N Engl J Med 353:1711–1723 91. Ramamoorthy S, Cidlowski JA (2016) Corticosteroids: mechanisms of action in health and disease. Rheum Dis Clin N Am 42:15–31, vii 92. De Francesco PN, Valdivia S, Cabral A, Reynaldo M, Raingo J, Sakata I, OsborneLawrence S, Zigman JM, Perello M (2015)

Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug. . . Neuroanatomical and functional characterization of CRF neurons of the amygdala using a novel transgenic mouse model. Neuroscience 289:153–165 93. Bolton JL, Molet J, Regev L, Chen Y, Rismanchi N, Haddad E, Yang DZ, Obenaus A, Baram TZ (2018) Anhedonia following early-life adversity involves aberrant interaction of reward and anxiety circuits and is reversed by partial silencing of amygdala corticotropin-releasing hormone gene. Biol Psychiatry 83:137–147 94. Lemos JC, Wanat MJ, Smith JS, Reyes BA, Hollon NG, Van Bockstaele EJ, Chavkin C, Phillips PE (2012) Severe stress switches CRF action in the nucleus accumbens from appetitive to aversive. Nature 490:402–406 95. Chen Y, Bender RA, Frotscher M, Baram TZ (2001) Novel and transient populations of corticotropin-releasing hormone-expressing neurons in developing hippocampus suggest unique functional roles: a quantitative spatiotemporal analysis. J Neurosci 21:7171–7181 96. Ivy AS, Rex CS, Chen Y, Dube C, Maras PM, Grigoriadis DE, Gall CM, Lynch G, Baram TZ (2010) Hippocampal dysfunction and cognitive impairments provoked by chronic early-life stress involve excessive activation of CRH receptors. J Neurosci 30:13005–13015 97. Chen Y, Baram TZ (2016) Toward understanding how early-life stress reprograms cognitive and emotional brain networks. Neuropsychopharmacology 41:197–206 98. Ivy AS, Brunson KL, Sandman C, Baram TZ (2008) Dysfunctional nurturing behavior in rat dams with limited access to nesting material: a clinically relevant model for early-life stress. Neuroscience 154:1132–1142 99. Kolber BJ, Boyle MP, Wieczorek L, Kelley CL, Onwuzurike CC, Nettles SA, Vogt SK, Muglia LJ (2010) Transient early-life forebrain corticotropin-releasing hormone elevation causes long-lasting anxiogenic and despair-like changes in mice. J Neurosci 30:2571–2581 100. Brunson KL, Grigoriadis DE, Lorang MT, Baram TZ (2002) Corticotropin-releasing hormone (CRH) downregulates the function of its receptor (CRF1) and induces CRF1 expression in hippocampal and cortical regions of the immature rat brain. Exp Neurol 176:75–86 101. Wang XD, Su YA, Wagner KV, Avrabos C, Scharf SH, Hartmann J, Wolf M, Liebl C, Kuhne C, Wurst W, Holsboer F, Eder M, Deussing JM, Muller MB, Schmidt MV (2013) Nectin-3 links CRHR1 signaling to

21

stress-induced memory deficits and spine loss. Nat Neurosci 16:706–713 102. Wang XD, Labermaier C, Holsboer F, Wurst W, Deussing JM, Muller MB, Schmidt MV (2012) Early-life stress-induced anxietyrelated behavior in adult mice partially requires forebrain corticotropin-releasing hormone receptor 1. Eur J Neurosci 36:2360–2367 103. Bolton JL, Molet J, Ivy A, Baram TZ (2017) New insights into early-life stress and behavioral outcomes. Curr Opin Behav Sci 14:133–139 104. Refojo D, Schweizer M, Kuehne C, Ehrenberg S, Thoeringer C, Vogl AM, Dedic N, Schumacher M, von Wolff G, Avrabos C, Touma C, Engblom D, Schutz G, Nave KA, Eder M, Wotjak CT, Sillaber I, Holsboer F, Wurst W, Deussing JM (2011) Glutamatergic and dopaminergic neurons mediate anxiogenic and anxiolytic effects of CRHR1. Science 333:1903–1907 105. Piazza PV, Deroche-Gamonet V (2013) A multistep general theory of transition to addiction. Psychopharmacology 229:387–413 106. Namba MD, Tomek SE, Olive MF, Beckmann JS, Gipson CD (2018) The winding road to relapse: forging a new understanding of cue-induced reinstatement models and their associated neural mechanisms. Front Behav Neurosci 12:17 107. Epstein DH, Preston KL, Stewart J, Shaham Y (2006) Toward a model of drug relapse: an assessment of the validity of the reinstatement procedure. Psychopharmacology 189:1–16 108. Kasanetz F, Deroche-Gamonet V, Berson N, Balado E, Lafourcade M, Manzoni O, Piazza PV (2010) Transition to addiction is associated with a persistent impairment in synaptic plasticity. Science 328:1709–1712 109. Berton O, McClung CA, Dileone RJ, Krishnan V, Renthal W, Russo SJ, Graham D, Tsankova NM, Bolanos CA, Rios M, Monteggia LM, Self DW, Nestler EJ (2006) Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 311:864–868 110. Bourke CH, Glasper ER, Neigh GN (2014) SSRI or CRF antagonism partially ameliorate depressive-like behavior after adolescent social defeat. Behav Brain Res 270:295–299 111. Ayala AR, Pushkas J, Higley JD, Ronsaville D, Gold PW, Chrousos GP, Pacak K, Calis KA, Gerald M, Lindell S, Rice KC, Cizza G (2004) Behavioral, adrenal, and sympathetic responses to long-term administration of an

22

Arie Kaffman et al.

oral corticotropin-releasing hormone receptor antagonist in a primate stress paradigm. J Clin Endocrinol Metab 89:5729–5737 112. Habib KE, Weld KP, Rice KC, Pushkas J, Champoux M, Listwak S, Webster EL, Atkinson AJ, Schulkin J, Contoreggi C, Chrousos GP, McCann SM, Suomi SJ, Higley JD, Gold PW (2000) Oral administration of a corticotropin-releasing hormone receptor antagonist significantly attenuates behavioral, neuroendocrine, and autonomic responses to stress in primates. Proc Natl Acad Sci U S A 97:6079–6084 113. Walker D, Yang Y, Ratti E, Corsi M, Trist D, Davis M (2009) Differential effects of the

CRF-R1 antagonist GSK876008 on fearpotentiated, light- and CRF-enhanced startle suggest preferential involvement in sustained vs phasic threat responses. Neuropsychopharmacology 34:1533–1542 114. Garcia-Garcia AL, Newman-Tancredi A, Leonardo ED (2014) 5-HT(1A) [corrected] receptors in mood and anxiety: recent insights into autoreceptor versus heteroreceptor function. Psychopharmacology 231:623–636 115. Gross C, Hen R (2004) The developmental origins of anxiety. Nat Rev Neurosci 5:545–552

Chapter 2 Qualitative vs. Quantitative Methods in Psychiatric Research: Updated A. Benjamin Srivastava, Firas H. Kobiessy, and Mark S. Gold Abstract Since the incipiency of psychiatry as a medical specialty, the “holy grail” has been neuroscience-based diagnostic system and treatment strategies, but this lofty, yet necessary, goal has eluded the greatest minds for centuries. Now, with advances in molecular genetics and resting-state neuroimaging, neurosciencebased diagnosis and treatment are now more possible than ever. However, clinical symptomatology, longitudinal course, and delimitation of illnesses (i.e., phenotypic classification) remain indispensable for responsible, reproducible, and meaningful use of these new methodologies. Key words Psychiatric illness, History, Classification, Genetics, Neuroimaging

1

Introduction Psychiatric illness represents one of the leading health burdens, both in the United States and worldwide. In the United States, approximately 44.7 million adults (18.3%) suffer from mental illness, 19 million (7.7%) suffer from a substance use disorder, and 8.2 million suffer from both [1]. The consequences are enormous, as mental illness is the leading cause of disability in developed countries and is associated with increased risk of co-occurring chronic illnesses including cardiovascular disease, diabetes, obesity, asthma, epilepsy, and cancer [2, 3]. Further, among developed countries, disease burden and mortality are highest in the United States [2, 4], and all-cause mortality and suicide have increased in the United States across many demographic groups [5]. Though medications to treat affective, psychotic, and substance use disorders have existed for decades, treatment resistance and recidivism for all conditions remain high, and virtually all FDA-approved treatments for psychiatric disorders today were discovered by serendipity; that is, none were developed with the intention of targeting a putative pathophysiologic target. Since the advent notion of “biological psychiatry” beginning in the 1950s, the introduction of

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_2, © Springer Science+Business Media, LLC, part of Springer Nature 2019

23

24

A. Benjamin Srivastava et al.

mechanism-based diagnosis and treatments has been the ‘holy grail’ of psychiatry [6]. In this chapter we will cover briefly a history of psychiatric research and classification as well as the current state of qualitative and quantitative research, focusing on two principal areas of translational research in psychiatry: genetics and neuroimaging. The idea of psychiatric illness originating in the brain dates back to ancient Greece but was not given serious consideration for scientific research until the latter half of the nineteenth century. At that time, psychiatry in Europe was mostly based in asylums removed from academic medical centers, staffed by physicians without much interest in teaching or research. In 1865, however, Wilhelm Greisinger became Professor of Psychiatry at Charite´ hospital in Berlin and instituted a unit for patients with primarily psychiatric illness. This unit served as a teaching service for medical students and residents and a corresponding research unit headed by the great pathologist Rudolph Virchow. Pathological exploration of the brain in search of the causes of psychopathology became en vogue, with some of the greatest pathologists of their generation making seminal contributions. Theodor Meynert of Vienna made fundamental discoveries regarding the cytoarchitecture of the brain and spinal cord, and Paul Fleschig and Eduard Hitzig made seminal contributions to understanding brain function in terms of cerebral localization and cortical excitability, respectively [7]. Perhaps the greatest mind to search for a “cerebral” origin of mental illness was Carl Wernicke, having described the eponymous aphasia and its localization to the posterior portion of the left superior temporal gyrus. In 1900, he shifted his approach toward psychiatric illness with the publication of Grundriss der Psychiatrie in Klinischen Vorlesungen, and though his work in this area is often considered “localizationist” and “reductionist,” he did describe phenomena consistent with Hebbian plasticity and circuit-level localizations forming dynamic, multilevel processing systems, offering a prescient description of systems neuroscience [8, 9]. Nevertheless, his “bottom-up” (i.e., neuroscience-based) approach to symptom classification never gained true footing. Thus began a push, principally from Ewald Hecker and Karl Kahlbaum, using the already validated concept of neurosyphilis to define psychiatric illness based on symptomatology, longitudinal course, and delimitation from other illnesses [10]. Neurosyphilis characteristically begins as a painless chancre with transition to maculopapular rash on palms/soles and condyloma lata. Additional manifestations can include the ArgyllRobertson pupil, gummas (granulomatous formations), and aortitis. Then begins a latent phase that can last for decades without symptoms but eventually presents as a diverse array of neuropsychiatric symptoms, culminating in tabes dorsalis (dorsal column disease) or general paresis of the insane (GPI), the latter of which

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated

25

was responsible for a bulk of psychiatric admissions in the late nineteenth and early twentieth centuries [7, 10, 11]. Using this construct of symptoms, course, and outcome, Hecker and Kahlbaum described hebephrenia and catatonia, respectively, as separate entities, which was fundamental to the insights provided by perhaps the greatest psychiatric nosologist in history, Emil Kraepelin [10]. Kraepelin, who help professorships at Dorpat, Munich, and Heidelberg, developed an interest in experimental psychology from an early age. Though he attended medical school because the salary of a physician was better than that of a psychologist, he did some training under the great experimental psychologist Wilhelm Wundt, whose teachings were applied to psychiatric illness in the context of the models outlined by Hecker and Kahlbaum. Though he surrounded himself with some of the great neuropathologists of his day including Alois Alzheimer (with Alzheimer he coined the term for the eponymous dementia) and Franz Nissil, he was unconvinced that work by Wernicke or anyone else with a bottom up approach provided a reliable diagnostic scheme for psychiatric pathology. Instead, he believed that psychiatric illness could be separated into “natural kinds”; by following the natural course, symptomatology, and outcome of his patients all the while remaining etiologically agnostic, he could effectively “carve nature at its joints.” Through this method Kraepelin ultimately made his fundamental contribution to psychiatry: the delimitation of the dementia praecox (DP, modern-day schizophrenia), encompassing both hebephrenia and catatonia, and the manic-depressive insanity (MDI, modern-day bipolar disorder). “Issue” or terminal state was crucial to be diagnosed with the DP, patients had to be emotionally and/or volitionally impaired, while patients with the MDI retained these aspects of mental functioning. This diagnostic system was broadly adopted and influential, with various degrees of orthodoxy Kraepelin himself remained unconvinced that the delimitation between the DP and MDI was finite [7, 10, 12–14]. Kraepelin had his fair share of detractors including prominent psychiatrists Karl Jaspers and Adolf Meyer, who, along with psychoanalytically oriented psychiatrists, saw folly in ignoring the patient’s interpretations of his or her unique symptoms [7, 15]. Additionally, various other diagnostic schemes appeared. For example, Karl Kleist, a student of Wernicke, and Kleist’s student Karl Leonhard, developed the Wernicke-Kleist-Leonhard system of classification that rejected the orthodoxy of Kraepelin’s dichotomy and posited brain legions for individual psychoses [16]. In the United States, psychoanalysis dominated academic psychiatry as well as the American Psychiatric Association (APA), though only about 10% of practicing psychiatrists were analysts. In an attempt to consolidate the vast array of diagnoses, the APA commissioned the DSM. The first two iterations of the DSM were heavily influenced by analysts, effectively mitigating Kraepelin’s influence in the United States [7].

26

A. Benjamin Srivastava et al.

However, psychoanalysis became increasingly removed from medicine as it attempted to answer society-level questions, political philosophy, and human nature, rather than diagnose and treat psychiatric illness, perhaps best exemplified in Sigmund Freud’s classic work Civilization and Its Discontents [12, 17]. In the 1950s–1970s, a revolution occurred in the United States. At Washington University in St. Louis, Samuel B. Guze, George Winokur, and Eli Robins sought to reorient psychiatry as a medical discipline and began using the approach of empirical, systematic study of patients and their illnesses over time, with illnesses being described in terms of clinical presentation, longitudinal course, biomarkers, family history, and delimitation [12, 18]. By adhering to the dictates of etiological agnosticism, first proffered by Robins’ mentor at Harvard, the great psychiatrist and contrarian Mandel Cohen, they were pejoratively described as “neo-Kraepelinians,” taking an almost identical approach to their German intellectual godfather [12]. Their work culminated in the 1972 “Feighner criteria,” an operationalized set of 16 separate illnesses described by the aforementioned approach and named after the then psychiatry resident, John Feighner, who organized the project [19]. An important point to keep in mind is that this paper, which at one time was the most highly cited in psychiatry, was a research-oriented paper and was not meant for regular clinical use. However, a psychoanalytically trained psychiatrist at Columbia University, Robert Spitzer, who ironically had a great disdain for psychoanalysis, was heavily influenced by the Washington University group and the Feigner paper, became chair of the DSM III task force, and expanded the Feighner criteria into the Research Diagnostic Criteria, which laid the groundwork for the DSM III in 1980 [12]. Ever since, psychiatry has relied on this operationalized set of criteria (revised four times since then) for both clinical and research diagnostic purposes. 1.1 Limits of a “TopDown” Approach

The vast improvements made in psychiatric research and clinical care since the advent of DSM III notwithstanding, many issues still persist. First, with each iteration of the DSM, the number of diagnoses has continued to increase, seemingly without regard for the same focus on course, issue, and “natural kinds” employed by Kraepelin. At the root of this trend, aside from political and social forces, is the debate among psychiatrists between “lumpers” and “splitters.” “Lumpers” favor more parsimonious explanations for psychiatric phenomena—psychiatric diagnoses are few in number and prima facie heterogeneous. Splitters broadly believe the approach to the most valid set of criteria is continuing to make divisions in clinical-level descriptions in order to reach the “natural kind” [10, 20]. A leading proponent of the “splitter” method is noted psychiatrist Hagop Akiskal of the University of California, San Diego, who has now theorized the existence of six types of

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated

27

bipolar disorder, often based on clinical impressions or case series and not the rigorous methodologies proposed by the Feighner criteria [21]. The fact is, in using descriptive criteria, there will always be arguments about “correct” nosology; but the original proposals of the Wash U group for illness classification stand; in the Clinic 500 study at Wash U that examined the delimitation between affective psychosis and psychosis from schizophrenia, rather than attempt to “force” patients into groups, patients were labeled as “probable” and “uncertain” if a correct diagnosis could not be reached [22]. Obviously, a top-down level of classification is then inadequate; neural substrate at some level is necessary for carving nature at its joints. A concrete example of this evolution in diagnosis is the categorization of dementia. While dementia is a clinical diagnosis, the etiology of the type of dementia can only be classified histopathologically (i.e., post-mortem). Every medical student learns the casual associations: beta amyloid plaques and neurofibrillary tangles (3R/4R Tau) with Alzheimer’s disease, TDP-43/other tau aggregates for the frontotemporal dementias, and Lewy bodies (alpha synuclein) for Parkinson’s disease/dementia with Lewy bodies. Similarly, each of these dementias has a characteristic phenotype, though increased evidence demonstrates that the core underlying neuropathology may overlap. For example, a patient may present with a clinical symptom course consistent with behavioral variant frontotemporal dementia, but on postmortem exam, the pathology can demonstrate beta amyloid plaques and neurofibrillary tangles. To reconcile this clinicopathologic discrepancy, biomarkers including cerebrospinal fluid, FDG-PET, and MRI are used to aid in the refinement of clinical diagnosis. Thus, the amalgamation of multiple levels of classification including genetics, molecular signature, and a network-level conceptualization may be necessary for both clinical and research purposes [23]. Obviously, no primary psychiatric illness has a demonstrable cellular and molecular signature (in fact, a level of convergence may be at the network level, as Wernicke might have anticipated), but the idea of multiple levels of classification already present in dementia nosology may be readily translatable.

2

Genetics: Family Studies, Twin Studies, and Genome-Wide Association Studies Perhaps the first psychiatrist to rigorously investigate the role of genetics in psychiatric illness was Ernst Ru¨din, a Swiss psychiatrist and prote´ge´ of Kraepelin. Ru¨din, working with the preeminent statistical geneticist Wilhelm Weinberg, applied Mendelian principles to larger-scale studies of patients diagnosed with schizophrenia (Kraepelin’s dementia praecox) focusing on systematic recruitment based on rigorous diagnostic history, phenotypic characterization

28

A. Benjamin Srivastava et al.

methods, powerful statistical tools, and minimizing bias. Rudin and Weinberg found (1) the risk for schizophrenia in offspring of probands is 5–7%, (2) parental risk is lower than sibling risk, and (3) the sibling segregation pattern is non-Mendelian, three findings that have remained uncontested ever since. Unfortunately, Ru¨din, like many of his contemporaries under the Weimar Republic and eventually the Third Reich, became leading voices of German racial hygiene theories and eugenics, leaving a dark stain on his earlier, academic legacy. Nevertheless, his methods remained largely influential in the realm of psychiatric genetics [24]. Arguably the most influential American psychiatric geneticist was Dr. Theodore Reich of Washington University, and given what we have discussed heretofore about the reconciliation between quantitative and qualitative methods in psychiatry, a significant section of this chapter will be devoted to Reich. Early in his career with colleagues George Winokur and Paula Clayton, Reich performed, using rigorous clinical phenotyping, fundamental family studies on mania [25]. Following his psychiatry residency at Wash U, Reich undertook a postdoctoral fellowship at the University of Edinburgh with the acclaimed quantitative geneticist DS Falconer, under whose mentorship he would develop a multiple threshold model of inheritance (e.g., introducing the idea of more and less severe forms of disease) as a means of non-Mendelian inheritance [26, 27]. Reich then returned to Wash U and along with Sam Guze and C. Robert Cloninger applied his model to psychiatric illness, sociopathy (modern-day antisocial personality disorder) and Briquet’s syndrome (modern-day somatic symptom disorder) [28]. Reich later became interested in environmental influence and cultural transition on psychiatric illness, as at the time it was assumed that the reason that psychiatric illness ran in families was purely genetic. Along with Cloninger and colleague John Rice, Reich used path analysis, combining genetic and environmental influences and demonstrated that only in intact families could genetic transmission mimic cultural transmission [29–31]. Reich was also prescient in his realization that molecular genetics would supplant family studies in the realm of psychiatric genetics, and at that time biomarkers were becoming available to permit whole genome screens. In the early 1990s, Reich was the principal investigator of an NIMH-funded bipolar genetics study and developed the diagnostic interview for genetic studies and family history interview (FIGS) that could provide reliable diagnoses and family histories, respectively, for large-scale studies [32]. Reich realized this method of large-scale, phenotypic level of characterization of both probands and families was essential to contextualize and understand the quantitative data from biomarkers. Reich was also the principal investigator for the Collaborative Study on the Genetics of Alcoholism (COGA), which was innovated for its extensive (>10,000 individuals) diagnostic and family histories as well as

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated

29

quantitative measures including electroencephalogram (EEG), evoked-related potentials (ERP), personality measures, and biomarkers as well as reassessment of diagnosis over time and quantification of diagnostic error [26, 33]. We discuss at length Dr. Reich’s career because his insights underscore how quantitative and qualitative measures are inextricably linked in psychiatric genetics (and genetics in general). From his early days in Edinburgh and Wash U, he understood the necessity of rigorous phenotypic classification and that all biomarker data, while essential for a progression in understanding of genetics at a more nuanced level, must be taken into context [26]. Now more than ever are Reich’s lessons indispensable, given the latest iteration of progress in psychiatric genetics (and all of genetics, for that matter), the genome-wide association study (GWAS). The general concept behind GWAS is to compare large sample sizes (usually >10,000) of subjects diagnosed with a given illness and a large group of control subjects [34, 35]. Large-scale sequencing has enabled detection of single nucleotide polymorphisms (SNPs), variations in single DNA nucleotides that may implicate a gene (either regulatory or coding) in the pathophysiology of disease [35]. Copy number variants (CNVs) are sections of repeated portions of the genome that vary between populations, representing structural variations in the genome that may underlie a genetic signature of an illness [34]. The sample size required to detect GWAS-significant SNPs and CNVs is enormous, which has dictated collaboration and data sharing [34]. Additionally, statistical rigor is indispensable; a threshold finding of p < 5  10 8 has been adopted as a putative threshold for both minimizing Type I error and maximizing power [35]. The Psychiatric Genomics Consortium (PGC) is comprised of over 800 investigators and 900,000 subjects, originally focusing on attention-deficit/hyperactivity disorder (ADHD), autism, bipolar affective disorder, major depressive disorder, and schizophrenia [36]. It since has expanded with large-scale studies on eating disorders, obsessive-compulsive and tick disorders, post-traumatic stress disorder, and substance use disorders [36]. Overall, size appears to be the predominant factor in uncovering GWASsignificant loci. For example, in schizophrenia, in 2011 with 9394 cases, the PGC reported 5 GWAS-significant loci and in 2014 with 35,500 cases 108 GWAS-significant loci [37, 38]. Though the anticipated pathway of genes to mechanism to treatable target in retrospect is simplistic, GWAS findings have opened the door for many different avenues of scientific inquiry. For example, Jaffe et al., using advanced RNA sequencing technology in postmortem samples, demonstrated that RNA transcripts corresponding to high-risk genes were preferentially expressed in the prefrontal cortex during the fetal developmental period, indicating that the phenotypic manifestation of schizophrenia must be

30

A. Benjamin Srivastava et al.

to a significant extent environmentally mediated [39]. In a groundbreaking, recent study analyzing multiple longitudinal cohorts, Ursini and colleagues demonstrated that sets of GWAS-significant genes for schizophrenia with high polygenic risk scores that interact with early life (i.e., in utero, delivery, neonatal) complications are differentially expressed in the placenta. Moreover these genes are involved in cellular stress response, suggesting that the pathogenesis in schizophrenia may be in part driven through the placental response to stress [40]. Conversely, using a novel design and rich phenotypic classification of existing data sets and advanced statistical methods, Arnedo and colleagues found that certain phenotypes (symptoms and course) corresponding with certain genotypes (sets of SNPs) imply gene-gene (epistatic) underpinnings of schizophrenia heritability [41]. Collectively, genetic information can support a stress diathesis model of disease: in the case of schizophrenia, a high-risk patient may be conceptualized as having a predictive genetic liability that with exposure to environmental stressors (intrauterine pathology, urban rearing, marijuana, etc.) may manifest in the resultant phenotype [42]. Obviously, for complex phenotypes such as bipolar and schizophrenia, GWAS presents a dilemma: rigorous phenotyping across samples dictates the validity of the GWAS findings, but this task becomes more challenging with the large number of sample sizes that is required to produce GWAS-significant findings. In the future, artificial intelligence and machine learning methods may be exploited to facilitate this requirement [43].

3 3.1

Translational Research in Psychiatry Neuroimaging

As we mentioned earlier, defining the neural structures implicit in psychiatric illness has been an elusive task for centuries. One of the first attempts at localizing function to brain structure was the practice of phrenology, the measurement of ‘bumps’ on a skull to measure mental function, pioneered by Franz Joseph Gall in 1796. Now considered a pseudoscience, it had enormous impact and would later influence Paul Broca and Carl Wernicke in extrapolating function from cortical and subcortical lesions [44]. Postmortem, lesion-focused studies, while instrumental, have their own issues; namely, they preclude studying live patients, and one can only extrapolate necessity, not sufficiency for a given brain region and function. The advent of in vivo neuroimaging, specifically positron emission tomography (PET) scanning, task functional magnetic resonance imaging (fMRI), and resting-state fMRI has allowed for the development of a systems neuroscience approach to psychiatric illness, and research continues to evolve at an ever more rapid pace [45]. Since 1990, the “doubling time” for publications

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated

31

involving fMRI has been 11 years, and for resting state, since 2004, the “doubling time” has been 2.1 years [45]. Briefly, both PET and fMRI are based on the well-established principle that neural activity corresponds to changes in cerebral blood flow. Specifically, in PET imaging, changes in cerebral blood flow, as indexed by the release of a radioactive nucleotide, correspond with changing neural activity in the brain. In fMRI, changes in blood oxygenation level-dependent (BOLD) signal reflect changes in neural activity. Glutamatergic signaling (neural activity) leads to increased blood flow; however, this paradoxically does not result in an increase in tissue oxygen uptake (i.e., the blood flows too quickly with increased neural activity), resulting in increased BOLD signal [45, 46]. While task-based fMRI research (i.e., a task measuring some psychological construct is given during a scan for the purposes of correlating brain function) has fundamentally advanced systems neuroscience research, several limitations must be mentioned. First, all fMRI research presupposes that the psychological construct of the paradigm is known and serves as a marker of a “natural kind” [47]. Second, reproducibility is continually a problem; most studies are underpowered and subject to reporting bias [48]. Third, signal to noise ratio is low: a single task does not significantly alter measurable brain function; many responses (on the order of 101 to 102) produce meaningful results [45]. From these issues arose the question of measuring intrinsic neural activity at rest (“resting state”). In a landmark paper in 1995, Biswal and colleagues demonstrated that conventional, task fMRI investigating finger tapping produced increased BOLD signal in the sensorimotor cortex; yet when the subject remained still, fMRI data were acquired as well [49]. Choosing a region of interest (ROI) as an area in the sensorimotor cortex that displayed increased BOLD signal during the aforementioned task, the authors demonstrated that voxel-wise ROI connectivity (Pearson correlation) demonstrated a map showing increased BOLD signal in a nearly identical sensorimotor cortical area as the task state, proving that intrinsic neural activity can be measured even when the brain is not actively engaged in a task [49]. From this fundamental finding followed an explosion in research investigating properties of the brain in its “resting state.” In 2001, using PET data, Distinguished Professor Marcus E. Raichle and colleagues at Washington University School of Medicine published a seminal paper describing a “default mode” of brain function, now called the default mode network (DMN) [50]. When the brain is at rest (not engaged in a task), certain areas—the precuneus, posterior cingulate cortex, and medial prefrontal cortex—were found to have increased CBF, leading authors to conclude that a “default state” exists and that, when more specific tasks are required, activity in this “default system”

32

A. Benjamin Srivastava et al.

attenuates [50]. The DMN has since become the basis for an abundance of research into brain function, consciousness, and psychopathology [51]. Following this landmark paper, in 2005, Fox and colleagues demonstrated that spontaneous fluctuations in BOLD signaling occur between (1) the putative DMN areas (“task negative”) and (2) areas known to be activated during task conditions which included the intraparietal sulcus (IPS), frontal eye fields (FEF), and portions of the ventral prefrontal and dorsolateral prefrontal cortices, insula, and supplementary motor area (“task positive”) [52]. Strikingly, in functional connectivity analyses, these two systems were anticorrelated, further validating the concept of the “default mode” system responsible for self-referential processing and a separate system responsible for performing goal-directed tasks. When engaged in a task, functional connectivity in the DMN is decreased and increased in the task-positive system [52]. The task-positive system has been the subject of intensive investigation, with key findings being the definition of attentional and top-down control networks [53, 54]. The next landmark paper in resting-state functional imaging came in 2011, when Power and colleagues, using a graph theoretic approach in both meta-analytic-based region of interest and datadriven, voxel-wise approaches, defined functional areas arranged in spatially distinct networks that correspond to task-specific activations, outlining the functional organization of the human brain [55]. A limitation at this time was that most analyses were group level, possibly limiting understanding applicability to the functional organization of an individual [55]. In 2015, Laumann and colleagues tackled this problem by demonstrating that when using a large amount of data (upward of 100 min of resting-state scan time), the signal to noise ratio is optimized and though the individual subject shows a similar network architecture to the group, there are distinct topological differences not apparent at group-level analyses [56]. As an extension of these findings, in 2017, Gordon and colleagues gathered resting-state and task fMRI in ten highly sampled subjects (approximately 300 min of scan time/subject), generating individual-specific functional connectomes [57]. Network topologies manifested differently both between individual subjects and between each subject and the group average. For example, several subjects had an area of salience network in the medial prefrontal cortex (mPFC), which was not evident in the group average [57]. Much has been argued about the stability of resting-state networks. A substantial body of literature has been devoted toward so-called “dynamic” RSFC, wherein correlation fluctuates dramatically over seconds to minutes [58]. Physiologic significance has been ascribed to “dynamic” RSFC as biomarkers for different forms of psychopathology. However, Laumann and colleagues

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated

33

demonstrated that rather than existing as “natural kinds,” “dynamic” RSFC findings may reflect failure to control for certain systematic sources of error including sampling variability, head motion, and sleep/wake state [59]. Further, Gratton and colleagues, using highly sampled resting state data, demonstrated that cortical RSNs are most stable in the individual subjects, with minimal contributions from session to session variability or tasks [60]. In fact, when intrinsic resting-state data is removed, the individual effects of the individual tasks and sessions are enhanced, underscoring the stability of the RSNs [60]. The clinical implications of valid and stable resting-state functional connectivity are immediately clear. First, network connectivity may represent an endophenotype, and alterations in network connectivity may function as biomarkers of disease states, providing another level of classification based on neural substrate [55]. Further, these alterations in functional connectivity may provide direct targets for personalized medicine including psychopharmacology and neuromodulation [61]. As an example, Drysdale and colleagues recently demonstrated that RSFC could be used to depict “biotypes” in MDD that transcended clinical phenotype and predicted repetitive transcranial magnetic stimulation (rTMS) treatment response [62]. 3.2 Imaging Genomics

Heretofore we have described the background, current state, and methodological issues relating to two of the dominating areas in psychiatric research, genetics, and neuroimaging. Recently, the field of “imaging genomics” has emerged, with the goal of gaining insights into the impact of human genetic variation on the structure and function of neural systems in both health and disease [63]. However, powerful imaging and large-scale genomic approaches may be subject to methodological errors as described in this article, which can significantly obfuscate findings, especially when combined. Recently Carter and colleagues issued the following guidelines to enhance reliability, validity, and replicability: (1) rigorous phenotyping measured using tools with construct validity, sensitivity, and reliability, (2) examination of genetic associations based on prior evidence of involvement of a certain gene in the studied disease, (3) methods for addressing heterogeneity, (4) transparent imaging analyses explicitly describing processing and control methods and corrections for multiple comparisons, and (5) appropriate powering of a study with independent replication [63]. The last point relates to the increased use of metaanalytic approaches for synthesizing and integrating large amounts of data, but again this underscores the need for consistency in accuracy of phenotyping, methods of experimentation and processing (especially for imaging studies), and controlling for common sources of bias [63, 64].

34

4

A. Benjamin Srivastava et al.

Conclusions Psychiatric illness is a major global public health burden, and available treatments have remained largely stagnant over the past two decades. An informed, neuroscience-based approach is essential to the advancement of psychiatric research both in understanding of disease and improvement in treatments. However, as methods become more complex and require increasing magnitudes of sample size, qualitative aspects—rigorous phenotyping—can (and unfortunately does) become easily neglected, the obvious issues in quantitative methods (variability in fMRI data processing, GWAS analysis, etc.). Data sharing and meta-analytic approaches demanding rigorous criteria for inclusion may help resolve some of these issues.

References 1. Center for Behavioral Health Statistics and Quality (2017) 2016 National survey on drug use and health: detailed tables. Substance Abuse and Mental Health Services Administration, Rockville, MD 2. Murray CJL, Lopez AD (2013) Measuring the global burden of disease. N Engl J Med 369:448–457 3. Vigo D, Thornicroft G, Atun R (2016) Estimating the true global burden of mental illness. Lancet Psychiatry 3:171–178 4. Murray CJ, Atkinson C, Bhalla K, Birbeck G, Burstein R, Chou D, Dellavalle R, Danaei G, Ezzati M, Fahimi A, Flaxman D, Foreman, Gabriel S, Gakidou E, Kassebaum N, Khatibzadeh S, Lim S, Lipshultz SE, London S, Lopez, MacIntyre MF, Mokdad AH, Moran A, Moran AE, Mozaffarian D, Murphy T, Naghavi M, Pope C, Roberts T, Salomon J, Schwebel DC, Shahraz S, Sleet DA, Murray, Abraham J, Ali MK, Atkinson C, Bartels DH, Bhalla K, Birbeck G, Burstein R, Chen H, Criqui MH, Dahodwala, Jarlais, Ding EL, Dorsey ER, Ebel BE, Ezzati M, Fahami, Flaxman S, Flaxman AD, Gonzalez-Medina D, Grant B, Hagan H, Hoffman H, Kassebaum N, Khatibzadeh S, Leasher JL, Lin J, Lipshultz SE, Lozano R, Lu Y, Mallinger L, McDermott MM, Micha R, Miller TR, Mokdad AA, Mokdad AH, Mozaffarian D, Naghavi M, Narayan KM, Omer SB, Pelizzari PM, Phillips D, Ranganathan D, Rivara FP, Roberts T, Sampson U, Sanman E, Sapkota A, Schwebel DC, Sharaz S, Shivakoti R, Singh GM, Singh D, Tavakkoli M, Towbin JA, Wilkinson JD, Zabetian A, Murray, Abraham J, Ali MK, Alvardo M, Atkinson C, Baddour LM,

Benjamin EJ, Bhalla K, Birbeck G, Bolliger I, Burstein R, Carnahan E, Chou D, Chugh SS, Cohen A, Colson KE, Cooper LT, Couser W, Criqui MH, Dabhadkar KC, Dellavalle RP, Jarlais, Dicker D, Dorsey ER, Duber H, Ebel BE, Engell RE, Ezzati M, Felson DT, Finucane MM, Flaxman S, Flaxman AD, Fleming T, Foreman, Forouzanfar MH, Freedman G, Freeman MK, Gakidou E, Gillum RF, Gonzalez-Medina D, Gosselin R, Gutierrez HR, Hagan H, Havmoeller R, Hoffman H, Jacobsen KH, James SL, Jasrasaria R, Jayarman S, Johns N, Kassebaum N, Khatibzadeh S, Lan Q, Leasher JL, Lim S, Lipshultz SE, London S, Lopez, Lozano R, Lu Y, Mallinger L, Meltzer M, Mensah GA, Michaud C, Miller TR, Mock C, Moffitt TE, Mokdad AA, Mokdad AH, Moran A, Naghavi M, Narayan KM, Nelson RG, Olives C, Omer SB, Ortblad K, Ostro B, Pelizzari PM, Phillips D, Raju M, Razavi H, Ritz B, Roberts T, Sacco RL, Salomon J, Sampson U, Schwebel DC, Shahraz S, Shibuya K, Silberberg D, Singh JA, Steenland K, Taylor JA, Thurston GD, Vavilala MS, Vos T, Wagner GR, Weinstock MA, Weisskopf MG, Wulf S, Murray (2013) The state of US health, 1990–2010: burden of diseases, injuries, and risk factors. JAMA 310:591–608 5. Case A, Deaton A (2015) Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proc Natl Acad Sci U S A 112:15078–15083 6. Zorumski CF, Rubin EH (2011) Psychiatry and clinical neuroscience: a primer. Oxford University Press, New York, NY

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated 7. Shorter E (1997) A history of psychiatry. Wiley, New York, NY 8. Gage N, Hickok G (2005) Multiregional cell assemblies, temporal binding and the representation of conceptual knowledge in cortex: a modern theory by a “classical” neurologist, Carl Wernicke. Cortex 41:823–832 9. Wernicke C (1900) Grundriss der Psychiatrie. Verlag von Georg Thieme, Leipzig 10. Kendler KS, Engstrom EJ (2017) Kahlbaum, Hecker, and Kraepelin and the transition from psychiatric symptom complexes to empirical disease forms. Am J Psychiatry 174:102–109 11. Berger JR, Dean D (2014) Neurosyphilis. Handb Clin Neurol 121:1461–1472 12. Decker HS (2013) The making of DSM-III: a diagnostic manual’s conquest of American psychiatry. Oxford University Press, New York, NY 13. Engstrom EJ, Kendler KS (2015) Emil Kraepelin: icon and reality. Am J Psychiatry 172:1190–1196 14. Kraepelin E (1899) Psychiatrie: Ein Lehrbuch fu¨r Studirende und Aerzte. JA Barth, Leipzig 15. Kendler KS, Engstrom EJ (2018) Criticisms of Kraepelin’s psychiatric nosology: 1896–1927. Am J Psychiatry 175:316–326 16. Ungvari GS (1993) The Wernicke-KleistLeonhard school of psychiatry. Biol Psychiatry 34:749–752 17. Freud S (1930) Civilization and its discontents. Hogarth, London 18. Robins E, Guze SB (1970) Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia. Am J Psychiatry 126:983–987 19. Feighner JP, Robins E, Guze SB, Woodruff RA Jr, Winokur G, Munoz R (1972) Diagnostic criteria for use in psychiatric research. Arch Gen Psychiatry 26:57–63 20. Kendler KS, Parnas J (2008) Philosophical issues in psychiatry: explanation, phenomenology, and nosology. Johns Hopkins University Press, Baltimore, MD 21. Ng B, Camacho A, Lara DR, Brunstein MG, Pinto OC, Akiskal HS (2008) A case series on the hypothesized connection between dementia and bipolar spectrum disorders: bipolar type VI? J Affect Disord 107:307–315 22. Guze SB, Cloninger CR, Martin RL, Clayton PJ (1983) A follow-up and family study of schizophrenia. Arch Gen Psychiatry 40:1273–1276 23. Elahi FM, Miller BL (2017) A clinicopathological approach to the diagnosis of dementia. Nat Rev Neurol 13:457–476

35

24. Propping P (2005) The biography of psychiatric genetics: from early achievements to historical burden, from an anxious society to critical geneticists. Am J Med Genet B Neuropsychiatr Genet 136b:2–7 25. Reich T, Clayton PJ, Winokur G (1969) Family history studies: V. The genetics of mania. Am J Psychiatry 125:1358–1369 26. Suarez BK, Rice JP (2004) Theodore Reich, M.D.: October 14, 1938–December 25, 2003. Am J Med Genet B Neuropsychiatr Genet 127b:1–4 27. Reich T, James JW, Morris CA (1972) The use of multiple thresholds in determining the mode of transmission of semi-continuous traits. Ann Hum Genet 36:163–184 28. Cloninger CR, Reich T, Guze SB (1975) The multifactorial model of disease transmission: III. Familial relationship between sociopathy and hysteria (Briquet’s syndrome). Br J Psychiatry 127:23–32 29. Cloninger CR, Rice J, Reich T (1979) Multifactorial inheritance with cultural transmission and assortative mating. III. Family structure and the analysis of separation experiments. Am J Hum Genet 31:366–388 30. Cloninger CR, Rice J, Reich T (1979) Multifactorial inheritance with cultural transmission and assortative mating. II. A general model of combined polygenic and cultural inheritance. Am J Hum Genet 31:176–198 31. Rice J, Cloninger CR, Reich T (1978) Multifactorial inheritance with cultural transmission and assortative mating. I. Description and basic properties of the unitary models. Am J Hum Genet 30:618–643 32. Nurnberger JI Jr, Blehar MC, Kaufmann CA, York-Cooler C, Simpson SG, HarkavyFriedman J, Severe JB, Malaspina D, Reich T (1994) Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. Arch Gen Psychiatry 51:849–859; discussion 863–844 33. Reich T, Edenberg HJ, Goate A, Williams JT, Rice JP, Van Eerdewegh P, Foroud T, Hesselbrock V, Schuckit MA, Bucholz K, Porjesz B, Li TK, Conneally PM, Nurnberger JI Jr, Tischfield JA, Crowe RR, Cloninger CR, Wu W, Shears S, Carr K, Crose C, Willig C, Begleiter H (1998) Genome-wide search for genes affecting the risk for alcohol dependence. Am J Med Genet 81:207–215 34. Collins AL, Sullivan PF (2013) Genome-wide association studies in psychiatry: what have we learned? Br J Psychiatry 202:1–4 35. Jannot AS, Ehret G, Perneger T (2015) P < 5 x 10( 8) has emerged as a standard of statistical

