Computational Psychiatry

A Primer

ISBN: 9780262364652 | Copyright 2020

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The first introductory textbook in the emerging, fast-developing field of computational psychiatry.

Computational psychiatry applies computational modeling and theoretical approaches to psychiatric questions, focusing on building mathematical models of neural or cognitive phenomena relevant to psychiatric diseases. It is a young and rapidly growing field, drawing on concepts from psychiatry, psychology, computer science, neuroscience, electrical and chemical engineering, mathematics, and physics. This book, accessible to nonspecialists, offers the first introductory textbook in computational psychiatry.

After more than 100 years of psychological theories, psychopharmacological research, and clinical experience, the challenges of understanding and treating mental illness remain. Computational psychiatry seeks to explain how psychiatric dysfunction may emerge mechanistically, and how it may be classified, predicted, and clinically addressed. It has the potential to bridge advances in neuroscience and clinical applications, connecting low-level biological features with high-level cognitive features. After a survey of computational psychiatry methods, the book covers biologically detailed models of working memory and decision making and computational models of cognitive control. It then describes the application of computational approaches to schizophrenia, depression, anxiety, addiction, and Tourette's syndrome. Finally, the book briefly discusses additional disorders and offers guidelines for future research. Chapters also offer discussions of related issues, chapter summaries, and suggestions for further study. The book can be used as a textbook by students and as a reference for scientists and clinicians interested in applying computational models to diagnosis and treatment strategies.

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Contents (pg. v)
Preface (pg. xi)
1 Introduction: Toward a Computational Approach to Psychiatry (pg. 1)
1.1 A Brief History of Psychiatry: Clinical Challenges and Treatment Development (pg. 1)
1.2 Computational Approaches in Neuroscience and Psychiatry (pg. 10)
1.3 Structure of the Book (pg. 19)
1.4 Chapter Summary (pg. 19)
1.5 Further Study (pg. 20)
2.1 Neural Networks and Circuits Approach (pg. 21)
2.2 Drift-Diffusion Models2 (pg. 28)
2.3 Reinforcement Learning Models3 (pg. 32)
2.4 Bayesian Models and Predictive Coding4 (pg. 39)
2.5 Model Fitting and Model Comparison (pg. 48)
2.6 Chapter Summary (pg. 53)
2.7 Further Study (pg. 54)
3.1 Introduction (pg. 57)
3.2 What Is Biophysically Based Neural Circuit Modeling? (pg. 59)
3.3 Linking Propositions for Cognitive Processes (pg. 62)
3.4 Attractor Network Models for Core Cognitive Computations in Recurrent Cortical Circuits (pg. 66)
3.5 Altered Excitation—Inhibition Balance as a Model of Cognitive Deficits (pg. 69)
3.6 Future Directions (pg. 76)
3.7 Chapter Summary (pg. 80)
3.8 Further Study (pg. 80)
3.9 Acknowledgments (pg. 81)
4 Computational Models of Cognitive Control: Past and Current Approaches (pg. 83)
4.1. Introduction (pg. 83)
4.2 Past and Current Models of Cognitive Control (pg. 87)
4.3 Discussion: Evaluating Models of Cognitive Control (pg. 100)
4.4 Chapter Summary (pg. 103)
4.5 Further Study (pg. 104)
5 The Value of Almost Everything: Models of the Positive and Negative Valence Systems and Their Relevance to Psychiatry (pg. 105)
5.1 Introduction (pg. 105)
5.2. Utility and Value in Decision Theory (pg. 106)
5.3 Utility and Value in Behavior and the Brain (pg. 111)
5.4 Discussion (pg. 119)
5.5 Chapter Summary (pg. 121)
5.6 Further Study (pg. 121)
5.7 Acknowledgments (pg. 122)
6 Psychosis and Schizophrenia from a Computational Perspective (pg. 123)
6.1 Introduction (pg. 123)
6.2 Past and Current Computational Approaches (pg. 125)
6.3 Case Study Example: Attractor-like Dynamics in Belief Updating in Schizophrenia (pg. 134)
6.4 Chapter Summary (pg. 143)
6.5 Further Study (pg. 144)
7 Depressive Disorders from a Computational Perspective (pg. 145)
7.1 Introduction (pg. 145)
7.2 Cognitive Neuroscience of Depression (pg. 146)
7.3 Past and Current Computational Approaches (pg. 148)
7.4 Case Study: How Does Reward Learning Relate to Anhedonia? (pg. 154)
7.5 Discussion (pg. 160)
7.6 Chapter Summary (pg. 164)
7.7 Further Study (pg. 164)
8.1 Introduction (pg. 165)
8.2 Past and Current Computational Approaches (pg. 167)
8.3 Case Study Example: Anxious Individuals Have Difficulty in Learning about the Uncertainty Associated with Negative Outcomes (from Browning et al. 2015) (pg. 171)
8.4 Discussion (pg. 180)
8.5 Chapter Summary (pg. 182)
8.6 Further Study (pg. 182)
9 Addiction from a Computational Perspective (pg. 185)
9.1 Introduction: What Is Addiction? (pg. 185)
9.2 Past Approaches (pg. 187)
9.3 Interacting Multisystem Theories (pg. 196)
9.4 Implications (pg. 199)
9.5 Chapter Summary (pg. 204)
9.6 Further Study (pg. 204)
10 Tourette Syndrome from a Computational Perspective (pg. 205)
10.1. Introduction (pg. 205)
10.2 Past and Current Computational Approaches to Tourette Syndrome (pg. 216)
10.3 Case Study: An Integrative, Theory-Driven Account of Tourette Syndrome (pg. 220)
10.4 Discussion (pg. 240)
10.5 Chapter Summary (pg. 244)
10.6 Further Study (pg. 245)
10.7 Acknowledgments (pg. 246)
11 Perspectives and Further Study in Computational Psychiatry (pg. 247)
11.1 Processes and Disorders Not Covered in This Book (pg. 247)
11.2 Data-Driven Approaches (pg. 251)
11.3 Realizing the Potential of Computational Psychiatry (pg. 252)
11.4 Chapter Summary (pg. 254)
Notes (pg. 255)
Chapter 1 (pg. 255)
Chapter 2 (pg. 255)
Chapter 3 (pg. 255)
Chapter 5 (pg. 256)
Chapter 7 (pg. 256)
Chapter 9 (pg. 256)
Chapter 10 (pg. 257)
References (pg. 259)
Index (pg. 321)
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