36

A. Benjamin Srivastava et al.

significance for genome-wide association studies. J Clin Epidemiol 68:460–465 36. O’Donovan MC (2015) What have we learned from the Psychiatric Genomics Consortium. World Psychiatry 14:291–293 37. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011) Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43:969–976 38. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427 39. Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, Kleinman JE (2016) Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci 19:40–47 40. Ursini G, Punzi G, Chen Q, Marenco S, Robinson JF, Porcelli A, Hamilton EG, Mitjans M, Maddalena G, Begemann M, Seidel J, Yanamori H, Jaffe AE, Berman KF, Egan MF, Straub RE, Colantuoni C, Blasi G, Hashimoto R, Rujescu D, Ehrenreich H, Bertolino A, Weinberger DR (2018) Convergence of placenta biology and genetic risk for schizophrenia. Nat Med 24:792–801 41. Arnedo J, Svrakic DM, Del Val C, RomeroZaliz R, Hernandez-Cuervo H, Fanous AH, Pato MT, Pato CN, de Erausquin GA, Cloninger CR, Zwir I (2015) Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies. Am J Psychiatry 172:139–153 42. Millan MJ, Andrieux A, Bartzokis G, Cadenhead K, Dazzan P, Fusar-Poli P, Gallinat J, Giedd J, Grayson DR, Heinrichs M, Kahn R, Krebs MO, Leboyer M, Lewis D, Marin O, Marin P, Meyer-Lindenberg A, McGorry P, McGuire P, Owen MJ, Patterson P, Sawa A, Spedding M, Uhlhaas P, Vaccarino F, Wahlestedt C, Weinberger D (2016) Altering the course of schizophrenia: progress and perspectives. Nat Rev Drug Discov 15:485–515 43. Szymczak S, Biernacka JM, Cordell HJ, Gonzalez-Recio O, Konig IR, Zhang H, Sun YV (2009) Machine learning in genome-wide association studies. Genet Epidemiol 33(Suppl 1):S51–S57 44. Kaitaro T (2001) Biological and epistemological models of localization in the nineteenth century: from Gall to Charcot. J Hist Neurosci 10:262–276

45. Snyder AZ (2016) Intrinsic brain activity and resting state networks. In: Pfaff DW, Volkow ND (eds) Neuroscience in the 21st century: from basic to clinical. Springer, New York, NY, pp 1625–1676 46. Raichle ME, Mintun MA (2006) Brain work and brain imaging. Annu Rev Neurosci 29:449–476 47. Coltheart M (2006) What has functional neuroimaging told us about the mind (so far)? Cortex 42:323–331 48. David SP, Ware JJ, Chu IM, Loftus PD, FusarPoli P, Radua J, Munafo MR, Ioannidis JP (2013) Potential reporting bias in fMRI studies of the brain. PLoS One 8:e70104 49. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echoplanar MRI. Magn Reson Med 34:537–541 50. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682 51. Raichle ME (2015) The brain’s default mode network. Annu Rev Neurosci 38:433–447 52. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102:9673–9678 53. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 103:10046–10051 54. Dosenbach NU, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, Fox MD, Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL, Petersen SE (2007) Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A 104:11073–11078 55. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, Petersen SE (2011) Functional network organization of the human brain. Neuron 72:665–678 56. Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Gilmore AW, McDermott KB, Nelson SM, Dosenbach NU, Schlaggar BL, Mumford JA, Poldrack RA, Petersen SE (2015) Functional system and areal organization of a highly sampled individual human brain. Neuron 87:657–670 57. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, Ortega M, Hoyt-Drazen C, Gratton C, Sun H, Hampton

Qualitative vs. Quantitative Methods in Psychiatric Research: Updated JM, Coalson RS, Nguyen AL, McDermott KB, Shimony JS, Snyder AZ, Schlaggar BL, Petersen SE, Nelson SM, Dosenbach NUF (2017) Precision functional mapping of individual human brains. Neuron 95:791–807.e797 58. Calhoun VD, Miller R, Pearlson G, Adali T (2014) The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:262–274 59. Laumann TO, Snyder AZ, Mitra A, Gordon EM, Gratton C, Adeyemo B, Gilmore AW, Nelson SM, Berg JJ, Greene DJ, McCarthy JE, Tagliazucchi E, Laufs H, Schlaggar BL, Dosenbach NUF, Petersen SE (2017) On the stability of BOLD fMRI correlations. Cereb Cortex 27:4719–4732 60. Gratton C, Laumann TO, Nielsen AN, Greene DJ, Gordon EM, Gilmore AW, Nelson SM, Coalson RS, Snyder AZ, Schlaggar BL, Dosenbach NUF, Petersen SE (2018) Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98:439–452.e435

37

61. Fox MD, Buckner RL, Liu H, Chakravarty MM, Lozano AM, Pascual-Leone A (2014) Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc Natl Acad Sci U S A 111:E4367–E4375 62. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, PascualLeone A, Voss HU, Casey BJ, Dubin MJ, Liston C (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:28–38 63. Carter CS, Bearden CE, Bullmore ET, Geschwind DH, Glahn DC, Gur RE, MeyerLindenberg A, Weinberger DR (2017) Enhancing the informativeness and replicability of imaging genomics studies. Biol Psychiatry 82:157–164 64. Ioannidis JP (2017) Meta-analyses can be credible and useful: a new standard. JAMA Psychiat 74:311–312

Part II Overview of Animal Models of Psychiatric Illness

Chapter 3 Animal Models of Self-Injurious Behavior: An Update Darragh P. Devine Abstract Although self-injurious behavior is a common comorbid behavior problem among individuals with neurodevelopmental disorders, little is known about its etiology and underlying neurobiology. Interestingly, it shows up in various forms across patient groups with distinct genetic errors and diagnostic categories. This suggests that there may be shared neuropathology that confers vulnerability in these disparate groups. Convergent evidence from clinical pharmacotherapy, brain imaging studies, postmortem neurochemical analyses, and animal models indicates that dopaminergic insufficiency is a key contributing factor. This chapter provides an overview of studies in which animal models have been used to investigate the biochemical basis of self-injury and highlights the convergence in findings between these models and expression of self-injury in humans. Key words Self-injurious behavior, Lesch-Nyhan syndrome, Prader-Willi syndrome, Dopamine, Striatum, Animal model

1

Introduction

1.1 Clinical Relevance

Self-injurious behavior (SIB) is a devastating characteristic that is commonly expressed in a variety of genetic disorders, including Lesch-Nyhan [1, 2], Prader-Willi [3, 4], and Fragile X [5, 6] syndromes. SIB is also prevalent in children with autism [7, 8] and intellectual handicaps, including a broad array of etiological backgrounds [9–11]. The forms of SIB differ somewhat between these diagnostic groups, and in most groups, there is heterogeneity in terms of incidence and severity of expression even within the affected population. One exception is Lesch-Nyhan syndrome in which all patients exhibit severe self-biting behaviors [12]. In contrast, estimates of prevalence in Prader-Willi syndrome (mostly obsessive skin-picking) range from 80% to 90% of patients [4, 13], and up to about 50% of children with autism bang their heads and punch or slap themselves [14]. The reasons for these individual differences are not well-characterized. However, the homogeneity of occurrence of SIB across neurodevelopmental disorders and the heterogeneity of expression within specific disorders

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_3, © Springer Science+Business Media, LLC, part of Springer Nature 2019

41

42

Darragh P. Devine

suggest that shared biochemical mechanisms may confer vulnerability for SIB that is then shaped by environmental circumstances across and within these patient groups. SIB is often reported among patients who exhibit impaired communication skills [10, 11, 15–20] or who live in impoverished institutional environments [11, 21–27]. There also appears to be a high prevalence of SIB in syndromes that are associated with chronic limbic dysfunction and pathological irritability [28]. This includes patients with Lesch-Nyhan syndrome [12] and other disorders [29, 30], wherein specific episodes of SIB are often related to the presence of environmental challenges and disturbances. In addition, among patients with non-syndromic intellectual handicaps, there is evidence that SIB is more highly prevalent in those who have a greater degree of intellectual handicap [31]. Accordingly, lower communication skills, environmental deprivation, emotional distress, and lower intellectual functioning are implicated in individual differences in vulnerability for SIB. A substantial body of clinical research has been targeted at this behavioral pathology. Most of this research focuses on intervention for reinforcing social interactions that maintain SIB. This approach has yielded treatment programs that are partially effective for many self-injurers [17, 18, 25, 26, 32–36], and behavior therapy is clearly the treatment of choice. However, functional analyses reveal that social interactions do not reinforce SIB in at least 30% of cases [37, 38]. Furthermore, many self-injurious patients are highly resistant to behavioral interventions [12, 36, 39], and some groups (especially Lesch-Nyhan syndrome) are especially unresponsive [12, 40]. It has been suggested that function-based treatment in combination with pharmacotherapy may be the best intervention strategy, although there is still little research on the potential efficacy of this kind of combination therapy [41]. 1.2 Biological Basis of Clinical SIB

The most frequent and severe expressions of SIB are commonly seen in Lesch-Nyhan syndrome [1], an X-linked recessive disorder that results from any of a variety of mutations in a gene that encodes the purine salvage enzyme hypoxanthine-guanine phosphoribosyl transferase (HPRT). Afflicted boys express a biologically inert HPRT molecule [42]. Although the mechanisms are unknown, this results in a nearly total elimination of dopaminergic innervation of the striatum, severe dystonia, and SIB [43, 44]. Diminished dopamine content or function has also been reported in patients with autism [45] and Rett syndrome [46], where SIB is a common feature. Furthermore, research on Lesch-Nyhan syndrome has revealed that striatal D1 and D2 receptors are upregulated in postmortem immunohistochemical analyses [47]. Accordingly, dysregulation of dopamine neurotransmission is strongly implicated in Lesch-Nyhan syndrome, but the connection between dysregulated dopamine and self-injury is not understood in any of these

Animal Models of Self-Injurious Behavior: An Update

43

disorders. Moreover, it is important to note that dopamine systems are probably not the only neurotransmitter systems that are dysregulated in Lesch-Nyhan syndrome and other neurodevelopmental disorders. Evaluations in Lesch-Nyhan, autistic, and intellectually handicapped self-injurers have also revealed abnormal markers of adenosine [42, 48–50], opioid [47, 51–56], and serotonergic [43, 57] functions, and there is evidence that dysregulation of the limbic hypothalamic pituitary adrenal (LHPA) axis may be an important characteristic of self-injurers [51, 58, 59]. However, the biochemical bases of all these abnormalities and mechanisms by which they may contribute to vulnerability for SIB are not clear, so additional investigations are mandated. 1.3 Pharmacological Trials for Clinical SIB

2

A great variety of pharmacological agents have been evaluated for treatment of SIB in clinical trials. Unfortunately, these trials have yielded largely equivocal results. For example, it has been reported that opioid receptor antagonists (e.g., naloxone, naltrexone) reduce SIB in some studies [60–68], but not in others [69, 70]. Similar contradictions have been reported in terms of neuroleptic [61, 71–75] and serotonergic [76–83] interventions for SIB. In light of the lack of consistent therapeutic effects of these interventions, it is difficult to draw conclusions about the neurobiological basis of SIB from these clinical trials. One interpretation of these data is that there may be subgroups of self-injurers in which differing (or at least partially differing) neuropathologies contribute to the expression of SIB. However, the difficulty in interpreting these clinical data is compounded by the fact that some studies have been conducted with imprecise dependent measures [63, 64, 73, 84, 85], open-label trials are common [63, 73, 77, 84–86], many trials include subjects who receive multiple drugs concurrently [84, 85], many drugs exert multiple pharmacological actions [84–86], and long-term follow-up studies are generally not done (some exceptions are refs. 60, 71, 74). The most parsimonious explanation remains that common neuropathology confers vulnerability across the multiple populations of self-injurers, and the expression of SIB may be influenced by additional social and biological factors.

Animal Models It is noteworthy that SIB is frequently observed in captive populations of animals. Spontaneous SIB is prevalent in caged monkeys [87, 88], farm animals [89, 90], household pets [91–93], and in a strain of inbred rabbits [94]. In all these cases, the expression of SIB is enhanced by distress and social isolation. Although less common, SIB has also been reported in the wild. For example, Jane Goodall described the case of a Gombe chimpanzee that exhibited severe

44

Darragh P. Devine

Table 1 An ordinal scale for rating the severity of self-inflicted tissue injury in animals Category

Example

Lesion models

Neonatal 6-OHDA lesions

Environmental manipulations

Early environmental deprivation

Pharmacological manipulations

Chronic pemoline

Genetic

HPRT enzyme knockouts

self-injury following the death of its mother [95]. Thus, it appears that SIB can be a naturally occurring behavior that is expressed in the context of stress or deprivation in animals. It follows then that the manipulations that invoke SIB in laboratory models likely impact the same endogenous neurobiological mechanisms that underlie the expression of SIB in these more naturalistic contexts. A variety of laboratory models have been formulated in which developmental and neurochemical manipulations result in expression of SIB. These models can be classified into four categories of experimental manipulations (Table 1). The most well-characterized of these models are the 6-hydroxydopamine (6-OHDA) lesion model; early environmental deprivation; administration of caffeine, pemoline, or Bay K 8644; and an HPRT knockout mouse. 2.1 The 6-OHDA Lesion Model

The 6-OHDA lesion model is a model wherein dopaminergic innervation of the striatum is destroyed in neonatal rats. Then, administration of dopamine agonists (e.g., L-dopa, apomorphine) in adulthood results in immediate and profound expression of SIB. Since the model is dependent upon early developmental destruction of dopaminergic neurons (i.e., lesions do not produce SIB if inflicted in adulthood), it has relevance for the developmental pathophysiology of Lesch-Nyhan syndrome [96, 97]. However, it should be noted that there is no clear relationship between any developmental milestone and the onset of SIB in Lesch-Nyhan syndrome or any other neurodevelopmental disorder, and in fact, the age of onset is quite variable in these disorders. Nevertheless, the neonatal 6-OHDA model has been characterized more extensively than any other animal model of SIB, and it has yielded very interesting behavioral observations and neurochemical data. Data from the 6-OHDA model have revealed interesting lesioninduced changes in striatal chemoarchitecture. Adult rats that were lesioned as neonates exhibit increases in striatal serotonin (5-HT), met-enkephalin, and substance P (SP) content [98, 99], as well as a reduction in [3H]naloxone binding to μ-opioid receptors [100]. These rats also exhibit unchanged or increased binding of [3H]spiroperidol or [3H]raclopride to the D2 class of dopamine

Animal Models of Self-Injurious Behavior: An Update

45

receptors in the striatum [98–100] and unchanged or decreased binding of [3H]SCH23390 to the D1 receptor class [98, 100–102]. The reports of increases in D2 binding concur with postmortem immunohistochemical data from Lesch-Nyhan brains, but the decrease in D1 binding conflicts with the data from LeschNyhan striata [47]. The meaning of this discrepancy is not clear. In this model, the decreases in binding to the D1 class and increases in binding to the D2 class might lead one to expect that the dopamine agonist-induced SIB results from enhanced actions on the D2 receptors. However, iontophoretic application of the D1 agonist SKF 38393 causes a greater inhibitory responsiveness of spontaneously firing striatal units in rats after neonatal 6-OHDA than it does in control rats, whereas responses to the D2 agonist PPHT are unchanged [102]. So, it appears that the density of D1 and D2 receptors are uncoupled from the sensitivity of neuronal responses to administration of dopamine and selective receptor agonists after 6-OHDA lesions. The mechanisms of these effects are unknown, but the importance of D1 receptors in SIB after 6-OHDA appears clear. Administration of SKF 38393 will induce SIB in rats after neonatal 6-OHDA lesions [99, 103–106]. Furthermore, the SIB-producing effects of L-dopa and SKF 38393 are reliably blocked by administration of D1 antagonists (Scheme 23390, Scheme 39166, NO-0756, A-69024; [99, 103–106]), but not by administration of a D2 antagonist (metoclopramide; [106]), although risperidone (a mixed D2/5-HT2 antagonist) attenuated L-dopa-induced SIB [107]. Administration of a D2 agonist (LY 171555) produces hyperlocomotion and stereotypy, but no SIB in lesioned rats [99, 103, 104]. In summary, although 6-OHDA lesions do not increase expression of or binding to the D1 class of receptors, it appears that increased sensitivity of signaling through D1 receptors is important in the induction of SIB in neonatally lesioned rats. This interpretation is further supported by a report that striatal phospho-p38MAPK (Thr180/Tyr182) and phosphoCREB (Ser133) are increased in the neonatal 6-OHDA model [98]. One of the most striking neurochemical effects of neonatal 6-OHDA lesions is a hyperinnervation of striatal serotonergic (5-HT) neurons [96, 108–111] in adulthood. This hyperinnervation is accompanied by increased 5-HT1B and 5-HT2 receptor binding and supersensitivity to 5-HT receptor agonists [108]. Accordingly, one might predict a serotonergic involvement in SIB in neonatally lesioned rats. However, there are conflicting reports on the effects of administration of 5-HT agonists in these animals. In one study, administration of 5-HT did not induce SIB [96], although another study reported that systemic administration of a 5-HT2c receptor agonist did produce self-injury [112]. Thus, the role of lesion-induced 5-HT dysregulation is unclear, but it appears that actions on 5-HT2c receptors may play a role in SIB in this model.

46

Darragh P. Devine

Stress may also play an important role in the expression of SIB in these animals, a finding that is redolent of the data from human clinical reports [12, 28–30]. Footshock stress potentiates the ability of apomorphine to induce SIB in neonatally lesioned rats [113]. Since emotional stress increases dopamine neurotransmission [114], there appears to be convergence in the actions of apomorphine (a direct dopamine receptor agonist) and stress in the induction of SIB in this animal model. Accordingly, this finding provides further support for the potential involvement of dopamine neurotransmission in exhibition of SIB. 2.2 The Early Environmental Deprivation Model

The early environmental deprivation model originated in observations that nonhuman primates exhibit a variety of abnormal behaviors, including spontaneous SIB, if they are reared in socially impoverished environments [87, 115, 116]. This model has particular relevance to the high incidence of SIB that is often seen in institutionalized populations [11, 21–27]. In fact, it has long been unclear if institutional environments specifically promote the etiology of SIB or if the prevalence of SIB in these populations derives from the fact that intellectually handicapped self-injurers tend to be placed into these institutions more often than non-injurers do. However, in the early 1990s, a large number of ostensibly normal children were released from highly impoverished Romanian orphanages. These children had been placed for economic reasons, not for any evidence of physical or intellectual handicap. Nevertheless, 24% of these children exhibited SIB when adopted into normal family homes [27]. This finding lends strong support to the idea that impoverished institutional environments specifically promote the etiology and expression of SIB. In the early environmental deprivation model, there are interesting connections to dopaminergic function. Maternally deprived rhesus macaques had lower concentrations of the metabolite 3,4-dihydroxyphenylacetic acid (DOPAC) in cerebrospinal fluid (CSF) samples than did maternally reared animals [117]. Furthermore, isolation-reared rhesus macaques exhibit increases in the occurrence and intensity of stereotyped behaviors after apomorphine administration at doses that do not produce stereotypy in group-housed controls [118]. This suggests that early environmental deprivation produces permanent alterations in dopamine receptor sensitivity, and this interpretation is further supported by neurochemical analyses of striatal function in isolated monkeys. Rhesus macaques that experienced early environmental deprivation exhibit a pronounced loss of striatal patch/matrix organization and chemoarchitecture in adulthood, 19–24 years later [119]. In the early environmental deprivation model, there is also an important connection with stress responsiveness. Isolation-reared nonhuman primates frequently express SIB in the context of emotional stress [116, 120, 121]. This connection is redolent of the

Animal Models of Self-Injurious Behavior: An Update

47

effects of stress in human self-injurers [12, 28–30]. Moreover, these self-injurious monkeys exhibit altered functioning of the LHPA axis, including blunted cortisol response to acute stress exposure [122, 123]. As in the neonatal 6-OHDA model, a connection may be drawn between the abnormal stress responsiveness and dopaminergic function because emotional stress is known to activate dopamine neurotransmission [114]. Interestingly, the stress-induced increases in extracellular dopamine concentrations were exaggerated in striata of isolation-reared rats, when compared with concentrations in group-housed rats [124]. However, it should also be noted that one study reported no differences in CSF monoamine metabolite concentrations when comparing samples taken from monkeys with and without a history of SIB [122]. 2.3 The Chronic Caffeine Model

The chronic caffeine model is a model in which rats are exposed to extremely high doses of caffeine for 10–12 days, until they exhibit SIB. Caffeine has been administered by the oral route, usually in the rats’ food or drinking water [125–129], or by daily subcutaneous injections [130–132]. The connection between caffeine and altered dopaminergic function is mediated by the antagonist actions of this methylxanthine on adenosine receptors [133]. This can result in modulatory presynaptic actions on dopamine neurotransmission [134], as well as changes in postsynaptic responses to dopamine [134, 135]. Caffeine administration has also been shown to exaggerate L-DOPA-induced SIB in neonatal 6-OHDA-lesioned rats [136, 137], whereas intrastriatal administration of a variety of adenosine agonists (NECA, CPA, and 2-CLA) is protective [105]. Furthermore, chronic caffeine-induced SIB was exaggerated by handling stress. Treated rats exhibited SIB whenever they were picked up by the tail [126]. In summary, caffeine-induced SIB may involve actions on dopamine neurotransmission or closely related systems, and as we saw in other animal models of SIB, there is evidence of an involvement of stress responses in caffeineinduced SIB. However, we evaluated the caffeine model of SIB and found that only a small percentage of the rats actually exhibited SIB, and the SIB was minor, even when we used high doses that are toxic in all the rats. Evidence of toxicity included weight loss, chromodacryorrhea, thymus involution, and death [132]. The low levels of caffeine-induced self-injury in our study do not concur with some previous reports [125–131, 136], but the toxic actions of these high doses of caffeine have been noted in other studies [125–127, 130]. Because the incidence of SIB was restricted to a very small percentage of the rats in our study, and especially because of the severe toxic actions of caffeine at the doses that were required to produce this minor SIB, we have concluded that this model is not suitable for ongoing studies on the neurobiological basis of SIB [132].

48

Darragh P. Devine

2.4 The Pemoline Model

The pemoline model is a model in which high doses of this psychostimulant are administered to rats. Pemoline is a long-lasting [138] indirect monoamine agonist that blocks reuptake of dopamine, 5-HT, and norepinephrine [139]. SIB can be produced within 48 h by acute administration of a single 300 mg/kg dose [140–145] or gradually after 2–6 daily treatments with doses of 75–200 mg/kg/day [132, 146–148]. In this model, the rats appear healthy throughout the treatment regimen, and assays of aspartate and alanine aminotransferase activity indicate that there is no organotoxicity [149]. Furthermore, cortical damage enhances pemoline-induced SIB [143], loosely linking this animal model of SIB with the vulnerability to self-injure in intellectually handicapped human populations. Administration of dopamine antagonists (haloperidol or pimozide) eliminates pemoline-induced SIB [144], providing evidence that the pemoline-induced behavioral syndrome is mediated (at least in part) through dopaminergic mechanisms. Furthermore, repeated treatment with pemoline causes approximately 30% depletion of striatal dopamine content [150]. Although this is much less than the degree of dopamine loss that is seen in Lesch-Nyhan syndrome [43, 44], it is comparable to the depletion seen in some other populations with neurodevelopmental disorders [45, 46] and raises the possibility that alterations in postsynaptic signaling underlie the induction of SIB during repeated pemoline treatment. This contention is further supported by evidence that cortically evoked striatal depolarizing postsynaptic potentials (DPSPs) are decreased by bath application of dopamine in slices from vehicletreated rats and in slices from rats that were treated with pemoline, but did not acquire self-injury, whereas these DPSPs are increased by dopamine application in slices from pemoline-treated rats that self-injured [140]. Serotonergic neurotransmission is also implicated in the pemoline model of SIB. Co-administration of the selective 5-HT reuptake inhibitor paroxetine exaggerates pemoline-induced SIB [147]. Thus excess serotonergic function may play an important role in the etiology and expression of SIB. This idea is further supported by the fact that serotonin hyperinnervation is found in striata of individuals with Lesch-Nyhan syndrome [43, 57] and following neonatal lesions in the 6-OHDA model [96, 108–111]. Another interesting finding is that pemoline-induced SIB is blocked by administering the N-methyl-aspartate (NMDA) receptor antagonist MK-801 [141, 151]. In fact, pemoline-induced SIB was blocked if and only if the MK-801 was administered prior to administration of pemoline (i.e., not if MK-801 was administered 8 h after pemoline administration [141], even though this time point precedes the actual expression of SIB by the rats). Transient actions of glutamate on NMDA receptors are an important initial step in classical glutamate-mediated synaptic plasticity, as these

Animal Models of Self-Injurious Behavior: An Update

49

actions initiate cascades of intracellular signaling that invoke enduring changes in neuronal function (for review see ref. 152). Thus, it seems that this acute phase of glutamate-mediated neuroplasticity was blocked in rats that were pre-treated with MK-801, but not in the rats that were treated 8 h after the pemoline injection. Overall, the SIB-suppressing actions of MK-801 implicate glutamate neurotransmission, and the time dependence of these actions suggests a role for glutamate-induced neuroplasticity in the pathophysiology of pemoline-induced SIB. This interpretation is further supported by our recent demonstration that the loweraffinity NMDA receptor antagonist memantine, which does not disrupt classical glutamate-mediated neuroplasticity [153, 154], failed to block pemoline-induced SIB [151]. We have been using the pemoline model to evaluate factors that may predispose animals to become vulnerable for SIB. In these studies, we identified that individual outbred rats differ in vulnerability for pemoline-induced SIB. If a moderately high dose of pemoline (75–100 mg/kg/day) is administered, approximately 50% of the rats develop patterns of SIB [132, 148]. We examined this phenomenon and identified that innate individual differences in stress responsiveness contribute to these individual differences in vulnerability for pemoline-induced SIB. Rats were pre-screened for behavioral responsiveness to the mild stress of a novel environment. Those that exhibited high rates of locomotor activation developed SIB when subsequently treated with pemoline, whereas the rats that exhibited lower rates of behavioral activation did not self-injure [155]. It appears that these individual differences in stress responsiveness arise from a combination of genetic and environmental determinants [156], and individual differences in regional expression of glucocorticoid receptors (GR) and corticotropin-releasing hormone (CRH) appear to make important contributions to the phenotype [157]. Overall, these observations have important heuristic value for ongoing analyses of the biochemical basis of vulnerability for SIB. We also examined the contribution of innate anxiety phenotypes in individual differences in vulnerability for pemoline-induced SIB. After pre-screening rats on neophobic tests of anxiety-related behavior (elevated plus maze and open-field emergence tests), we found that the rats did not exhibit individual differences in vulnerability for SIB based upon trait anxiety [148]. However, we reasoned that the outbred rats did not exhibit severe anxiety-related phenotypes, so we tested rats that were treated with FG7142, a benzodiazepine inverse agonist that is highly anxiogenic [158], and found that the FG7142-treated rats exhibited significantly more SIB than did vehicle-treated rats during the pemoline regimen [148]. Accordingly, it appears that anxiety may be another important driver of vulnerability for self-injury, but only if the anxiety phenotype is in the pathological range.

50

Darragh P. Devine

In addition, we found that innate vulnerability for pemolineinduced SIB can be modified by environmental manipulations. Since stress exposure appears to exacerbate SIB in clinical samples [12, 29], we examined the impact of repeated social defeat stress on pemoline-induced SIB. In this experiment, emotionally stressed rats exhibited earlier onset, greater incidence, and more severe expression of self-injury than handled controls did [159, 160]. In contrast, Bloom and colleagues [161] reported that stress exposure did not alter expression of SIB when rats were treated with pemoline. The discrepancy between these experimental outcomes is likely due to differences in experimental design, including the types of stressor used. In the study that reported that stress enhances vulnerability for SIB, pemoline-treated rats with a history of repeated social defeat stress were contrasted with pemoline-treated unstressed controls. In the study with the negative outcome, pemoline-treated rats that were exposed to discriminated avoidance were contrasted with pemoline-treated rats that were exposed to discriminated positive reinforcement. Most importantly, both studies used a high dose of pemoline (200 mg/kg). We have consistently found that lower pemoline doses reliably differentiate innate characteristics and manipulations that contribute to individual differences in vulnerability for pemoline-induced SIB [148, 155], whereas higher doses produce less sensitive outcomes. Vulnerability for pemoline-induced SIB was also exacerbated by early environmental deprivation. In this experiment, rats were housed in enriched environments (complex cages with toys, shelters, and social pairs) or impoverished environments (isolation in austere stainless steel cages) for 65 days postweaning. The rats in the impoverished environments exhibited greater tissue injury than did the enriched rats when treated with pemoline during the final 5 days of the experiment [160]. Overall, these findings concur with data from human self-injurers, and they provide additional tools with which to manipulate and study individual differences in vulnerability for SIB. 2.5

Bay K 8644

Bay K 8644 is a dihydropyridine L-type Ca2+ channel agonist that induces dystonia and self-injurious behavior in mice [162–164], especially if administered during early postweaning development [164]. Importantly, the Bay K 8644-induced SIB was blocked specifically by administration of the dihydropyridine L-type Ca2+ channel antagonists nifedipine, nimodipine, and nitrendipine, but not by the nondihydropyridine antagonists diltiazem, flunarizine, or verapamil [164]. The SIB was augmented by administration of the indirect dopamine agonists amphetamine and GBR 12909 [162], the monoamine oxidase inhibitor clorgyline [163], and the serotonin uptake inhibitor fluoxetine [163]. Bay K 8644-induced SIB was attenuated when vesicular stores of dopamine were depleted by administration of reserpine or tetrabenazine [162] or when

Animal Models of Self-Injurious Behavior: An Update

51

serotonin was depleted by administration of p-chlorophenylalanine or 5,7-dihydroxytryptamine [163], suggesting involvement of dopaminergic and serotonergic systems. Bay K 8644-induced SIB was also attenuated by co-administration of SCH-23390, SKF-38566 (D1/D5 dopamine receptor antagonists), U-99194, or GR-103691 (D3 antagonists) [165]. On the other hand, L-741,626 and L-745,870 (D2 and D4 antagonists, respectively) were ineffective. The effects of Bay K 8644 were also attenuated in D3 receptor knockout mice, but they were exaggerated in D1 knockouts [165]. The contrary effects in D1 knockout mice were attributed to delays in physical maturation of these mice. Thus, taken all together, the data suggest that dopaminergic and serotonergic neurotransmissions play important roles in the induction of SIB by Bay K 8644 in mice and its behavioral effects appear to be mediated through D1, D3, and/or D5 receptor signaling actions. 2.6 HPRT Knockout Mice

HPRT knockout mice have been developed as an animal model of the biological impairment found in Lesch-Nyhan syndrome. These mice exhibit complete enzymatic inactivity of the HPRT molecule [166, 167] and greatly elevated purine biosynthesis [168, 169], closely resembling neuropathological features of Lesch-Nyhan syndrome. They also have significant reductions in striatal dopamine content [170–172], but it should be noted that the dopamine deficiency (approximately 19% depletion) is not as extreme as in Lesch-Nyhan patients [171]. Despite the biochemical similarities between HPRT knockout mice and Lesch-Nyhan patients, these knockouts do not show any of the behavioral symptoms that are seen in Lesch-Nyhan syndrome. In particular, the mice do not exhibit motoric dysfunction or SIB [171, 173], even when challenged with apomorphine [171]. Furthermore, these mice do not differ from wild-type mice in expression of SIB when treated with clonidine or Bay K 8644 [174], both of which produce SIB in mice [162–164, 175, 176]. It is currently unclear why HPRT knockout mice fail to exhibit behavioral symptoms of LeschNyhan syndrome (apart from the differences in the levels of dopamine neurotransmission). One possibility is that mice are not as dependent upon this purine salvage enzyme as humans are (in which case another purine salvage enzyme, PRPP, might compensate for the missing HPRT). However, a recent study may help to cast light on the discrepancy between the behavioral data in HPRT KOs and humans with Lesch-Nyhan syndrome. Phosphoribosyl transferase domain containing 1 (PRTFDC1) gene is a paralog of HPRT. It is expressed as a functional protein in humans but not in mice. Interestingly, when human PTRFDC1 was expressed in HPRT knockout mice, it exhibited elevated aggression and amphetamine-induced stereotypy, which is thought to partially resemble the phenotype of humans with Lesch-Nyhan syndrome [177]. Further characterization of the biochemistry and behavior of these HPRT/PTRFDC1 transgenic mice is warranted.

52

3

Darragh P. Devine

Summary and Discussion Dysregulation of dopamine function constitutes a neurobiological feature upon which our knowledge of clinical SIB [43–47] and animal models of SIB [96–100, 117–119, 138–140, 149–150, 162] converge. Accordingly, the mechanisms that produce this dysregulation need to be investigated thoroughly in animal models of SIB, so that we might develop a greater understanding of the neurobiological basis of this behavioral pathology. Additional convergence is found in assays of serotonergic [43, 108–112, 147, 163] and opioid [51–56, 67–68, 147, 178] function. There is a clear need to investigate these potential neurotransmitter contributions more extensively. Individual differences in vulnerability to exhibit SIB are also apparent in both clinical populations [4, 13–14, 31, 179–180] and experimentally induced SIB [132, 148, 155, 159]. These individual differences have not received a lot of attention, but early environmental impoverishment [27, 87] and exposure to environmental stress [28–30, 95, 115–116, 121, 159] are common elements that may help to drive vulnerability for SIB. In fact, a key common element in vulnerability for SIB in clinical samples and animal models may be individual differences in affective and physiological responsiveness to stress. Degree of intellectual handicap is also an important determinant of vulnerability for SIB in clinical samples [31], and this concurs with evidence from cortical lesions in pemoline-treated rats [143]. The utility of the neonatal 6-OHDA, early environmental impoverishment, and pemoline models is well-established, and further investigations of the biochemical basis of SIB in these models are important. In contrast, the caffeine model appears to have limited utility, owing to the toxic actions of the doses that are required [132]. The HPRT knockout mouse surprisingly does not exhibit motor abnormalities or SIB [171, 173], but the PRTFDC1-expressing HPRT KO mouse [177] may be a promising lead. Since SIB is prevalent in multiple genetically distinct syndromes and can be invoked by multiple manipulations in animal models, we could conclude that this behavior is driven by multiple distinct pathophysiological processes in different syndromes and across different animal models. However, it appears more reasonable to conclude that multiple different neurochemical abnormalities may contribute to the pathophysiology of SIB in the various diagnostic groups and in the various animal models, but there appear to be common elements upon which these inputs coalesce. For example, it was recently reported that the Ca2+ channel antagonist nifedipine blocks the induction of SIB in four distinct animal models of SIB. Those models are the neonatal 6-OHDA, Bay K 8644, pemoline,

Animal Models of Self-Injurious Behavior: An Update

53

and methamphetamine model of SIB in mice [181]. Furthermore, NMDA receptor antagonists have attenuated SIB in both the neonatal 6-OHDA [182] and pemoline models of SIB [141, 151]. It is unclear if the beneficial effects of nifedipine will carry over to clinical populations, since this drug has not yet been evaluated in clinical trials. However, the convergent findings with MK-801 in animal models appear to be particularly compelling. This glutamate receptor antagonist cannot be administered to humans because it has psychotomimetic actions [183]. However, drugs that diminish glutamate release (e.g., riluzole, lamotrigine) are showing promise for attenuating the expression of SIB in a broad variety of clinical disorders [184–187]. References 1. Lesch M, Nyhan WL (1964) A familial disorder of uric acid metabolism and central nervous system function. Am J Med 36:561–570 2. Nyhan WL (1968) Seminars on the LeschNyhan syndrome: summary of clinical features. Fed Proc 27:1034–1041 3. Stein DJ, Keating J, Zar HJ, Hollander E (1994) A survey of the phenomenology and pharmacotherapy of compulsive and impulsive-aggressive symptoms in PraderWilli syndrome. J Neuropsychiatry Clin Neurosci 6:23–29 4. Symons FJ, Butler MG, Sanders MD, Feurer ID, Thompson T (1999) Self-injurious behavior and Prader-Willi syndrome: behavioral forms and body locations. Am J Ment Retard 104:260–269 5. Hardiman RL, McGill P (2018) How common are challenging behaviours amongst individuals with Fragile X syndrome? A systematic review. Res Dev Disabil 76:99–109 6. Hall SS, Hustyi KM, Barnett RP (2018) Examining the influence of socialenvironmental variables on self-injurious behaviour in adolescent boys with fragile X syndrome. J Intellect Disabil Res 62 (12):1072–1085 7. Poustka F, Lisch S (1993) Autistic behaviour domains and their relation to self-injurious behaviour. Acta Paedopsychiatr 56:69–73 8. Wing L (1975) The syndrome of early childhood autism. Br J Psychiatry Spec No 9:349–360 9. Oliver C, Murphy GH, Corbett JA (1987) Self-injurious behavior in people with mental handicap: a total population study. J Ment Defic Res 31:147–162 10. Ando H, Yoshimura I (1979) Speech skill levels and prevalence of maladaptive behaviors

in autistic and mentally retarded children: a statistical study. Child Psychiatry Hum Dev 10:85–90 11. Schroeder SR, Schroeder CS, Smith B, Dalldorf J (1978) Prevalence of self-injurious behaviors in a large state facility for the retarded: a three-year follow-up study. J Autism Child Schizophr 8:261–269 12. Anderson LT, Ernst M (1994) Self-injury in Lesch-Nyhan disease. J Autism Dev Disord 24:67–81 13. Clarke DJ, Waters J, Corbett JA (1989) Adults with Prader-Willi syndrome: abnormalities of sleep and behaviour. J R Soc Med 82:21–24 14. Baghdadli A, Pascal C, Grisi S, Aussilloux C (2003) Risk factors for self-injurious behaviours among 222 young children with autistic disorders. J Intellect Disabil Res 47:622–627 15. Sigafoos J, Elkins J, Kerr M, Attwood T (1994) A survey of aggressive behaviour among a population of persons with intellectual disability in Queensland. J Intellect Disabil Res 38:369–381 16. Durand VM, Carr EG (1985) Self-injurious behavior: motivating conditions and guidelines for treatment. School Psychol Rev 14:171–176 17. Carr EG, Durand VM (1985) Reducing behavior problems through functional communication training. J Appl Behav Anal 18:111–126 18. Iwata BA, Dorsey MF, Slifer KJ, Bauman KE, Richman GS (1982) Toward a functional analysis of self-injury, analysis & intervention. Dev Dis 2:3–30 19. Bird F, Dores PA, Moniz D, Robinson J (1989) Reducing severe aggressive and self-

54

Darragh P. Devine

injurious behaviors with functional communication training. Am J Ment Retard 94:37–48 20. Sigafoos J, Meikle B (1996) Functional communication training for the treatment of multiply determined challenging behavior in two boys with autism. Behav Modif 20:60–84 21. Barron JL, Sandman CA (1984) Self-injurious behavior and stereotypy in an institutionalized mentally retarded population. Appl Res Ment Retard 5:499–511 22. Griffin JC, Williams DE, Stark MT, Altmeyer BK, Mason M (1986) Self-injurious behavior: a state-wide prevalence survey of the extent and circumstances. Appl Res Ment Retard 7:105–116 23. Maurice P, Trudel G (1982) Self-injurious behavior prevalence and relationships to environmental events. Monogr Am Assoc Ment Defic 5:81–103 24. Singh NN (1977) Prevalence of self-injury in institutionalised retarded children. N Z Med J 86:325–327 25. Favell JE, McGimsey JF, Schell RM (1982) Treatment of self-injury by providing alternate sensory activities, analysis & intervention. Dev Dis 2:83–104 26. Favell JE, Azrin NA, Baumeister AA, Carr EG, Dorsey MF, Forehand R, Foxx RM, Lovaas OI, Rincover A, Risley T, Romanczyk RG, Russo DC, Schroeder SR, Solnick JV (1982) The treatment of self-injurious behavior. Behav Ther 13:529–554 27. Beckett C, Bredenkamp D, Castle J, Groothues C, O’Connor TG, Rutter M (2002) Behavior patterns associated with institutional deprivation: a study of children adopted from Romania. J Dev Behav Pediatr 23:297–303 28. Sovner R, Fogelman S (1996) Irritability and mental retardation. Semin Clin Neuropsychiatry 1:105–114 29. Lindauer SE, DeLeon IG, Fisher WW (1999) Decreasing signs of negative affect and correlated self-injury in an individual with mental retardation and mood disturbances. J Appl Behav Anal 32:103–106 30. Verhoeven WMA, Tuinier S, van den Berg YWMM, Coppus AMW, Fekkes D, Pepplinkhuizen L, Thijssen JHH (1999) Stress and self-injurious behavior; hormonal and serotonergic parameters in mentally retarded subjects. Pharmacopsychiatry 32:13–20 31. Rojahn J, Esbensen AJ (2002) Epidemiology of self-injurious behavior in mental retardation: a review. In: Schroeder SR, OsterGranite ML, Thompson T (eds) Self-injurious

behavior: gene-brain-behavior relationships. American Psychological Association, Washington, DC, pp 41–77 32. Durand VM, Carr EG (1991) Functional communication training to reduce challenging behavior: maintenance and application in new settings. J Appl Behav Anal 24:251–264 33. Azrin NH, Gottlieb L, Hughart L, Wesolowski MD, Rahn T (1975) Eliminating selfinjurious behavior by educative procedures. Behav Res Ther 13:101–111 34. Cowdery GE, Iwata BA, Pace GM (1990) Effects and side effects of DRO as treatment for self-injurious behavior. J Appl Behav Anal 23:497–506 35. Tarpley HD, Schroeder SR (1979) Comparison of DRO and DRI on rate of suppression of self-injurious behavior. Am J Ment Defic 84:188–194 36. Bruhl HH, Fielding L, Joyce M, Peters W, Wieseler N (1982) Thirty-month demonstration project for treatment of self-injurious behavior in severely retarded individuals. Monogr Am Assoc Ment Defic 5:191–275 37. Vollmer TR (1994) The concept of automatic reinforcement: implications for behavioral research in developmental disabilities. Res Dev Disabil 15:187–207 38. Iwata BA, Pace GM, Dorsey MF, Zarcone JR, Vollmer TR, Smith RG, Rodgers TA, Lerman DC, Shore BA, Mazalesk JL et al (1994) The functions of self-injurious behavior: an experimental-epidemiological analysis. J Appl Behav Anal 27:215–240 39. Underwood LA, Figueroa RG, Thyer BA, Nzeocha A (1989) Interruption and DRI in the treatment of self-injurious behavior among mentally retarded and autistic selfrestrainers. Behav Modif 13:471–481 40. Olson L, Houlihan D (2000) A review of behavioral treatments used for Lesch-Nyhan syndrome. Behav Modif 24:202–222 41. Newcomb ET, Hagopian LP (2018) Treatment of severe problem behaviour in children with autism spectrum disorder and intellectual disabilities. Int Rev Psychiatry 30:96–109 42. Seegmiller JE, Rosenbloom FM, Kelley WN (1967) Enzyme defect associated with a sex-linked human neurological disorder and excessive purine synthesis. Science 155:1682–1684 43. Lloyd KG, Hornykiewicz O, Davidson L, Shannak K, Farley I, Goldstein M, Shibuya M, Kelley WN, Fox IH (1981) Biochemical evidence of dysfunction of brain neurotransmitters in the Lesch-Nyhan syndrome. N Engl J Med 305:1106–1111

Animal Models of Self-Injurious Behavior: An Update 44. Ernst M, Zametkin AJ, Matochik JA, Pascualvaca D, Jons PH, Hardy K, Hankerson JG, Doudet DJ, Cohen RM (1996) Presynaptic dopaminergic deficits in Lesch-Nyhan disease. N Engl J Med 334:1568–1572 45. Ernst M, Zametkin AJ, Matochik JA, Pascualvaca D, Cohen RM (1997) Low medial prefrontal dopaminergic activity in autistic children. Lancet 350:638 46. Brucke T, Sofic E, Killian W, Rett A, Riederer P (1987) Reduced concentrations and increased metabolism of biogenic amines in a single case of Rett-syndrome: a postmortem brain study. J Neural Transm 68:315–324 47. Saito Y, Ito M, Hanaoka S, Ohama E, Akaboshi S, Takashima S (1999) Dopamine receptor upregulation in Lesch-Nyhan syndrome: a postmortem study. Neuropediatrics 30:66–71 48. Rosenberger-Debiesse J, Coleman M (1986) Preliminary evidence for multiple etiologies in autism. J Autism Dev Disord 16:385–392 49. Page T, Coleman M (1998) De novo purine synthesis is increased in the fibroblasts of purine autism patients. Adv Exp Med Biol 431:793–796 50. Sweetman L, Nyhan WL (1970) Detailed comparison of the urinary excretion of purines in a patient with the Lesch-Nyhan syndrome and a control subject. Biochem Med 4:121–134 51. Sandman CA, Barron JL, Chicz-DeMet A, DeMet EM (1990) Plasma β-endorphin levels in patients with self-injurious behavior and stereotypy. Am J Ment Retard 95:84–92 52. Coid J, Allolio B, Rees LH (1983) Raised plasma metenkephalin in patients who habitually mutilate themselves. Lancet 2:545–546 53. Gillberg C, Terenius L, Lonnerholm G (1985) Endorphin activity in childhood psychosis. Spinal fluid levels in 24 cases. Arch Gen Psychiatry 42:780–783 54. Sandman CA (1988) β-endorphin disregulation in autistic and self-injurious behavior: a neurodevelopmental hypothesis. Synapse 2:193–199 55. Willemsen-Swinkels SHN, Buitelaar JK, Weijnen FG, Thijssen JHH, Van Engeland H (1996) Plasma beta-endorphin concentrations in people with learning disability and self-injurious and/or autistic behaviour. Br J Psychiatry 168:105–109 56. Sandman CA, Hetrick W, Talyor D, Marion S, Chicz-DeMet A (2000) Uncoupling of proopiomelanocortin (POMC) fragments is related to self-injury. Peptides 21:785–791

55

57. Jankovic J, Caskey TC, Stout JT, Butler IJ (1988) Lesch-Nyhan syndrome: a study of motor behavior and cerebrospinal fluid neurotransmitters. Ann Neurol 23:466–469 58. Symons FJ, Sutton KA, Walker C, Bodfish JW (2003) Altered diurnal pattern of salivary substance P in adults with developmental disabilities and chronic self-injury. Am J Ment Retard 108:13–18 59. Kemp AS, Fillmore PT, Lenjavi MR, Lyon M, Chicz-DeMet A, Touchette PE, Sandman CA (2008) Temporal patterns of self-injurious behavior correlate with stress hormone levels in the developmentally disabled. Psychiatry Res 157:181–189 60. Crews WDJ, Bonaventura S, Rowe FB, Bonsie D (1993) Cessation of long-term naltrexone therapy and self-injury: a case study. Res Dev Disabil 14:331–340 61. Gillman MA, Sandyk R (1985) Opiatergic and dopaminergic function in Lesch-Nyhan syndrome. Am J Psychiatry 142:1226 62. Herman BH, Hammock MK, Arthur-SmithA, Egan J, Chatoor I, Werner A, Zelnick N (1987) Naltrexone decreases self-injurious behavior. Ann Neurol 22:550552 63. Richardson JS, Zaleski WA (1983) Naloxone and self-mutilation. Biol Psychiatry 18:99–101 64. Sandman CA, Barron JL, Crinella FM, Donnelly JF (1987) Influence of naloxone on brain and behavior of a self-injurious woman. Biol Psychiatry 22:899–906 65. Sandman CA, Barron JL, Colman H (1990) An orally administered opiate blocker, naltrexone, attenuates self-injurious behavior. Am J Ment Retard 95:93102 66. Sandyk R (1985) Naloxone abolishes selfinjuring in a mentally retarded child. Ann Neurol 17:520 67. Benjamin E, Buot-Smith T (1993) Naltrexone and fluoxetine in Prader-Willi syndrome. J Am Acad Child Adolesc Psychiatry 32:870–873 68. Symons FJ, Thompson A, Rodriguez MC (2004) Self-injurious behavior and the efficacy of naltrexone treatment: a quantitative synthesis. Ment Retard Dev Disabil Res Rev 10:193–200 69. Willemsen-Swinkels SHN, Buitelaar JK, Nijhof GJ, Van Engeland H (1995) Failure of naltrexone hydrochloride to reduce selfinjurious and autistic behavior in mentally retarded adults - double-blind placebo-controlled studies. Arch Gen Psychiatry 52:766–773

56

Darragh P. Devine

70. Davidson PW, Kleene BM, Carroll M, Rockowitz RJ (1983) Effects of naloxone on selfinjurious behavior: a case study. Appl Res Ment Retard 4:1–4 71. Allen SM, Rice SN (1996) Risperidone antagonism of self-mutilation in a Lesch-Nyhan patient. Prog Neuropsychopharmacol Biol Psychiatry 20:793–800 72. Cohen SA, Ihrig K, Lott RS, Kerrick JM (1998) Risperidone for aggression and selfinjurious behavior in adults with mental retardation. J Autism Dev Disord 28:229–233 73. Horrigan JP, Barnhill LJ (1997) Risperidone and explosive aggressive autism. J Autism Dev Disord 27:313–323 74. Gualtieri CT, Schroeder SR (1989) Pharmacotherapy for self-injurious behavior: preliminary tests of the D1 hypothesis. Psychopharmacol Bull 25:364–371 75. Singh NN, Winton AS (1984) Behavioral monitoring of pharmacological interventions for self-injury. Appl Res Ment Retard 5:161–170 76. Gedye A (1990) Dietary increase in serotonin reduces self-injurious behaviour in a Down’s syndrome adult. J Ment Defic Res 34:195–204 77. Markowitz PI (1992) Effect of fluoxetine on self-injurious behavior in the developmentally disabled: a preliminary study. J Clin Psychopharmacol 12:27–31 78. Sovner R, Fox CJ, Lowry MJ, Lowry MA (1993) Fluoxetine treatment of depression and associated self-injury in two adults with mental retardation. J Intellect Disabil Res 37:301–311 79. Warnock JK, Kestenbaum T (1992) Pharmacologic treatment of severe skin-picking behaviors in Prader-Willi syndrome. Two case reports. Arch Dermatol 128:1623–1625 80. Frith CD, Johnstone EC, Joseph MH, Powell RJ, Watts RWE (1976) Double-blind clinical trial of 5-hydroxytryptophan in a case of Lesch-Nyhan syndrome. J Neurol Neurosurg Psychiatry 39:656–662 81. Ciaranello RD, Anders TF, Barchas JD, Berger PA, Cann HM (1976) The use of 5-hydroxytryptophan in a child with LeschNyhan syndrome. Child Psychiatry Hum Dev 7:127–133 82. Davanzo PA, Belin TR, Widawski MH, King BH (1998) Paroxetine treatment of aggression and self-injury in persons with mental retardation. Am J Ment Retard 102:427–437 83. Anders TF, Cann HM, Ciaranello RD, Barchas JD, Berger PA (1978) Further observations on the use of 5-hydroxytryptophan in

a child with Lesch-Nyhan syndrome. Neuropadiatrie 9:157–166 84. Janowsky DS, Kraus JE, Barnhill J, Elamir B, Davis JM (2003) Effects of topiramate on aggressive, self-injurious, and disruptive/ destructive behaviors in the intellectually disabled: an open-label retrospective study. J Clin Psychopharmacol 23:500–504 85. Smathers SA, Wilson JG, Nigro MA (2003) Topiramate effectiveness in Prader-Willi syndrome. Pediatr Neurol 28:130–133 86. Shapira NA, Lessig MC, Murphy TK, Driscoll DJ, Goodman WK (2002) Topiramate attenuates self-injurious behaviour in PraderWilli syndrome. Int J Neuropsychopharmacol 5:141–145 87. Harlow HF, Harlow MK (1962) Social deprivation in monkeys. Scient Am 207:136–146 88. Novak MA, Crockett CM, Sackett GP (2002) Self-injurious behavior in captive macaque monkeys. In: Schroeder SR, Oster-Granite ML, Thompson T (eds) Self-injurious behavior: gene-brain-behavior relationships. American Psychological Association, Washington, DC, pp 151–161 89. Dodman NH, Normile JA, Shuster L, Rand W (1994) Equine self-mutilation syndrome (57 cases). J Am Vet Med Assoc 204:1219–1223 90. McDonnell SM (2008) Practical review of self-mutilation in horses. Anim Reprod Sci 107:219–228 91. Jenkins JR (2001) Feather picking and selfmutilation in psittacine birds. Vet Clin North Am Exot Anim Pract 4:651–667 92. Schwartz S (1996) Animal behavior case of the month. A cat was referred for evaluation of self-mutilation. J Am Vet Med Assoc 208:1813–1814 93. Schwartz S (2002) Separation anxiety syndrome in cats: 136 cases (1991–2000). J Am Vet Med Assoc 220:1028–1033 94. Iglauer F, Beig C, Dimigen J, Gerold S, Gocht A, Seeburg A, Steier S, Willmann F (1995) Hereditary compulsive selfmutillating behaviour in laboratory rabbits. Lab Anim 29:385–393 95. Goodall J (1986) The chimpanzees of Gombe: patterns of behavior. Belknap Press of Harvard University Press, Cambridge, MA 96. Breese GR, Baumeister AA, McCown TJ, Emerick SG, Frye GD, Crotty K, Mueller RA (1984) Behavioral differences between neonatal and adult 6-hydroxydopamine-treated rats to dopamine agonists: relevance to neurological symptoms in clinical syndromes

Animal Models of Self-Injurious Behavior: An Update with reduced brain dopamine. J Pharmacol Exp Ther 231:343–354 97. Breese GR, Baumeister AA, McCown TJ, Emerick SG, Frye GD, Mueller RA (1984) Neonatal-6-hydroxydopamine treatment: model of susceptibility for self-mutilation in the Lesch-Nyhan syndrome. Pharmacol Biochem Behav 21:459–461 98. Sivam SP, Pugazhenthi S, Pugazhenthi V, Brown H (2008) L-DOPA-induced activation of striatal p38MAPK and CREB in neonatal dopaminergic denervated rat: relevance to self-injurious behavior. J Neurosci Res 86:339–349 99. Sivam SP (1989) D1 dopamine receptormediated substance P depletion in the striatonigral neurons of rats subjected to neonatal dopaminergic denervation: implications for self-injurious behavior. Brain Res 500:119–130 100. Neal BS, Joyce JN (1992) Neonatal 6-OHDA lesions differentially affect striatal D1 and D2 receptors. Synapse 11:35–46 101. Molina-Holgado E, Van Gelder NM, Dewar KM, Reader TA (1995) Dopamine receptor alterations correlate with increased GABA levels in adult rat neostriatum: effects of a neonatal 6-hydroxydopamine denervation. Neurochem Int 27:443–451 102. Radja F, El Mansari M, Soghomonian J-J, Dewar KM, Ferron A, Reader TA, Descarries L (1993) Changes of D1 and D2 receptors in adult rat neostriatum after neonatal dopamine denervation: in situ hybridization and iontophoresis. Neuroscience 57:635–648 103. Breese GR, Baumeister AA, Napier TC, Frye GD, Mueller RA (1985) Evidence that D-1 dopamine receptors contribute to the supersensitive behavioral responses induced by l-dihydroxyphenylalanine in rats treated neonatally with 6-hydroxydopamine. J Pharmacol Exp Ther 235:287–295 104. Criswell HE, Mueller RA, Breese GR (1992) Pharmacologic evaluation of SCH-39166, A-69024, NO-0756, and SCH-23390 in neonatal-6-OHDA-lesioned rats. Further evidence that self-mutilatory behavior induced by L-DOPA is related to D1 dopamine receptors. Neuropsychopharmacology 7:95–103 105. Criswell HE, Mueller RA, Breese GR (1988) Assessment of purine-dopamine interactions in 6-hydroxydopamine-lesioned rats: evidence for pre- and postsynaptic influences by adenosine. J Pharmacol Exp Ther 244:493–500 106. Fletcher GH, Starr MS (1987) Topography of dopamine behaviours mediated by D1 and

57

D2 receptors revealed by intrastriatal injection of SKF 38393, lisuride and apomorphine in rats with a unilateral 6-hydroxydopamineinduced lesion. Neuroscience 20:589–597 107. Allen SM, Freeman JN, Davis WM (1998) Evaluation of risperidone in the neonatal 6-hydroxydopamine model of Lesch-Nyhan syndrome. Pharmacol Biochem Behav 59:327–330 108. El Mansari M, Radja F, Ferron A, Reader TA, Molina-Holgado E, Descarries L (1994) Hypersensitivity to serotonin and its agonists in serotonin-hyperinnervated neostriatum after neonatal dopamine denervation. Eur J Pharmacol 261:171–178 109. Jackson D, Abercrombie ED (1992) In vivo neurochemical evaluation of striatal serotonergic hyperinnervation in rats depleted of dopamine at infancy. J Neurochem 58:890–897 110. Mrini A, Soucy J-P, Lafaille F, Lemoine P, Descarries L (1995) Quantification of the serotonin hyperinnervation in adult rat neostriatum after neonatal 6-hydroxydopamine lesion of nigral dopamine neurons. Brain Res 669:303–308 111. Towle AC, Criswell HE, Maynard EH, Lauder JM, Joh TH, Mueller RA, Breese GR (1989) Serotonergic innervation of the rat caudate following a neonatal 6-hydroxydopamine lesion: an anatomical, biochemical and pharmacological study. Pharmacol Biochem Behav 34:367–374 112. Allen SM, Davis WM (1999) Relationship of dopamine to serotonin in the neonatal 6-OHDA rat model of Lesch-Nyhan syndrome. Behav Pharmacol 10:467–474 113. Stodgell CJ, Loupe PS, Schroeder SR, Tessel RE (1998) Cross-sensitization between footshock stress and apomorphine on selfinjurious behavior and neostriatal catecholamines in a rat model of Lesch-Nyhan syndrome. Brain Res 783:10–18 114. Sorg BA, Kalivas PW (1991) Effects of cocaine and footshock stress on extracellular dopamine levels in the ventral striatum. Brain Res 559:29–36 115. Cross HA, Harlow HF (1965) Prolonged and progressive effects of partial isolation on the behavior of macaque monkeys. J Exp Res Pers 1:39–49 116. Gluck JP, Sackett GP (1974) Frustration and self-aggression in social isolate rhesus monkeys. J Abnorm Psychol 83:331–334 117. Clarke AS, Ebert MH, Schmidt DE, McKinney WT, Kraemer GW (1999) Biogenic amine activity in response to fluoxetine and

58

Darragh P. Devine

desipramine in differentially reared rhesus monkeys. Biol Psychiatry 46:221–228 118. Lewis MH, Gluck JP, Beauchamp AJ, Keresztury MF, Mailman RB (1990) Long-term effects of early social isolation in Macaca mulatta: changes in dopamine receptor function following apomorphine challenge. Brain Res 513:67–73 119. Martin LJ, Spicer DM, Lewis MH, Gluck JP, Cork LC (1991) Social deprivation of infant rhesus monkeys alters the chemoarchitecture of the brain: I. Subcortical regions. J Neurosci 11:3344–3358 120. Young LD, Suomi SS, Harlow HF, McKinney WT Jr (1973) Early stress and later response to separation in rhesus monkeys. Am J Psychiatry 130:400–405 121. Davenport MD, Lutz CK, Tiefenbacher S, Novak MA, Meyer JS (2008) A rhesus monkey model of self-injury: effects of relocation stress on behavior and neuroendocrine function. Biol Psychiatry 63:990–996 122. Tiefenbacher S, Novak MA, Jorgensen MJ, Meyer JS (2000) Physiological correlates of self-injurious behavior in captive, sociallyreared rhesus monkeys. Psychoneuroendocrinology 25:799–817 123. Tiefenbacher S, Novak MA, Marinus LM, Chase WK, Miller JA, Meyer JS (2004) Altered hypothalamic-pituitary-adrenocortical function in rhesus monkeys (Macaca mulatta) with self-injurious behavior. Psychoneuroendocrinology 29:501–515 124. Fulford AJ, Marsden CA (1998) Effect of isolation-rearing on conditioned dopamine release in vivo in the nucleus accumbens of the rat. J Neurochem 70:384–390 125. Peters JM (1967) Caffeine-induced hemorrhagic automutilation. Arch Int Pharmacodyn Ther 169:139–146 126. Hoefnagel D (1968) Seminars on the LeschNyhan syndrome: summary. Fed Proc 27:1042–1046 127. Ferrer I, Costell M, Grisolia S (1982) LeschNyhan syndrome like behavior in rats from caffeine ingestion. FEBS Lett 141:275–278 128. Minana MD, Portoles M, Jorda A, Grisolia S (1984) Lesch-Nyhan syndrome, caffeine model: increases of purine and pyrimidine enzymes in rat brain. J Neurochem 43:1556–1560 129. Minana MD, Grisolia S (1986) Caffeine ingestion by rats increases noradrenaline turnover and results in self-biting. J Neurochem 47:728–732 130. Mueller K, Saboda S, Palmour RA, Nyhan WL (1982) Self-injurious behaviour

produced in rats by daily caffeine and continuous amphetamine. Pharmacol Biochem Behav 17:613–617 131. Mueller K, Nyhan WL (1983) Clonidine potentiates drug induced self-injurious behavior in rats. Pharmacol Biochem Behav 18:891–894 132. Kies SD, Devine DP (2004) Self-injurious behaviour: a comparison of caffeine and pemoline models in rats. Pharmacol Biochem Behav 79:587–598 133. Snyder SH (1985) Adenosine as a neuromodulator. In: Cowan WM, Shooter EM, Stevens CF, Thompson RF (eds) Annual review of neuroscience. Annual Reviews Inc., Palo Alto, CA, pp 103–124 134. Ferre´ S, Fuxe K, von Euler G, Johansson B, Fredholm BB (1992) Adenosine-dopamine interactions in the brain. Neuroscience 51:501–512 135. Brown SJ, James S, Reddington M, Richardson PJ (1990) Both A1 and A2a purine receptors regulate striatal acetylcholine release. J Neurochem 55:31–38 136. Brown SJ, Gill R, Evenden JL, Iversen SD, Richardson PJ (1991) Striatal A2 receptor regulates apomorphine-induced turning in rats with unilateral dopamine denervation. Psychopharmacology 103:78–82 137. Casas-Bruge M, Almenar C, Grau IM, Jane J, Herrera-Marschitz M, Ungerstedt U (1985) Dopaminergic receptor supersensitivity in self-mutilatory behavior of Lesch-Nyhan disease. Lancet 1(8435):991–992 138. King BH, Cromwell HC, Lee HT, Behrstock SP, Schmanke T, Maidment NT (1998) Dopaminergic and glutamatergic interactions in the expression of self-injurious behavior. Dev Neurosci 20:180–187 139. Everett GM (1976) Comparative pharmacology of amphetamine and pemoline on biogenic amine systems. Fed Proc 35:405 140. Cromwell HC, King BH, Levine MS (1997) Pemoline alters dopamine modulation of synaptic responses of neostriatal neurons in vitro. Dev Neurosci 19:497–504 141. King BH, Au D, Poland RE (1995) Pretreatment with MK-801 inhibits pemolineinduced self-biting behavior in prepubertal rats. Dev Neurosci 17:47–52 142. Mueller K, Hsiao S (1980) Pemoline-induced self-biting in rats and self-mutilation in the deLange syndrome. Pharmacol Biochem Behav 13:627–631 143. Cromwell HC, Levine MS, King BH (1999) Cortical damage enhances pemoline-induced

Animal Models of Self-Injurious Behavior: An Update self-injurious behavior in prepubertal rats. Pharmacol Biochem Behav 62:223–227 144. Mueller K, Nyhan WL (1982) Pharmacologic control of pemoline induced self-injurious behavior in rats. Pharmacol Biochem Behav 16:957–963 145. Genovese E, Napoli PA, Bolego-Zonta N (1969) Self-aggressiveness: a new type of behavioural change induced by pemoline. Life Sci 8:513–515 146. Mueller K, Hollingsworth E, Pettit H (1986) Repeated pemoline produces self-injurious behavior in adult and weanling rats. Pharmacol Biochem Behav 25:933938 147. Turner CA, Panksepp J, Bekkedal M, Borkowski C, Burgdorf J (1999) Paradoxical effects of serotonin and opioids in pemolineinduced self-injurious behavior. Pharmacol Biochem Behav 63:361–366 148. Yuan X, Devine DP (2016) The role of anxiety in vulnerability for self-injurious behaviour: studies in a rodent model. Behav Brain Res 311:201–209 149. Muehlmann AM, Brown BD, Devine DP (2008) Pemoline-induced self-injurious behavior: a rodent model of pharmacotherapeutic efficacy. J Pharmacol Exp Ther 324:214–223 150. Muehlmann AM, Devine DP (2008) Selfinjurious behavior: individual differences in neurotransmitter concentrations using an animal model. Keystone Symposium: Towards Identifying the Pathophysiology of Autistic Syndromes. C2:104 151. Muehlmann AM, Devine DP (2008) Glutamate-mediated neuroplasticity in an animal model of self-injurious behaviour. Behav Brain Res 189:32–40 152. Soderling TR, Derkach VA (2000) Postsynaptic protein phosphorylation and LTP. Trends Neurosci 23:75–80 153. Barnes CA, Danysz W, Parsons CG (1996) Effects of the uncompetitive NMDA receptor antagonist memantine on hippocampal longterm potentiation, short-term exploratory modulation and spatial memory in awake, freely moving rats. Eur J Neurosci 8:565–571 154. Chen HS, Wang YF, Rayudu PV, Edgecomb P, Neill JC, Segal MM, Lipton SA, Jensen FE (1998) Neuroprotective concentrations of the N-methyl-D-aspartate open-channel blocker memantine are effective without cytoplasmic vacuolation following post-ischemic administration and do not block maze learning or long-term potentiation. Neuroscience 86:1121–1132

59

155. Muehlmann AM, Wilkinson JA, Devine DP (2011) Individual differences in vulnerability for self-injurious behavior: studies using an animal model. Behav Brain Res 217:148–154 156. Stead JDH, Clinton S, Neal C, Schneider J, Jama A, Miller S, Vazquez DM, Watson SJ, Akil H (2006) Selective breeding for divergence in novelty-seeking traits: heritability and enrichment in spontaneous anxietyrelated behaviors. Behav Genet 36:697–712 157. Kabbaj M, Devine DP, Savage VR, Akil H (2000) Neurobiological correlates of individual differences in novelty-seeking behavior in the rat: differential expression of stress-related molecules. J Neurosci 20:6983–6986 158. Evans AK, Lowry CA (2007) Pharmacology of the beta-carboline FG-7,142, a partial inverse agonist at the benzodiazepine allosteric site of the GABA A receptor: neurochemical, neurophysiological, and behavioral effects. CNS Drug Rev 13:475–501 159. Muehlmann AM, Kies SD, Turner CA, Wolfman S, Lewis MH, Devine DP (2012) Self-injurious behavior: limbic dysregulation and stress effects in an animal model. J Intellect Disabil Res 56:490–500 160. Devine DP, Muehlmann AM (2009) Tiermodelle fu¨r selbstverletzendes Verhalten (Animal models of self-injurious behavior). In: Schmahl C, Stiglmayr C (eds) Selbstverletzendes Verhalten bei Stressassoziierten Erkrankungen (Self-injurious behaviour in stress-associated disorders). Verlag W. Kohlhammer, Stuttgart, Germany, pp 39–60 161. Bloom CM, Holly S, Miller AM (2012) Selfinjurious behavior vs. nonsuicidal self-injury, the CNS stimulant pemoline as a model of self-destructive behavior. Crisis 33:106–112 162. Kasim S, Jinnah HA (2003) Self-biting induced by activation of L-type calcium channels in mice: dopaminergic influences. Dev Neurosci 25:20–25 163. Kasim S, Egami K, Jinnah HA (2002) Selfbiting induced by activation of L-type calcium channels in mice: serotonergic influences. Dev Neurosci 24:322–327 164. Jinnah HA, Yitta S, Drew T, Kim BS, Visser JE, Rothstein JD (1999) Calcium channel activation and self-biting in mice. Proc Natl Acad Sci U S A 96:15228–15232 165. Kasim S, Blake BL, Fan X, Chartoff E, Egami K, Breese GR, Hess EJ, Jinnah HA (2006) The role of dopamine receptors in the neurobehavioral syndrome provoked by activation of L-type calcium channels in rodents. Dev Neurosci 28:505517

60

Darragh P. Devine

166. Hooper M, Hardy K, Handyside A, Hunter S, Monk M (1987) HPRT-deficient (LeschNyhan) mouse embryos derived from germline colonization by cultured cells. Nature 326:292–295 167. Kuehn MR, Bradley A, Robertson EJ, Evans MJ (1987) A potential animal model for Lesch-Nyhan syndrome through introduction of HPRT mutations into mice. Nature 326:295–298 168. Jinnah HA, Hess EJ, Wilson MC, Gage FH, Friedmann T (1992) Localization of hypoxanthine-guanine phosphoribosyltransferase mRNA in the mouse brain by in situ hybridization. Mol Cell Neurosci 3:64–78 169. Jinnah HA, Page T, Friedmann T (1993) Brain purines in a genetic mouse model of Lesch-Nyhan disease. J Neurochem 60:2036–2045 170. Dunnett SB, Sirinathsinghji DJ, Heavens R, Rogers DC, Kuehn MR (1989) Monoamine deficiency in a transgenic (Hprt-) mouse model of Lesch-Nyhan syndrome. Brain Res 501:401–406 171. Finger S, Heavens RP, Sirinathsinghji DJS, Kuehn MR, Dunnett SB (1988) Behavioral and neurochemical evaluation of a transgenic mouse model of Lesch-Nyhan syndrome. J Neurol Sci 86:203–213 172. Jinnah HA, Wojcik BE, Hunt M, Narang N, Lee KY, Goldstein M, Wamsley JK, Langlais PJ, Friedmann T (1994) Dopamine deficiency in a genetic mouse model of LeschNyhan disease. J Neurosci 14:1164–1175 173. Engle SJ, Womer DE, Davies PM, Boivin G, Sahota A, Simmonds HA, Stambrook PJ, Tischfield JA (1996) HPRT-APRT-deficient mice are not a model for Lesch-Nyhan syndrome. Hum Mol Genet 5:1607–1610 174. Kasim S, Jinnah HA (2002) Pharmacologic thresholds for self-injurious behavior in a genetic mouse model of Lesch-Nyhan disease. Pharmacol Biochem Behav 73:583–592 175. Razzak A, Fujiwara M, Oishi MR, Ueki S (1977) Possible involvement of a central noradrenergic system in automutilation induced by clonidine in mice. Jpn J Pharmacol 27:145–152 176. Bhattacharya SK, Jaiswal AK, Mukhopadhyay M, Datla KP (1988) Clonidine-induced automutilation in mice as a laboratory model for clinical self-injurious behaviour. J Psychiatr Res 22:43–50

177. Keebaugh AC, Mitchell HA, Gaval-Cruz M, Freeman KG, Edwards GL, Weinshenker D, Thomas JW (2011) PRTFDC1 is a genetic modifier of HPRT-deficiency in the mouse. PLoS One 6:e22381 178. King BH, Au D, Poland RE (1993) Low dose naltrexone inhibits pemoline-induced selfbiting behavior in prepubertal rats. J Child Adolsc Psychopharmacol 3:71–79 179. Rojahn J (1986) Self-injurious and stereotypic behavior of noninstitutionalized mentally retarded people: prevalence and classification. Am J Ment Defic 91:268–276 180. Rojahn J (1984) Self-injurious behavior in institutionalized severely/profoundly retarded adults: prevalence data and staff agreement. J Behav Assess 6:13–27 181. Blake BL, Muehlmann AM, Egami K, Breese GR, Devine DP, Jinnah HA (2007) Nifedipine suppresses self-injurious behaviors in animals. Dev Neurosci 29:241–250 182. Criswell HE, Johnson KB, Mueller RA, Breese GR (1993) Evidence for involvement of brain dopamine and other mechanisms in the behavioral action of the N-methyl-Daspartic acid antagonist MK-801 in control and 6-hydroxydopamine-lesioned rats. J Pharmacol Exp Ther 265:1001–1010 183. Ellison G (1995) The N-methyl-D-aspartate antagonists phencyclidine, ketamine and dizocilpine as both behavioral and anatomical models of the dementias. Brain Res Brain Res Rev 20:250–267 184. Davanzo PA, King BH (1996) Open trial lamotrigine in the treatment of self-injurious behavior in an adolescent with profound mental retardation. J Child Adolesc Psychopharmacol 6:273–279 185. Sasso DA, Kalanithi PS, Trueblood KV, Pittenger C, Kelmendi B, Wayslink S, Malison RT, Krystal JH, Coric V (2006) Beneficial effects of the glutamate-modulating agent riluzole on disordered eating and pathological skin-picking behaviors. J Clin Psychopharmacol 26:685–687 186. Pittenger C, Krystal JH, Coric V (2005) Initial evidence of the beneficial effects of glutamate-modulating agents in the treatment of self-injurious behavior associated with borderline personality disorder. J Clin Psychiatry 66:1492–1493 187. Rizvi ST (2002) Lamotrigine and borderline personality disorder. J Child Adolesc Psychopharmacol 12:365–366

Chapter 4 Bipolar Disorder: Its Etiology and How to Model in Rodents Nadja Freund and Georg Juckel Abstract Characterized by the switch of manic and depressive phases, bipolar disorder was described as early as the fifth century BC. Nevertheless up to date, the underlying neurobiology is still largely unclear, assuming a multifactor genesis with both biological-genetic and psychosocial factors. Significant process has been achieved in recent years in researching the causes of bipolar disorder with modern molecular biological (e.g., genetic and epigenetic studies) and imaging techniques (e.g., positron emission tomography (PET) and functional magnetic resonance imaging (fMRI)). In this chapter we will first summarize our recent knowledge on the etiology of bipolar disorder. We then discuss how several factors observed to contribute to bipolar disorder in human patients can be manipulated to generate rodent models for bipolar disorder. Finally, we will give an overview on behavioral test that can be used to assess bipolar-disorder-like behavior in rodents. Key words Bipolar disorder, Etiology, Rodent models, Genetic models, Environmental manipulation, Behavioral test

1 1.1

Introduction: Etiology of Bipolar Disorder Genetics

Twin, family, and adoption studies have shown that genetic factors highly contribute to the etiology of bipolar disorder. The concordance rate of monozygotic twins lies between 43% and 75% [1]. When a distinction between unipolar and bipolar disorder is made, the concordance rate is higher in bipolar twins than in unipolar ones, indicating a greater influence of genetic factors in bipolar disorder. The polarity (unipolar, bipolar) is usually consistent in monozygotic twins suggesting that unipolar and bipolar disorder show genetic similarities. The risk of disease for firstdegree relatives is significantly increased in bipolar disorder and is around 10% with a lifetime risk of 1% among the general population [2]. Finally, even in larger adoption studies, rates of bipolar disorder were greatly increased compared with the entire population. These results suggest that the disease is genetically determined and that environmental factors play an important role in the development of bipolar disorder. Despite the significant influence of genetic factors

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_4, © Springer Science+Business Media, LLC, part of Springer Nature 2019

61

62

Nadja Freund and Georg Juckel

on the development of bipolar disorder, the responsible genes have not yet been identified [3]. One reason for this is that complex genetic heterogeneity exists, i.e., several susceptibility genes interact with the environment and predispose to similar clinical symptoms. Gene linkage studies have so far yielded multiple replicated findings on candidate regions on various chromosomes [4]. Similarly, several epigenetic modifications have been reported in patients with bipolar disorder indicating an interaction of environment and genes in the onset of the disorder [5]. 1.2 Circadian Rhythm

Both the depressive and the manic phase are characterized by significant alterations in circadian rhythm in bipolar patients. In addition to diurnal affect variation, some depressive patients wake up too early, show a shortened REM sleep latency, changes in endocrinological secretion profiles or a delayed attainment of minimal body temperature. Overall, there seems to be a shortening of the circadian period in bipolar patients. Sleep deprivation can result in clinical improvement in about 50% of depressive patients but leads to a provocation of hypomanic and manic phases in 5–25% of bipolar patients [6]. As the phasic occurrence of affect disturbances is characteristic for bipolar disorder, disruption of the biological rhythm, the internal clock, and its genetics could play a pathogenic role. The biological rhythm is controlled by a combination of internal circadian pacemakers and external key stimuli like day-night cycle. In mammals, the internal circadian clock is located in the nucleus suprachiasmaticus (SCN) of the hypothalamus and regulates physiological and behavioral rhythms to a period of approximately 24 h. Among other genes, the transcription factors CLOCK plays a major role in regulating this oscillatory function [7].

1.3 Neurotrophins, Neurotransmitters, and Endocrinology

Neurotrophins are essential for the development of the central nervous system, in particular for the sprouting of neurites, for the phenotypic differentiation of neurons, and for synaptogenesis. In addition to their role in the development, they contribute to brain plasticity, the structural and functional remodeling of the central nervous system in response to sensorimotor, emotional, and psychosocial stimuli. BDNF is one member of the family of neurotrophins. In the context of depression, there is a reduction of the serum levels of BDNF correlating with the severity of disease [8]. Stress as a major risk factor for affective disorders may play a role in the suppression of BDNF transcription. It is assumed that in affective disorders, a congenital or acquired lack of neurotrophins results in an inability of the brain to structurally and functionally adapt to changing environmental stimuli (plasticity). Therefore, bipolar disease could be interpreted as the result of an inadequate stress response [9].

Bipolar Disorder: Its Etiology and How to Model in Rodents

63

BDNF is furthermore involved in intracellular signal transduction. Complex signaling networks play an important role in the central nervous system, as countless extracellular generated information needs to be reinforced and weighted before it can be transmitted by integrated nerve and glial cells to effectors. Each cell is equipped with a system of molecules for intracellular signal transduction which is built cascade-like and allows mutual interference. Binding of neurotransmitters to receptors on the cell surface leads to an allosteric conformational change of the receptor molecule and to the activation of so-called G proteins, which in turn mobilize second messengers such as cAMP, inositol 1,4,5trisphosphate, and diacylglycerol. These intracellular messenger substances activate protein kinases directly (e.g., PKA, KC) or indirectly via increasing the intracellular calcium concentration (e.g., CaMK). Growth factors like BDNF bind to receptors which self-develop tyrosine kinase activity (TrkB) and initiate ERK-MAP kinase and PI-3K/AKT cascades. Patients with bipolar disorder show characteristic changes in intracellular signal transduction. In particular, postmortem studies have shown increased levels of stimulatory G protein (Gsα) and enhanced adenylate cyclase activity in brains of bipolar patients [10]. The involvement of monoaminergic neurotransmitters as essential pathogenetic factor in affective disorders became of scientific interest already in the 1960s with the amine hypotheses [11]. On the basis of this hypothesis, a reduction of noradrenaline and serotonin was postulated as the cause of depression, while in mania an increase of the biogenic amines was postulated. The effect of noradrenergic or serotonergic substances in the treatment of depressive symptoms on one hand and the other hand the onset of manic states induced by the same substances was repeatedly considered evidence for an essential role of catecholamines in affective disorders. Even though measurements of the pathogenetically relevant neurotransmitter and their metabolites in the cerebrospinal fluid or plasma of bipolar patients provided indication for a dysfunction of noradrenergic or serotonergic neurotransmission, the diverse findings do not provide a completely clarified neurochemical disease model of bipolar disorders [12]. It was furthermore not possible to find clear differences in nature or extend of the noradrenergic or serotonergic dysfunction between unipolar and bipolar affective disorder. Only postmortem studies suggest evidence for structural and functional differences in dysfunctional neurotransmission of biogenic amines between unipolar and bipolar disorders. Thus, bipolar affective patients showed a higher number of neurons in locus coeruleus, the brain nucleus containing mainly noradrenaline, compared to unipolar patients and healthy controls. Due to the close functional connection between noradrenergic system and drive, this could be a correlate to the clinical differences between unipolar and bipolar disorder

64

Nadja Freund and Georg Juckel

[13]. It is also still unclear to what extent the changes in the dopaminergic system mentioned for the affective disorders can differentiate between unipolar and bipolar disorder in the sense of reduced neurotransmission in the case of depressive patients and a normalization or increase of the same in the case of mania. An important neuroendocrinological system whose pathogenetic significance for affective disorders has been researched for a long time is the hypothalamic-pituitary-thyroid axis [14]. The occurrence of psychiatric symptoms, especially depressive and cognitive disorders, in primary thyroid disease is common, but relatively unspecific [15]. While the majority of depressive patients are laboratory (peripheral) euthyroid, there is evidence that rather subclinical deviations of the thyroid metabolic rate present an etiologic and/or disease-sustaining factor in bipolar disease, especially in rapid cycling. In addition, this becomes clear ex juvantibus: thyroid hormones seem to accelerate the effect of conventional medication, and a high-dosed thyroid hormone administration (in the sense of augmentation) in addition to classical medication initially has a positive effect on therapy-resistant acute depressive symptoms and the course of affective disorders. This applies in particular to acute and prophylaxis-resistant bipolar disorders and rapid cycling [16]. It has not yet been clarified in which way thyroid hormones show their effect in affective diseases. Possible mechanisms of effect could be the balancing of “central hypothyroidism,” the influence of neurotransmitter systems that are involved in affect modulation, and the effects on gene expression of various target structures. The interactions of thyroid hormones with other functional systems of the adult CNS are complex. Overall, the thyroid hormones in the sense of a “crosstalk” seem to be in connection with various other functional units of the CNS and to be able to modulate these or to be modified by them themselves. The therapeutic effects of augmentation treatment with triiodothyronine (T3) and L-thyroxine (L-T4) in affective disorders may result from such an interaction, which increases serotonergic neurotransmission [17, 18]. 1.4

Neuroanatomy

Postmortem neuropathological explorations in bipolar affective patients did provide evidence for dysfunctional corticolimbic and corticostriatal control circuits, specifically with changes in the frontal lobe. The microscopic and macroscopic changes described so far, however, could only contribute little to the nosalogic specification and differentiation of bipolar affective disorders. An essential explanation for this could be that brain functional and brain morphological changes find their expression in individual symptoms and not in nosalogic entities. Imaging techniques such as computer tomography (CT) and magnetic resonance imaging (MRI) revealed numerous structural brain changes in affective patients in general and in bipolar patients in particular. However, these findings could

Bipolar Disorder: Its Etiology and How to Model in Rodents

65

not contribute to a distinction between the unipolar and bipolar progress [19]. Another disadvantage is that most imaging studies in bipolar patients were performed in euthymic and/or depressive phases and rarely during the manic phase. This disadvantage also applies for functional imaging techniques like PET (positron emission tomography), SPECT (single-photon emission computer tomography), or fMRI (functional magnetic resonance imaging). The main findings are: l

An enlargement of the third ventricle has been demonstrated in bipolar patients but even stronger in unipolar patients.

l

Increased subcortical hyperintensities have been reported in bipolar patients as well as in older patients with unipolar depression.

l

Frontal/prefrontal volume reduction seems to appear more frequently in bipolar patients and in unipolar patients compared to healthy controls.

l

Decreased global brain activity was found in both bipolar and unipolar depressive patients, but also an increase has been reported.

l

Reduction in dosolateral-prefontal activity is the most prominent finding in bipolar depressive patients but also in unipolar depressive patients.

l

Reduced activity of the temporal cortex and the basal ganglia in bipolar patients and unipolar depressive patients could not be consistently replicated.

l

Increased amygdala activity was found in both bipolar and unipolar depressive patients.

l

Variable anterior cingulate/medial prefrontal activity was found in both unipolar and bipolar depressive patients, and these differences in activity are related to therapy response.

Functional magnetic resonance imaging (fMRI) in bipolar patients has not been reported often. The few studies showed abnormalities during emotional paradigms in involved brain regions such as the amygdala, ACC, and medial prefrontal cortex. Here some studies show a difference when comparing to unipolar depressive patients. 1.5 Psychosocial Factors

Cognitive models of mania were formulated analogously to the hopelessness model of depression. According to this model, a given vulnerability promotes dysfunctional schemes and facilitates cognitive errors (e.g., overgeneralization, global and stable attributions as a result of anticipated or current positive experiences). Mood then becomes more positive or more irritable. As a result, for instance, self-esteem increases, more activities are planned, warnings of others are ignored, and taking of medication is being

66

Nadja Freund and Georg Juckel

questioned. This may start a loop that can lead to an escalation of manic symptoms [20]. Mansell and Pedley [21] try to understand mood swings in general. Their focus lies on the subjective interpretation of changes of internal states, e.g., increased energy, waking up too early, etc. By attributing a high personal significance to internal changes (e.g., anxiety that fatigue signalizes a new depression, morning awakening is a sign for a creative phase) and attempting to control these internal states, functional emotion regulation is therefore disturbed. They classify these counterproductive control agendas that increase the risk for clinically relevant depressive or manic symptoms as “ascent behavior” and “descent behavior.” The former leads to an increase in activating (e.g., taking stimulants against fatigue; activating behavior), and the latter is responsible for a decrease in the current state of arousal (e.g., avoiding social contacts due to fears of not being entertaining; deactivating behavior). Initial studies demonstrate that this approach may improve our psychological understanding and treatment of bipolar disorders [22]. 1.6

Perspectives

Despite all research efforts, the neurobiology of bipolar affective disorders has so far been significantly less well-known compared to unipolar depression. Furthermore, bipolar patients are often examined only in depressive states, but not in manic phases so that the neurobiological findings are often similar to those in unipolar patients. Above all, a greater genetic influence can certainly be determined in bipolar patients. Larger studies that systematically examine bipolar patients on a neurobiological basis intraindividually in transition from the depressive to the manic state and vice versa are missing so far. Only case studies of rare ultrarapid cycling cases, in which highly rhythmically each 24 h a depressive state pattern alternates with a manic one, allow to estimate in which way neurochemical, neuroendocrinological, and neuroanatomical parameters rhythmically change like a “chronobiological clockwork” in bipolar patients [23]. Thus, various, generally empirical less supported pathogenetic models for bipolar disorder have been developed in the past. In the so-called Kindling model, it is assumed that the increasing sequence of manic depressive phases triggers, i.e., “kindles,” an increase in the phasic disorder intensity, an acceleration of the phase frequency, an increasing independence of disease episodes from stressful life events, as well as a progressive therapy resistance. Based on chronobiological considerations, it is assumed, with regard to the switch model, that slowing down a mood effective oscillator in the brainstem and weakening the hemispheric top-down regulation of this oscillator result in increased sensitivity for external stimuli and this to an increased switch risk in either a depressive phase (dominance of right hemisphere) or a manic phase (dominance of left hemisphere). Urgently required empirical data will in the future certainly lead to precise

Bipolar Disorder: Its Etiology and How to Model in Rodents

67

pathophysiological models of bipolar affective disorders. Despite partly good level of knowledge about molecular and neurobiological mechanisms that are involved in the development, course, and therapy of bipolar diseases, there is still no explanatory model that associates the entire findings of previous research, including psychological explanations, thus providing a holistic picture for the understanding of these diseases. This represents the challenge for the next few years. Animal models can be useful tools in addressing this challenge.

2

Animal Models Genetic Models

Nearly 50 years ago the first genetically manipulated mouse was introduced [24]. Since then genetic manipulation has widely been used to generate animal models for several human diseases [25] including bipolar disorder [26]. Various forms of transgene animal models exist. In knockout animals one or more genes are deactivated [27], while knock-in animals carry additional genes [28]. Newest methods allow conditional knock-in or knockout and cell- or region-specific manipulation. Recent invention of the clustered regularly interspaced short palindromic repeats (CRISPR) system significantly facilitates genetic manipulation in animal models [29]. Furthermore viral vectors have widely been used for genome editing. Here genetic information is transferred with the use of a viral construct (e.g., herpes simplex virus, lenti- or adenovirus) [30]. Genetic manipulation of genes involved in the circadian rhythm (e.g., mutation or knockdown of CLOCK) induces mania-like behavior [31–35]. Similarly, mutations in the BDNF gene [36–38] and knockout of the ERK1 gene [39] resulted in manialike behavior. Comparable behavioral affects can be achieved by genetic manipulation of the dopaminergic system, namely, dopamine transporter (DAT) knockout or knockdown [40–48]. Viral manipulation that increases dopamine D1 receptor expression in the medial prefrontal cortex induces mania-like behavior, while the termination of this overexpression leads to depressive-like behavior [49, 50].

2.2 Influencing Circadian Rhythm in Rodents

As discussed above several genetic models address disturbances in genes related to the regulation of circadian rhythms. However, also models that directly influence the sleep-wake cycle have been described. Sleep deprivation in rodents increases aggression, exploratory behavior, and activity in general indicating a manialike phenotype [51–54]. Manipulation of the length of day can also induce bipolar-like behavior. Extended daylight periods are associated with anxiety and depressive-like behavior, while the opposite (i.e., reduced anxiety and depressive-like behavior) is observed

2.1

68

Nadja Freund and Georg Juckel

when the daylight period is decreased [40]. Recently, the combination of genetic and circadian rhythm manipulation was successful to induce bipolar-like behavior. Mice with reduced expression of the dopamine transporter showed mania-like behavior after their daytime was shortened, while the same strain of mice shows depressivelike behavior after an extended length of day [55]. 2.3 Pharmacological Manipulation

Imbalances in the neurotransmitter systems can induce bipolar-like behavior. Therefore the administration of psychostimulants like amphetamine and cocaine has long been used to induce several behavioral and neurobiological aspects that are associated with mania [56–67]. Interestingly, during withdrawal from these psychostimulants, behavioral and neurobiological changes associated with depression have been observed [68–76]. Therefore administration of psychostimulants and its withdrawal is one of the few models that allow investigation of both mania- and depressive-like behavior in the same animal [77]. However, administration of psychostimulants affects several neurotransmitter systems and does not allow precise examination of the neurobiology underlying the bipolar-like behavior. Therefore approaches to pharmacologically manipulate specific neurotransmitters exist. Administration of GBR12909, an inhibitor of DAT, results in mania-like behavior, while the acetylcholinesterase inhibitor physostigmine induces depressive-like behavior [78, 79].

2.4 Environmental Manipulation

Apart from influencing sleep patterns (see Sect. 2.2), the induction of stress has been widely used in animal models. It can be applied as early as during embryonic development. In this case the pregnant dam receives predictable or unpredictable stress, e.g., by restraining her, exposing her to inescapable swimming, or overcrowding the cage. Prenatal stress has been associated with depressive-like behavior [80]. The most common paradigm to induce stress directly after birth is maternal separation [81, 82]. In this paradigm pups are separated from their mothers for several hours (from 1 up to 24 h). This separation can start right after birth (usually at postnatal day 2) and can be conducted until weaning at postnatal day 21. This paradigm requires taking care that the pups are kept in a warm environment, especially as long as they do not have fur (usually until day 14). Furthermore, enough time for nursing has to be ensured to allow for a normal weight increase. Depending on the protocol, pups are housed in individual cages during separation or kept together as litter. Variations of early postnatal stress by manipulating maternal behavior are early weaning (at postnatal day 14) [83] and communal nursing [84]. For the latter two litters that differ in age (e.g., 4 days) are reared together in one cage with both dams. Maternal separation, early weaning, and communal nursing can induce depressive-like behavior and anxiety that may last until adulthood [83–85]. After weaning stress can be induced by socially

Bipolar Disorder: Its Etiology and How to Model in Rodents

69

isolating the animal resulting in increased anxiety and fear [86]. Several stress paradigms in adult animals have been reported including restrained stress, unpredicted repeated stress, predator stress, and many more (for overview see ref. 87). Even stress during adulthood can induce anxiety and depressive-like behavior. Taken together, stress plays an important role in bipolar disorder [9]. Animal models also show depressive-like and anxiety behavior after stress, but an animal model for bipolar disorder induced by stress has to our knowledge never been reported. 2.5 Behavioral Readouts 2.5.1 Depressive-Like Behavior

The best known behavioral test for depressive-like behavior in rodents might be the so-called forced swim test [88, 89]. Here, the animal is placed in a container with water, and its immobility time, i.e., the time the animal is just floating without trying to escape, is accounted as a measure of helplessness which corresponds to depressive-like behavior. Depending on the protocol, habituation of several minutes is allowed before the tests starts, and immobility time is scored, or the test is conducted 24 h after a training run. A very similar test that was developed as a simpler version with the same background is the tail suspension test that can be used in mice [90, 91]. Briefly, mice are suspended above ground by their tails, and their immobility time versus the time they move to try to escape is recorded. Again, immobility time is used as the score for depressive-like behavior. A combination to induce and test depressive-like behavior is the learned helplessness paradigm originally used in dogs [92]. Here, animals are exposed to an uncontrollable stressor (i.e., mild electric shock). In the next session, usually 24 h later, the possibility to escape the shock by shuttling to the other compartment of the behavioral box is introduced. Time to escape the shock and trials without escape behavior are used to measure depressive-like behavior. Alternative protocols use, e.g., a lever press to stop the shock [93]. In the triadic version of this paradigm, three different experimental groups are utilized [94]. The first exposure to the stressor takes place in a different environment (e.g., box with a turning wheel and tail shocks). During this session the first group, the escapable group, is able to turn off the shock by turning the wheel. The second group, the inescapable group, is connected to the shock of the first group and therefore receives shocks that are for them uncontrollable. This group is very similar to the previously described learned helplessness test. The third group is placed in the apparatus on day 1 but does not receive any shocks. Twenty-four hours later, the second session takes place in a different environment, namely, a shuttle box. Animals of all groups can terminate a shock to their feed by shuttling to a second compartment. In this version of the paradigm, several aspects of helplessness can be assessed. In the first group, the transfer of the learned control over a stressor can be tested; as mentioned in the second group, learned helplessness is

70

Nadja Freund and Georg Juckel

investigated. The third group is a handling control, and their first time exposure to the shock is examined. Anxiety can also be used as a measurement for depression as depression and anxiety disorder often occur together [95]. The open field test is widely used to measure anxiety in rodents [96]. Animals are placed is a squared box with high walls, and their behavior is recorded for several minutes, usually 5 min. Rodents prefer covered dark over bright exposed places but at the same time are curious to explore new spaces. Therefore they will spend time close to the wall but also explore the center of the squared box. Time spent close to the walls serves as measurement for anxiety. Variations of this test place a novel object in the center to increase interest to enter the center and explore the object [97]. In the novelty-suppressed feeding test, food-restricted animals are placed in the open field apparatus, and food is placed in the center to induce a conflict between the aversive exposed area and the drive to eat [98]. Time to approach the food and latency to eat are recorded to measure anxiety. Another anxiety task that makes use of rodents’ preference for covered, dark places is the elevated plus maze. The apparatus looks like a plus with two covered and two expose arms [99, 100]. Animals are put in this maze that is elevated above ground, and time in the open arms as well as entries to the open arms is recorded. Again, the more time animals spent in the open arms/the more often they enter the open arms, the less anxious they are. As both tests measure time spent in an exposed area, results of the open field test and elevated plus maze show a correlation [101]. Anxiety can not only be measured through a behavior that is avoided (i.e., exposed area in open field test and elevated plus maze) but through active coping with anxiety. For this measurement the marble burying test can be used. Here, animals are placed in a box filled with 5 cm of bedding and 20 glass marbles evenly distributed [102]. The number of buried marbles after 15 min is counted and indicates anxiety. Other tests try to reflect emotional states observed in patients with depression. The successive negative contrast test is based on disappointment after reward loss [103]. Animals are trained in an operant conditioning paradigm and learn that a certain action (e.g., nose poke on touch screen) results in the delivery of a reward (e.g., four food pellets). Once the animal has learned the task, the reward is reduced (to, e.g., one food pellet), and the latency to respond and to collect the pellet reflects the devalue effect and serves as a measurement of depressive-like behavior. A similar effect can be observed when the qualitative value of the reward is reduced instead of the quantitative value, e.g., if the animal is trained on a 32% sucrose reward that is diminished to 4% sucrose. Similarly, a test has been developed to represent the pessimistic state of depressive patients [104]. Animals are trained that a certain tone is

Bipolar Disorder: Its Etiology and How to Model in Rodents

71

associated with the delivery of a reward after a lever press. A second tone indicates that in this trial, a lever press results in a negative event, an aversive tone. When the animals have learned the task, an ambiguity with frequencies between the two learned tones is presented. The decision not to press the lever reflects the animal’s anticipation of a negative event, and the latency to press and omissions serve as a sign of depressive-like behavior. Anhedonia, the inability to experience pleasure, is another emotional state that is prominent in patients with depression [105]. The possibilities to test anhedonia in rodents will be discussed in Subheading 2.5.2 as they capture anhedonia but also hedonia. 2.5.2 Mania-Like Behavior

Hyperactivity is a core symptom of mania. Therefore measuring locomotion is probably the simplest paradigm to assess mania-like behavior. It can be conducted with automated video tracking systems in the home cage [106]. A more complex version is the behavioral pattern monitor, an activity box with holes in the wall and floor [107]. Here, not only locomotion but also whole pokes and rearing can be monitored. Similarly, sleep patterns of the animals can be recorded [108]. Impulsivity is another main characteristic of manic phases in bipolar disorder [109]. The delayed discounting task is used to measure impulsivity in rodents [110, 111]. Either in a T-maze or in an operant conditioning box, animals learn that one side (e.g., left arm, left lever) is associated with a large reward, while the other side (e.g., right arm, right lever) contains a small reward. After learning a delay before the large reward is presented is introduced. Briefly, decisions for the immediate, small reward over decisions for the large delayed reward are counted as measurement for impulsivity and mania-like behavior. Another task assessing impulsivity but also risk-taking behavior is the rodent version of the Iowa gambling task [112]. Here, different arms or levers are associated with a certain probability to gain a certain amount of reward (e.g., different amounts of food pellets) but also the probability to receive punishment (time-outs that differ in duration). The animals’ number of choices for high risk and high gain serves as measure for mania-like behavior. As mentioned above hedonia can be used as a sign of mania-like behavior, while anhedonia is associated with depressive-like behavior. One method to examine rodents’ interest in pleasurable activities is to observe their activity. Male rodents are placed in an apparatus with a sexually receptive female [113]. Number of and latency for mounts, intromissions, and ejaculations are recorded. Increased or decreased sexual activity compared to controls reflects hedonia or anhedonia, respectively. In the two bottles, sucrose preference test animals are presented with two water bottles in their home cage [114]. One bottle contains water, while the

72

Nadja Freund and Georg Juckel

other is filled with a sucrose solution. Usually the position of the bottles is switched after 24 h to avoid a side bias. The amount of water and sucrose consumed is recorder for 48 h. Amount of sucrose consumed relative to the consumed water can be used as measurement of hedonia or anhedonia. 2.5.3 Cycling Between Mania- and DepressiveLike Behavior

The problem in behaviorally phenotyping animal models for bipolar disorder is that ideal models should show mania-like and depressive-like behavior in a cycling manner [115]. However, habituation in rodents can occur fast. In the elevated plus maze, e.g., some rodents avoid open arms in general when retested [116]. Similarly, stressful test like the helplessness test can induce depressive-like behavior and might have effects on a potential following mania-like phase. Therefore testing one animal in both phases can be difficult. Sexual activity has so far been assessed in animal models for bipolar disorder and during both phases [49, 68]. Stable behavior over time in operant tasks like Iowa gambling or delay discounting task should be assessed and tested if they could be used to measure not only mania-like but also depressive-like behavior.

2.6

Several approaches to model bipolar-like behavior in rodents have been made. While most models focus on either mania- or depressive-like behavior, only few were able to model the switch from mania to depression. However, to investigate the neurobiology of this switch, the model obviously has to show both maniaand depressive-like behavior in a cycling manner. The use of viral techniques [49] and the combination of several manipulations [55] might help in overcoming this obstacle. So far, however, broad mechanisms were targeted that might have resulted in affecting multiple secondary mechanisms. Specification of the exact mechanisms underlying certain bipolar-like behavior in animal models will be very helpful to understand the exact neurobiology in human patients. Furthermore, human patients show a broad range of behavioral disturbances. Therefore, it is important that also a battery of several behavioral tests is conducted in animal models to investigate several behavioral aspects. In addition to already wellestablished tests (e.g., open field, forced swim, locomotion), the use and establishment of approaches that aim to investigate emotional states (e.g., disappointment, pessimism) can be a very useful tool to depict various facets of the disorder.

Perspectives

References 1. Kiesepp€a T, Partonen T, Haukka J, Kaprio J, Lo¨nnqvist J (2004) High concordance of bipolar I disorder in a nationwide sample of twins. Am J Psychiatry 161:1814–1821

2. Kato T, Kunugi H, Nanko S, Kato N (2000) Association of bipolar disorder with the 5178 polymorphism in mitochondrial DNA. Am J Med Genet 96:182–186

Bipolar Disorder: Its Etiology and How to Model in Rodents 3. Schulze TG (2010) Genetic research into bipolar disorder: the need for a research framework that integrates sophisticated molecular biology and clinically informed phenotype characterization. Psychiatr Clin North Am 33:67–82 4. Budde M, Forstner AJ, Adorjan K, Schaupp SK, No¨then MM, Schulze TG (2017) Genetische Grundlagen der bipolaren Sto¨rung. Nervenarzt 88:755–759 5. Ludwig B, Dwivedi Y (2016) Dissecting bipolar disorder complexity through epigenomic approach. Mol Psychiatry 21:1490–1498 6. Colombo C, Benedetti F, Barbini B, Campori E, Smeraldi E (1999) Rate of switch from depression into mania after therapeutic sleep deprivation in bipolar depression. Psychiatry Res 86:267–270 7. Dunlap JC (1999) Molecular bases for circadian clocks. Cell 96:271–290 8. Karege F, Perret G, Bondolfi G, Schwald M, Bertschy G, Aubry J-M (2002) Decreased serum brain-derived neurotrophic factor levels in major depressed patients. Psychiatry Res 109:143–148 9. Ising M, Holsboer F (2006) Genetics of stress response and stress-related disorders. Dialogues Clin Neurosci 8:433–444 10. Gould TD, Manji HK (2002) The Wnt signaling pathway in bipolar disorder. Neuroscientist 8:497–511 11. Schildkraut JJ (1965) The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am J Psychiatry 122:509–522 12. Elhwuegi AS (2004) Central monoamines and their role in major depression. Prog Neuropsychopharmacol Biol Psychiatry 28:435–451 13. Baumann B, Bogerts B (2001) Neuroanatomical studies on bipolar disorder. Br J Psychiatry Suppl 41:s142–s147 14. Bauer M, Juckel G, Correll CU, Leopold K, Pfennig A (2008) Diagnosis and treatment in the early illness phase of bipolar disorders. Eur Arch Psychiatry Clin Neurosci 258:50–54 15. Bauer M, London ED, Silverman DH, Rasgon N, Kirchheiner J, Whybrow PC (2003) Thyroid, brain and mood modulation in affective disorder: insights from molecular research and functional brain imaging. Pharmacopsychiatry 36(Suppl 3):S215–S221 16. Bauer M, London ED, Rasgon N, Berman SM, Frye MA, Altshuler LL, Mandelkern MA, Bramen J, Voytek B, Woods R, Mazziotta JC, Whybrow PC (2005) Supraphysiological doses of levothyroxine alter regional cerebral

73

metabolism and improve mood in bipolar depression. Mol Psychiatry 10:456–469 17. Bauer M (2002) Thyroid hormone augmentation with levothyroxine in bipolar depression. Bipolar Disord 4(Suppl 1):109–110 18. Bauer M, Papenbrock J (2002) Identification and characterization of single-domain thiosulfate sulfurtransferases from Arabidopsis thaliana. FEBS Lett 532:427–431 19. Hallahan B, Newell J, Soares JC, Brambilla P, Strakowski SM, Fleck DE, Kiesepp€a T, Altshuler LL, Fornito A, Malhi GS, McIntosh AM, Yurgelun-Todd DA, Labar KS, Sharma V, MacQueen GM, Murray RM, McDonald C (2011) Structural magnetic resonance imaging in bipolar disorder: an international collaborative mega-analysis of individual adult patient data. Biol Psychiatry 69:326–335 20. Meyer TD, Finucane L, Jordan G (2011) Is risk for mania associated with increased daydreaming as a form of mental imagery? J Affect Disord 135:380–383 21. Mansell W, Pedley R (2008) The ascent into mania: a review of psychological processes associated with the development of manic symptoms. Clin Psychol Rev 28:494–520 22. Searson R, Mansell W, Lowens I, Tai S (2012) Think Effectively About Mood Swings (TEAMS): a case series of cognitivebehavioural therapy for bipolar disorders. J Behav Ther Exp Psychiatry 43:770–779 23. Juckel G, Hegerl U, Mavrogiorgou P, Gallinat J, Mager T, Tigges P, Dresel S, Schro¨ter A, Stotz G, Meller I, Greil W, Mo¨ller HJ (2000) Clinical and biological findings in a case with 48-hour bipolar ultrarapid cycling before and during valproate treatment. J Clin Psychiatry 61:585–593 24. Jaenisch R, Mintz B (1974) Simian virus 40 DNA sequences in DNA of healthy adult mice derived from preimplantation blastocysts injected with viral DNA. Proc Natl Acad Sci U S A 71:1250–1254 25. LePage DF, Conlon RA (2006) Animal models for disease: knockout, knock-in, and conditional mutant mice. Methods Mol Med 129:41–67 26. Beyer DKE, Freund N (2017) Animal models for bipolar disorder: from bedside to the cage. Int J Bipolar Disord 5:35 27. Zan Y, Haag JD, Chen K-S, Shepel LA, Wigington D, Wang Y-R, Hu R, LopezGuajardo CC, Brose HL, Porter KI, Leonard RA, Hitt AA, Schommer SL, Elegbede AF, Gould MN (2003) Production of knockout rats using ENU mutagenesis and a yeast-

74

Nadja Freund and Georg Juckel

based screening assay. Nat Biotechnol 21:645–651 28. Manis JP (2007) Knock out, knock in, knock down—genetically manipulated mice and the Nobel prize. N Engl J Med 357:2426–2429 29. Feng W, Liu H-K, Kawauchi D (2018) CRISPR-engineered genome editing for the next generation neurological disease modeling. Prog Neuropsychopharmacol Biol Psychiatry 81:459–467 30. Howarth JL, Lee YB, Uney JB (2010) Using viral vectors as gene transfer tools (Cell Biology and Toxicology Special Issue: ETCS-UK 1 day meeting on genetic manipulation of cells). Cell Biol Toxicol 26:1–20 31. Coque L, Mukherjee S, Cao J-L, Spencer S, Marvin M, Falcon E, Sidor MM, Birnbaum SG, Graham A, Neve RL, Gordon E, Ozburn AR, Goldberg MS, Han M-H, Cooper DC, McClung CA (2011) Specific role of VTA dopamine neuronal firing rates and morphology in the reversal of anxiety-related, but not depression-related behavior in the ClockΔ19 mouse model of mania. Neuropsychopharmacology 36:1478–1488 32. McClung CA (2013) How might circadian rhythms control mood? Let me count the ways. . . . Biol Psychiatry 74:242–249 33. Mukherjee S, Coque L, Cao J-L, Kumar J, Chakravarty S, Asaithamby A, Graham A, Gordon E, Enwright JF, DiLeone RJ, Birnbaum SG, Cooper DC, McClung CA (2010) Knockdown of Clock in the ventral tegmental area through RNA interference results in a mixed state of mania and depression-like behavior. Biol Psychiatry 68:503–511 34. Roybal K, Theobold D, Graham A, DiNieri JA, Russo SJ, Krishnan V, Chakravarty S, Peevey J, Oehrlein N, Birnbaum S, Vitaterna MH, Orsulak P, Takahashi JS, Nestler EJ, Carlezon WA, McClung CA (2007) Manialike behavior induced by disruption of CLOCK. Proc Natl Acad Sci U S A 104:6406–6411 35. van Enkhuizen J, Minassian A, Young JW (2013) Further evidence for ClockΔ19 mice as a model for bipolar disorder mania using cross-species tests of exploration and sensorimotor gating. Behav Brain Res 249:44–54 36. Kernie SG, Liebl DJ, Parada LF (2000) BDNF regulates eating behavior and locomotor activity in mice. EMBO J 19:1290–1300 37. Lyons WE, Mamounas LA, Ricaurte GA, Coppola V, Reid SW, Bora SH, Wihler C, Koliatsos VE, Tessarollo L (1999) Brainderived neurotrophic factor-deficient mice develop aggressiveness and hyperphagia in

conjunction with brain serotonergic abnormalities. Proc Natl Acad Sci U S A 96:15239–15244 ˜ os AM, Li CJ, Gal Toth J, Bath KG, 38. Magarin Jing D, Lee FS, McEwen BS (2011) Effect of brain-derived neurotrophic factor haploinsufficiency on stress-induced remodeling of hippocampal neurons. Hippocampus 21:253–264 39. Engel SR, Creson TK, Hao Y, Shen Y, Maeng S, Nekrasova T, Landreth GE, Manji HK, Chen G (2008) The extracellular signalregulated kinase pathway contributes to the control of behavioral excitement. Mol Psychiatry 14:448–461 40. Dulcis D, Jamshidi P, Leutgeb S, Spitzer NC (2013) Neurotransmitter switching in the adult brain regulates behavior. Science 340:449–453 41. Giros B, Jaber M, Jones SR, Wightman RM, Caron MG (1996) Hyperlocomotion and indifference to cocaine and amphetamine in mice lacking the dopamine transporter. Nature 379:606–612 42. Ralph RJ, Paulus MP, Fumagalli F, Caron MG, Geyer MA (2001) Prepulse inhibition deficits and perseverative motor patterns in dopamine transporter knock-out mice: differential effects of D1 and D2 receptor antagonists. J Neurosci 21:305–313 43. Ralph-Williams RJ, Paulus MP, Zhuang X, Hen R, Geyer MA (2003) Valproate attenuates hyperactive and perseverative behaviors in mutant mice with a dysregulated dopamine system. Biol Psychiatry 53:352–359 44. van Enkhuizen J, Henry BL, Minassian A, Perry W, Milienne-Petiot M, Higa KK, Geyer MA, Young JW (2014) Reduced dopamine transporter functioning induces highreward risk-preference consistent with bipolar disorder. Neuropsychopharmacology 39:3112–3122 45. van Enkhuizen J, Geyer MA, Halberstadt AL, Zhuang X, Young JW (2014) Dopamine depletion attenuates some behavioral abnormalities in a hyperdopaminergic mouse model of bipolar disorder. J Affect Disord 155:247–254 46. Young JW, van Enkhuizen J, Winstanley CA, Geyer MA (2011) Increased risk-taking behavior in dopamine transporter knockdown mice: further support for a mouse model of mania. J Psychopharmacol 25:934–943 47. Young JW, Goey AKL, Minassian A, Perry W, Paulus MP, Geyer MA (2010) The mania-like exploratory profile in genetic dopamine transporter mouse models is diminished in a

Bipolar Disorder: Its Etiology and How to Model in Rodents familiar environment and reinstated by subthreshold psychostimulant administration. Pharmacol Biochem Behav 96:7–15 48. Zhuang X, Oosting RS, Jones SR, Gainetdinov RR, Miller GW, Caron MG, Hen R (2001) Hyperactivity and impaired response habituation in hyperdopaminergic mice. Proc Natl Acad Sci U S A 98:1982–1987 49. Freund N, Thompson BS, Sonntag K, Meda S, Andersen SL (2016) When the party is over: depressive-like states in rats following termination of cortical D1 receptor overexpression. Psychopharmacology 233:1191–1201 50. Sonntag KC, Brenhouse HC, Freund N, Thompson BS, Puhl M, Andersen SL (2014) Viral over-expression of D1 dopamine receptors in the prefrontal cortex increase high-risk behaviors in adults: comparison with adolescents. Psychopharmacology. https://doi.org/ 10.1007/s00213-013-3399-8 51. Benedetti F, Fresi F, Maccioni P, Smeraldi E (2008) Behavioural sensitization to repeated sleep deprivation in a mice model of mania. Behav Brain Res 187:221–227 52. Gessa GL, Pani L, Fadda P, Fratta W (1995) Sleep deprivation in the rat: an animal model of mania. Eur Neuropsychopharmacol 5 (Suppl 1):89–93 53. Hicks RA, Moore JD, Hayes C, Phillips N, Hawkins J (1979) REM sleep deprivation increases aggressiveness in male rats. Physiol Behav 22:1097–1100 54. Morden B, Mullins R, Levine S, Cohen H, Dement W (1968) Effect of REM deprivation on the mating behavior of male rats. Psychophysiology 5:241–242 55. Young JW, Cope ZA, Romoli B, Schrurs E, Joosen A, Enkhuizen J, Sharp RF, Dulcis D (2018) Mice with reduced DAT levels recreate seasonal-induced switching between states in bipolar disorder. Neuropsychopharmacology. https://doi.org/10.1038/s41386-0180031-y 56. Borison RL, Sabelli HC, Maple PJ, Havdala HS, Diamond BI (1978) Lithium prevention of amphetamine-induced ‘manic’ excitement and of reserpine-induced ‘depression’ in mice: possible role of 2-phenylethylamine. Psychopharmacology 59:259–262 57. Davies C, Sanger DJ, Steinberg H, Tomkiewicz M, U’Prichard DC (1974) Lithium and alpha-methyl-p-tyrosine prevent “manic” activity in rodents. Psychopharmacologia 36:263–274 58. Frey BN, Valvassori SS, Re´us GZ, Martins MR, Petronilho FC, Bardini K, Dal-Pizzol F,

75

Kapczinski F, Quevedo J (2006) Effects of lithium and valproate on amphetamineinduced oxidative stress generation in an animal model of mania. J Psychiatry Neurosci 31:326–332 59. Fries GR, Valvassori SS, Bock H, Stertz L, Magalha˜es PV, Mariot E, Varela RB, KauerSant’Anna M, Quevedo J, Kapczinski F, Saraiva-Pereira ML (2015) Memory and brain-derived neurotrophic factor after subchronic or chronic amphetamine treatment in an animal model of mania. J Psychiatr Res 68:329–336 60. Gould TJ, Keith RA, Bhat RV (2001) Differential sensitivity to lithium’s reversal of amphetamine-induced open-field activity in two inbred strains of mice. Behav Brain Res 118:95–105 61. Kilbey MM, Ellinwood EH (1977) Reverse tolerance to stimulant-induced abnormal behavior. Life Sci 20:1063–1075 62. Maceˆdo DS, de Lucena DF, Queiroz AIG, Cordeiro RC, Arau´jo MM, Sousa FC, Vasconcelos SM, Hyphantis TN, Quevedo J, McIntyre RS, Carvalho AF (2013) Effects of lithium on oxidative stress and behavioral alterations induced by lisdexamfetamine dimesylate: relevance as an animal model of mania. Prog Neuropsychopharmacol Biol Psychiatry 43:230–237 63. Maceˆdo DS, Medeiros CD, Cordeiro RC, Sousa FC, Santos JV, Morais TA, Hyphantis TN, McIntyre RS, Quevedo J, Carvalho AF (2012) Effects of alpha-lipoic acid in an animal model of mania induced by d-amphetamine. Bipolar Disord 14:707–718 64. Rezin GT, Furlanetto CB, Scaini G, Valvassori SS, Gonc¸alves CL, Ferreira GK, Jeremias IC, Resende WR, Cardoso MR, Varela RB, Quevedo J, Streck EL (2014) Fenproporex increases locomotor activity and alters energy metabolism, and mood stabilizers reverse these changes: a proposal for a new animal model of mania. Mol Neurobiol 49:877–892 65. Rygula R, Szczech E, Kregiel J, Golebiowska J, Kubik J, Popik P (2015) Cognitive judgment bias in the psychostimulantinduced model of mania in rats. Psychopharmacology 232:651–660 66. Seiden LS, Sabol KE, Ricaurte GA (1993) Amphetamine: effects on catecholamine systems and behavior. Annu Rev Pharmacol Toxicol 33:639–677 67. Zheng W, Zeng Z, Bhardwaj SK, Jamali S, Srivastava LK (2013) Lithium normalizes amphetamine-induced changes in striatal FoxO1 phosphorylation and behaviors in rats. Neuroreport 24:560–565

76

Nadja Freund and Georg Juckel

68. Barr AM, Fiorino DF, Phillips AG (1999) Effects of withdrawal from an escalating dose schedule of d-amphetamine on sexual behavior in the male rat. Pharmacol Biochem Behav 64:597–604 69. Barr AM, Phillips AG (2002) Increased successive negative contrast in rats withdrawn from an escalating-dose schedule of D-amphetamine. Pharmacol Biochem Behav 71:293–299 70. Markou A, Koob GF (1991) Postcocaine anhedonia. An animal model of cocaine withdrawal. Neuropsychopharmacology 4:17–26 71. Marszalek-Grabska M, Gibula-Bruzda E, Jenda M, Gawel K, Kotlinska JH (2016) Memantine improves memory impairment and depressive-like behavior induced by amphetamine withdrawal in rats. Brain Res 1642:389–396 72. Mutschler NH, Miczek KA (1998) Withdrawal from a self-administered or non-contingent cocaine binge: differences in ultrasonic distress vocalizations in rats. Psychopharmacology 136:402–408 73. Paulson PE, Camp DM, Robinson TE (1991) Time course of transient behavioral depression and persistent behavioral sensitization in relation to regional brain monoamine concentrations during amphetamine withdrawal in rats. Psychopharmacology 103:480–492 74. Schindler CW, Persico AM, Uhl GR, Goldberg SR (1994) Behavioral assessment of high-dose amphetamine withdrawal: importance of training and testing conditions. Pharmacol Biochem Behav 49:41–46 75. Schwartz JM, Ksir C, Koob GF, Bloom FE (1982) Changes in locomotor response to beta-endorphin microinfusion during and after opiate abstinence syndrome—a proposal for a model of the onset of mania. Psychiatry Res 7:153–161 76. Wise RA, Munn E (1995) Withdrawal from chronic amphetamine elevates baseline intracranial self-stimulation thresholds. Psychopharmacology 117:130–136 77. Pathak G, Ibrahim BA, McCarthy SA, Baker K, Kelly MP (2015) Amphetamine sensitization in mice is sufficient to produce both manic- and depressive-related behaviors as well as changes in the functional connectivity of corticolimbic structures. Neuropharmacology 95:434–447 78. van Enkhuizen J, Milienne-Petiot M, Geyer MA, Young JW (2015) Modeling bipolar disorder in mice by increasing acetylcholine or dopamine: chronic lithium treats most, but

not all features. Psychopharmacology 232:3455–3467 79. Queiroz AIG, de Arau´jo MM, da Silva Arau´jo T, de Souza GC, Cavalcante LM, de Jesus Souza Machado M, de Lucena DF, Quevedo J, Maceˆdo D (2015) GBR 12909 administration as an animal model of bipolar mania: time course of behavioral, brain oxidative alterations and effect of mood stabilizing drugs. Metab Brain Dis 30:1207–1215 80. Fatima M, Srivastav S, Mondal AC (2017) Prenatal stress and depression associated neuronal development in neonates. Int J Dev Neurosci 60:1–7 81. Freund N, Thompson BS, Denormandie J, Vaccarro K, Andersen SL (2013) Windows of vulnerability: maternal separation, age, and fluoxetine on adolescent depressive-like behavior in rats. Neuroscience 249:88–97 82. Leussis MP, Freund N, Brenhouse HC, Thompson BS, Andersen SL (2012) Depressive-like behavior in adolescents after maternal separation: sex differences, controllability, and GABA. Dev Neurosci 34:210–217 83. Kikusui T, Mori Y (2009) Behavioural and neurochemical consequences of early weaning in rodents. J Neuroendocrinol 21:427–431 84. Macrı` S, Laviola G, Leussis MP, Andersen SL (2010) Abnormal behavioral and neurotrophic development in the younger sibling receiving less maternal care in a communal nursing paradigm in rats. Psychoneuroendocrinology 35:392–402 85. Nishi M, Horii-Hayashi N, Sasagawa T (2014) Effects of early life adverse experiences on the brain: implications from maternal separation models in rodents. Front Neurosci 8:166 86. Lukkes JL, Watt MJ, Lowry CA, Forster GL (2009) Consequences of post-weaning social isolation on anxiety behavior and related neural circuits in rodents. Front Behav Neurosci. https://doi.org/10.3389/neuro.08.018. 2009 87. Deslauriers J, Toth M, Der-Avakian A, Risbrough VB (2017) Current status of animal models of posttraumatic stress disorder: behavioral and biological phenotypes, and future challenges in improving translation. Biol Psychiatry. https://doi.org/10.1016/j. biopsych.2017.11.019 88. Porsolt RD, Bertin A, Jalfre M (1977) Behavioral despair in mice: a primary screening test for antidepressants. Arch Int Pharmacodyn Ther 229:327–336

Bipolar Disorder: Its Etiology and How to Model in Rodents 89. Yankelevitch-Yahav R, Franko M, Huly A, Doron R (2015) The forced swim test as a model of depressive-like behavior. J Vis Exp. https://doi.org/10.3791/52587 90. Can A, Dao DT, Terrillion CE, Piantadosi SC, Bhat S, Gould TD (2012) The tail suspension test. J Vis Exp. https://doi.org/10.3791/ 3769 91. Steru L, Chermat R, Thierry B, Simon P (1985) The tail suspension test: a new method for screening antidepressants in mice. Psychopharmacology 85:367–370 92. Seligman MEP (1972) Learned helplessness. Annu Rev Med 23:407–412 93. Vollmayr B, Henn FA (2001) Learned helplessness in the rat: improvements in validity and reliability. Brain Res Brain Res Protoc 8:1–7 94. Durgam, R. C. (2001) Rodent models of depression: learned helplessness using a triadic design in rats. Curr Protoc Neurosci Chapter 8, Unit 8 10B 95. Hirschfeld RMA (2001) The comorbidity of major depression and anxiety disorders: recognition and management in primary care. Prim Care Companion J Clin Psychiatry 3:244–254 96. Seibenhener ML, Wooten MC (2015) Use of the Open Field Maze to measure locomotor and anxiety-like behavior in mice. J Vis Exp. https://doi.org/10.3791/52434 97. Belzung C (1992) Hippocampal mossy fibres: implication in novelty reactions or in anxiety behaviours? Behav Brain Res 51:149–155 98. Blasco-Serra A, Gonza´lez-Soler EM, CerveraFerri A, Teruel-Martı´ V, Valverde-Navarro AA (2017) A standardization of the NoveltySuppressed Feeding Test protocol in rats. Neurosci Lett 658:73–78 99. Montgomery KC (1955) The relation between fear induced by novel stimulation and exploratory drive. J Comp Physiol Psychol 48:254–260 100. Pellow S, Chopin P, File SE, Briley M (1985) Validation of open:closed arm entries in an elevated plus-maze as a measure of anxiety in the rat. J Neurosci Methods 14:149–167 101. Carola V, D’Olimpio F, Brunamonti E, Mangia F, Renzi P (2002) Evaluation of the elevated plus-maze and open-field tests for the assessment of anxiety-related behaviour in inbred mice. Behav Brain Res 134:49–57 102. Njung’e K, Handley SL (1991) Evaluation of marble-burying behavior as a model of anxiety. Pharmacol Biochem Behav 38:63–67 103. Mitchell EN, Marston HM, Nutt DJ, Robinson ESJ (2012) Evaluation of an operant

77

successive negative contrast task as a method to study affective state in rodents. Behav Brain Res 234:155–160 104. Harding EJ, Paul ES, Mendl M (2004) Animal behaviour: cognitive bias and affective state. Nature 427:312 105. Heshmati M, Russo SJ (2015) Anhedonia and the brain reward circuitry in depression. Curr Behav Neurosci Rep 2:146–153 106. York JM, Blevins NA, McNeil LK, Freund GG (2013) Mouse short- and long-term locomotor activity analyzed by video tracking software. J Vis Exp. https://doi.org/10. 3791/50252 107. Young JW, Minassian A, Paulus MP, Geyer MA, Perry W (2007) A reverse-translational approach to bipolar disorder: rodent and human studies in the Behavioral Pattern Monitor. Neurosci Biobehav Rev 31:882–896 108. Bastianini S, Berteotti C, Gabrielli A, Lo Martire V, Silvani A, Zoccoli G (2015) Recent developments in automatic scoring of rodent sleep. Arch Ital Biol 153:58–66 109. Swann AC (2009) Impulsivity in mania. Curr Psychiatry Rep 11:481 110. Mar, A. C., and Robbins, T. W. (2007) Delay discounting and impulsive choice in the rat. Curr Protoc Neurosci Chapter 8, Unit 8 22 111. Mitchell SH (2014) Assessing delay discounting in mice. Curr Protoc Neurosci 66:8.30.1–8.30.12 112. de Visser L, Homberg JR, Mitsogiannis M, Zeeb FD, Rivalan M, Fitoussi A, Galhardo V, van den Bos R, Winstanley CA, DelluHagedorn F (2011) Rodent versions of the Iowa gambling task: opportunities and challenges for the understanding of decisionmaking. Front Neurosci. https://doi.org/ 10.3389/fnins.2011.00109 113. A˚gmo A (1997) Male rat sexual behavior. Brain Res Protocol 1:203–209 114. Willner P, Towell A, Sampson D, Sophokleous S, Muscat R (1987) Reduction of sucrose preference by chronic unpredictable mild stress, and its restoration by a tricyclic antidepressant. Psychopharmacology 93:358–364 115. Nestler EJ, Hyman SE (2010) Animal models of neuropsychiatric disorders. Nat Neurosci 13:1161–1169 ˜eda AF, Corre116. Rico JL, Penagos-Gil M, Castan dor K (2016) Gerbils exhibit stable open-arms exploration across repeated testing on the elevated plus-maze. Behav Process 122:104–109

Chapter 5 Recent Updates in Modeling Risky Decision Making in Rodents Caitlin A. Orsini, Shelby L. Blaes, Barry Setlow, and Nicholas W. Simon Abstract Excessive preference for risky over safe options is a hallmark of several psychiatric disorders. Here we describe a behavioral task that models such risky decision making in rats. In this task, rats are given choices between small, safe rewards and large rewards accompanied by risk of footshock punishment. The risk of punishment changes within a test session, allowing quantification of decision making at different levels of risk. Importantly, this task can yield a wide degree of reliable individual variability, allowing the characterization of rats as “risk-taking” or “risk-averse.” The task has been demonstrated to be effective for testing the effects of pharmacological agents and neurobiological manipulations, and the individual variability (which mimics the human population) allows assessment of behavioral and neurobiological distinctions among subjects based on their risk-taking profile. Key words Risk, Risk taking, Decision making, Cost/benefit, Rat, Psychiatric disorders, Behavior, Animal models

1

Introduction Excessive risk taking is characteristic of several psychiatric disorders, including schizophrenia, attention deficit-hyperactivity disorder, and substance use disorders [1–4]. Therefore, animal models of risky decision making have great potential utility for psychiatric research. We have developed a task for use in rats based on previous two-choice discrimination tasks [5–8] that assesses preference for safe vs. risky rewards. Performance in this “risky decision making task” (RDT) has been demonstrated to be replicable, sensitive, and highly effective for pharmacological testing [9]. Importantly, there is a high degree of between-subjects variability among rats in this task, which resembles that found in the human population. This reliable variability can be useful for delineating both neurobiological and behavioral differences among subjects, thereby offering insight into mechanisms underlying risky decision making [10–12]. Critically, risk preference assessed by this task is unrelated

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_5, © Springer Science+Business Media, LLC, part of Springer Nature 2019

79

80

Caitlin A. Orsini et al.

to measures of motivation, anxiety, or pain sensitivity, suggesting that individual differences in risky decision making are not solely a by-product of other behavioral differences [13]. In the RDT, rats are given choices between a small, “safe” food reward and a large food reward accompanied by variable probabilities of punishment (a mild footshock). Each session consists of five blocks of ten choice trials, with the probability of punishment (risk) accompanying the large reward increasing with each consecutive block (0, 25, 50, 75, 100%). Preference for the large, “risky” reward typically decreases with increased punishment probability (i.e., the risk of punishment “discounts” the value of the large reward). This task design provides measures of risky choice at varying degrees of risk from each session, allowing for repeated testing using a within-subjects experimental design. Additionally, this task is among the few multi-choice animal decision making tasks that combine rewarding outcomes with potential punishment, as opposed to potential lost reward opportunity ([14–17], but see ref. 18). This approach captures the ambiguous nature of “realworld” risky decision making, in which choices are often associated with both rewards and risks of adverse consequences that may be physically unrelated to each other [19, 20]. Although most work in this task has been conducted in male rats, in the last several years, our laboratory has begun to use both sexes in experiments employing the RDT. Through a series of experiments [21], we observed that females as a group tend to be more risk-averse than males and, consequently, the parameters used to obtain optimal performance in males did not translate as easily to females. Hence, in this review, additional details about task parameters for females are included where necessary. Finally, it is important to note that the description of the RDT in this chapter is formatted for use with rats. Although the task could in theory be used for mice with appropriately mouse-sized equipment, our own (admittedly limited) experience is that this could prove challenging.

2

Materials In order to minimize extraneous factors, all behavioral procedures in this task are fully automated and utilize the equipment listed in this section in the configuration detailed in Subheading 3.1.

2.1 Habitest Behavioral Test System (Coulbourn Instruments, Whitehall, PA)

Note that similar systems from other manufacturers, such as those from Med-Associates using Med-PC software (Fairfax, VA), work equally well [22]. 1. System Power Base with Lincs (can control up to 16 test cages). 2. Computer with hardware interface and Graphic State software for experiment control and data collection.

Recent Updates in Modeling Risky Decision Making in Rodents

81

3. Environmental Connection Board with 1.12 W house light and connector cable. 4. Sound attenuating cubicle. 5. Rat test chamber with extra panels, shock floor, and drop pan. 6. 45 mg food pellet dispenser. 7. Food trough equipped with 1.12 W house light and photobeam to detect head entries, placed 2 cm above the chamber floor. 8. Two retractable levers, placed 11 cm above the chamber floor. 9. Shock generator and cable. 10. Activity monitor (optional, though useful for monitoring shock reactivity). 2.2 Grain-Based Rodent Tablets (Test Diet, Richmond, IN)

These are 45 mg food pellets, which are compatible with a variety of pellet dispensers (see Note 1). The risky decision making task requires up to 230 pellets/session.

2.3 Data Analysis Software

To extract data from Coulbourn Instruments Graphic State 3.0 files, we have used a custom Excel macro written by Dr. Jonathan Lifshitz (University of Kentucky). The newer Graphic State 4.0 software, however, allows for generation of data analysis templates specific to the test protocol within Graphic State itself, providing more flexibility in how the data can be analyzed. The Coulbourn data files can also be converted into .csv files, which can be analyzed with a variety of data analysis packages (e.g., Matlab or the opensource statistical software R).

2.4 Nolvasan Cleaning Solution (2%)

The solution is placed in a spray bottle to clean the test chambers between rats. Nolvasan has been deemed safe for use on surfaces that come into contact with animals.

3

Methods Prior to assessment of risky decision making, it is necessary to train the rats on different aspects of the task in order to ensure optimal task performance. These procedures include magazine training (during which rats learn to associate the food trough with food delivery (Subheading 3.2.2)), lever press shaping (during which rats learn to associate lever presses with food delivery into the food trough (Subheading 3.2.3)), and nosepoke shaping (during which rats learn how to initiate a trial via a nosepoke into the food trough and become acclimated to extension/retraction of the levers (Subheading 3.2.4)). The methods outlined below describe

82

Caitlin A. Orsini et al.

these shaping procedures as well as the RDT itself, followed by the procedures for analyzing data obtained from the task. In addition, an example experimental design is provided for a within-session behavioral pharmacological experiment using the task. 3.1 Test Chamber Preparation

1. Place test chamber into sound attenuating cubicle. 2. Insert shock floor connected to shock generator into the chamber. 3. Insert food trough in the center of the front wall (use combinations of the blank panels to achieve the desired height). 4. Insert two retractable levers to the right and left of the food trough. 5. Insert food dispenser above the food trough. 6. Mount 1.12 W-house light on the rear wall of the cubicle (in the Coulbourn Instruments system, this light plugs directly into the Environmental Connection Board).

3.2 Shaping Procedures (See Note 2) 3.2.1 Food Restriction

1. Reduce rats to 85% of their free feeding weight, and maintain as such throughout the duration of behavioral testing. This process typically requires 5–6 days, and food restriction should not begin until at least a week after any invasive procedure (such as surgery). If testing occurs over the course of many weeks (and particularly if young rats are used), the “target weight” should be increased by 5 g/week to account for growth. Consult your veterinarian for guidelines specific to your institution. 2. If females are included in the study, it is recommended that, unless already maintained on such a diet, all rats (including males) are switched to soy-free chow (e.g., Envigo Teklad Irradiated Global 19% Protein Extruded Rodent Diet, #2919) to avoid phytoestrogens that are commonly found in rodent diets. To avoid additional sources of external estrogens, it is also recommended that, unless rats are already housed as such, bedding be changed from corncob bedding to paperbased or wood shaving bedding (e.g., Sani-Chip, P.J. Murphy Forest Products) for all rats.

3.2.2 Magazine Training

1. Put four of the 45 mg food pellets (see Note 1) into the rats’ home cage the day before magazine training begins—this reduces neophobia to the food and facilitates magazine training. 2. The magazine training session (during which rats learn to associate the sounds that accompany food pellet delivery with food availability within the food trough) lasts 64 min and consists of 38 discrete deliveries of a single food pellet with an intertrial interval (ITI) of 100
40 s. This generally takes no more than one session, but it is a good idea to check the data

Recent Updates in Modeling Risky Decision Making in Rodents

83

from this session to make sure that rats are reliably entering the food trough within a few seconds of food delivery (if not, more sessions can be run as needed). 3.2.3 Lever Press Shaping

1. Place rat in chamber with single lever extended for 30 min session. 2. Houselight is illuminated throughout the session. 3. Each lever press is reinforced with a single food pellet delivery and illumination of the food trough (the food trough remains illuminated until the rat enters the trough). 4. After criterion of 50 lever presses is met in a single session, repeat the procedure with the other lever extended. 5. The order in which levers are presented should be counterbalanced across all subjects (see Note 3). 6. Lever press shaping can take anywhere from 1 to 4 sessions per lever, depending on the rat (see Note 4).

3.2.4 Nosepoke Shaping

1. In a 60 min session, the rat is shaped to nosepoke into the food trough during simultaneous illumination of the trough and house lights. This light cue lasts 10 s, and the ITI is 40
10 s. If no nosepoke is made during this cue, the lights are extinguished for the remainder of the ITI. 2. Immediately following a nosepoke during the cue, the trough light is extinguished, and a single lever (either left or right) is extended. The order in which the levers are extended is pseudorandom, such that there are no more than two consecutive presentations of the same lever. A lever press results in immediate delivery of a single food pellet, retraction of the lever, and extinction of the house light. 3. Each session has a maximum of 70 trials. Rats are trained to a criterion of at least 30 presses of each lever within the 60 min session.

3.3 The Risky Decision-Making Task

1. Each session lasts 60 min and consists of five blocks of 18 trials each. Each block consists of two trial types: forced-choice and free-choice trials. Different trial configurations may be employed for specific applications, however (e.g., optogenetic manipulations—see Note 5 for details). 2. Each 40 s trial begins with a 10 s illumination of the food trough and house lights. 3. A nosepoke into the illuminated food trough triggers extension of one of the two levers (on forced-choice trials) or both levers simultaneously (on free-choice trials) for 10 s. Failure to press a lever during this window causes the lights to be extinguished, and the trial proceeds to the ITI and is scored as an omission.

84

Caitlin A. Orsini et al.

The purpose of this nosepoke is to position the rat in the center of the chamber in order to reduce positional (left or right) response biases. 4. The magnitude of the food pellet reward associated with each lever is fixed throughout the task (i.e., it is the same in every session). One lever consistently produces a single food pellet (the “safe” lever), while the other lever produces a larger food reward (two to three food pellets, see Note 6; the “risky” lever). The other important distinction between the levers is the associated risk of punishment. Selection of the safe lever is never associated with punishment, whereas selection of the risky lever is accompanied immediately by a possible 1 s scrambled footshock contingent on a preset probability specific to each trial block. Importantly, food pellets are delivered following every choice of the risky lever, regardless of whether or not footshock occurs. 5. The probability of footshock accompanying the risky lever is set at 0% during the first 18-trial block. In subsequent 18-trial blocks, the probability of footshock increases to 25, 50, 75, and 100% (see Note 7). On forced-choice trials, the shock probability within a block is dependent on shock delivery on previous trials in that block. On free-choice trials, however, the shock probability is independent for each trial. 6. The optimal starting footshock intensity for this task is typically 0.20–0.25 mA for males and 0.15–0.20 mA for females (see Note 8). 7. Each trial block begins with eight forced-choice trials, during which each lever is presented four times in pseudorandom order. These forced-choice trials serve as a reminder of the probability of punishment specific to each block of trials. 8. Following the forced-choice trials, there are ten free-choice trials in which both levers are extended simultaneously. 9. After selection of a lever (in either the forced-choice or freechoice trials), the lever(s) are immediately retracted. Delivery of food (and shock) is accompanied by re-illumination of both the food trough and house lights, which are then extinguished upon entry to the food trough to collect the food or after 10 s, whichever occurs sooner. 10. After food trough entry, there is an ITI period ranging from 20 to 35 s (ITI duration is a function of the latency to respond during the light cue, lever extension, and food delivery; however, each full trial lasts 40 s, irrespective of response latencies; see Note 9). See Fig. 1 for task schematic. 11. Approximately 15–25 sessions are typically required to achieve stable performance (see Subheading 3.4, step 3) for a large group of rats (n ¼ 12–18) (see Note 10).

Recent Updates in Modeling Risky Decision Making in Rodents

85

Fig. 1 Schematic of the risky decision-making task. Each trial begins with simultaneous illumination of the house light and trough light, which lasts for 10 s or until the rat performs a nosepoke into the food trough. Following the nosepoke, either one (forced-choice trial) or both levers (free-choice trial) are extended, and this extension lasts for 10 s or until the rat presses a lever. Selection of one lever (the “safe” lever) causes delivery of a single food pellet, and selection of the other (the “risky” lever) causes delivery of two or three food pellets (see Note 6); however, food delivery following choice of the risky lever is accompanied by the possibility of a 1 s footshock, the probability of which changes throughout the session (0, 25, 50, 75, and 100%). Following an ITI period, the next trial is initiated. Each trial (light cue, lever extension, reward/punishment, ITI) lasts 40 s

3.4 Risky DecisionMaking Task Data Analysis

1. For each block of free-choice trials, data are expressed as the percentage of completed trials on which the rat chose the large reward, calculated by dividing the total number of choices of the risky lever by the total number of choice trials completed (excluding omissions). For example, if the risky lever were selected on five trials, the safe lever selected on three trials, and two trials were omitted, the percent choice of the risky lever for that block would be 62.5%. 2. Each rat should produce data from five blocks in each session. These five data points can then be plotted with the percentage of risky lever choices on the Y axis and the risk of punishment (representative of each block) on the X axis (Fig. 2). 3. Before data can be interpreted, it is critical that the rats have reached a point at which responding can be considered stable, as performance can fluctuate considerably prior to full acquisition of the task rules. Stable performance can be quantified using a session X trial block repeated measures ANOVA across a series of 3–5 consecutive sessions (see Note 11). Stable performance is defined as the absence of a main effect of session or interaction between session and trial block [6]. We typically use group sizes of no fewer than 12 rats. However, it is important to note that with smaller group sizes, statistical power is reduced and it becomes less likely that an effect of session or an interaction between session and trial block will be observed. In this case, it is important to make the criteria for stable

86

Caitlin A. Orsini et al.

Fig. 2 Risky decision-making task. (a) Performance of cohorts of male and female rats on the risky decisionmaking task (mean
SEM). Note that females as a group show less choice of the large, risky reward (i.e., are more risk-averse) compared to males. (b and c) Distribution of individual performance of the male (b) and female (c) rats shown in panel (a). Each line represents data from a single rat. Note the wide degree of variability in performance in both sexes, allowing rats to be classified according to their risk preference

Fig. 3 Illustration of the criteria for stable performance in the risky decisionmaking task. Each line represents data from a single session of training. (a) Depiction of stable performance across a five-session span (session X trial block ANOVA, p > 0.05 for effects of session). (b) Depiction of unstable performance across a five-session span ( p < 0.05 for main effect of session)

performance more stringent by either increasing the value of alpha for effects involving the session factor or observing the subjects’ behavior qualitatively across sessions to determine whether it appears consistent from session to session (Fig. 3). 4. When analyzing performance, it is ideal to utilize the mean performance data across a 3–5 session series (see Note 12) rather than an individual session for each subject. This accounts for subtle differences between sessions that occur as a result of day-to-day differences in environmental conditions and other confounding factors that may influence performance and

Recent Updates in Modeling Risky Decision Making in Rodents

87

promote enhanced omissions or slight behavioral biases (see Notes 10 and 12). This approach may not be desirable, however, when using within-subject experimental designs (see below). 5. In order to compare two groups of subjects, a mixed-design repeated measures ANOVA should be used, with group as the between-subject factor and trial block (risk of punishment) as the repeated measure factor. 3.5 Repeated Measure Treatment Procedure (WithinSubject Designs)

1. Prior to any treatment (e.g., acute systemic drug administration, intracranial microinjections, acute behavioral manipulations), it is critical that all groups of rats have achieved stable performance in the RDT. If performance is unstable, sessionto-session fluctuations in behavior could either promote a falsepositive effect (type 1 error) or mask an effect of treatment (type 2 error). 2. We will use systemic amphetamine administration as an example of a within-subjects treatment regimen with four different conditions. For the first session, each rat will be given one of three doses of amphetamine (0.33, 1.0, 1.5 mg/kg) or 0.9% saline vehicle prior to testing. In the second “washout” session, each subject will perform the RDT with no treatment. This pattern will continue for a total of eight consecutive sessions, with the order in which the treatments are administered counterbalanced across subjects. 3. After this eight-session experimental schedule, data will be available from four treatment sessions (sessions 1, 3, 5, and 7) and four baseline “washout” sessions (sessions 2, 4, 6, and 8). The four treatment sessions can be compared using a repeated measure analysis (trial block X treatment) to detect any effects of treatment on choice performance. The four baseline sessions should also be compared using a similar analysis, to determine whether the treatment exerted any long-term effects on behavior that outlasted the individual treatment sessions (i.e., “carryover effects”). If an effect of session is revealed with this latter analysis, performance underwent a “baseline shift,” and therefore any effects of treatment may be confounded. If a treatment produces a significant baseline shift, the simple repeated measure statistical design described above may not be an effective method of assessing differences between doses and treatment parameters, and alternative analyses may be necessary (e.g., normalizing performance in each treatment session to the level of performance in the immediately preceding baseline session). To avoid such confounds, additional baseline sessions may be used between the treatment sessions.

88

Caitlin A. Orsini et al.

4. If multiple rounds of experimental manipulations are to be performed, rats should be tested for a minimum of five untreated baseline sessions between each treatment schedule. The last 3–5 sessions should be analyzed for stability (see Subheading 3.4); if performance is not stable in five sessions, baseline testing should continue until stability is achieved (see Note 12).

4

Notes 1. Once we started incorporating females into our experiments, we began using 45 mg soy-free food pellets (Test Diet, 5TUL) rather than the grain-based pellets (Test Diet, 5TUM) we had used previously. This was done to avoid potential confounds of phytoestrogens found in soy that can have effects similar to estrogen. Although the use of soy-free pellets may not be necessary even when females are included in an experiment, it becomes critical if experiments involve hormonal manipulations in either males or females. Both grain-based and soy-free pellets, however, are readily consumed by foodrestricted rats, and we find them easier to work with than sucrose pellets as they do not as readily absorb moisture and become sticky when exposed to air. They do produce dust that can clog pellet dispensers if not cleaned regularly; however, dispensers can be cleaned easily with a compressed air duster. 2. These shaping procedures were adapted from [6, 7] and can be used for any two-choice decision making task [8, 9]. 3. The identity and positions of the response levers should be balanced, such that for half of the test chambers, the left lever is the “large, risky” lever and for the other half of the chambers, the right lever is the “large, risky” lever. This can be accomplished most easily in the Coulbourn Instruments system at the hardware level, by specifying the same set of inputs/outputs to correspond to the “large risky” and “small safe” lever across all test chambers at the software level but alternating the left/right configuration of which lever is actually plugged into which set of connections across chambers (e.g., so that “switch 1” at the software level controls either the left or right lever at the hardware level, depending on the chamber). This ensures that factors such as proximity to the door of the test chamber do not bias preference for one or the other levers. In addition, it is important to counterbalance the subjects in different treatment groups across the two different types of chambers (e.g., so that rats in a given treatment condition do not all have the “large, risky” lever on the right).

Recent Updates in Modeling Risky Decision Making in Rodents

89

4. Different rat strains seem to shape more readily than others. For example, in our experience, Long-Evans and SpragueDawley rats typically acquire lever pressing for food reward at a faster rate than Fischer 344 or Fischer 344  Brown Norway F1 hybrid rats (unpublished observations, Simon and Setlow; Orsini, Blaes and Setlow). To facilitate shaping, particularly in strains slow to shape, placing a food pellet on either the extended lever within the operant chamber or on the lever mechanism behind the wall of the chamber may encourage rats to interact with lever, rendering them more likely to press it. 5. To improve compatibility with optogenetics, we modified the standard RDT to increase the number of free-choice trials within each block [23]. To do this, the number of trial blocks was reduced from five to three, across which the risk of punishment increases (0, 25, 75%). Each block consists of 8 forcedchoice trials and 20 free-choice trials, resulting in a total of 84 trials across a 56 min session. The structure and duration of each trial are the same as those in the standard RDT. This task design yields more trials on which optogenetic manipulations can occur and, consequently, more data for each risk condition. 6. To avoid satiation and promote continued performance, particularly in females, the magnitude of the large reward can be decreased from three pellets [9, 13] to two pellets [21, 24]. Extensive parametric work in our laboratory shows that rats perform comparably across different reward magnitude conditions (i.e., one pellet vs. two pellets or one pellet vs. three pellets; [11]). 7. Reversing the order of probabilities of footshock across the session (100, 75, 50, 25, 0%) also yields comparable patterns of choice behavior, whereby as the risk of punishment decreases across the session, choice of the large, risky reward increases. This “descending” RDT is useful as it permits determination of whether an experimental manipulation affects risky choice or behavioral flexibility [24]. 8. Although these recommended footshock intensities can produce a robust discounting curve [9], there is considerable variability between rats in sensitivity to shock in this task. For example, some cohorts of rats (either male or female) may be insensitive to 0.25 mA shock (i.e., they show a strong preference for the large, risky reward), in which case the shock intensity can be increased in small increments (no greater than 0.05 mA for males and 0.025 mA for females). Conversely, some cohorts of rats may avoid this shock intensity, which would require a reduction of intensity between sessions. Additional changes to shock intensities should occur only if

90

Caitlin A. Orsini et al.

choice behavior has not changed after three consecutive sessions on the adjusted shock intensity. Note that the recommendations of 0.20–0.25 mA and 0.15–0.20 mA for males and females, respectively, are optimal intensities for Long-Evans rats; higher or lower intensities may be optimal for other rat strains. Before performing any experimental manipulations, it is recommended to test a small group of pilot subjects in order to determine ideal footshock parameters, as there are differences in shock apparatus, environment, and rat strain/age that may influence shock sensitivity. Importantly, when determining ideal shock intensity, we have found that behavioral performance is typically more consistent if the intensity begins low and is increased until an optimal point is determined (rather than beginning at a higher intensity and decreasing). Relatedly, it is important to consider the nature of the experimental question to determine how shock intensities should be adjusted over the course of the experiment. If individual differences in choice performance are being assessed in relation to another factor (e.g., correlations between risk preference and drug self-administration), shock intensities should be adjusted equivalently across all rats based on their mean performance across the last 3 consecutive days of testing. If both males and females are included in such an experiment, adjustments of shock values should be based on the mean performance of each sex separately. In contrast, if the goal of the experiment is to determine whether a manipulation (e.g., drug administration) changes choice behavior relative to stable baseline performance, it is prudent to adjust shock intensities on an individual rat basis to generate a mean choice curve that is as close to the center of the parametric space as possible. This reduces between-subjects variance and ensures sufficient parametric space to observe either increases or decreases in choice performance relative to baseline. 9. While this task was initially designed with a fixed trial duration (40 s), a recent version of the task utilized a variable, abbreviated trial duration, in which an ITI of 10
4 s was initiated upon pellet delivery. With this alteration, session duration differs between subjects as a function of response latency, averaging approximately 25
5 min. Critically, this change in task parameters did not induce gross alterations in response latency, trial omissions, or individual variability in male rats (unpublished observations, Gabriel and Simon). 10. It is critical to monitor each rat’s task performance carefully on a daily basis. Significant changes in the choice distribution from 1 day to the next (or a reduction in the overall number of choices) can indicate a problem, such as a clogged feeder,

Recent Updates in Modeling Risky Decision Making in Rodents

91

inoperable lever, rats placed in incorrect test chambers, or health problems. 11. While we have historically used mean performance across five consecutive sessions as the basis by which to determine stability, we have also observed that behavioral stability after five sessions is generally evident during the first three of the five consecutive sessions. Together with the fact that others use performance across three sessions from which to determine a stable baseline [25, 26], we now analyze the mean performance across three consecutive sessions to establish behavioral stability. This change has proven to be useful in that it makes experiments more efficient across what are already long periods of training and testing. 12. After extended periods of testing, rats often demonstrate some degree of habituation to the shock. This is manifested as a gradual increase in choice of the risky reward across multiple sessions. If this occurs, it may be necessary to increase the footshock intensity by 0.05 mA prior to any subsequent treatment regimen. After any shifts in intensity, it is critical to obtain behavioral stability over a 3–5 session period (see Notes 10 and 11) before conducting further experiments. References 1. Bechara A, Dolan S, Denburg N, Hindes A, Anderson SW, Nathan PE (2001) Decisionmaking deficits, linked to dysfunctional ventromedial prefrontal cortex, revealed in alcohol and stimulant abusers. Neuropsychologia 39:376–389 2. Drechsler R, Rizzo P, Steinhausen HC (2008) Decision-making on an explicit risk-taking task in preadolescents with attention-deficit/ hyperactivity disorder. J Neural Transm 115:201–209 3. Ernst M, Kimes AS, London ED, Matochik JA, Eldreth D, Tata S, Contoreggi C, Leff M, Bolla K (2003) Neural substrates of decision making in adults with attention deficit hyperactivity disorder. Am J Psychiatry 160:1061–1070 4. Ludewig K, Paulus MP, Vollenweider FX (2003) Behavioural dysregulation of decisionmaking in deficit but not nondeficit schizophrenia patients. Psychiatry Res 119:293–306 5. Evenden JL, Ryan CN (1996) The pharmacology of impulsive behavior in rats: the effects of drugs on response choice with varying delays of reinforcement. Psychopharmacology 128:161–170 6. Cardinal RN, Robbins TW, Everitt BJ (2000) The effects of d-amphetamine, chlordiazepoxide, α-flupenthixol and behavioural

manipulations on choice of signalled and unsignalled delayed reinforcement in rats. Psychopharmacology 152:362–375 7. Winstanley CA, Dalley JW, Theobald DE, Robbins MJ (2003) Global 5-HT depletion attenuates the ability of amphetamine to decrease impulse choice on a delay-discounting task in rats. Psychopharmacology 170:320–331 8. Simon NW, Mendez IA, Setlow B (2007) Cocaine exposure causes long term increases in impulsive choice. Behav Neurosci 121:543–549 9. Simon NW, Gilbert RJ, Mayse JD, Bizon JL, Setlow B (2009) Balancing risk and reward: a rat model of risky decision making. Neuropsychopharmacology 34:2208–2217 10. Mitchell MR, Weiss VG, Beas BS, Morgan D, Bizon JL, Setlow B (2014) Adolescent risk taking, cocaine self-administration, and striatal dopamine signaling. Neuropsychopharmacology 39:955–962 11. Shimp KG, Mitchell MR, Beas BS, Bizon JL, Setlow B (2015) Affective and cognitive mechanisms of risky decision making. Neurobiol Learn Mem 117:60–70

92

Caitlin A. Orsini et al.

12. Deng JV, Orsini CA, Shimp KG, Setlow B (2018) MeCP2 expression in a rat model of risky decision making. Neuroscience 369:212–221 13. Simon NW, Montgomery KS, Beas BS, Mitchell MR, LaSarge CL, Mendez IA, Banuelos C, Vokes CM, Taylor AB, Haberman RP, Bizon JL, Setlow B (2011) Dopaminergic modulation of risky decision making. J Neurosci 31:17460–17470 14. Van Den Bos R, Lasthius W, Den Heijer E, Van Der Harst J, Spruijt B (2006) Toward a rodent model of the Iowa gambling task. Behav Res Methods 38:470–478 15. Zeeb FD, Robbins TW, Winstanley CA (2009) Serotonergic and dopaminergic modulation of gambling behavior as assessed using a novel rat gambling task. Neuropsychopharmacology 34:2329–2343 16. Jentsch JD, Woods JA, Groman SM, Seu E (2010) Behavioral characteristics and neural mechanisms mediating performance in a rodent version of the Balloon Analog Risk Task. Neuropsychopharmacology 35:1797–1806 17. Cardinal R, Howes N (2005) Effects of lesions of the nucleus accumbens core on choice between small certain rewards and large uncertain rewards in rats. BMC Neurosci 6:37 18. Negus S (2005) Effects of punishment on choice between cocaine and food in rhesus monkeys. Psychopharmacology 181:244–252 19. Talmi D, Dayan P, Kiebel SJ, Frith CD, Dolan RJ (2009) How humans integrate the

prospects of pain and reward during choice. J Neurosci 29:14617–14626 20. Anselme P (2015) Does reward unpredictability reflect risk? Behav Brain Res 280C:119–127 21. Orsini CA, Willis ML, Gilbert RJ, Bizon JL, Setlow B (2016) Sex differences in a rat model of risky decision making. Behav Neurosci 130:50–61 22. Gabriel, Freels, Setlow, & Simon (2019). Risky Decision-making is associated with impulsive action and sensitivity to first-time nicotine exposure. Behavioural Brain Research, 359, 579–88 23. Orsini CA, Hernandez CM, Singhal S, Kelly KB, Frazier CJ, Bizon JL, Setlow B (2017) Optogenetic inhibition reveals distinct roles for basolateral amygdala activity at discrete time points during risky decision making. J Neurosci 37:11537–11548 24. Orsini CA, Heshmati SC, Garman TS, Wall SC, Bizon JL, Setlow B (2018) Contributions of medial prefrontal cortex to decision making involving risk of punishment. Neuropharmacology 139:205–216 25. Zeeb FD, Winstanley CA (2011) Lesions of the basolateral amygdala and orbitofrontal cortex differentially affect acquisition and performance of a rodent gambling task. J Neurosci 31:2197–2204 26. Zeeb FD, Winstanley CA (2013) Functional disconnection of the orbitofrontal cortex and basolateral amygdala impairs acquisition of a rat gambling task and disrupts animals’ ability to alter decision-making behavior after reinforcer devaluation. J Neurosci 33:6434–6443

Part III Methods of Animal Models of Psychiatric Illness

Chapter 6 The Pemoline Model of Self-Injurious Behavior: An Update Darragh P. Devine Abstract Neurodevelopmental disorders typically comprise a complex constellation of behavioral symptoms and neurochemical abnormalities. However, many of the symptoms are inconsistently expressed within any one particular patient group or overlap between patient groups. In other words, there is usually heterogeneity of symptoms between diagnostic groups, and there is often partial homogeneity of symptoms across these groups. These include cognitive deficits, emotional lability, and perseverative or aberrant behaviors. Animal models of neurodevelopmental disorders typically reproduce or mimic specific genetic, neurochemical, and/or behavioral sequelae, although they typically fail to replicate the entire spectrum of biological and behavioral characteristics. Indeed, it may be impractical or even impossible to model the entire spectrum of characteristics of a disorder in any single animal model. A focus on one or more specific behavioral characteristics that occur in multiple neurodevelopmental disorders (e.g., self-injury) may be a fruitful strategy. The development of these behaviorally focused models may yield increased understanding of the endogenous and environmental factors that confer vulnerability for aberrant behaviors that commonly occur in these disorders. One such behaviorally focused animal model is the pemoline model of self-injurious behavior. Key words Self-injurious behavior, Autism, Pemoline, Methodology, Behavioral pharmacology, Dopamine, Striatum, Animal model

1

Introduction Self-injurious behavior (SIB) is arguably the most debilitating of all the maladaptive features that are seen in children with neurodevelopmental disorders. It carries the risk of severe physical harm, and it interferes with all normal functions of daily living, including educational and socializing activities [1–3]. In addition, SIB is destructive for families of self-injurers [3–5], and the annual cost of specialized care is over $3 billion in the USA [6]. Despite the prevalence of this behavioral pathology among intellectually handicapped populations, little investment has been made in examining the neurobiological basis of SIB. Therefore, progress in assessment and treatment of self-injury has been hampered. For example, there is very little understanding of factors that promote vulnerability for SIB.

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_6, © Springer Science+Business Media, LLC, part of Springer Nature 2019

95

96

Darragh P. Devine

We have refined a preclinical model using chronic pemoline administration in rats. Our studies show that this model shares several characteristics with human SIB, and we propose that pemoline model of SIB demonstrates face, etiological, construct, convergent, and predictive validity. The behavioral symptoms resemble human SIB, evincing face validity. Each rat targets one or two specific tissue sites, and repeatedly injures those specific sites [7–12], in a manner that resembles the stereotypic body site preferences that are seen human self-injurers [13]. We also found that the self-injury increased after rearing in impoverished environments [10, 14] which resembles the etiological effects of institutional deprivation in humans [15]. The model has construct validity as monoamine involvement is implicated both in clinical SIB [16] and in the pharmacological actions of pemoline [17] (although the roles of the monoamines need to be clarified). The pemoline model also has convergent validity. The L-type calcium channel antagonist nifedipine diminishes SIB in the Bay K 8644, amphetamine, 6-hydroxydopamine (6-0HDA), and pemoline models [8], and the N-methyl-D-aspartate (NMDA) receptor antagonist MK-801 attenuates SIB in the 6-OHDA, dorsal root ganglionectomy, and pemoline models [11, 18–20]. In addition, the model has predictive validity. Although there is no gold standard for pharmacotherapy for SIB, risperidone, valproate, and topiramate, each reduces SIB across groups of clinical self-injurers [21–24] and in the pemoline model [9]. Furthermore, glutamate antagonists (e.g., lamotrigine and riluzole) are showing promise in human clinical trials [25–28], and this concurs with evidence that MK-801 diminishes SIB in the pemoline model [11, 20]. Overall, it appears that the pemoline model shares very important characteristics with human clinical SIB within the translational limitations of behavioral models of human pathology.

2 2.1

Materials Drugs

1. Pemoline (2-amino-5-phenyl-4(5H)-oxazolone; SigmaAldrich) has been suspended (at various concentrations) in 5% acacia gum (also known as gum arabic) and administered oral gavage [29–31]. 2. Pemoline has also been suspended in a carboxymethylcellulose vehicle (0.9% NaCl; 0.5% Na+ carboxymethyl cellulose; 0.4% (v/v) polysorbate 80; 0.86% (v/v) EtOH) and administered by subcutaneous (s.c.) injection [20, 32, 33]. This preparation has been used only for an acute high-dose regimen. 3. In most recent experiments, pemoline has been suspended in sterile peanut oil and administered by s.c. injection [7–9, 11, 12, 34–37] (see Note 1). This preparation has been used for acute treatment with a high dose and for chronic treatment regimens with more moderate doses (see Note 2).

The Pemoline Model of Self-Injurious Behavior: An Update

2.2 Animals and Doses

97

1. The first reports of pemoline-induced self-injury were in Swiss albino mice (20
2 g) and Sprague-Dawley rats (150
10 g) [29, 30]. The expression of tissue injury was greater in male than female mice, but there were no differences in tissue injuries between male and female rats [30]. In these experiments, pemoline was suspended in acacia gum and administered in a single dose by oral gavage (62.5–1000 mg/kg at 20 ml/kg in mice, and 62.5–500 mg/kg at 10 ml/kg in rats). Fasting or feeding did not affect the expression of injury in these experiments. 2. Pemoline-induced self-injury has also been reported in juvenile Sprague-Dawley rats (80
15 g). In these experiments, pemoline was administered by a single s.c. injection at 145–300 mg/kg [20, 32–35], and self-injury was observed within 2–48 h, depending upon the dose. 3. Pemoline can be administered by repeated daily injections at doses ranging from 75 to 300 mg/kg/day [7–9, 11, 12, 34–37]. In these experiments, self-injury was induced in Sprague-Dawley and Long-Evans rats ranging from 80 to 670 g. SIB was expressed in 100% of the rats within the first few days after administration of 300 mg/kg/day and in more than 75% of the rats after administration of 200 mg/kg/day [7]. We typically find that doses in the range of 75–100 mg/kg/day are effective in 50% of Long-Evans rats (ED5O) during 5–10 days of treatment. In these experiments, pemoline was suspended in peanut oil (see Note 3). 4. A recent report indicated that C57Bl/6J mice did not selfinjure after acute treatment by gastric lavage with pemoline (100–500 mg/kg) in 5% acacia gum [30], in contrast to a previous report [30, 31]. This discrepant finding was attributed to strain differences. 5. In our investigations, we identified that C57Bl/6 mice self-injure after repeated daily pemoline injections (250 mg/kg/day, s.c. in peanut oil) but that they are not as vulnerable as outbred BALBc mice (unpublished). These preliminary findings support the conclusion that there are strain differences in vulnerability in mice but also indicate that administration by s.c. injection may enhance vulnerability in a resistant strain. We are continuing to characterize the utility of the pemoline model in mice.

3

Methods Most recent research on pemoline-induced self-injury has focused on self-injury in rats that are injected daily with pemoline suspended in peanut oil. Accordingly, the methods described herein focus on this regimen.

98

3.1

Darragh P. Devine

Animal Welfare

3.2 Drug Injections and Monitoring of SelfInjury

All procedures using live animals must be carried out in accordance with the Animal Welfare Act and the Guide for the Care and Use of Laboratory Animals (National Research Council), or the equivalent regulations for investigations that are conducted outside of the USA. Since the experimental end points are focused on selfinflicted injury in the animals, there must be very careful monitoring of all injuries. The following procedures were conducted in accordance with the above principles, and all the procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Florida. 1. After 5–7 days acclimation to the housing room, each rat is weighed and injected with pemoline in peanut oil (generally, 75–150 mg/kg, depending upon the needs of the experiment, as described in Subheading 2.2, item 3). These injections are given at approximately 8:00 a.m. each day for 3–10 days (see Note 4). 2. In our experience, the behavioral outcomes typically exhibit greater variability than the biochemical measures do. Accordingly, our power analyses have been based upon the behavioral measures, and we generally find that we need about 12–18 rats/group in our studies. 3. Each rat is examined twice daily (8:00 a.m. and 6:00 p.m.) for evidence of self-injury on each of the days of drug treatments and again on the morning of the final experimental day. The examinations consist of visual and digitally recorded inspection of each rat’s head, forepaws, ventrum, hind paws, and tail. The presence of injuries is scored by the experimenter and then rescored from the recordings by a trained observer who is blind to the treatment conditions. Each rat is assigned a tissue injury rating according to the scale in Table 1. The placement of each injury and the number of sites of injury are also recorded. In addition, images of all injury sites are digitally captured, and the area of each injury is measured using an MCID camera system with NIH Image software. 4. If an open lesion is detected during any visual inspection (i.e., score ¼ 4; Table 1), the rat is euthanized immediately (see Note 5). This has occurred in less than 5% of rats treated with pemoline at 100 mg/kg in our studies. 5. The rats’ behavior is also digitally recorded in 5-min time samples collected every 3 h throughout the experiment. The frequency and duration of self-injurious oral contact with any site on the rats’ skin are scored using “Observer” software (Noldus Information Technology). This contact is differentiated from normal grooming because rats exhibit constant movements from one body site to another when grooming,

The Pemoline Model of Self-Injurious Behavior: An Update

99

Table 1 Examples of the four categories of manipulations that induce selfinjurious behavior in animals Score Classification Description 0

No injury

No tissue damage

1

Very mild

Slight edema, pink moist skin, involves small area

2

Mild

Moderate edema, slight erythema/denuding, involves medium area or multiple sites

3

Moderate

Substantial edema/erythema/denuding. Involves large area, can include multiple sites

4

Open lesion

any open break in the skin, requires immediate euthanasia

whereas the self-injurious contact consists of prolonged contact between the mouth and a single body site (usually, forepaw or ventrum). Self-injurious oral contact is defined as all oral contact that remains fixed on any body part for more than 5 s. Trained observers who are blind to the treatment conditions score the digital video recordings, and inter-observer reliability is evaluated. 6. On the final day of the experiment (8:00–10:00 a.m.), each rat is checked again for injuries and terminated by methods that are determined by the requirements of the pre-planned biochemical assays (i.e., yielding fixed or fresh-frozen tissue samples). Pemoline is not injected on this final morning, but our assays indicate that plasma pemoline concentrations are stable at about 40 μg/ml, 24 h after five daily injections at 150 mg/ kg/day [38]. 3.3 Statistical Analyses of SIB

1. If any rats are terminated early (due to tissue injury), all the scores of injury rating, location, number, and area that were measured at the final inspection and the total oral contact score that was measured on the final day are assigned for each missing time point so that the measures do not underestimate the contributions of these rats (i.e., to avoid selectively removing the worst self-injurers in any treatment group). 2. Measures of self-injury can be analyzed using repeated measure analyses of variance (ANOVAs). However, since the expression of SIB involves an onset phase (during which the area of injury and duration of self-injurious oral contact each increase over days) and a maintenance phase (during which these measures remain fairly constant), we recommend that the onset and maintenance of self-injury are each compared using profile

100

Darragh P. Devine

analysis. Hotelling’s T2 tests can be used as an initial omnibus test to determine if the profiles of area of injury and selfinjurious oral contact are parallel between groups or if one is bigger. The features of these two phases for each of the dependent variable can then be examined using logistic growth curves with parameters that measure slope during the onset phase and asymptotic expression during the maintenance phase. 3. When additional dependent measures are taken (e.g., behavioral and hormonal concomitants, measures of gene and protein expression), the relationship between these factors and the size of injury is assessed by calculating a total area of injury score for each rat (sum of all daily measures of all injury sites) and analyzing the Pearson correlation between the injury area and the additional dependent measure. Similarly, a total oral contact score is calculated, and Pearson correlations are run against each additional dependent measure. 3.4 Validation of the Tissue Injury Rating Scale in the Pemoline Model of Self-Injurious Behavior

4

We validated the use of the tissue injury rating scale by assessing its relationship with the actual self-injurious oral contact in digitally recorded time samples of rats treated with pemoline [39]. In this case, 12 rats were treated with pemoline (200 mg/kg/day) for 5 days and terminated on the morning of the sixth day. Each night, the rats were digitally video-recorded for eight hourly 10-min time samples. The recordings were scored for self-injurious oral contact with body parts, and the duration of self-injurious oral contact was compared for each night with the tissue injury score the following morning. Spearman rho correlation between the total oral contact duration for each night with the ensuing tissue injury score was 0.672 ( p < 0.001).

Notes 1. The maximum pemoline concentration that goes into suspension is 23 mg/ml (carboxymethylcellulose vehicle) or 50 mg/ ml (peanut oil vehicle). Lower concentrations would require excessively large injection volumes. 2. Pemoline was previously used under the trade name Cylert for treatment of attention deficit hyperactivity disorder and for treatment of narcolepsy. Its approval was revoked by the US Food and Drug Administration (FDA) in 2005 due to reports of hepatotoxicity after long-term use. However, our analyses reveal that there are no behavioral signs of malaise, no thymus involution or adrenal hyperplasia [7, 9], and no evidence of organotoxicity (assayed by aspartate aminotransferase-induced extinction of NADH in plasma samples) during the treatment regimens we have used [9].

The Pemoline Model of Self-Injurious Behavior: An Update

101

3. Pemoline is difficult to get into suspension in any vehicle, but effective suspension is extremely important. In the case of peanut oil suspension, this is best achieved by making a fresh suspension and stirring constantly for 4–6 h on a temperaturecontrolled hot plate at 37  C before each daily administration. The pemoline suspension can be administered through a 23-gauge needle (failing to warm and stir necessitates the use of a larger bore needle, which may not be appropriate for small rodents, especially mice). 4. When designing trials of pharmacological challenges, the pharmacodynamics of the drugs should be considered. Since pemoline in suspension has a long duration of action, pharmacological challenges might need to be administered multiple times each day of the experiment [9]. Since pemoline actions may peak shortly after the daily injections, one of the daily injections of the challenge drug might need to be administered prior to the daily pemoline injection [11, 20]. 5. Timely termination of rats that engage in self-injury is an important ethical concern, and it is highly recommended that clear termination criteria are established a priori. Whereas one could terminate individual rats immediately upon the first sign of injury, this criterion appears to be overly restrictive, allowing for only a categorical data analysis (i.e., identifying the percentages of rats that do or do not self-injure). In fact, some rats start to engage in self-injurious oral contact and then stop when they have caused only very minor edema or erythema. This is seen in some of our graphs. The injury heals, and the percentage of self-injurious rats actually decreases on subsequent days of pemoline treatment [9]. Most self-injuring rats induce local edema or erythema, and a small percentage do more serious damage. We never see the gross injuries that are reported with clonidine [40] and caffeine [41] in our pemoline model when we administer 75–150 mg/kg/day. Thus, allowing a reasonable expression of SIB has allowed a more detailed analysis of pemoline’s dose-orderly effects [7], a characterization of the actual expression of self-injurious oral contact [9, 11], elucidation of biochemical factors that underlie vulnerability for pemoline-induced SIB [37], and measures of effectiveness when screening potential pharmacotherapeutic interventions [8, 9]. Accordingly, we have adopted a termination criterion that allows for statistically meaningful continuous (i.e., rather than yes/no) measures of severity while preventing any individual rat from progressing beyond any initial break in the skin.

102

Darragh P. Devine

References 1. Matson JL, Minshawi NF, Gonzalez ML, Mayville SB (2006) The relationship of comorbid problem behaviors to social skills in persons with profound mental retardation. Behav Modif 30:496–506 2. Matson JL, Nebel-Schwalm M (2007) Assessing challenging behaviors in children with autism spectrum disorders: a review. Res Dev Disabil 28:567–579 3. Findling RL (2005) Pharmacologic treatment of behavioral symptoms in autism and pervasive developmental disorders. J Clin Psychiatry 66 (Suppl 10):26–31 4. Sarimski K (1997) Communication, socialemotional development and parenting stress in Cornelia-de-Lange syndrome. J Intellect Disabil Res 41(Pt1):70–75 5. Bromley J, Emerson E (1995) Beliefs and emotional reactions of care staff working with people with challenging behaviour. J Intellect Disabil Res 39(Pt 4):341–352 6. National Institutes of Health Consensus Statement (1989) Treatment of destructive behaviors in persons with developmental disabilities. J Autism Dev Disord 20:403–429 7. Kies SD, Devine DP (2004) Self-injurious behaviour: a comparison of caffeine and pemoline models in rats. Pharmacol Biochem Behav 79:587–598 8. Blake BL, Muehlmann AM, Egami K, Breese GR, Devine DP, Jinnah HA (2007) Nifedipine suppresses self-injurious behaviors in animals. Dev Neurosci 29:241–250 9. Muehlmann AM, Brown BD, Devine DP (2008) Pemoline-induced self-injurious behavior: a rodent model of pharmacotherapeutic efficacy. J Pharmacol Exp Ther 324:214–223 10. Devine DP, Muehlmann AM (2009) Tiermodelle fur selbstverletzendes Verhalten (Animal models of self-injurious behavior). In: Schmahl C, Stiglmayr C (eds) Selbstverletzendes Verhaltenbei Stressassoziierten Erkrankungen (Self-injurious behaviour in stressassociated disorders). Verlag W. Kohlhammer, Stuttgart, Germany, pp 39–60 11. Muehlmann AM, Devine DP (2008) Glutamate-mediated neuroplasticity in an animal model of self-injurious behaviour. Behav Brain Res 189:32–40 12. Yuan X, Devine DP (2016) The role of anxiety in vulnerability for self-injurious behaviour: studies in a rodent model. Behav Brain Res 311:201–209 13. Symons FJ, Thompson T (1997) Self injurious behaviour and body site preference. J Intellect Disabil Res 41:456–468

14. Kies SD, Turner CA, Lewis MH, Devine DP (2002) Effects of environmental complexity in an animal model of self-injury. Soc Neurosci Abstr 28:207.8 15. Beckett C, Bredenkamp D, Castle J, Groothues C, O’Connor TG, Rutter M (2002) Behavior patterns associated with institutional deprivation: a study of children adopted from Romania. J Dev Behav Pediatr 23:297–303 16. Saito Y, Takashima S (2000) Neurotransmitter changes in the pathophysiology of LeschNyhan syndrome. Brain Dev 22(Suppl 1): S122–S131 17. Everett GM (1976) Comparative pharmacology of amphetamine and pemoline on biogenic amine systems. Fed Proc 35:405 18. Criswell HE, Johnson KB, Mueller RA, Breese GR (1993) Evidence for involvement of brain dopamine and other mechanisms in the behavioral action of the N-methyl-D-aspartic acid antagonist MK-801 in control and 6-hydroxydopamine-lesioned rats. J Pharmacol Exp Ther 265:1001–1010 19. Tseng SH, Lin SM (1998) Substantia nigra lesion suppresses the antagonistic effects of N-methyl-D-aspartate receptor antagonist (MK-801) on the autotomy in the rat. Neurosci Lett 255:167–171 20. King BH, Au D, Poland RE (1995) Pretreatment with MK-801 inhibits pemoline-induced self-biting behavior in prepubertal rats. Dev Neurosci 17:47–52 21. Winchel RM, Stanley M (1991) Self-injurious behavior: a review of the behavior and biology of self-mutilation. Am J Psychiatry 148:306–317 22. Accardo P (2003) Risperidone in children with autism and serious behavioral problems. J Pediatr 142:86–87 23. Davis LL, Ryan W, Adinoff B, Petty F (2000) Comprehensive review of the psychiatric uses of valproate. J Clin Psychopharmacol 20:1S–17S 24. Smathers SA, Wilson JG, Nigro MA (2003) Topiramate effectiveness in Prader-Willi syndrome. Pediatr Neurol 28:130–133 25. Davanzo PA, King BH (1996) Open trial lamotrigine in the treatment of self-injurious behavior in an adolescent with profound mental retardation. J Child Adolesc Psychopharmacol 6:273–279 26. Sasso DA, Kalanithi PS, Trueblood KV, Pittenger C, Kelmendi B, Wayslink S, Malison RT, Krystal JH, Coric V (2006) Beneficial effects of the glutamate-modulating agent

The Pemoline Model of Self-Injurious Behavior: An Update riluzole on disordered eating and pathological skin-picking behaviors. J Clin Psychopharmacol 26:685–687 27. Pittenger C, Krystal JH, Coric V (2005) Initial evidence of the beneficial effects of glutamatemodulating agents in the treatment of selfinjurious behavior associated with borderline personality disorder. J Clin Psychiatry 66:1492–1493 28. Rizvi ST (2002) Lamotrigine and borderline personality disorder. J Child Adolesc Psychopharmacol 12:365–366 29. Genovese E, Napoli PA, Bolego ZN (1968) Group toxicity and autoaggressivity induced by 5-phenyl-2-imino-4-oxo-oxazolidine (pemoline). Boll Soc Ital Biol Sper 44:1953–1957 30. Genovese E, Napoli PA, Bolego-Zonta N (1969) Self-aggressiveness: a new type of behavioural change induced by pemoline. Life Sci 8:513–515 31. Kasim S, Jinnah HA (2002) Pharmacologic thresholds for self-injurious behavior in a genetic mouse model of Lesch-Nyhan disease. Pharmacol Biochem Behav 73:583–592 32. King BH, Au D, Poland RE (1993) Low dose naltrexone inhibits pemoline-induced self-biting behavior in prepubertal rats. J Child Adol Psychopharmacol 3:71–79 33. King BH, Cromwell HC, Lee HT, Behrstock SP, Schmanke T, Maidment NT (1998) Dopaminergic and glutamatergic interactions in the expression of self-injurious behavior. Dev Neurosci 20:180–187

103

34. Cromwell HC, Levine MS, King BH (1999) Cortical damage enhances pemoline-induced self-injurious behavior in prepubertal rats. Pharmacol Biochem Behav 62:223–227 35. Cromwell HC, King BH, Levine MS (1997) Pemoline alters dopamine modulation of synaptic responses of neostriatal neurons in vitro. Dev Neurosci 19:497–504 36. Turner CA, Panksepp J, Bekkedal M, Borkowski C, Burgdorf J (1999) Paradoxical effects of serotonin and opioids in pemolineinduced self-injurious behavior. Pharmacol Biochem Behav 63:361–366 37. Muehlmann AM, Wilkinson JA, Devine DP (2011) Individual differences in vulnerability for self-injurious behavior: studies using an animal model. Behav Brain Res 217(1):148–154 38. Muehlmann AM, Devine DP (2008) Selfinjurious behavior: individual differences in neurotransmitter concentrations using an animal model. Keystone Symp: Towards Identifying the Pathophysiology of Autistic Syndromes C2:206 39. Kies SD, Devine DP (2002) Quantification of self-injurious behavior in an animal model of pemoline-induced self-injury. Int Meeting Autism Res 2:Pl.2.4 40. Razzak A, Fujiwara M, Ueki S (1975) Automutilation induced by clonidine in mice. Eur J Pharmacol 30:356–359 41. Peters JM (1967) Caffeine-induced hemorrhagic automutilation. Arch Int Pharmacodyn Ther 169:139–146

Chapter 7 Rodent Models of Adaptive Value Learning and Decision-Making Alicia Izquierdo, Claudia Aguirre, Evan E. Hart, and Alexandra Stolyarova Abstract Real-world decisions are rarely as straightforward as choosing between clearly “good” vs. “bad” options. More often, options must be evaluated carefully because they differ in relative value. For example, we typically learn about (and make decisions between) options in comparison, where one outcome may be more costly or risky than the other. Several neuropsychiatric conditions are characterized by atypical evaluation of effort and risk costs, including major depression, schizophrenia, autism, obsessive-compulsive disorder, and substance use disorders. Aberrant value learning and decision-making have long been considered a cognitive-behavioral endophenotype of these disorders and can be modeled in rodents. This chapter presents two general methodological domains that the experimenter can manipulate in animal decision-making tasks: risk and effort. Here, we present detailed methods of rodent tasks frequently employed within these domains: probabilistic reversal learning (PRL) and effort choice. These tasks recruit regions within rodent frontal cortex, the amygdala, and the striatum, and performance is heavily modulated by dopamine, making these assays highly valid measures in the study of behavioral and substance addictions, in particular. Key words Reversal learning, Effort discounting, Orbitofrontal cortex, Anterior cingulate cortex, Basolateral amygdala

1

Introduction Over the past decades, there have been major advances in experimental techniques and functional neuroimaging analyses in human subject research. In more recent years, there has also been a notable increase in the use of artificial intelligence, virtual systems, and in silico models to learn about, mimic, and reproduce the computations of real brains. However, due to the practical and ethical limitations of human brain research and the scarcity of biophysically plausible computational models, we continue to rely on the animal model to uncover the neurobiological mechanisms of complex cognitive processes. The use of mice and rats in neuroscience research has also been supported by powerful technological advances in manipulating and recording from neurons with unprecedented

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_7, © Springer Science+Business Media, LLC, part of Springer Nature 2019

105

106

Alicia Izquierdo et al.

specificity [1–3]. Ideally, these techniques will be partnered with well-characterized behavior [4, 5] for maximum impact in understanding neuropsychiatric conditions. Organisms must evaluate the cost of rewards and respond to significant changes in the motivational value of rewards for survival. Adaptations in these abilities are of interest to a variety of fields of study including psychology, neuroscience, behavioral ecology, and ethology [6]. For example, in contemplating a behavioral task to implement in the laboratory, the researcher may consider first if it is important to test how animals gather information or evidence for the options or in an unknown or changing reward environment (i.e., learning) and/or if it is of interest to test how well animals maximize or exploit rewards in well-known conditions (i.e., performance). Another factor to consider in choosing a behavioral task is that animals experience rewards in the context of foraging, even in laboratory tasks and in highly controlled conditions. For example, costs are given priority consideration: risk of shock is weighted more heavily than risk of non-reward. Similarly, the opportunity to exploit a resource comes at the expense of exploring another [7]. Finally, overcoming these costs is likely also very influenced by the reward environment’s volatility (i.e., whether it is dynamic or stable) and valence (i.e., whether it is impoverished or profitable), since animals tend to evaluate such factors in relative terms [8], based on their history of experiences. Usually, the value of rewards such as food can be predicted by sensory cues in the environment, and the ability to associate cues (i.e., stimuli) with reward and respond to changes in reward is highly predictive of survival and success. If this associative learning is plastic, we are better at coping with changes that affect our ability to procure the goal. One may, for example, select an option with an outcome that is better or worse than expected (i.e., prediction errors) that then requires an update to our expectations to learn from the experience. Adaptive value learning may therefore influence any stage of this process (i.e., representation of the outcomes, valuation of the outcomes, and action selection), thereby enabling reward maximization in the future [9]. One platform to probe this ability is a touchscreen-response system, where experimenters can present visual stimuli to rodents, monkeys, or humans [4] and have them learn about stimulus-outcome associations. There are commercially available systems, but individual laboratories often configure and optimize their own hardware and software [10, 11]. Experimenters can manipulate the value of visual stimuli in a number of different ways: by changing probabilities of reinforcement, toying with the magnitude of rewards, or by imposing delays to reward associated with different stimuli [12]. By introducing uncertainty in probabilistic reversal learning (PRL) or bandit tasks, the experimenter can obtain many more trials from the subject, making it more challenging for rodents and more

Rodent Models of Adaptive Value Learning and Decision-Making

107

interesting and engaging for the primate species. Another benefit is that with hundreds to potentially thousands of trials to learn from in the rodent, one can then better apply models of dynamic learning and reinforcement learning to explain behavior. The neural substrates of this behavior have been reviewed recently [4, 13]. Here we present methods for testing rats on PRL, though similar applications have also been used in mice [14, 15]. Rats must also overcome different costs to procure rewards, and one such cost is physical effort. The ventral striatum, basolateral amygdala, and medial cortical regions (viz., anterior cingulate cortex) support decisions involving different effort costs, for example, when choosing to work for a preferred option instead of selecting the freely available reward of lesser value [16–27]. Effort choice tasks have been useful for modeling motivational symptomatology that occurs in diseases and for testing potential therapeutics and pharmacotherapies to ameliorate deficits. Several paradigms have been employed in this domain, including manually administered tests such as effort t-maze tasks [28, 29], as well as a variety of effort discounting tasks in carefully controlled operant conditioning settings [21, 30]. One paradigm we have recently used has provided a fairly quick and easy way to probe and dissociate relative value decisions (i.e., choices between qualitatively different options that are more or less preferred) from a more general willingness to work (i.e., responding on a progressive ratio schedule). Partnered with control tasks that our lab implements to assess rats’ willingness to work with and without another option (single option control), and their consumption of either reward without a work requirement (free-choice control), this paradigm can provide meaningful insights into cost-benefit evaluations in rodents, rats in particular [26, 27]. A beneficial feature of the task is that animals can selftitrate the amount of effort they are willing to exert, mimicking the human condition. Here we present methods for testing rats on effort choices. Impairments in adaptive value learning and decision-making can manifest as perseverative behaviors and/or the inability to monitor and update one’s own behavior, prominent features of obsessive-compulsive disorder (OCD) and substance use disorder (SUD). Although humans engage in complex decision-making, the use of rodents has been key in uncovering the neural mechanisms and the pharmacological modulation of such adaptive behavior, in its simplest form. The aim of this chapter is to provide researchers with two well-validated behavioral tools by which value learning and decision-making can be assessed in the rodent. We also note that this chapter does not review the procedures that manipulate brain mechanisms (the independent variables) but rather the dependent variables or measures by which we frequently assess this learning and choice behavior in rats.

108

Alicia Izquierdo et al.

Following a detailed protocol for food restriction in rodents (a procedure common to many assays of instrumental learning), we describe each task in turn. These methods have been optimized for male, adult Long-Evans strain of rat, though we have also used and/or adapted similar protocols in both male and female rats [31] and adolescent rats [32].

2

Materials

2.1 General Guidelines

1. It is necessary to have access to vivaria and behavioral testing rooms to conduct experiments that have been approved by the Institutional Animal Care and Use Committee (IACUC), beforehand. 2. The experimenter should be able to house rats in temperatureand humidity-controlled rooms with the ability to reverse the light/dark cycle. 3. The methods described here may work best if rats are maintained under a 12/12 h reverse light cycle at 22
C with lab chow and water available ad libitum prior to behavioral testing. 4. Behavioral testing should be conducted during the dark phase, when rats are most active.

2.2

PRL

1. Training and testing are conducted in operant chambers (Lafayette Instrument Co., Lafayette, IN) that are housed within sound- and light-attenuating cubicles. 2. Each chamber is equipped with a house light, tone generator, video camera, and LCD touchscreen opposing the pellet dispenser. The pellet dispenser delivers 45-mg dustless precision sucrose pellets. 3. In these chambers, the investigator can similarly choose to deliver liquid rewards. ABET II TOUCH software (Lafayette Instrument Co., Lafayette, IN) controls the hardware and can be customized.

2.3

Effort Choice

1. Training and testing are conducted in operant chambers outfitted with a house light, internal stimulus lights (located over levers), a single food-delivery magazine on the opposite side of the chamber to the levers, and two retractable levers positioned to the left and right of the chamber. 2. These chambers are similarly enclosed in sound- and lightattenuating cubicles. Additionally, such chambers can include a liquid swivel attached to a steel leash to an external syringe pump for drug self-administration. 3. All hardware is controlled and easily modified in Med-PC IV (Med-Associates, St. Albans, VT).

Rodent Models of Adaptive Value Learning and Decision-Making

3 3.1

109

Methods Food Restriction

3.1.1 Establishing FreeFeeding, Baseline Weights

1. Dietary restriction is used as a motivator to enhance the pursuit of the reward (in our tasks, food rewards) in rats. A sated or overfed rat is not likely to engage in motivated behavior to procure food rewards. The restriction level is commonly no lower than 85% of a rat’s free-feeding weight (this is typically deemed an acceptable level by most IACUCs) and is in keeping with NIH Guide for the Care and Use of Laboratory Animals. Food rewards (sucrose, grain, or other-flavored pellets) vary widely in animal tasks. The following can be used as a guide for food rewards in behavioral studies in the rat. 2. Rats that are food-restricted should have free access to water in their home cage at all times; it would likely be a very special exemption to have permission to impose both food and water restriction. 3. For rats on “rest” (animals not currently being tested, with free access to food), a baseline is established by taking their weights after 1 week or more of free feeding and at least 1 week after their arrival to the vivarium from the supplier. 4. If rats are bred in-house, they should be at least 190 g to undergo a food-restricted diet to avoid stunting of growth. We have published average weights even for young animals that may be used for comparison [32]. 5. Additionally, vendors provide standard growth curves that can be used as a reference. An established baseline weight serves as the highest weight from which to calculate the 85% “minimum” weight.

3.1.2 Establishing 85% Free-Feeding Weight

1. For behavioral studies requiring reward-motivated responding, rats should be individually housed in order to carefully monitor food consumption and weight. This does not typically require special permission from one’s IACUC. However, with extra care, dividers could be placed in pair-housed cages during feeding time and removed following consumption. 2. In the event that a rat reaches a new highest weight, this number should then be used to calculate the new 85% minimum body weight. This recalculation occurs frequently with younger rats (4 h per day in consummatory activities. We use the term cost-based anorexia (CBA) for this type of protocol (see Note 20). 6. A variant of fixed unit price protocol is to make the cost of food variable. One instantiation of this is a progressive ratio in which the cost of successive pellets within a food opportunity (session) increases by either a fixed or fractional amount. 7. In such a protocol, animals will stop responding for food at a point when the cost is apparently too high (the break point). Such protocols often require prior food deprivation (e.g., overnight) and are generally used in an open economy to study motivation, and intake would not be sufficient to produce satiation if no cost were imposed on food.

Protocols Using Rodents to Model Eating Disorders in Humans 3.2.2 Appetitive Cost Protocols

323

1. Appetitive cost is that incurred to gain access to food, such as foraging (see Note 18), and can be emulated in an operant task in at least two ways. 2. The first [18] is to have a food behind a door that can be opened automatically contingent upon performance of a designated appetitive or access cost. 3. The second is to have two (preferably different) response devices, only one of which delivers food pellets according to a unit price (as in Subheading 4.1) but that consummatory device is only made operational once designated response (s) have been made on the other appetitive device [17]. In either version of appetitive cost, it is necessary to program a criterion that will terminate availability of food unless and until a new appetitive cost is emitted. A criterion of 10 min without consummatory responses is suitable. 4. In rats and mice, as appetitive cost increases, the number of eating bouts or meals decreases but intake remains normal by a reciprocal increase in bout size. 5. Thus, the optimal strategy in this case is manifested as frequency of eating, and, within parameters that have been studied, there is little or no CBA. One disadvantage of this protocol (over, say, a TR version) is that the time at which a subject will initiate responding or eating is not as precisely predictable as might be ideal for mechanistic or interventional studies.

4

Notes 1. There may be a good scientific reason for using an “unusual” strain or species, including genetic modification or physiological trait. 2. The basis of good science is replication and extension; you cannot assume that a result reported by a different laboratory using different procedure, strain, and/or species will generalize to the conditions that you are proposing to use. 3. Studies of food intake often require handling animals on a daily basis; handling constitutes an often variable or uncontrolled amount of stress. In general, confident and skilled handlers reduce such stress. Rats of most strains become quite docile with repeated handling, but mice can be more challenging. A large object (hand) entering the home territory from overhead may appear as a threat, and some animals adapt to stepping onto a platform (e.g., petri dish lid) for removal from their cage.

324

Neil E. Rowland

4. You may not have much control over the standard conditions present in your vivarium, including whether cages are ventilated or conventional, but you should always specify those conditions in the methods sections of publications. Facilities often use unnecessarily intense lighting, and (nocturnal) animals on the top shelf of a rack may be differentially affected by this photic stress. 5. Specialty caging and equipment is available to measure metabolic rate at frequent intervals over 24-h periods, often in conjunction with behavioral variables such as food intake and activity. Likewise, some specialty group housing allows identification and recording food intake of each individual. Such housing has substantial capital cost and is not reasonable for studies with large numbers of subjects. 6. Commercially available rodent food (chow) is made from natural animal or plant ingredients that may vary from batch to batch and between suppliers. Semi-purified diets are synthesized from pure ingredients, with well-defined and replicable compositions (e.g., fat type and content), but are more expensive. Chow is NOT an appropriate control for a semi-purified experimental diet. 7. In all cases, intake should be corrected for evaporative loss and spillage. Evaporative loss is usually quite small and can be assessed by placing a similar food source in or on a cage in the same room, but with no animals. Spillage can be significant, especially in mice. Assessing food spillage in solid bottom cages is often difficult or unreliable. In cages with mesh or rod floors beneath the feeder area, spilled food may be retrieved from a tray or other suitable catch such as a small sheet of absorbent paper. An accuracy of 1% in food measurement is recommended for most purposes, but sometimes cannot be achieved because of imprecision in measurement of spillage or other wastage. 8. Powdered solid food is especially prone to digging and spillage (above). Using liquid diets, the amount consumed per lick is usually very constant for a given individual and spout characteristic, in which case the number of spout contacts (e.g., lickometer) or approaches (e.g., infrared beam) can be acquired and the size of bouts derived by linear interpolation from the volume consumed over the 24-h session. 9. Absent food hoarding, feeding episodes are quantitatively equivalent to pellets delivered. In this context, food items are usually small but uniform and nutritionally complete pellets or tablets that can be purchased commercially. Common pellet sizes for rats and mice are 45 and 20 mg, representing ~0.25% and 0.5% of daily intake, respectively. They are dispensed, one

Protocols Using Rodents to Model Eating Disorders in Humans

325

or more at a time, from a size-specific hopper that is controlled electronically. An open-source and relatively inexpensive version of a “take and replace” device that can fit in a standard cage has been described [19]. Commercial operant behavior chambers or systems are relatively expensive but are durable, which is not a small concern with rodents that are prone to chew on just about anything. Animals rarely need special training to perform lever press of nose poke responses for food: leaving the animal in such a chamber overnight with a low response ratio is generally sufficient to establish robust operant behavior. 10. While meals may be a useful construct, there are exceptions. Mice often exhibit several hours per night grazing [19–21], behavior that is not well described by meal analysis. Further, meal patterns can be greatly altered by what may appear to be small environmental costs (Subheading 4). 11. Several commercial suppliers offer high-fat diets (45–60% metabolizable energy, ME, from fat) and suitable low-fat (1 week after the stressor is terminated), measure more than one behavioral outcome variable for reliability and/or robustness, and

Updates in PTSD Animal Models Characterization

333

Fig. 1 Published papers on PTSD animal models. Using the keywords “PTSD animal models,” 792 literatures were found in PubMed on August 20, 2018. The distribution of the publications is shown yearly

have replicable effects across more than one laboratory. Most models present an unpredictable, or inescapable severe stressor (e.g., vary stressor intensity, duration) to avoid habituation and mimic life-threatening aspects of trauma associated with PTSD [18]. There is no identical or specific behavior that is shown in animal models. Many simply refer to the variety of behaviors in animal studies as PTSD-like. Although objective biological measures (e.g., imaging, peripheral biomarkers) are being researched in many clinical studies, the link between behavioral changes in animal models and clinically observed markers is undetermined. In addition, across species, researchers have not defined the common area of PTSDlike behavior. For example, mice and rats may show different responses to the same stressor. Preclinical researchers utilize current rodent models to probe biological phenotypes of PTSD (e.g., sleep disturbances, hippocampal and fear-circuit dysfunction, inflammation, glucocorticoid receptor hypersensitivity). However, within those models, there exist some issues across animal species as the biological responses to the traumatic stressor can be quite different. Between humans and animals, the difference is even more obvious. Fortunately, most models reliably produce enduring generalized anxiety-like or depression-like behaviors, as well as hyperactive fear circuits, glucocorticoid receptor hypersensitivity, and response to long-term selective serotonin reuptake inhibitors. Overall, preclinical (and clinical) PTSD researchers are increasingly incorporating homologous biological measures to assess markers of risk, response, and treatment outcome. Whether or not these biological observations combine with behavior testing will help us to establish a reliable PTSD animal model is currently unknown. In general, there are three criteria for animal models. The criteria include a phenomenological resemblance to the modeled

334

Lei Zhang et al.

condition (“face validity”), demonstrating identical underlying mechanisms as the human disorder (“construct validity”) and providing predictions about therapeutic response (“predictive validity”) [8, 19]. PTSD animal models are paradigms with appropriate stressors provoking long-lasting behavioral symptoms similar to PTSD, such as hyperarousal, hypervigilance, social withdrawal, and cognitive alterations. Furthermore, these models may provide the tools needed to examine the therapeutic responses and may meet the criterion of predictive validity. Since the exact pathomechanisms of PTSD are still unknown, it is difficult to define the exact conditions of construct validity. However, animal models have demonstrated a number of biological factors that have been implicated in the development of PTSD in humans.

2

To Establish an Animal Model of PTSD Using Highly Stringent Criteria Stress paradigms result in a wide range of biobehavioral responses in animals; thus, a number of animal models have been introduced. In animals, stressful events, aversive challenges, and situational reminders of a traumatic stress result in long-term effects on behavioral, autonomic, and hormonal responses that mimic many of the changes seen in human subjects with PTSD. Those stresses, for example, include electric shock [20–22], social confrontations, stress–restress [23], underwater trauma [24, 25], exposure of an animal to a predator [26, 27], and the inescapable tail shock [15, 22]. Here, we briefly list and discuss several common models for PTSD studies. We do not intend to compare these models. Rather, we are reviewing these models to expand our understanding of PTSD-like behavior, clinical PTSD symptoms, and the molecular mechanisms that may underlie the disorder.

3

PTSD Animal Models Here, we show the common PTSD rodent models (Fig. 2).

3.1 Learned Helplessness

The learned helplessness test is a behavior model in which an animal endures repeatedly painful or otherwise aversive stimulus that is unable to escape or avoid (https://en.wikipedia.org/wiki/ Learned_helplessness) [18]. In this situation, the animals may fail to learn or accept “escape” or “avoidance” in new situations where such behavior would likely be effective. Thus, the animals learn that it is helpless in situations where there is a presence of aversive stimuli. The animal manifests acquired learned helplessness (https://en.wikipedia.org/wiki/Learned_helplessness) [18]. The learned helplessness model has been used for PTSD study since it was first developed by Seligman and Maier in the late 1960s based

Fig. 2 An overview of common animal models for PTSD. Post-traumatic stress disorder (PTSD) is a chronic, debilitating mental disorder. Despite recent progress, the molecular mechanisms underlying the pathology of PTSD are poorly understood. To promote research on PTSD, many animal models have been developed. Currently, several stress paradigms mimic the behavioral symptoms and neurological alterations seen in PTSD. This figure shows an overview of PTSD rodent models, including learned helplessness, footshock, restraint stress, inescapable tail shock, single-prolonged stress, underwater trauma, social isolation, social defeat, early-life stress, and predator-based stress. These rodent models reproduce some of the behavioral and biotical phenotypes seen in PTSD

Updates in PTSD Animal Models Characterization 335

336

Lei Zhang et al.

on Pavlov’s “classical conditioning” [28]. In that model, they used dogs that had previously experienced inescapable electric shocks and failed to escape in a different setting afterward. The experimental dogs were unable to avoid further shocks, showing a “behavioral despair” [28]. These animals demonstrate a unique motivational deficit feature. Thus, the model was initially used as a cognitive depression model [29] and was considered a PTSD-like model later on [18, 30]. This model has been established in several species, including rats and mice [31]. In addition to measuring behavior alteration, researchers also examine biological parameters in this model, observing the levels of neurotransmitters (e.g., catecholamines) and psychopharmacological responses [32, 33]. However, its use as a PTSD model is uncertain since it provides inconsistent behavioral data [34]. The behavioral alterations in this animal model may not reflect the behavioral alterations in human PTSD [35]. To avoid such controversy, numerous modified models have emerged in the past decades, each offering a different approach to specific PTSD symptoms [18]. 3.2

Footshock

Electric footshock has been used to develop models examining PTSD-like behavior and biological changes indicative of PTSD [31, 36]. The footshock model consists of two major components: physical and emotional. The physical component includes acute or chronic exposures of shocks of varying intensity and duration on an electrified grid floor in an electric footshock apparatus [36]. In this model, animals are often placed into a shock chamber with a metalgrid floor and subjected to a sequence of electric shocks of varying intensities, 0.1–0.25 mA, and durations; shock periods generally last 10–15 min and footshocks themselves last from 1 to 6 s [18, 31, 37]. Animals do not habituate to footshocks in comparison to other stressors (loud noise, bright light, and hot/cold temperatures). The footshock model reproduces some of the core behaviors seen in PTSD, including avoidance, anxiety, hyperarousal, aggression, re-experiencing, and sleep disorders. This model can be combined with other behavioral testing. For example, fear conditioning in the footshock exposure followed by situational reminders (SR) has been used to study PTSD [36]. Combining footshock with a SR serves as a contextually conditioned Pavlovian cue (e.g., re-exposure to the box where the shocks were administered) [20]. Animals exposed to a SR exhibit a distinct behavioral response (e.g., crouching near the back wall of the box, increase in respiratory rate, etc.) despite the absence of the stressor itself [20]. This paradigm has been used in the studies of PTSD symptoms such as re-experiencing and intrusions.

3.3

Restraint Stress

Immobilization stress or restraint stress uses plastic restraint tubes to restrain or immobilize the animal [23, 38]. There may be single sessions lasting from 15 min to 2 h, subchronic session (3 days; 1 h

Updates in PTSD Animal Models Characterization

337

per day), or chronic restraint stress (40 days; 1 h per day) [23, 25, 39]. These restraint stress paradigms increase anxiety-like behaviors and enhance nociception [40, 41]. 3.4 Inescapable Tail Shock (ITS)

In general, stress exposure consists of a 2 h per day session of immobilization and tail shocks for three consecutive days. Animals are restrained by being immobilized in a ventilated plexiglass tube. Forty electric shocks (2–3 mA, 3 s duration) are delivered to their tails at semi-random intervals of 150–210 s [42]. The ITS model mimics the pathophysiology of PTSD and shows the behavioral and biological changes seen in PTSD [42]. In this model, rodents exhibit a delayed and exaggerated startle response, changed plasma corticosterone levels, and significant decline of body weight, indicating dysfunctions of the HPA axis and of metabolic regulation, both of which are seen in PTSD. This model has been employed with other paradigms to examine acoustic startle response and gene expression and to address the neuropsychological molecular mechanism in PTSD [43].

3.5 Single-Prolonged Stress (SPS)

The SPS model was firstly described in 1997 as a “time-dependent [stress] sensitization” [39]. It is a multimodal traumatic stress exposure protocol including sequential exposure to three stressors (2 h of restraint, a 20-min group swim, and exposure to ether until loss of consciousness) during a single continuous session. This protocol was originally designed to cause a robust stress response through three different pathways—psychological (restraint), physiological (forced group swim), and pharmacological (ether). The SPS model has been partially used for PTSD studies [11, 44–49]. It is demonstrated that SPS animals mimic the pathophysiological abnormalities and behavioral characteristics of PTSD, including enhanced anxiety-like behavior and glucocorticoid negative feedback [11]. The SPS animals also show the expected therapeutic response to paroxetine on enhanced fear memory, enhanced freezing in response to contextual fear conditioning, and impaired extinction of fear memory [11]. There may be a 7–14-day interval between SPS and behavioral experiments [11]. SPS-induced reproducible PTSD-like alterations of the HPA axis are observed [25]. SPS rats show a significant change in fast feedback and the HPA axis dysregulation which are often seen in PTSD [25]. There are paradoxical findings about levels of cortisol levels in PTSD [50, 51]. PTSD may not show an elevated tonic cortisol levels [52, 53] as it may be a chronic stress-related disorder. It is assuming that PTSD may shut down the HPA axis due to enhanced sensitivity to negative feedback [54]. Thus, this model has been used to study the dysfunction of the HPA axis in stressrelated disorders such as PTSD and depression. In addition to its use in the study of the HPA axis, this model is also used to study PTSDinvolved neurotransmitters. It has been found that SPS alters

338

Lei Zhang et al.

neurotransmitter signaling and reduces hippocampal NMDA receptor density, which may contribute to impairments in spatial memory [55]. Data indicates that the SPS model is capable of producing several symptoms characteristic of PTSD, such as exaggerated HPA axis negative feedback, heightened anxiety, hyperarousal, enhanced fear, retention of extinguished fear, anxiety-like behavior, and stressinduced nociceptive sensitivity [56]. 3.6 Underwater Trauma (UT)

The UT model involves placing an animal in a container with water and allowing it to swim for 30 s before gently submerging it and allowing the animal to struggle completely under the surface for 30 s before removing it from the container [24, 41, 57]. This model is used to examine anxiety-like behavior. As a stressor, UT is more ethologically relevant than electric shocks since the threat of drowning may be an accurate representation of the real threats faced by wild animals, including rats and mice. The results show that UT results in the impairment of learning in the Morris water maze, indicating that it may be a useful model in PTSD studies [58].

3.7 Social Isolation (SI)

SI is a state of complete or near-complete lack of contact between an individual and society. Its occurring during adulthood of rodent for a period of 1 day to 8 weeks or maternal isolation can produce symptoms of PTSD [59]. Animal maternal separation is a model of SI. The long-term SI model results in animals demonstrating relevant behavioral and physiological disturbances, including hyperlocomotion, anxiety-like behavior, aggression, cognitive alterations, and neuroendocrine changes [60–62]. These are often seen in human PTSD. Three weeks of SI results in an increase of contextual fear responses and impaired fear extinction. Such behavior disturbance is correlated with a decrease of 5alpha-reductase type I (5alpha-RI) mRNA expression and decreased allopregnanolone (Allo) levels in selected neurons of the medial prefrontal cortex, hippocampus, and basolateral amygdala [63]. This model is also used for testing the effect of S-norfluoxetine, a selective brain steroidogenic stimulant on corticolimbic Allo levels and on contextual fear responses resulting from social isolation [63].

3.8 Social Defeat (SD)

To understand the role of social defeat in the etiology of PTSD, the SD model has been widely used. This model is used to examine behavioral and physiological sequelae of social stress, mainly in mice [64, 65]. This model produces the anxiety-like and avoidance behavior seen in PTSD. In general, mice (C57BL/6) are exposed to a single aggressor mouse (CD1) for either 1 or 5 days [41, 66]. SD animals can be classified into susceptible and resilient populations [41, 66]. Increased avoidance behaviors were only obtained in susceptible animals at day 11 and day 39 post-stress with no changes in morning corticosterone levels at day 11. However, resilient animals have increased corticosterone levels at day

Updates in PTSD Animal Models Characterization

339

39 post-stress [66]. There is a long-lasting increase in social avoidance and submissive behavior. The animals subjected to SD present persisting PTSD-like behaviors such as increased freezing, lack of tail rattling, enhanced and prolonged response to acoustic startle, weight loss, inflammatory cardiac histopathologies, and reduced dendritic spine density in the medial prefrontal cortex [67]. 3.9 Early-Life Stress (ELS)

The ELS plays a profound short- and long-term effect on human physiology and psychology [68]. It is significantly associated with a diagnosis of PTSD and increases vulnerability to the development of PTSD [69–73]. Exposure to trauma as a child increases PTSDlike symptoms following the traumatic event and produces PTSDlike symptoms as an adult [68]. Animal maternal separation mimics childhood trauma by separating pups from their mother for 1–3 h per day on postnatal days 2–9 [41]. Data demonstrated that 3 h/ day of maternal separation in rats during the first 2 weeks of life results in significant increases of acoustic startle and anxiety-like behaviors, as well as increased corticosterone levels in response to mild handling stress in adulthood [74]. Moreover, rats with a history of ELS show increased PTSD-like symptoms in the underwater traumatic paradigm [75]. Adult animals that had been subjected to a single-prolonged episode of maternal deprivation (MD) [24 h, postnatal day (PND) 9–10] show long-term behavioral alterations. The behavioral abnormalities in MD animals are associated with neurodevelopmental processes triggered by MD-induced elevated glucocorticoids in relevant specific brain regions. Long-term effects of MD on the cholinergic system within rat brains and the HPA axis have also been observed [76].

3.10 Predator-Based Stress

Confrontation with a natural predator has been shown to provoke high levels of intense fear and stress in rodents. It may also result in alterations of long-lasting behavioral and endocrine responses [18, 77, 78]. Predator (protected or unprotected) and predator odor/scent exposures are psychological stressors, which have been used to establish an animal stress model used for the study of PTSD [27, 41]. Animals are exposed to predator odor in a variety of ways, including indirect exposure to a predator (cat), predator urine (bobcat, fox), predator feces or litter, or trimethylthiazoline (TMT), a synthetic compound isolated from fox feces, for a very brief period of time (typically 5–10 min) [41, 79–81]. Exposing a rodent to a cat results in a significant increase in anxiety-like behavior [82, 83]. Such alterations can still be observed even after 3 weeks [82, 83]. Diamond and co-workers modified the predator-based stress model [84]. In their experiment, adult male Sprague-Dawley rats were exposed to either two or three predatorbased fear conditioning sessions. They found that stress groups demonstrated increased anxiety on the elevated plus maze, impaired object recognition memory, and robust contextual and

340

Lei Zhang et al.

cued fear conditioned memory 3 months after the last conditioning session [84]. In addition, although it is known that PTSD is associated with high risks of cardiovascular disease, few studies use this model to study the possible mechanism of cardiovascular disease under PTSD conditions. One such study demonstrates that, after undergoing the modified version of predator-based stress, male rats had an increased sensitivity to ischemic heart injury, such as larger myocardial infarcts and attenuated post-ischemic recovery [85]. Therefore, the data indicates that this model may be a useful tool to further explore the association between PTSD and cardiovascular disease.

4

Summary Epidemiological findings show that not everyone who experiences traumatic stress develops PTSD. Obviously, physical and psychological responses to traumatic events are diverse. Some individuals show time-limited distress, while others develop long-term PTSD. In addition, like other neuropsychiatric disorders, PTSD is a disorder with a heterogeneous syndrome. Subjects with PTSD may manifest comorbid symptoms and may exhibit divergent responses to treatment. Thus, diverse animal models, in which different traumatic stress paradigms and perimeter evaluations are implemented, have been developed. We notice that, in most animal models, PTSD-like behaviors are determined by comparing the average of the behaviors assessed between exposed and non-exposed rodent. The results are presented as a mean of the entire samples. Similar to humans, some animals may be more vulnerable than others to stress. Thus, they do not represent certain subjects. Existing animal models may overlook the evidence that only a proportion of individuals who are exposed to a “traumatic event” will eventually develop PTSD. Therefore, purer samples from categorized animals in the model have been greatly demanded in this study area [8]. Sometimes, the individual profiling approach may be needed to assess and screen for possible phenotypes of stress response, revealing the existence of distinct behavioral subgroups. Currently, biomarker studies are increasingly incorporating homologous biological measures and animal models to assess PTSD risk and treatment outcome [8]. The objective identification of biomarkers by revealing from the animal behavioral phenotype and biological genotype of traumatic stress allows us to use those information to develop the clinical tools for diagnosis and evaluation of therapeutic efficacy [86]. Therefore, combining the PTSD animal models with modern techniques such as brain imaging and deep sequencing will benefit our understanding of the etiology of PTSD and may improve prevention and treatment of PTSD.

Updates in PTSD Animal Models Characterization

341

Acknowledgments We thank Supriya Prabhakar, Berwin Yuan, Ze Chen, Alexis Shahidi, and Nora Wang for their editing of this manuscript. References 1. Carson MA, Paulus LA, Lasko NB, Metzger LJ, Wolfe J, Orr SP, Pitman RK (2000) Psychophysiologic assessment of posttraumatic stress disorder in Vietnam nurse veterans who witnessed injury or death. J Consult Clin Psychol 68:890–897 2. Peri T, Ben-Shakhar G, Orr SP, Shalev AY (2000) Psychophysiologic assessment of aversive conditioning in posttraumatic stress disorder. Biol Psychiatry 47:512–519 3. Orr SP, Lasko NB, Metzger LJ, Berry NJ, Ahern CE, Pitman RK (1998) Psychophysiologic assessment of women with posttraumatic stress disorder resulting from childhood sexual abuse. J Consult Clin Psychol 66:906–913 4. Shalev AY, Peri T, Gelpin E, Orr SP, Pitman RK (1997) Psychophysiologic assessment of mental imagery of stressful events in Israeli civilian posttraumatic stress disorder patients. Compr Psychiatry 38:269–273 5. Shalev AY, Orr SP, Pitman RK (1993) Psychophysiologic assessment of traumatic imagery in Israeli civilian patients with posttraumatic stress disorder. Am J Psychiatry 150:620–624 6. Nugent NR, Amstadter AB, Koenen KC (2008) Genetics of post-traumatic stress disorder: informing clinical conceptualizations and promoting future research. Am J Med Genet C Semin Med Genet 148C:127–132 7. Afifi TO, Asmundson GJ, Taylor S, Jang KL (2010) The role of genes and environment on trauma exposure and posttraumatic stress disorder symptoms: a review of twin studies. Clin Psychol Rev 30:101–112 8. Zhang L, Zhou R, Xing G, Hough CJ, Li X, Li H (2006) Identification of gene markers based on well validated and subcategorized stressed animals for potential clinical applications in PTSD. Med Hypotheses 66:309–314 9. Goswami S, Samuel S, Sierra OR, Cascardi M, Pare D (2012) A rat model of post-traumatic stress disorder reproduces the hippocampal deficits seen in the human syndrome. Front Behav Neurosci 6:26 10. Yehuda R, Kahana B, Schmeidler J, Southwick SM, Wilson S, Giller EL (1995) Impact of cumulative lifetime trauma and recent stress on current posttraumatic stress disorder

symptoms in holocaust survivors. Am J Psychiatry 152:1815–1818 11. Yamamoto S, Morinobu S, Takei S, Fuchikami M, Matsuki A, Yamawaki S, Liberzon I (2009) Single prolonged stress: toward an animal model of posttraumatic stress disorder. Depress Anxiety 26:1110–1117 12. Kolb LC (1987) A neuropsychological hypothesis explaining posttraumatic stress disorders. Am J Psychiatry 144:989–995 13. Cohen H, Kotler M, Matar MA, Kaplan Z, Miodownik H, Cassuto Y (1997) Power spectral analysis of heart rate variability in posttraumatic stress disorder patients. Biol Psychiatry 41:627–629 14. Yehuda R, Giller EL, Southwick SM, Lowy MT, Mason JW (1991) Hypothalamicpituitary-adrenal dysfunction in posttraumatic stress disorder. Biol Psychiatry 30:1031–1048 15. Zhang L, Li H, Su TP, Barker JL, Maric D, Fullerton CS, Webster MJ, Hough CJ, Li XX, Traumatic Stress Brain Study Group, and Ursano R (2008) p11 is up-regulated in the forebrain of stressed rats by glucocorticoid acting via two specific glucocorticoid response elements in the p11 promoter. Neuroscience 153:1126–1134 16. Zhang L, Su TP, Choi K, Maree W, Li CT, Chung MY, Chen YS, Bai YM, Chou YH, Barker JL, Barrett JE, Li XX, Li H, Benedek DM, Ursano R (2011) P11 (S100A10) as a potential biomarker of psychiatric patients at risk of suicide. J Psychiatr Res 45:435–441 17. Kaufer D, Friedman A, Seidman S, Soreq H (1998) Acute stress facilitates long-lasting changes in cholinergic gene expression. Nature 393:373–377 18. Schoner J, Heinz A, Endres M, Gertz K, Kronenberg G (2017) Post-traumatic stress disorder and beyond: an overview of rodent stress models. J Cell Mol Med 21:2248–2256 19. Willner P (1984) The validity of animal models of depression. Psychopharmacology 83:1–16 20. Pynoos RS, Ritzmann RF, Steinberg AM, Goenjian A, Prisecaru I (1996) A behavioral animal model of posttraumatic stress disorder featuring repeated exposure to situational reminders. Biol Psychiatry 39:129–134

342

Lei Zhang et al.

21. Servatius RJ, Ottenweller JE, Natelson BH (1995) Delayed startle sensitization distinguishes rats exposed to one or three stress sessions: further evidence toward an animal model of PTSD. Biol Psychiatry 38:539–546 22. Li H, Rogawski MA (1998) GluR5 kainate receptor mediated synaptic transmission in rat basolateral amygdala in vitro. Neuropharmacology 37:1279–1286 23. Gameiro GH, da Silva Andrade A, Nouer DF, Ferraz de Arruda Veiga MC (2006) How may stressful experiences contribute to the development of temporomandibular disorders? Clin Oral Investig 10:261–268 24. Richter-Levin G (1998) Acute and long-term behavioral correlates of underwater trauma— potential relevance to stress and post-stress syndromes. Psychiatry Res 79:73–83 25. Liberzon I, Krstov M, Young EA (1997) Stress-restress: effects on ACTH and fast feedback. Psychoneuroendocrinology 22:443–453 26. Adamec R (1997) Transmitter systems involved in neural plasticity underlying increased anxiety and defense—implications for understanding anxiety following traumatic stress. Neurosci Biobehav Rev 21:755–765 27. Cohen H, Zohar J (2004) An animal model of posttraumatic stress disorder: the use of cut-off behavioral criteria. Ann N Y Acad Sci 1032:167–178 28. Seligman ME (1972) Learned helplessness. Annu Rev Med 23:407–412 29. Vollmayr B, Gass P (2013) Learned helplessness: unique features and translational value of a cognitive depression model. Cell Tissue Res 354:171–178 30. van der Kolk B, Greenberg M, Boyd H, Krystal J (1985) Inescapable shock, neurotransmitters, and addiction to trauma: toward a psychobiology of post traumatic stress. Biol Psychiatry 20:314–325 31. Van Dijken HH, Van der Heyden JA, Mos J, Tilders FJ (1992) Inescapable footshocks induce progressive and long-lasting behavioural changes in male rats. Physiol Behav 51:787–794 32. Anisman H, Sklar LS (1979) Catecholamine depletion in mice upon reexposure to stress: mediation of the escape deficits produced by inescapable shock. J Comp Physiol Psychol 93:610–625 33. Glazer HI, Weiss JM, Pohorecky LA, Miller NE (1975) Monamines as mediators of avoidance-escape behavior. Psychosom Med 37:535–543 34. Yehuda R, Antelman SM (1993) Criteria for rationally evaluating animal models of

posttraumatic stress disorder. Biol Psychiatry 33:479–486 35. Hamilton JD, Workman RH Jr (1998) Persistence of combat-related posttraumatic stress symptoms for 75 years. J Trauma Stress 11:763–768 36. Bali A, Jaggi AS (2015) Electric foot shock stress: a useful tool in neuropsychiatric studies. Rev Neurosci 26:655–677 37. Daviu N, Fuentes S, Nadal R, Armario A (2010) A single footshock causes long-lasting hypoactivity in unknown environments that is dependent on the development of contextual fear conditioning. Neurobiol Learn Mem 94:183–190 38. Kvetnansky R, Mikulaj L (1970) Adrenal and urinary catecholamines in rats during adaptation to repeated immobilization stress. Endocrinology 87:738–743 39. Liberzon I, Young EA (1997) Effects of stress and glucocorticoids on CNS oxytocin receptor binding. Psychoneuroendocrinology 22:411–422 40. Gameiro GH, Gameiro PH, Andrade Ada S, Pereira LF, Arthuri MT, Marcondes FK, Veiga MC (2006) Nociception- and anxiety-like behavior in rats submitted to different periods of restraint stress. Physiol Behav 87:643–649 41. Whitaker AM, Gilpin NW, Edwards S (2014) Animal models of post-traumatic stress disorder and recent neurobiological insights. Behav Pharmacol 25:398–409 42. Jiang X, Zhang ZJ, Zhang S, Gamble EH, Jia M, Ursano RJ, Li H (2011) 5-HT2A receptor antagonism by MDL 11,939 during inescapable stress prevents subsequent exaggeration of acoustic startle response and reduced body weight in rats. J Psychopharmacol 25:289–297 43. Su YA, Zhang Q, Su DM, Tang MX (2011) Rat mitochondrion-neuron focused microarray (rMNChip) and bioinformatics tools for rapid identification of differential pathways in brain tissues. Int J Biol Sci 7:308–322 44. Siegmund A, Wotjak CT (2006) Toward an animal model of posttraumatic stress disorder. Ann N Y Acad Sci 1071:324–334 45. Perrine SA, Eagle AL, George SA, Mulo K, Kohler RJ, Gerard J, Harutyunyan A, Hool SM, Susick LL, Schneider BL, Ghoddoussi F, Galloway MP, Liberzon I, Conti AC (2016) Severe, multimodal stress exposure induces PTSD-like characteristics in a mouse model of single prolonged stress. Behav Brain Res 303:228–237 46. Eagle AL, Knox D, Roberts MM, Mulo K, Liberzon I, Galloway MP, Perrine SA (2013)

Updates in PTSD Animal Models Characterization Single prolonged stress enhances hippocampal glucocorticoid receptor and phosphorylated protein kinase B levels. Neurosci Res 75:130–137 47. Knox D, Nault T, Henderson C, Liberzon I (2012) Glucocorticoid receptors and extinction retention deficits in the single prolonged stress model. Neuroscience 223:163–173 48. Knox D, George SA, Fitzpatrick CJ, Rabinak CA, Maren S, Liberzon I (2012) Single prolonged stress disrupts retention of extinguished fear in rats. Learn Mem 19:43–49 49. Knox D, Perrine SA, George SA, Galloway MP, Liberzon I (2010) Single prolonged stress decreases glutamate, glutamine, and creatine concentrations in the rat medial prefrontal cortex. Neurosci Lett 480:16–20 50. Tanke MA, Kema IP, Dijck-Brouwer J, Doornbos B, De Vries EG, Korf J (2008) Low plasma tryptophan in carcinoid patients is associated with increased urinary cortisol excretion. Psychoneuroendocrinology 33:1297–1301 51. Yehuda R, Southwick SM, Nussbaum G, Wahby V, Giller EL Jr, Mason JW (1990) Low urinary cortisol excretion in patients with posttraumatic stress disorder. J Nerv Ment Dis 178:366–369 52. Yehuda R (2002) Clinical relevance of biologic findings in PTSD. Psychiatry Q 73:123–133 53. Yehuda R, Halligan SL, Bierer LM (2002) Cortisol levels in adult offspring of Holocaust survivors: relation to PTSD symptom severity in the parent and child. Psychoneuroendocrinology 27:171–180 54. Pitman RK, Rasmusson AM, Koenen KC, Shin LM, Orr SP, Gilbertson MW, Milad MR, Liberzon I (2012) Biological studies of posttraumatic stress disorder. Nat Rev Neurosci 13:769–787 55. Harvey BH, Oosthuizen F, Brand L, Wegener G, Stein DJ (2004) Stress-restress evokes sustained iNOS activity and altered GABA levels and NMDA receptors in rat hippocampus. Psychopharmacology 175:494–502 56. Zhang Y, Gandhi PR, Standifer KM (2012) Increased nociceptive sensitivity and nociceptin/orphanin FQ levels in a rat model of PTSD. Mol Pain 8:76 57. Moore NL, Gauchan S, Genovese RF (2012) Differential severity of anxiogenic effects resulting from a brief swim or underwater trauma in adolescent male rats. Pharmacol Biochem Behav 102:264–268 58. Diamond DM, Campbell AM, Park CR, Woodson JC, Conrad CD, Bachstetter AD,

343

Mervis RF (2006) Influence of predator stress on the consolidation versus retrieval of longterm spatial memory and hippocampal spinogenesis. Hippocampus 16:571–576 59. Imanaka A, Morinobu S, Toki S, Yamawaki S (2006) Importance of early environment in the development of post-traumatic stress disorderlike behaviors. Behav Brain Res 173:129–137 60. Brain P, Benton D (1979) The interpretation of physiological correlates of differential housing in laboratory rats. Life Sci 24:99–115 61. Malkesman O, Maayan R, Weizman A, Weller A (2006) Aggressive behavior and HPA axis hormones after social isolation in adult rats of two different genetic animal models for depression. Behav Brain Res 175:408–414 62. Kim JW, Kirkpatrick B (1996) Social isolation in animal models of relevance to neuropsychiatric disorders. Biol Psychiatry 40:918–922 63. Pibiri F, Nelson M, Guidotti A, Costa E, Pinna G (2008) Decreased corticolimbic allopregnanolone expression during social isolation enhances contextual fear: a model relevant for posttraumatic stress disorder. Proc Natl Acad Sci U S A 105:5567–5572 64. Pizarro JM, Lumley LA, Medina W, Robison CL, Chang WE, Alagappan A, Bah MJ, Dawood MY, Shah JD, Mark B, Kendall N, Smith MA, Saviolakis GA, Meyerhoff JL (2004) Acute social defeat reduces neurotrophin expression in brain cortical and subcortical areas in mice. Brain Res 1025:10–20 65. Siegfried B, Frischknecht HR, Waser PG (1984) Defeat, learned submissiveness, and analgesia in mice: effect of genotype. Behav Neural Biol 42:91–97 66. Krishnan V, Han MH, Graham DL, Berton O, Renthal W, Russo SJ, Laplant Q, Graham A, Lutter M, Lagace DC, Ghose S, Reister R, Tannous P, Green TA, Neve RL, Chakravarty S, Kumar A, Eisch AJ, Self DW, Lee FS, Tamminga CA, Cooper DC, Gershenfeld HK, Nestler EJ (2007) Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 131:391–404 67. Hammamieh R, Chakraborty N, De Lima TC, Meyerhoff J, Gautam A, Muhie S, D’Arpa P, Lumley L, Carroll E, Jett M (2012) Murine model of repeated exposures to conspecific trained aggressors simulates features of posttraumatic stress disorder. Behav Brain Res 235:55–66 68. Cloitre M, Stolbach BC, Herman JL, van der Kolk B, Pynoos R, Wang J, Petkova E (2009) A developmental approach to complex PTSD: childhood and adult cumulative trauma as

344

Lei Zhang et al.

predictors of symptom complexity. J Trauma Stress 22:399–408 69. Binder EB, Bradley RG, Liu W, Epstein MP, Deveau TC, Mercer KB, Tang Y, Gillespie CF, Heim CM, Nemeroff CB, Schwartz AC, Cubells JF, Ressler KJ (2008) Association of FKBP5 polymorphisms and childhood abuse with risk of posttraumatic stress disorder symptoms in adults. JAMA 299:1291–1305 70. Binder EB, Salyakina D, Lichtner P, Wochnik GM, Ising M, Putz B, Papiol S, Seaman S, Lucae S, Kohli MA, Nickel T, Kunzel HE, Fuchs B, Majer M, Pfennig A, Kern N, Brunner J, Modell S, Baghai T, Deiml T, Zill P, Bondy B, Rupprecht R, Messer T, Kohnlein O, Dabitz H, Bruckl T, Muller N, Pfister H, Lieb R, Mueller JC, Lohmussaar E, Strom TM, Bettecken T, Meitinger T, Uhr M, Rein T, Holsboer F, Muller-Myhsok B (2004) Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment. Nat Genet 36:1319–1325 71. Halldorsdottir T, de Matos APS, Awaloff Y, Arnarson EO, Craighead WE, Binder EB (2017) FKBP5 moderation of the relationship between childhood trauma and maladaptive emotion regulation strategies in adolescents. Psychoneuroendocrinology 84:61–65 72. Harms MB, Birn R, Provencal N, Wiechmann T, Binder EB, Giakas SW, Roeber BJ, Pollak SD (2017) Early life stress, FK506 binding protein 5 gene (FKBP5) methylation, and inhibition-related prefrontal function: a prospective longitudinal study. Dev Psychopathol 29:1895–1903 73. Yehuda R, Daskalakis NP, Bierer LM, Bader HN, Klengel T, Holsboer F, Binder EB (2016) Holocaust exposure induced intergenerational effects on FKBP5 methylation. Biol Psychiatry 80:372–380 74. Kalinichev M, Easterling KW, Plotsky PM, Holtzman SG (2002) Long-lasting changes in stress-induced corticosterone response and anxiety-like behaviors as a consequence of neonatal maternal separation in Long-Evans rats. Pharmacol Biochem Behav 73:131–140 75. Ardi Z, Albrecht A, Richter-Levin A, Saha R, Richter-Levin G (2016) Behavioral profiling as a translational approach in an animal model of posttraumatic stress disorder. Neurobiol Dis 88:139–147 76. De la Fuente M, Llorente R, Baeza I, De Castro NM, Arranz L, Cruces J, Viveros MP (2009) Early maternal deprivation in rats: a proposed animal model for the study of

developmental neuroimmunoendocrine interactions. Ann N Y Acad Sci 1153:176–183 77. Blanchard RJ, Nikulina JN, Sakai RR, McKittrick C, McEwen B, Blanchard DC (1998) Behavioral and endocrine change following chronic predatory stress. Physiol Behav 63:561–569 78. Masini CV, Sauer S, White J, Day HE, Campeau S (2006) Non-associative defensive responses of rats to ferret odor. Physiol Behav 87:72–81 79. Cohen H, Matar MA, Richter-Levin G, Zohar J (2006) The contribution of an animal model toward uncovering biological risk factors for PTSD. Ann N Y Acad Sci 1071:335–350 80. Cohen H, Ziv Y, Cardon M, Kaplan Z, Matar MA, Gidron Y, Schwartz M, Kipnis J (2006) Maladaptation to mental stress mitigated by the adaptive immune system via depletion of naturally occurring regulatory CD4+CD25+ cells. J Neurobiol 66:552–563 81. Cohen H, Zohar J, Gidron Y, Matar MA, Belkind D, Loewenthal U, Kozlovsky N, Kaplan Z (2006) Blunted HPA axis response to stress influences susceptibility to posttraumatic stress response in rats. Biol Psychiatry 59:1208–1218 82. Adamec R (2001) Does long term potentiation in periaqueductal gray (PAG) mediate lasting changes in rodent anxiety-like behavior (ALB) produced by predator stress?—effects of low frequency stimulation (LFS) of PAG on place preference and changes in ALB produced by predator stress. Behav Brain Res 120:111–135 83. Adamec RE, Shallow T (1993) Lasting effects on rodent anxiety of a single exposure to a cat. Physiol Behav 54:101–109 84. Zoladz PR, Park CR, Fleshner M, Diamond DM (2015) Psychosocial predator-based animal model of PTSD produces physiological and behavioral sequelae and a traumatic memory four months following stress onset. Physiol Behav 147:183–192 85. Rorabaugh BR, Krivenko A, Eisenmann ED, Bui AD, Seeley S, Fry ME, Lawson JD, Stoner LE, Johnson BL, Zoladz PR (2015) Sex-dependent effects of chronic psychosocial stress on myocardial sensitivity to ischemic injury. Stress 18:645–653 86. Jia M, Meng F, Smerin SE, Xing G, Zhang L, Su DM, Benedek D, Ursano R, Su YA, Li H (2012) Biomarkers in an animal model for revealing neural, hematologic, and behavioral correlates of PTSD. J Vis Exp. https://doi. org/10.3791/3361

Chapter 20 Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy Yasser Medlej, Houssein Salah, Lara Wadi, Sarah Saad, Rita Asdikian, Nabil Karnib, Dima Ghazal, Bashir Bashir, Jad Allam, and Makram Obeid Abstract Psychiatric and cognitive disturbances are the most common comorbidities of epileptic disorders in children. The successful treatment of these comorbidities faces many challenges including their etiologically heterogonous nature. Translational neurobehavioral research in age-tailored and clinically relevant rodent seizure models offers a controlled setting to investigate emotional and cognitive behavioral disturbances, their causative factors, and potentially novel treatment interventions. In this review, we propose a conceptual framework that provides a nonsubjective approach to rodent emotional behavioral testing with a focus on the clinically relevant outcome of behavioral response adaptability. We also describe the battery of neurobehavioral tests that we tailored to seizure models with prominent amygdalo-hippocampal involvement, including testing panels for anxiety-like, exploratory, and hyperactive behaviors (the open-field and light-dark box tests), depressive-like behaviors (the forced swim test), and visuospatial navigation (Morris water maze). The review also discusses the modifications we introduced to active avoidance testing in order to simultaneously test auditory and hippocampal-dependent emotionally relevant learning and memory. When interpreting the significance and clinical relevance of the behavioral responses obtained from a given testing panel, it is important to avoid a holistic disease-based approach as a specific panel may not necessarily mirror a disease entity. The analysis of measurable behavioral responses has to be performed in the context of outcomes obtained from multiple related and complementary neurobehavioral testing panels. Behavioral testing is also complemented by mechanistic electrophysiological and molecular investigations. Key words Emotions, Cognition, Behavior, Rodents, Epilepsy, Pediatric, Seizure

1

Introduction Cognitive and emotional behavioral derangements are the most common comorbidities of both acquired and genetic epileptic disorders [1–6] and may accompany even the relatively most benign types of inherited epilepsies [7–9]. Indeed, epilepsy, a very common neurological disorder in children [10], is associated with a high 60% risk of cognitive and emotional behavioral disturbances [1–3, 6], the most common of which are depression [11], anxiety [12, 13], attentional problems [14], learning disorders [15], aggression

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_20, © Springer Science+Business Media, LLC, part of Springer Nature 2019

345

346

Yasser Medlej et al.

[16, 17], and autism [18]. Far from being subtle clinical accompaniments of the epilepsies, these comorbidities tend to impact quality of life, at times more drastically than seizures themselves [19, 20], and are associated with a high risk of suicide in adolescents [21]. Unfortunately, the successful treatment of these comorbidities faces a slew of challenges, the most important of which is their etiologically heterogeneous nature. Indeed, multiple factors may contribute to behavioral disturbances in epilepsy, including the underlying pathology, the location and extent of a possible epileptogenic lesion, seizure frequency and duration [9, 22], the number and types of anti-seizure medications [23–29], and potentially epileptiform discharges [8, 22, 30, 31]. The type and location of the underlying pathology are usually the most important contributors. For instance, lesional epilepsies of the frontal lobe tend to be associated with rigidity, disinhibition, and autistic-like features, while temporal lobe lesions may result in mood disturbances as well as language and memory deficits. A growing body of clinical and experimental evidence also suggests that a prolonged seizure or status epilepticus (SE) in and of itself is also a key contributor to emotional and cognitive behavioral deficits [6, 32–36]. In the clinical arena, it has been very difficult to accurately delineate the contributory role of these various factors and therefore to direct treatment strategies. Rodent seizure models offer a controlled setting that allows a more precise study of the multifaceted epilepsy comorbidities and their potential response to desperately needed novel treatment strategies. This is primarily achieved by employing neurobehavioral testing panels following various epileptogenic brain insults where the effect and contribution of different factors to emotional and cognitive issues can be isolated and dissected with complementary proteomics and histological techniques. Contributory factors amenable to investigations include acquired brain insults, their nature, severity and location, as well as various lesional or non-lesional genetic defects in seizure models that recapitulate human conditions [32, 37, 38]. While a detailed description of all rodent neurobehavioral panels is beyond the scope of one review, here we discuss the neurobehavioral testing panels that we employ in immature rat seizure models in our rodent behavioral facility in the translational epilepsy laboratory at the American University of Beirut. The battery of testing panels was tailored to models with prominent amygdalo-hippocampal circuitry involvement, specifically those that echo the common age-tailored and clinically relevant scenarios of hypoxic encephalopathy of the newborn [37, 38] and adolescent temporal lobe epilepsy (TLE) [32]. Indeed, hypoxic encephalopathy is the most common cause of seizures in the term neonate [39] and is associated with later life cognitive and behavioral derangements [40, 41], while TLE, believed to pathologically start in childhood and adolescence [42], is one of the most

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

347

common causes of refractory epilepsy and has one of the highest incidence of psychiatric disorders among the epilepsies [43, 44].

2

The Approach to Neurobehavioral Panels in Rodent Seizure Models

2.1 A Conceptual Framework for the Study of Emotional Behaviors: Can We Study Emotions?

Antecedent to the relatively recent emergence of brain science, certain human behaviors qualifying for “emotional behaviors” gained their emotional nuances based on the simple observation of accompanying “feelings” that are self-perceived and “facial and bodily emotional signs” that are expressed to observers. Due to the highly subjective and cultural nature of these feelings and emotional signs, it is almost impossible to study them in rodents. Short of being able to study feelings, investigators in the field of emotions have focused on their nonsubjective aspects, specifically the observed measurable emotional behaviors in rodents’ testing of both implicit and explicit emotionally relevant learning and memory [45]. Along those lines, in our laboratory we employ a conceptual framework (Fig. 1) that segregates human responses to stimuli into three different components: the feelings, the bodily expressions, and the relatively measurable observed behaviors. According to this conceptual framework, in response to a new environment, task, or survival threat, subjects can act with either instinctive behavioral responses or learned behavioral responses, and these can be adaptive or maladaptive with various degrees of accompanying expressed and perceived emotional nuances. This is definitely not a naı¨ve attempt to provide a comprehensive explanation of all animal emotions and behaviors, including humans, but a simple elemental stratification that provides a conceptual framework for modeling and studying emotional behaviors and their disturbances. The framework provides a reasonable behavioral parallelism between humans and rodents while respecting the differences in brain circuitry that generates innate versus learned responses to threat [46] and stressing the clinically relevant practical outcomes of behavioral response adaptability. According to this paradigm, all behavioral responses can be “emotional” to some extent, depending on the degree of accompanying feelings and expressions. For example, the adaptive learned response of steering a car takes different emotional nuances when performing a routine task such as driving through an intersection versus the more threatening task of avoiding an incoming truck. As discussed in the sections below, the modified active avoidance (MAAV) test developed in our laboratory as a modification to the standard shuttle box [46] reveals how an excessive innate maladaptive freezing response to contextual and auditory cues is replaced by a learned adaptive shockavoiding shuttling behavior [46]. Neurodevelopment is a specific area where such a stratification of responses is particularly important, as many innate responses become maladaptive over time and

348

Yasser Medlej et al.

Fig. 1 A conceptual framework for the study of emotional behaviors in rodents. We have devised this conceptual approach in order to study the nonsubjective measurable parameters of emotional behaviors. A subject’s response to a stimulus is stratified into three elemental components: the self-perceived feelings, the accompanying bodily and facial expressions, and the behavioral response that ensues. Feelings and emotional expressions are almost impossible to study in rodents due to the subjective nature of self-perceived feelings and the rather unique forms of facial and bodily expressions to humans and their various cultures. On the other hand, emotionally relevant behavioral responses to stimuli can be objectively studied and classified as learned and innate, adaptive or maladaptive, depending on the behavioral outcomes. While providing a reasonable behavioral parallelism between humans and rodent models, the framework facilitates the study of measurable behaviors, and their adaptability to environmental challenges, which has a translational clinical value. According to this paradigm, all behavioral responses can be “emotional” to some extent depending on the accompanying feelings and expressions. The framework therefore respects the cognitive-emotional continuum in providing a platform for the elemental examination of measurable behavioral responses and their adaptability in tests of emotionally relevant learning and memory, which is particularly important in epilepsies involving the amygdalo-hippocampal circuitry

are replaced by learned behavioral responses. Not only intended for humor, but to readily illustrate this, consider the highly adaptive yet instinctual and highly emotional act of crying for food and other needs in the first few years of life that is gradually replaced by the rather cognitively adaptive learned responses of getting a meal; a behavioral response not usually accompanied by prominent emotional feelings and expressions, unless it occurs in a special context such as after prolonged fasting. 2.2 The CognitiveEmotional Behavioral Continuum

It is important to note that the above described focus on the nonsubjective measurable behaviors in the study of emotions is difficult to neurobiologically separate from the study of cognition. Indeed, many of the neurobehavioral rodent tests described below, such as learning behavioral avoidance of electrical shocks, intuitively seem to be reminiscent of cognitive behaviors that subserve survival. We currently know that emotional behaviors involve emotional circuitry including reward or survival networks that are tightly related to cognitive processing [45, 47]. While not unique to the epilepsies, this relation is very prominent in seizure disorders,

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

349

specifically those of hippocampal origin. Indeed, the amygdalohippocampal circuitry is implicated in learning, memory, emotions, and when disrupted, in seizure generation [48]. In addition, emerging functional and structural data point to the role of the hippocampus in disambiguation and pattern separation such as in contextual fear conditioning and in providing the cognitive flexibility required to escape from anxiety-provoking situations [47, 49, 50]. Therefore, from a pathophysiological and anatomical standpoint, the blurred margin between the study of cognitive and emotional behaviors is not an obstacle in animal research, but on the contrary, it has an important clinical translational relevance especially in animal seizure models. It is of clinical interest to study and investigate new treatment modalities to disturbances in emotional behavioral adaptability to stimuli and to new environments irrespective of the semantics of the rather blurred cognitionemotion continuum. Indeed, the close relation between cognitive adaptability and emotional homeostasis echoes what is commonly encountered in children and adolescents with epilepsy, where deficits in learning and memory and inability to cope with psychosocial demands may be key contributors to emotional behavioral derangements and psychiatric diseases. Not surprisingly, the risk of psychopathology in epilepsy is the highest (>50%) in those with underlying intellectual disability [3]. The child with epilepsy and emotional behavioral disturbances is often referred to neuropsychological testing to understand his or her cognitive and attentional capacities in order to tailor pharmacological and potentially environmental interventions. More suitable academic and home environments tailored to the child’s intellectual abilities can attenuate emotional behavioral disturbances and substantially complement or even obviate the need for psychiatric medications. In the translational epilepsy laboratory, we aim at performing comprehensive rodent neurobehavioral testing panels akin to these neuropsychological tests. This allows assessing the interplay between disrupted emotional behaviors, learning deficits, and the underlying pathologies including potential hippocampal structural and functional deficits. 2.3 An Elemental Behavioral OutcomeBased Test Interpretation

Despite the drug-based historical validities of some neurobehavioral panels as tests for entities such as “depression” or “anxiety” disorders, we approach the results of each testing panel using an elemental behavior-based interpretation instead of a holistic psychiatric condition-based interpretation. In other terms, instead of mirroring specific psychiatric entities as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), each panel is considered to be a test for elemental behavioral responses to stressful or new environments (e.g., locomotor hyperactivity, exploration, struggling, “giving up,” etc.). This interpretative approach to testing panels respects the herein advanced behavioral framework

350

Yasser Medlej et al.

without sacrificing translational clinical merit and face validity since such elemental behavioral disturbances are seen in children with epilepsy without necessarily qualifying for DSM-V diagnoses. The focus on the elemental approach may be more clinically relevant to the child with epilepsy than a condition-based approach, since behavioral disturbances in these children likely represent clinical manifestations pathophysiologically tightly related to the seizures, their etiology, and treatment, rather than to “run of the mill” DSM-V psychiatric entities. Moreover, in order to approximate the potential psychiatric condition that fits best the measured elemental behavioral responses from various panels, we perform a comprehensive interpretation where responses from individual tests are interpreted in the context of the responses obtained from other related panels.

3 Neurobehavioral Tests for Cognitive and Emotional Behavioral Disturbances in Rodent Seizure Models Below is a discussion of the five main behavioral panels we perform following brain insults in rodent seizure models including the kainic acid (KA) model of TLE in peri-adolescent rats and the global gradient hypoxia model of hypoxic encephalopathy in 10-day-old rat pups. The light-dark box test (LDT) and openfield test (OFT) are used to assess anxious-like, exploratory, and hyperactive behaviors. The forced swim test (FST) is employed to examine learned despair, struggling behaviors, and depressive-like behaviors. The Morris water maze (MWM) is a test for visuospatial navigation. The MAAV assesses the recognition of auditory emotional cues as well as hippocampal-dependent contextual emotional cues and the acquisition of learned adaptive shock-avoiding behaviors. Depending on the posed research question, two main approaches are employed in our laboratory. We perform a rather comprehensive broad approach with multiple neurobehavioral tests when aiming at newly characterizing the type and extent of deficits in a given seizure model. In such cases, the testing battery sequence is designed to minimize interferences between tests, starting with the least aversive such as the LDT to the most aversive (MAAV conditioning experiments). However, in an already behaviorally characterized seizure model, and in order to investigate potential novel protective interventions, we employ a narrower approach with select neurobehavioral tests tailored to the anticipated deficits (Table 1).

Rodents are forced to swim in an enclosed cylinder filled with water, and their escapedirected behaviors are observed

Depressive-like behaviors

Learning of emotionally relevant auditory Rodents are placed in a modified and contextual cues, and acquisition of two-compartment shuttling box. In the left adaptive behavioral avoidance of contextcompartment, an electrical foot shock is cued and tone-signaled electrical foot signaled by a tone. In the contextually modified shocks right compartment (black-white wall patterns and visual cues such as dices and beads), the shock is not signaled by a tone but is regularly administered every 10 s spent on that side

Test used for

Time spent in each compartment, latency time to first transition, number of transitions between the two compartments, exploratory activity levels reflecting time spent next to novel objects on the lit side Escape-directed swimming behavior (escape latency, distance traveled)

Anxiety-like behaviors The setup consists of two interconnected compartments: one is black and dim, and the other is white, brightly illuminated, and contains novel objects. Here we study the conflict between a rodent’s exploratory behaviors versus a natural aversion to new lit environments

The maze consists of an open circular pool filled Visuospatial navigation with water that contains a hidden submerged escape platform. Rodents are placed in a designated starting location of the pool, and their escape-directed swimming behavior is observed during a predefined session of 2 min

Morris water maze (MWM)

Distance traveled, time spent in each zone (central versus peripheral), time spent exploring central objects, latency to enter the central area, immobility (freezing), speed in each zone

Time spent immobile, swimming and struggling activities, climbing attempts, latency time to the onset of immobility

Percentage of avoidance behaviors and avoidance latencies (time required to avoid the shock before its onset upon perceiving the conditioned stimulus)

Measurable behavioral responses

Light-darkbox test (LDT)

Open-field test A rodent is subjected to a novel open arena from Exploratory, hyperactive, and anxiety-like behaviors (OFT) which escape is prevented by surrounding walls. It is placed either in the center or next to the walls of the apparatus, and its behavior is observed over a predefined session of usually 5–10 min

Forced swim test (FST)

Modified active avoidance (MAAV)

Testing panels Test description

Table 1 Battery of neurobehavioral panels tailored to pediatric seizure models with prominent amygdalo-hippocampal involvement

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy 351

352

Yasser Medlej et al.

3.1 The Open-Field Test: Hyperactivity, Exploratory, and Anxiety-Like Behaviors

Maladaptive anxious behaviors are common accompaniment of seizures in both the clinical and the experimental arenas [12, 32, 51–53], and up to one third of children with epilepsy exhibit anxiety, which manifests behaviorally with excessive tantrums, hyperactivity, and negativity [51]. Hyperactivity in the context of impulsivity, and inattention, at times qualifying for attention deficit hyperactivity disorder (ADHD) also can accompany the epilepsies [54]. The OFT is frequently used in various rodent models to monitor locomotor activity (hyperactivity), as well as exploratory and anxiety-like behaviors [6, 32, 37, 55, 56]. It was first introduced by Hall and Ballachey in 1932, and it involves subjecting rodents to an open arena from which escape is prevented by surrounding walls [57]. The rodent is placed either in the center or next to the walls of the apparatus, and its behavior is observed over a predefined session of usually 5–10 min. The test is based on the conflict that arises between rodents’ natural innate aversion to open lit areas and their drive to explore. Rats may exhibit thigmotaxis (spend time next to the walls) as an innate response or move to the field’s central area as a manifestation of enhanced adaptive exploratory behavior. Several modifications to the test were introduced including the shape of the open-field arena (circular, rectangular, or square), its size, color (clear or opaque), lighting conditions (position and intensity), and the presence of objects on the field’s floor [32, 58–61]. The presence of a light source directed to the field’s center intensifies the contrast with the periphery and thus magnifies the conflict between innate aversion and adaptive exploration. A variety of measurable behavioral outcomes can be objectively obtained from automated video analyses using a dedicated video tracking software, where the central and peripheral areas are digitally defined [37]. The distance traveled, the time spent in each zone (central versus peripheral), the time spent exploring central objects, the latency to enter the central area, immobility (freezing), and the speed in each zone can be separately measured [32, 37, 62]. The effect of the known anxiolytic benzodiazepines on rodent behaviors was tested in the OFT. Treated animals exhibited increased distance and time spent in the central zone without an increase in the total distance traveled which can be interpreted as an increase in exploratory behaviors [63]. These results conferred to some extent predictive validity to the OFT as a test for anxiety-like behaviors. In rodent seizure models, OFT has been employed to assess anxiety-like behaviors that accompany seizures. For instance, in TLE animal models, and following SE induced by lithiumpilocarpine, peri-adolescent rats exhibit decreased exploratory activity as reflected by a shorter exploration time [64]. However, the outcomes in this test are likely a function of the animal’s age at the time of seizure induction. Indeed, rats exhibit hyperactivity in the OFT in the peri-adolescent and adult ages following SE though

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

353

not when SE is induced at, or before P14–15 with either KA, pentylenetetrazol (PTZ), or pilocarpine [32, 65–67]. In addition, the OFT results seem to be a function of the seizure model and the used strain of rats. Following early life hypoxic seizures, an increase in locomotor activity in the peripheral zones is seen in SpragueDawley and Wistar rats when tested in the peri-adolescent and adult ages [6, 68], but not in Long-Evans, irrespective of the age of testing, as shown by us and others [37, 69, 70]. Additional related behavioral panels were not performed in many of these studies to better dissect the nature of the elemental outcomes of increased locomotor activity and decreased exploration. However, in seizure models, these are generally considered to denote anxiety-like behaviors given the results of other related tests such as the LDT [71]. While measuring the different elemental rat’s behaviors in the OFT is relatively simple, their interpretation is rather complex, and has to be conducted in the context of other related panels, especially with the above discussed potential strain and age-related variabilities. To that end, the use of a battery of multiple complementary tests for novelty and lit space-dependent responses is helpful. 3.2 The Light-Dark Box Test: Anxiety-Like Behaviors

In addition to the OFT, other behavioral tests are used in epilepsy rodent models to study anxiety-like and exploratory behaviors, of which the LDT is one of the most commonly used ones. It was first proposed by Jacqueline Crawley and Frederick Goodwin based on rodents’ innate behavioral aversion to brightly illuminated areas and on their spontaneous tendency to explore novel environments [72]. The simplest used setup consists of two interconnected compartments: one is black and dim, and the other compartment is white and brightly illuminated. In this two-compartment environment, a conflict between two rodents’ behaviors arises: an exploratory behavior versus a natural aversion to new lit environments. Pharmacological testing conferred predictive validity to the LDT as a test for anxiety-like behaviors. Indeed, when treated with known anxiolytic drugs such as benzodiazepines, mice exhibit an increase in exploratory behaviors and time spent in the lit chamber with a higher number of transitions between the two compartments [72, 73]. Since its introduction in 1980, different experimental paradigms have been used in both mice and rats [74, 75], along with various modifications to the testing apparatus, including the size of the box and its compartments [76], the addition of a tunnel between the two compartments [77], or turning the box into a brightly illuminated corridor runway with two equally sized dark compartments at both ends [78]. In our laboratory, we divided the test apparatus into two equal compartments and added different novel objects to the lit chamber to further drive the exploratory behavior in our seizure rodent models with anticipated hippocampal insult-related increased anxiety-like behaviors [34, 37].

354

Yasser Medlej et al.

In this test, the multiple outcome parameters include the time spent in each compartment, latency time to the first transition, the number of transitions between the two compartments, and exploratory activity levels reflecting time spent next to novel objects on the lit side [79, 80]. In addition to its historical use in assessing behavioral anxiolysis and anxiogenesis with drugs and environmental stressors, the LDT is also used following brain insults including epileptogenic ones [81, 82]. Decreased exploratory behaviors in the LDT have been reported in various epilepsy rodent models, including hypoxic seizure models, as well as pilocarpine-induced and KA-induced SE seizure models [34, 53, 83, 84]. As opposed to controls, following SE, animals spend less time in the lit chamber, have lesser number of transitions between the two compartments, and a longer latency time to the first entry into the lit chamber. The interpretation of the measurable behavioral outcomes obtained from the LDT must be done in the context of other responses obtained from related and complementary behavioral tests. For example, activity level in the OFT can help in refining the interpretation of LDT outcomes. In the context of an increased locomotor activity in the OFT, an increase in LDT transitions suggests a drug or brain insult-induced maladaptive hyperactivity rather than an adaptive increased exploratory behavior. When performing neurodevelopmental studies, the age at the time of testing should also be carefully considered as exploratory behaviors may be higher in periadolescence as compared to the adult age [85]. 3.3 The Forced Swim Test (FST): Depression-Like Behaviors or Behavioral Response to Stress in a Closed Space?

In addition to anxiety, depression is also a very common comorbid condition seen in epilepsy patients [86–88]. Depression is behaviorally characterized by irritability, a tendency to easily “give up,” along with accompanying feelings of sadness and despair [51, 86–88]. While it is impossible to study the feeling of sadness in rodents, the FST allows assessing measurable behavioral surrogates of these feelings. It was developed by Roger D. Porsolt in 1977 based on the observation that a rat will become “immobile and give up” when forced to swim in an enclosed cylinder filled with water. In this test, rodents initially exhibit vigorous struggling activity, but eventually develop an immobile posture [89, 90]. The administration of clinically effective antidepressant agents enhances the escape-directed behaviors of rodents (i.e., more swimming and struggling), which conferred to this test its translational predictive clinical validity in screening novel antidepressants agents [89, 91]. Giving up or immobility and the lack of struggling are therefore generally considered measurable behavioral surrogates of depressive-like states. The test was subjected to several modifications to increase its sensitivity and reliability [89, 92, 93]. Some modifications included increasing the water height to prevent the rat from stabilizing itself with its tail on the bottom of the cylinder, narrowing the cylinder’s width to prevent free swimming, and tailoring its dimensions to test mice [89, 93].

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

355

Several behavioral outcomes can be measured in the FST including time spent immobile, swimming and struggling activities, climbing attempts, and latency time to the onset of immobility. Instead of a potentially biased observational scoring, these outcomes are automated through video tracking and activity softwares. In addition to its validity and reliability in testing new antidepressants [91], the FST is used to examine struggling and “giving up” responses in animal models of epilepsy. Following chemoconvulsant-induced SE (i.e., KA, pilocarpine, and PTZ), rodents show increased immobility in FST likely reflecting depression-like behaviors [94–96]. From a mechanistic end, our data suggest a role of TrkB pathway dysregulation in the FST depressive-like deficits in our hypoxic seizure model in Sprague Dawley rats [4], while others have shown that depressive-like behaviors are accompanied by serotonin receptor downregulation in the KA seizure model [94]. Although increased immobility is thought to correlate with a despair-like “giving up” behavior, animals with seizures can actually exhibit increased swimming activity compared to controls. For instance, we have found that following early life hypoxic seizures, unlike Sprague Dawley rats [4], the Long Evans strain of rats develop increased swimming activity compared to controls in the FST when tested in the peri-adolescent age [37]. Given the molecular [38, 97] and behavioral [6, 37] surrogates of functional hippocampal impairment in the hypoxic seizure model, this early life hypoxia-induced increased swimming was hypothesized to be secondary to hippocampal-related contextual deficits in learning despair in the context of a closed inescapable cylinder (Fig. 2). Such potential counterintuitive results illustrate the importance of an elemental approach to neurobehavioral testing. These tests do not necessarily mirror the same disease condition, mechanistically and behaviorally, in all brain injury models, ages, and even strains. Indeed, a psychiatric disease entity-based approach to FST as a “test for depression-like behaviors” would have yielded the very biologically unlikely result of “resistance to depression” following early life hypoxic seizures in the Long Evans strain of rats. However, an approach based on the measurable elemental behavioral outcomes obtained from FST and from other panels that test behavioral hyperactivity and hippocampal learning allowed a better multitest-based comprehensive interpretation of the FST outcome as a potential learning deficit-driven increased struggling. Along those lines and in the context of the conceptual framework discussed above, the FST can be considered as a test for measurable behavioral responses to stress in a closed container. Rather than diminishing its scientific merit, such an approach expands the application of FST beyond a test limited to depression-like behaviors. In addition to avoiding pitfalls in interpretation, technical confounders should also be considered. For example, the depth of the water, its temperature, and the width of

356

Yasser Medlej et al.

Fig. 2 Forced swim test (FST) performed in the peri-adolescent age following early life hypoxic seizures in the Long-Evans strain of rats [37]. The percentage of immobility is plotted against the minute of testing. At the fourth minute of testing, there was a statistically significant divergence in behaviors between the hypoxic and normoxic rats ( p < 0.05) that culminated in doubling of the immobility time at minutes 6 and 7 in the normoxic group as opposed to the hypoxic group that continued to exhibit struggling behaviors. A holistic test-based approach to the FST as a test for depression would have resulted in the counterintuitive interpretation of “hypoxia-induced decreased depression.” However, using an elemental behavioral approach to the obtained outcomes, our findings were interpreted in the light of results obtained from other complementary tests as hippocampal learning deficits-related increased struggling

the cylinders can significantly interfere with the rat’s behavior during the testing session. Also, similarly to LDT, it is helpful to complement the FST with an OFT to further validate that the swimming activity of the rodents is not confounded by hyperactivity. 3.4 The Modified Active Avoidance (MAAV) Test

In active avoidance conditioning, rodents learn to recognize emotionally relevant cues and to avoid aversive events with learned adaptive behaviors that replace innate automatic responses to threats (freezing) [37, 98]. Its concept is built on classical conditioning that was first introduced by Ivan Pavlov in 1927 [99, 100] and further developed by B.F. Skinner to include instrumental (operant) avoidance conditioning [101]. Pavlovian fear conditioning paradigms involve a conditioned stimulus (CS) that can be represented either by contextual visual cues, lights, or more commonly a tone and an aversive unconditioned stimulus (US) such as an electrical foot shock [102, 103]. In these conditioning paradigms, the rodent learns to couple the CS with an US, and this is mainly assessed by the emergence of innate freezing responses to the CS. Upon re-exposure to the same CS (contextual cues, or tone), the rodent generally exhibits a freezing behavior as it associates the CS with the aversive stimulus [104]. While such testing paradigms are limited to assessing survival-relevant memory to emotional cues, instrumental conditioning allows a higher level of

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

357

Fig. 3 Schematic design of the modified active avoidance (MAAV) test. We developed this test by modifying the standard shuttling box in order to simultaneously test contextual and auditory conditioning. In addition to testing the recognition of auditory emotional cue and hippocampal-related “visual relational” emotional cues, the test assesses learning of adaptive shock-avoiding shuttling responses that replace innate fear responses (freezing). In the left compartment, the foot shock is signaled by a tone. In the contextually modified right compartment (black-white wall patterns and visual cues such as dices and beads), the shock is not signaled by a tone but is regularly administered every 10 s spent on that side

investigations that include testing of learned adaptable responses that anticipate and avoid an unconditioned aversive stimulus. Active avoidance is a type of instrumental conditioning where the rodent learns to associate between the CS and US and to develop an anticipatory learned adaptive behavioral response to avoid or terminate an aversive stimulus such as stepping over a platform, jumping over a barrier [105], or shuttling between compartments [46]. In our laboratory we developed the MAAV (Fig. 3) as a simultaneous auditory and contextual instrumental active avoidance test by modifying the standard shuttle box [46]. A standard shuttle box consists of two compartments connected via a door for shuttling. The electrical shock is signaled by a CS such as a tone or a light on both sides, and shuttling between the compartments prevents an incoming shock or terminates an ongoing one. Over multiple training sessions, the rodent learns to adaptively shuttle upon exposure to the CS. In the MAAV test, the shock is signaled by a tone in one compartment and by visual cues in the other [37]. This allows the simultaneous testing of both contextual and auditory conditioning, as well as learning of adaptive shuttling behaviors that replace automatic maladaptive innate freezing. The recognition of

358

Yasser Medlej et al.

emotionally relevant cues with arousal/fear expression manifests with freezing responses that occur after CS-US pairing. This innate emotional freezing behavior is followed by adaptive acquired shock-avoidance responses characterized by shuttling to the other chamber. Such an adaptive response to learned emotionally relevant cues can be quantified by measuring the percentage of avoidance behaviors and avoidance latencies (the time required to avoid the shock before its onset upon perceiving the CS) [46, 106]. Additional outcomes include the retention subtest following acquisition testing, where the rodent is allowed to freely roam in both chambers without shock delivery, during which the time spent in each compartment as well as the number of transitions are quantified. Compared to other behavioral panels, the MAAV test requires some extensive fine-tuning. Optimizing learning of shuttling responses in control rats, prior to data collection experiments, requires an accurate determination of the relative durations of the tone, the electrical shock, and the inter-trial resting periods, which can be strain and age-dependent. However, compared with other testing panels, the MAAV provides multiple emotionally relevant learning and memory outcomes that are particularly relevant to amygdalo-hippocampal seizure models. We have employed the MAAV test in our work on the anti-seizure effect of tropomyosinrelated kinase B (TrkB) receptor blockade in the hypoxic seizure Long-Evans rodent model. We found that in addition to attenuating early life hypoxia-induced hyperexcitability [38], transient TrkB blockade also normalized the deficits in the MAAV [37]. When tested in the peri-adolescent age, rats that transiently received a TrkB blocker following early life hypoxic seizures made significantly less erroneous entries into the chamber with the contextual CS compared to vehicle-treated hypoxic rats during the retention subtest of the MAAV [37]. Of note, these deficits in MAAV were noted prior to the potential emergence of chronic recurrent spontaneous seizures described in this model [107]. The reversal of MAAV deficits with TrkB blockade suggests that a disruption in hippocampal synaptic homeostasis is implicated in the long-term learning deficits seen in this model with no overt hippocampal damage, but with well described molecular alterations in α-amino-3hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and TrkB receptors [38, 70, 97]. Moreover, the impairment in contextual but not in auditory conditioning in this hippocampal seizure model falls in line with other studies that revealed impaired Pavlovian contextual conditioning following KA-induced SE [108]. This is tightly related to the role of the hippocampus that can be best functionally summarized as a “relational structure” [50], where it plays a key role in tests requiring pattern separation such as

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

359

contextual fear conditioning though not in the rather relatively relationally simpler auditory fear conditioning [47]. 3.5 The Morris Water Maze (MWM) Test

The MWM assesses hippocampal-dependent visuospatial navigation. Initially designed in 1980 by Richard Morris, it aimed at studying rodent spatial mapping that is independent of visual, auditory, or olfactory senses. The maze consists of an open circular pool filled with water and contains a hidden submerged escape platform. Rodents are placed in a designated starting location of the pool, and their escape-directed swimming behavior during a defined session of usually 2 min is analyzed with parameters that include escape latency, distance traveled, and the route followed. At least three visual cues are placed at a distance from the borders of the maze so that the rodent relies on hippocampal-dependent navigation [109]. Following multiple days of spatial acquisition during which the rodent gradually learns to find the escape platform faster, the platform is removed, and the probe trial is performed as a test of retention for spatial learning [6, 32]. The MWM has been validated as a tool to reliably assess hippocampal function, as well as processes such as long-term potentiation and N-methyl-D-aspartate (NMDA) receptor functioning [110, 111]. Rodents with hippocampal lesions have been found to struggle significantly in localizing a hidden platform in the maze [112]. However, the hippocampus proper does not seem to play an essential role in platform localization, but rather in tasks requiring disambiguation such as finding the escape platform in a pool that contains a rigid platform as well as another fake floating one [113], further supporting a “relational role” of the hippocampus. Along those lines, the MWM is commonly performed in epilepsy rodent models accompanied by prominent structural [6, 32] and molecular [38, 97] hippocampal involvement. Rats that experience early life hypoxia-induced seizures or chemoconvulsant-induced SE have an increased latency period to reach the platform and are slower in reaching their full potential during spatial acquisition [6, 32, 65]. In TLE rodent epilepsy models, rats also spend less time in the probe quadrant during the probe trial test (Fig. 4, [32]). In the MWM test, the literature suggests that hippocampal-dependent spatial learning is impaired irrespective of the seizure model, rodent strain, age at time of SE induction, or the age at the time of testing [6, 32, 65, 114]. Moreover, in those various rodent seizure models, overt hippocampal damage seems to be associated with MWM impairments, as seen following KA-induced SE [32] or following early life hypoxic seizures in Sprague-Dawley rats [6]. While we highly rely on this test for cognitive hippocampal function, it is important to perform a visible platform test following the spatial

360

Yasser Medlej et al.

Fig. 4 Morris water maze following kainic acid (KA)-induced status epilepticus (SE). (a) Shown is the acquisition of place learning. As opposed to controls (saline group), the rats that experienced SE (KA group) were slower in reaching their full potential in locating the escape platform, as reflected by longer overall mean escape latencies (ANOVA, p > 0.05). In this study that we previously published [32], leptin administration (LEP group) did not prevent the deficits. The control group that received leptin (saline-leptin) had comparable overall mean escape latencies to the saline group. (b) Shown are the results of the probe trial retention subtest performed 24 h post-acquisition. The dashed circle in the water maze diagram corresponds to the previous location of the platform. The control groups (saline and saline-LEP) spent significantly more time in the probe quadrant than in the other quadrants reflecting retention of spatial learning, whereas the groups that experienced SE (KA and KALEP groups) spent a comparable duration in all four quadrants (permission obtained from Epilepsy and Behavior, Elsevier)

acquisition and probe trial testing, in order to verify that potential MWM deficits are not related to visual or locomotor impairments. 3.6

Additional Panels

In addition to the battery of panels described above, other neurobehavioral tests have been employed in immature epilepsy rodent models. For instance, in addition to the MWM, the eight-arm radial maze [56] and the Y-maze [115] are employed to examine spatial learning. The elevated plus maze [56, 116] has been used for hyperactivity and anxiety-like behaviors associated with epilepsy. Also, paralleling an increased clinical awareness about autistic spectrum disorder (ASD), researchers have focused on rodent testing panels that reproduce autistic features spanning deficits in social communication and interactions (three chamber social approach testing), as well as restricted, repetitive patterns of behaviors (marble burying and hole poke tasks) [117–119]. The etiologically elusive primary or classical autism or ASD as strictly defined by Kanner’s seminal cases [120] is believed to be predominantly genetic in nature and is associated with a high risk of later onset epilepsy in adolescence and adulthood [121]. Secondary ASD occurring in the context of known genetic syndromes (syndromic ASD), brain lesions (i.e., frontal), or metabolic conditions, can also

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy

361

be associated with epilepsy. While epilepsy and autism in these various primary and secondary conditions are likely both symptomatic of an underlying network dysfunction, there is an increasing research interest in investigating whether seizures and epileptiform features themselves contribute to autism, a legitimate question since epileptiform features in the form of continuous spike-waves in sleep (CSWS) are known to result in various forms of autistic-like regressions in behaviors and language (Landau-Kleffner syndrome) [31]. There is also definite scientific merit in studying the potential emergence of autistic features in models of acquired seizures specifically models of neonatal insults given the growing clinical and experimental literature on their association with a higher risk of autism [122–126]. The previously described elemental behavioral approach to rodent panels definitely applies to tests of autistic-like behaviors given that autistic features in acquired epilepsy models are unlikely to fit a primary autism disease entity. Moreover, when testing novel potential drugs and looking for potential molecular, histological, and electrophysiological contributors to rodent autistic-like features, it is important to keep in mind model and insult specificity as research findings may not necessarily apply to all forms of autism, given its pathophysiological heterogeneity and given the likely ensuing variations in clinical trajectories and medication response in the various primary and secondary autistic conditions [127].

4

Conclusions While the DSM-V classification has an unquestionable merit in the clinical arena, it is difficult to mirror its psychiatric disease entities in animal models, especially that major diagnostic components rely heavily on subjective feelings that are unmeasurable in rodents. We believe therefore that in preclinical research design and interpretation, efforts should be directed at the study of various measurable elements of behavioral disturbances using conceptual frameworks similar to the herein proposed one. We have provided an example of how an elemental behavior-based approach provides a better interpretation to hypoxic seizures-induced excessive swimming in the FST as a deficit in contextual learning rather than the counterintuitive interpretation of “resistance to depression” if we were to follow a DSM-V condition-based approach to this testing panel. The elemental measurable behavioral outcomes of a specific panel should therefore be analyzed in the context of behavioral responses obtained from other related and complementary panels without necessarily considering a certain panel as mirroring a disease entity or a mood disorder, as was also shown in the LDT discussion above. In order to mechanistically dissect the behavioral deficits in rodent seizure models, neurobehavioral studies are complemented by

362

Yasser Medlej et al.

long-term EEG recordings, histological studies, and proteomics. In our TLE and hypoxic seizure models, these are mainly performed to assess molecular and structural hippocampal changes as well as epileptiform and seizure activity [6, 32, 38, 97, 123]. Wireless EEG [128, 129] and live imaging of hippocampal activity [130, 131] are now increasingly being used. These techniques can be performed in freely moving rodents during neurobehavioral testing and therefore will markedly improve our ability to more accurately study the relation between molecular and electrophysiological changes in hippocampal synaptic homeostasis, seizures and epileptiform features, and emotional and cognitive behavioral disturbances in rodent seizure models.

Acknowledgments Sara Saad, Bashir Bashir, and Jad Allam were involved in this research under the Medical Research Volunteer Program (MRVP) at the American University of Beirut. References 1. Ott D, Caplan R, Guthrie D, Siddarth P, Komo S, Shields WD et al (2001) Measures of psychopathology in children with complex partial seizures and primary generalized epilepsy with absence. J Am Acad Child Adolesc Psychiatry 40:907–914 2. Ott D, Siddarth P, Gurbani S, Koh S, Tournay A, Shields WD et al (2003) Behavioral disorders in pediatric epilepsy: unmet psychiatric need. Epilepsia 44:591–597 3. Gredler GR, Rutter M, Graham P, Yule W (1980) A neuropsychiatric study in childhood. J. B. Lippincott, Philadelphia, 272 pp 4. Medlej Y, Salah H, Wadi L, Saad S, Bashir B, Allam J et al. (2019) Lestaurtinib (CEP-701) modulates the effects of early life hypoxic seizures on cognitive and emotional behaviors in immature rats. Epilepsy Behav 92:332–340. 5. Steffenburg U, Hagberg G, Viggedal G, Kyllerman M (1995) Active epilepsy in mentally retarded children. I. Prevalence and additional neuroimpairments. Acta Paediatr 84:1147–1152 6. Mikati MA, Zeinieh MP, Kurdi RM, Harb SA, El Hokayem JA, Daderian RH et al (2005) Long-term effects of acute and of chronic hypoxia on behavior and on hippocampal histology in the developing brain. Dev Brain Res 157:98–102 7. Fastenau PS, Johnson CS, Perkins SM, Byars AW, deGrauw TJ, Austin JK et al (2009)

Neuropsychological status at seizure onset in children: risk factors for early cognitive deficits. Neurology 73:526–534 8. Lee SY, Park J, Park S, Kim Y, Lee KY (2017) Cognitive function and neuropsychological comorbidities in children with newly diagnosed idiopathic epilepsy. J Korean Med Sci 33:e17 9. Lee GP (2010) Neuropsychological assessment in epilepsy. In: Neuropsychology of epilepsy and epilepsy surgery. Oxford University Press, Oxford, p 96 10. Cowan LD (2002) The epidemiology of the epilepsies in children. Dev Disabil Res Rev 8:171–181 11. Piazzini A, Canevini MP, Maggiori G, Canger R (2001) Depression and anxiety in patients with epilepsy. Epilepsy Behav 2:481–489 12. Williams J, Steel C, Sharp GB, DelosReyes E, Phillips T, Bates S et al (2003) Anxiety in children with epilepsy. Epilepsy Behav 4:729–732 13. Ettinger AB, Weisbrot DM, Nolan EE, Gadow KD, Vitale SA, Andriola MR et al (1998) Symptoms of depression and anxiety in pediatric epilepsy patients. Epilepsia 39:595–599 14. Davis SM, Katusic SK, Barbaresi WJ, Killian J, Weaver AL, Ottman R et al (2010) Epilepsy in

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy children with attention-deficit/hyperactivity disorder. Pediatr Neurol 42:325–330 15. Pavlou E, Gkampeta A (2011) Learning disorders in children with epilepsy. Childs Nerv Syst 27:373–379 16. Titus JB, Kanive R, Sanders SJ, Blackburn LB (2008) Behavioral profiles of children with epilepsy: parent and teacher reports of emotional, behavioral, and educational concerns on the BASC-2. Psychol Sch 45:893–904 17. Dunn DW, Austin JK, Harezlak J, Ambrosius WT (2003) ADHD and epilepsy in childhood. Dev Med Child Neurol 45:50–54 18. Steffenburg S, Gillberg C, Steffenburg U (1996) Psychiatric disorders in children and adolescents with mental retardation and active epilepsy. Arch Neurol 53:904–912 19. Boylan LS, Flint LA, Labovitz DL, Jackson SC, Starner K, Devinsky O (2004) Depression but not seizure frequency predicts quality of life in treatment-resistant epilepsy. Neurology 62:258–261 20. Gilliam F, Hecimovic H, Sheline Y (2003) Psychiatric comorbidity, health, and function in epilepsy. Epilepsy Behav 4(Suppl 4): S26–S30 21. Nilsson L, Ahlbom A, Farahmand BY, A˚sberg M, Tomson T (2002) Risk factors for suicide in epilepsy: a case control study. Epilepsia 43:644–651 22. Braun KP (2017) Preventing cognitive impairment in children with epilepsy. Curr Opin Neurol 30:140–147 23. Bourgeois BF, Prensky AL, Palkes HS, Talent BK, Busch SG (1983) Intelligence in epilepsy: a prospective study in children. Ann Neurol 14:438–444 24. Schubert R (2005) Attention deficit disorder and epilepsy. Pediatr Neurol 32:1–10 25. Calandre E, Dominguez-Granados R, Gomez-Rubio M, Molina-Font J (1990) Cognitive effects of long-term treatment with phenobarbital and valproic acid in school children. Acta Neurol Scand 81:504–506 26. Sheth RD, Goulden KJ, Ronen GM (1994) Aggression in children treated with clobazam for epilepsy. Clin Neuropharmacol 17:332–337 27. Kang H, Eun B, Wu Lee C, Ku Moon H, Kim J, Wook Kim D et al (2007) The effects on cognitive function and behavioral problems of topiramate compared to carbamazepine as monotherapy for children with benign rolandic epilepsy. Epilepsia 48:1716–1723 28. Coppola G, Verrotti A, Resicato G, Ferrarelli S, Auricchio G, Operto FF et al (2008) Topiramate in children and

363

adolescents with epilepsy and mental retardation: a prospective study on behavior and cognitive effects. Epilepsy Behav 12:253–256 29. Miyamoto T, Kohsaka M, Koyama T (2000) Psychotic episodes during zonisamide treatment. Seizure 9:65–70 30. Chiesa V, Gardella E, Tassi L, Canger R, Lo Russo G, Piazzini A et al (2007) Age-related gender differences in reporting ictal fear: analysis of case histories and review of the literature. Epilepsia 48:2361–2364 31. Besag FM (2018) Epilepsy in patients with autism: links, risks and treatment challenges. Neuropsychiatr Dis Treat 14:1 32. Obeid M, Frank J, Medina M, Finckbone V, Bliss R, Bista B et al (2010) Neuroprotective effects of leptin following kainic acid-induced status epilepticus. Epilepsy Behav 19:278–283 33. Mazarati A, Siddarth P, Baldwin RA, Shin D, Caplan R, Sankar R (2008) Depression after status epilepticus: behavioural and biochemical deficits and effects of fluoxetine. Brain 131:2071–2083 34. Liu G, Gu B, He X, Joshi RB, Wackerle HD, Rodriguiz RM et al (2013) Transient inhibition of TrkB kinase after status epilepticus prevents development of temporal lobe epilepsy. Neuron 79:31–38 35. van Gestel S, van Oud-Alblas HB, Malingre M, Ververs T, Braun K, Van Nieuwenhuizen O (2005) Propofol and thiopental for refractory status epilepticus in children. Crit Care 9:P273 36. Kim SJ, Lee DY, Kim JS (2001) Neurologic outcomes of pediatric epileptic patients with pentobarbital coma. Pediatr Neurol 25:217–220 37. Goenaga J, Appavu B, Fox J, Oelze T, Kuk R, 799 Fox D et al (2015) The protective effects of 8A0U01 lestaurtinib (CEP-701) against long-term behavioral deficits following early life hypoxic seizures. American Epilepsy Society Annual Meeting (Abstract) 38. Obeid M, Rosenberg EC, Klein PM, Jensen FE (2014) Lestaurtinib (CEP-701) attenuates “second hit” kainic acid-induced seizures following early life hypoxic seizures. Epilepsy Res 108:806–810 39. Glass HC, Shellhaas RA, Tsuchida TN, Chang T, Wusthoff CJ, Chu CJ et al (2017) Seizures in preterm neonates: a multicenter observational cohort study. Pediatr Neurol 72:19–24 40. van Handel M, Swaab H, de Vries LS, Jongmans MJ (2009) Behavioral outcome in children with a history of neonatal

364

Yasser Medlej et al.

encephalopathy following perinatal asphyxia. J Pediatr Psychol 35:286–295 41. van Handel M, de Sonneville L, de Vries LS, Jongmans MJ, Swaab H (2012) Specific memory impairment following neonatal encephalopathy in term-born children. Dev Neuropsychol 37:30–50 42. Berg AT (2008) The natural history of mesial temporal lobe epilepsy. Curr Opin Neurol 21:173–178 43. Thome-Souza S, Kuczynski E, Assumpc¸˜ao F, Rzezak P, Fuentes D, Fiore L et al (2004) Which factors may play a pivotal role on determining the type of psychiatric disorder in children and adolescents with epilepsy? Epilepsy Behav 5:988–994 44. Verrotti A, Carrozzino D, Milioni M, Minna M, Fulcheri M (2014) Epilepsy and its main psychiatric comorbidities in adults and children. J Neurol Sci 343:23–29 45. LeDoux J, Phelps L, Alberini C (2016) What we talk about when we talk about emotions. Cell 167:1443–1445 46. Choi JS, Cain CK, LeDoux JE (2010) The role of amygdala nuclei in the expression of auditory signaled two-way active avoidance in rats. Learn Mem 17:139–147 47. Izquierdo I, Furini CR, Myskiw JC (2016) Fear memory. Physiol Rev 96:695–750 48. McDonald AJ, Mott DD (2017) Functional neuroanatomy of amygdalohippocampal interconnections and their role in learning and memory. J Neurosci Res 95:797–820 49. Bannerman DM, Sprengel R, Sanderson DJ, McHugh SB, Rawlins JNP, Monyer H et al (2014) Hippocampal synaptic plasticity, spatial memory and anxiety. Nat Rev Neurosci 15:181–192 50. Gerlai R (2001) Behavioral tests of hippocampal function: simple paradigms complex problems. Behav Brain Res 125:269–277 51. Plioplys S (2003) Depression in children and adolescents with epilepsy. Epilepsy Behav 4 (Suppl 3):S39–S45 52. Crawley JN (1985) Exploratory behavior models of anxiety in mice. Neurosci Biobehav Rev 9:37–44 53. Gro¨ticke I, Hoffmann K, Lo¨scher W (2007) Behavioral alterations in the pilocarpine model of temporal lobe epilepsy in mice. Exp Neurol 207:329–349 54. Williams AE, Giust JM, Kronenberger WG, Dunn DW (2016) Epilepsy and attentiondeficit hyperactivity disorder: links, risks, and challenges. Neuropsychiatr Dis Treat 12:287–296

55. Hall CS (1934) Emotional behavior in the rat. I. Defecation and urination as measures of individual differences in emotionality. J Comp Psychol 18:385 56. Sayin U, Sutula TP, Stafstrom CE (2004) Seizures in the developing brain cause adverse long-term effects on spatial learning and anxiety. Epilepsia 45:1539–1548 57. Walsh RN, Cummins RA (1976) The openfield test: a critical review. Psychol Bull 83:482 58. Pardon M, Gould G, Garcia A, Phillips L, Cook M, Miller S et al (2002) Stress reactivity of the brain noradrenergic system in three rat strains differing in their neuroendocrine and behavioral responses to stress: implications for susceptibility to stress-related neuropsychiatric disorders. Neuroscience 115:229–242 59. Chakraborti A, Gulati K, Ray A (2008) Age related differences in stress-induced neurobehavioral responses in rats: modulation by antioxidants and nitrergic agents. Behav Brain Res 194:86–91 60. Krishna S, Dodd CA, Hekmatyar SK, Filipov NM (2014) Brain deposition and neurotoxicity of manganese in adult mice exposed via the drinking water. Arch Toxicol 88:47–64 61. Berton O, Ramos A, Chaouloff F, Mormede P (1997) Behavioral reactivity to social and nonsocial stimulations: a multivariate analysis of six inbred rat strains. Behav Genet 27:155–166 62. Dubovicky M, Paton S, Morris M, Mach M, Lucot J (2007) Effects of combined exposure to pyridostigmine bromide and shaker stress on acoustic startle response, pre-pulse inhibition and open field behavior in mice. J Appl Toxicol 27:276–283 63. Choleris E, Thomas A, Kavaliers M, Prato F (2001) A detailed ethological analysis of the mouse open field test: effects of diazepam, chlordiazepoxide and an extremely low frequency pulsed magnetic field. Neurosci Biobehav Rev 25:235–260 64. Kalemenev SV, Zubareva OE, Frolova EV, Sizov VV, Lavrentyeva VV, Lukomskaya NY, KKh K, Zaitsev AV, Magazanik LG (2015) Impairment of exploratory behavior and spatial memory in adolescent rats in lithiumpilocarpine model of temporal lobe epilepsy. Dokl Biol Sci 463:175–177 65. Stafstrom CE, Chronopoulos A, Thurber S, Thompson JL, Holmes GL (1993) Age-dependent cognitive and behavioral deficits after kainic acid seizures. Epilepsia 34:420–432 66. Shi X (2007) Long-term effects of recurrent seizures on learning, behavior and anxiety: an

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy experimental study in rats Xiu-Yu Shi, Ji-Wen Wang, Ge-Fei Lei, Ruo-Peng Sun. World J Pediatr 3:61–65 67. Huang L, Yang SN, Liou C, Hung P, Lai M, Wang C et al (2002) Pentylenetetrazolinduced recurrent seizures in rat pups: time course on spatial learning and long-term effects. Epilepsia 43:567–573 68. Shimomura C, Ohta H (1988) Behavioral abnormalities and seizure susceptibility in rat after neonatal anoxia. Brain Dev 10:160–163 69. Talos DM, Sun H, Zhou X, Fitzgerald EC, Jackson MC, Klein PM et al (2012) The interaction between early life epilepsy and autisticlike behavioral consequences: a role for the mammalian target of rapamycin (mTOR) pathway. PLoS One 7:e35885 70. Jensen F, Holmes G, Lombroso C, Blume H, Firkusny I (1992) Age-dependent changes in long-term seizure susceptibility and behavior after hypoxia in rats. Epilepsia 33:971–980 71. Umpierre AD, Remigio GJ, Dahle EJ, Bradford K, Alex AB, Smith MD et al (2014) Impaired cognitive ability and anxiety-like behavior following acute seizures in the Theiler’s virus model of temporal lobe epilepsy. Neurobiol Dis 64:98–106 72. Crawley J, Goodwin FK (1980) Preliminary report of a simple animal behavior model for the anxiolytic effects of benzodiazepines. Pharmacol Biochem Behav 13:167–170 73. Hascoe¨t M, Bourin M (1998) A new approach to the light/dark test procedure in mice. Pharmacol Biochem Behav 60:645–653 74. Milner L, Crabbe J (2008) Three murine anxiety models: results from multiple inbred strain comparisons. Genes Brain Behav 7:496–505 75. Pich EM, Samanin R (1989) A two-compartment exploratory model to study anxiolytic/anxiogenic effects of drugs in the rat. Pharmacol Res 21:595–602 76. Costall B, Kelly M, Naylor R, Onaivi E (1988) Actions of buspirone in a putative model of anxiety in the mouse. J Pharm Pharmacol 40:494–500 77. Belzung C, Misslin R, Vogel E, Dodd RH, Chapouthier G (1987) Anxiogenic effects of methyl-β-carboline-3-carboxylate in a light/ dark choice situation. Pharmacol Biochem Behav 28:29–33 78. Shimada T, Matsumoto K, Osanai M, Matsuda H, Terasawa K, Watanabe H (1995) The modified light/dark transition test in mice: evaluation of classic and putative anxiolytic and anxiogenic drugs. Gen Pharmacol Vasc S 26:205–210

365

79. Handley SL (1991) Evaluation of marbleburying behavior as a model of anxiety. Pharmacol Biochem Behav 38:63–67 80. Treit D (1990) A comparison of anxiolytic and nonanxiolytic agents in the shockprobe/burying test for anxiolytics. Pharmacol Biochem Behav 36:203–205 81. Mu¨ller CJ, Gro¨ticke I, Bankstahl M, Lo¨scher W (2009) Behavioral and cognitive alterations, spontaneous seizures, and neuropathology developing after a pilocarpine-induced status epilepticus in C57BL/6 mice. Exp Neurol 219:284–297 82. Varman DR, Rajan KE (2015) Environmental enrichment reduces anxiety by differentially activating serotonergic and neuropeptide Y (NPY)-ergic system in Indian field mouse (Mus booduga): an animal model of posttraumatic stress disorder. PLoS One 10: e0127945 83. Rodriguez-Alvarez N, Jimenez-Mateos EM, Dunleavy M, Waddington JL, Boylan GB, Henshall DC (2015) Effects of hypoxiainduced neonatal seizures on acute hippocampal injury and later-life seizure susceptibility and anxiety-related behavior in mice. Neurobiol Dis 83:100–114 84. Yow H, Ahmad N, Azmi N, Bakry M (2017) The effect of curcumin on anxiety and recognition memory in kainate model of epileptic rats. Indian J Pharm Sci 79:267–276 85. Arrant AE, Schramm-Sapyta NL, Kuhn CM (2013) Use of the light/dark test for anxiety in adult and adolescent male rats. Behav Brain Res 256:119–127 86. Fiest KM, Dykeman J, Patten SB, Wiebe S, Kaplan GG, Maxwell CJ et al (2013) Depression in epilepsy: a systematic review and metaanalysis. Neurology 80:590–599 87. Shehata GA, Bateh AEM (2009) Cognitive function, mood, behavioral aspects, and personality traits of adult males with idiopathic epilepsy. Epilepsy Behav 14:121–124 88. Kanner AM (2006) Depression and epilepsy: a new perspective on two closely related disorders. Epilepsy Curr 6:141–146 89. Porsolt RD, Le Pichon M, Jalfre M (1977) Depression: a new animal model sensitive to antidepressant treatments. Nature 266:730–732 90. Porsolt RD, Bertin A, Jalfre M (1977) Behavioral despair in mice: a primary screening test for antidepressants. Arch Int Pharmacodyn Ther 229:327–336 91. Petit-Demouliere B, Chenu F, Bourin M (2005) Forced swimming test in mice: a

366

Yasser Medlej et al.

review of antidepressant activity. Psychopharmacology 177:245–255 92. Slattery DA, Cryan JF (2012) Using the rat forced swim test to assess antidepressant-like activity in rodents. Nat Protoc 7:1009 93. Porsolt RD, Bertin A, Blavet N, Deniel M, Jalfre M (1979) Immobility induced by forced swimming in rats: effects of agents which modify central catecholamine and serotonin activity. Eur J Pharmacol 57:201–210 94. Koh S, Magid R, Chung H, Stine CD, Wilson DN (2007) Depressive behavior and selective downregulation of serotonin receptor expression after early-life seizures: reversal by environmental enrichment. Epilepsy Behav 10:26–31 95. Mazarati AM, Pineda E, Shin D, Tio D, Taylor AN, Sankar R (2010) Comorbidity between epilepsy and depression: role of hippocampal interleukin-1β. Neurobiol Dis 37:461–467 96. Mortazavi F, Ericson M, Story D, Hulce VD, Dunbar GL (2005) Spatial learning deficits and emotional impairments in pentylenetetrazole-kindled rats. Epilepsy Behav 7:629–638 97. Rakhade SN, Zhou C, Aujla PK, Fishman R, Sucher NJ, Jensen FE (2008) Early alterations of AMPA receptors mediate synaptic potentiation induced by neonatal seizures. J Neurosci 28:7979–7990 98. Motta SC, Goto M, Gouveia FV, Baldo MV, Canteras NS, Swanson LW (2009) Dissecting the brain’s fear system reveals the hypothalamus is critical for responding in subordinate conspecific intruders. Proc Natl Acad Sci U S A 106:4870–4875 99. LeDoux JE (2014) Coming to terms with fear. Proc Natl Acad Sci U S A 111:2871–2878 100. Pavlov IP (1927) (2010) Conditional reflexes: an investigation of the physiological activity of the cerebral cortex. Ann Neurosci 17(3):136–141. 101. McSweeney FK, Murphy ES (2014) The Wiley Blackwell handbook of operant and classical conditioning. John Wiley & Sons, Chichester 102. Pare D, Quirk GJ, Ledoux JE (2004) New vistas on amygdala networks in conditioned fear. J Neurophysiol 92:1–9 103. Fanselow MS, Sterlace SR (2014) Pavlovian fear conditioning. In: The Wiley Blackwell handbook of operant and classical conditioning. John Wiley & Sons, Chichester, p 117 104. Shoji H, Takao K, Hattori S, Miyakawa T (2014) Contextual and cued fear

conditioning test using a video analyzing system in mice. J Vis Exp (85). https://doi.org/ 10.3791/50871 105. Solomon RL, Wynne LC (1953) Traumatic avoidance learning: acquisition in normal dogs. Psychol Monogr Gen Appl 67:1 106. Jahn-Eimermacher A, Lasarzik I, Raber J (2011) Statistical analysis of latency outcomes in behavioral experiments. Behav Brain Res 221:271–275 107. Rakhade SN, Klein PM, Huynh T, HilarioGomez C, Kosaras B, Rotenberg A et al (2011) Development of later life spontaneous seizures in a rodent model of hypoxia-induced neonatal seizures. Epilepsia 52:753–765 108. Kemppainen E, Nissinen J, Pitk€anen A (2006) Fear conditioning is impaired in systemic kainic acid and amygdala-stimulation models of epilepsy. Epilepsia 47:820–829 109. Morris RG (1981) Spatial localization does not require the presence of local cues. Learn Motiv 12:239–260 110. Jeffery KJ, Morris RG (1993) Cumulative long-term potentiation in the rat dentate gyrus correlates with, but does not modify, performance in the water maze. Hippocampus 3:133–140 111. Bannerman DM, Good MA, Butcher SP, Ramsay M, Morris RG (1995) Distinct components of spatial learning revealed by prior training and NMDA receptor blockade. Nature 378:182–186 112. Morris R, Garrud P, Rawlins JA, O’Keefe J (1982) Place navigation impaired in rats with hippocampal lesions. Nature 297:681–683 113. Taylor AM, Bus T, Sprengel R, Seeburg PH, Rawlins JN, Bannerman DM (2013) Hippocampal NMDA receptors are important for behavioural inhibition but not for encoding associative spatial memories. Philos Trans R Soc Lond Ser B Biol Sci 369:20130149 114. Pearson JN, Schulz KM, Patel M (2014) Specific alterations in the performance of learning and memory tasks in models of chemoconvulsant-induced status epilepticus. Epilepsy Res 108:1032–1040 115. Hidaka N, Suemaru K, Li B, Araki H (2008) Effects of repeated electroconvulsive seizures on spontaneous alternation behavior and locomotor activity in rats. Biol Pharm Bull 31:1928–1932 116. Castelhano AS, Cassane GS, Scorza FA, Cysneiros RM (2013) Altered anxiety-related and abnormal social behaviors in rats exposed to early life seizures. Front Behav Neurosci 7:36

Overview on Emotional Behavioral Testing in Rodent Models of Pediatric Epilepsy 117. Yang M, Silverman JL, Crawley JN (2011) Automated three-chambered social approach task for mice. Curr Protoc Neurosci. Chapter 8:Unit 8.26, 8.26.1–8.26.16 118. Lugo JN, Swann JW, Anderson AE (2014) Early-life seizures result in deficits in social behavior and learning. Exp Neurol 256:74–80 119. Edition F (2013) Diagnostic and statistical manual of mental disorders. American Psychiatric Association, Philadelphia 120. Kanner L (1943) Autistic disturbances of affective contact. Nerv Child 2:217–250 121. Bolton PF, Carcani-Rathwell I, Hutton J, Goode S, Howlin P, Rutter M (2011) Epilepsy in autism: features and correlates. Br J Psychiatry 198:289–294 122. Driscoll DJO, Felice VD, Kenny LC, Boylan GB, O’Keeffe GW (2018) Mild prenatal hypoxia-ischemia leads to social deficits and central and peripheral inflammation in exposed offspring. Brain Behav Immun 69:418–427 123. Lippman-Bell JJ, Rakhade SN, Klein PM, Obeid M, Jackson MC, Joseph A et al (2013) AMPA Receptor antagonist NBQX attenuates later-life epileptic seizures and autistic-like social deficits following neonatal seizures. Epilepsia 54:1922–1932 124. Fezer GF, Matos MB, Nau AL, Zeigelboim BS, Marques JM, Liberalesso PBN (2017) Perinatal features of children with autism spectrum disorder. Rev Paul Pediatr 35:130–135

367

125. Angelidou A, Asadi S, Alysandratos K, Karagkouni A, Kourembanas S, Theoharides TC (2012) Perinatal stress, brain inflammation and risk of autism-review and proposal. BMC Pediatr 12:89 126. Froehlich-Santino W, Londono Tobon A, Cleveland S, Torres A, Phillips J, Cohen B et al (2014) Prenatal and perinatal risk factors in a twin study of autism spectrum disorders. J Psychiatr Res 54:100–108 127. Sztainberg Y, Zoghbi HY (2016) Lessons learned from studying syndromic autism spectrum disorders. Nat Neurosci 19:1408 128. Williams P, White A, Ferraro D, Clark S, Staley K, Dudek FE (2006) The use of radiotelemetry to evaluate electrographic seizures in rats with kainate-induced epilepsy. J Neurosci Methods 155:39–48 129. Kadam SD, White AM, Staley KJ, Dudek FE (2010) Continuous electroencephalographic monitoring with radio-telemetry in a rat model of perinatal hypoxia-ischemia reveals progressive post-stroke epilepsy. J Neurosci 30:404–415 130. Barcomb K, Goodell DJ, Arnold DB, Bayer KU (2015) Live imaging of endogenous Ca2 + /calmodulin-dependent protein kinase II in neurons reveals that ischemia-related aggregation does not require kinase activity. J Neurochem 135:666–673 131. Berdyyeva TK, Frady EP, Nassi JJ, Aluisio L, Cherkas Y, Otte S et al (2016) Direct imaging of hippocampal epileptiform calcium motifs following kainic acid administration in freely behaving mice. Front Neurosci 10:53

Part VIII Methods of Animal Models of Aging, Neurodegenerative Diseases and Traumatic Brain Injury

Chapter 21 Neurological Exam in Rats Following Stroke and Traumatic Brain Injury Hale Z. Toklu, Zhiui Yang, Mehmet Ersahin, and Kevin K. W. Wang Abstract Using the appropriate model for testing neurological symptoms in rats is essential for the assessment of functional outcome. A number of tests have been developed to quantify the severity of neurological deficits. These tests should meet criteria such as validity, specificity, sensitivity, and utility. Although analysis of motor function shows homology in primates and rodents, the total neurological exam scores may not always reflect the clinical outcome. Therefore, the selection of the appropriate tests has critical importance when evaluating therapeutic strategies. This chapter describes Toklu’s modified neurological exam score method which can be used practically to assess neurological symptoms following traumatic brain injury (TBI) and stroke. The method is a combination of balance, muscle strength, coordination, and reflex. Key words Neurological exam, Neurological score, Rat, Brain injury, Stroke, Trauma

1

Introduction

1.1 Assessing Neurological Deficits

A neurological exam typically provides information on the structural and functional integrity of the nervous system. Even though neurological examinations in animals are less sophisticated than those performed on humans, these examinations are still useful to assess the overall performance of humans following conditions such as traumatic brain injury (TBI) and stroke [1]. Using an appropriate model for testing neurological symptoms in rats is essential for the assessment of functional outcome. A number of tests have been developed to quantify the severity of neurological deficits. These tests should meet criteria such as validity, specificity, sensitivity, and utility. Although analysis of motor function shows homology in primates and rodents, the total neurological exam scores may not always reflect the clinical outcome. Therefore, selection of the appropriate test has critical importance when evaluating therapeutic strategies [2–4]. This chapter describes Toklu’s modified neurological exam score method which can be used practically to assess neurological symptoms following traumatic brain injury and stroke.

Firas H. Kobeissy (ed.), Psychiatric Disorders: Methods and Protocols, Methods in Molecular Biology, vol. 2011, https://doi.org/10.1007/978-1-4939-9554-7_21, © Springer Science+Business Media, LLC, part of Springer Nature 2019

371

372

Hale Z. Toklu et al.

The method is a combination of balance, muscle strength, coordination, and reflex.

2 2.1

Materials Animals

1. This neurological exam scale was tested on different strains of rats (Sprague Dawley, Wistar albino) of both sexes, 2–3 months of age, weighing between 250 and 350 g. 2. Upon arrival in the animal facility, animals were acclimated for at least 1 week. Animals were maintained on a 12:12-h lightdark cycle and provided standard rat chow and water ad libitum throughout the experimental protocol.

2.2 Drugs Used for Anesthesia for the Induction of Neurotrauma

3

1. Ketamine HCL (Ketalar®, Pfizer, Turkey) 100 mg/ kg ip. 2. Chlorpromazine (Largactil®, Eczacibasi, Turkey) 1 mg/ kg ip. 3. Isoflurane USP (Piramal Critical Care, Inc., Bethlehem, PA) 3%.

Methods

3.1 Toklu’s Modified Neurological Exam in Rats

3.2 Experimental Procedure

Since functional scoring is important in testing neuroprotective drugs, a simple set of criteria common for neurological tests was used to assess the normal and abnormal function in rats that have neurotrauma. Toklu’s modified neurological exam is a combination of Bederson’s neurological function test [5] and Garcia’s behavior score [6]. This test was first used to access neurological scores of rats with encephalopathy due to sepsis [7]. Later, we used this test in brain injury models such as ischemic stroke, weight drop trauma, and overpressure blast injury [8–12]. 1. A 20-point score was used to assess motor and behavioral deficits (Table 1). The higher the score, the more severe the neurological deficit. The sequence of testing animals for a given task was randomized for the animals. All observations were performed by an experienced blind investigator (see Note 1). In summary, the consciousness, performance in a smooth surface climbing platform (45
), extremity tonus, walking and postural reflexes, circling, and response to the nociceptive stimuli were assessed. 2. For walking and posture, the rats were observed as they moved around freely in their cages (see Note 2). 3. Finally, the responses to the nociceptive stimuli were assessed by pinching the tail.

Neurological Exam in Rats Following Stroke and Traumatic Brain Injury

373

Table 1 Toklu’s modified neurological exam Consciousness

Normal: 0 Agitation: 1 Lethargy: 2 Stupor: 3 Coma: 4

Walking/posture

Normal: 0 Limb adduction: 1 Hypomobility: 2 Turning to the paretic side: 3 Spontaneous circling: 4 No postural reflex: 5

Climbing platform

Climbing: 0 Staying on the hind limbs: 1 Hanging for >5 s: 2 Hanging for 1 s 180
: 0