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El. knyga: Computational Psychiatry

4.50/5 (20 ratings by Goodreads)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 24-Nov-2020
  • Leidėjas: MIT Press
  • Kalba: eng
  • ISBN-13: 9780262360715
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Computational PsychiatryDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 24-Nov-2020
  • Leidėjas: MIT Press
  • Kalba: eng
  • ISBN-13: 9780262360715
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"Computational psychiatry represents a novel and multidisciplinary approach to mental dysfunction. Computational psychiatry seeks to characterize mental dysfunction in terms of deviations from healthy brain computations over multiple time scales. It focuses on building mathematical models of neural or cognitive phenomena relevant to psychiatric diseases. One critical function of these models is their ability to bridge between low-level biological (neuroscience) and high-level cognitive features (psychiatric symptoms). This is the first textbook in the new field of computational psychiatry, designed for the next generation of scientists and clinicians who wish to apply computational models to modern diagnosis and treatment strategies"--

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.

Preface xi
1 Introduction: Toward a Computational Approach to Psychiatry
1(20)
1.1 A Brief History of Psychiatry: Clinical Challenges and Treatment Development
1(9)
1.1.1 Clinical Burden
1(1)
1.1.2 Diagnostic Complexity
1(2)
1.1.3 Treatment Development
3(4)
1.1.4 Toward the Future of Psychiatric Research
7(3)
1.2 Computational Approaches in Neuroscience and Psychiatry
10(9)
1.2.1 Computational Neuroscience
10(2)
1.2.2 Computational Psychiatry
12(2)
1.2.3 Data-Driven Approaches
14(1)
1.2.4 Theory-Driven Approaches
14(5)
1.3 Structure of the Book
19(1)
1.4
Chapter Summary
19(1)
1.5 Further Study
20(1)
2 Methods of Computational Psychiatry: A Brief Survey
21(36)
2.1 Neural Networks and Circuits Approach
21(7)
2.1.1 Artificial Neural Network Architectures
23(1)
2.1.2 Teaming in Feedforward Networks
23(2)
2.1.3 Recurrent Networks and Attractor Dynamics
25(1)
2.1.4 Application to Psychiatry
26(1)
2.1.5 Biological Networks
27(1)
2.2 Drift-Diffusion Models
28(4)
2.2.1 Optimality and Model Extensions
30(1)
2.2.2 Accumulation of Evidence in Biological Neurons
31(1)
2.3 Reinforcement Learning Models
32(7)
2.3.1 Learning the V or Q Values
33(2)
2.3.2 Reinforcement Learning in the Brain
35(1)
2.3.3 Evidence for Model-Based and Model-Free Systems
35(2)
2.3.4 Implications for Psychiatry
37(2)
2.4 Bayesian Models and Predictive Coding
39(9)
2.4.1 Uncertainty and the Bayesian Approach
39(1)
2.4.2 Testing Bayesian Predictions Experimentally
40(1)
2.4.3 Decision Theory
41(1)
2.4.4 Heuristics and Approximations: Implementation in the Brain
42(1)
2.4.5 Application to Psychiatry
43(1)
2.4.6 Predictive Coding and Bayesian Models Used in Psychiatry
43(5)
2.5 Model Fitting and Model Comparison
48(5)
2.5.1 Choosing a Suitable Model
48(2)
2.5.2 A Toy Example
50(3)
2.6
Chapter Summary
53(1)
2.7 Further Study
54(3)
3 Biophysically Based Neural Circuit Modeling of Working Memory and Decision Making, and Related Psychiatric Deficits
57(26)
3.1 Introduction
57(2)
3.2 What Is Biophysically Based Neural Circuit Modeling?
59(3)
3.3 Linking Propositions for Cognitive Processes
62(4)
3.4 Attractor Network Models for Core Cognitive Computations in Recurrent Cortical Circuits
66(3)
3.5 Altered Excitation-Inhibition Balance as a Model of Cognitive Deficits
69(7)
3.5.1 Working Memory Models
70(3)
3.5.2 Decision-Making Models
73(2)
3.5.3 State Diagram for the Role of Excitatory/Inhibitory Balance in Cognitive Function
75(1)
3.6 Future Directions
76(4)
3.6.1 Integrating Cognitive Function with Neurophysiological Biomarkers
76(2)
3.6.2 Incorporating Further Neurobiological Detail
78(1)
3.6.3 Informing Task Designs
78(1)
3.6.4 Studying Compensations and Treatments
79(1)
3.6.5 Distributed Cognitive Process in a Large-Scale Brain System
79(1)
3.7
Chapter Summary
80(1)
3.8 Further Study
80(1)
3.9 Acknowledgments
81(2)
4 Computational Models of Cognitive Control: Past and Current Approaches
83(22)
4.1 Introduction
83(4)
4.1.1 The Homunculus Problem of Cognitive Control
84(1)
4.1.2 Why Cognitive Control?
85(1)
4.1.3 Roadmap to This
Chapter
86(1)
4.2 Past and Current Models of Cognitive Control
87(13)
4.2.1 How Do We Determine When to Actively Maintain versus Rapidly Update Contextual Information in Working Memory?
87(2)
4.2.2 How Is the Demand for Cognitive Control Evaluated, and What Is the Computational Role of the Anterior Cingulate Cortex?
89(5)
4.2.3 How Do Contextual Representations Guide Action Selection toward Hierarchically Organized Task Goals, and What Is the Computational Role of the Prefrontal Cortex?
94(2)
4.2.4 How Are Task Sets Learned during Behavioral Performance, and When Are They Applied to Novel Contexts?
96(4)
4.3 Discussion: Evaluating Models of Cognitive Control
100(3)
4.3.1 Model Evaluation: Determining Whether a Computational Model Is Useful
100(2)
4.3.2 Cognitive Control Impairments in Schizophrenia
102(1)
4.4
Chapter Summary
103(1)
4.5 Further Study
104(1)
5 The Value of Almost Everything: Models of the Positive and Negative Valence Systems and Their Relevance to Psychiatry
105(18)
5.1 Introduction
105(1)
5.2 Utility and Value in Decision Theory
106(5)
5.2.1 Utility
106(2)
5.2.2 Value
108(3)
5.3 Utility and Value in Behavior and the Brain
111(8)
5.3.1 Utility
111(3)
5.3.2 Value
114(1)
5.3.3 Evaluation
114(2)
5.3.4 Aversive Values and Opponency
116(1)
5.3.5 Instrumental and Pavlovian Use of Values
117(2)
5.4 Discussion
119(2)
5.5
Chapter Summary
121(1)
5.6 Further Study
121(1)
5.7 Acknowledgments
122(1)
6 Psychosis and Schizophrenia from a Computational Perspective
123(22)
6.1 Introduction
123(2)
6.2 Past and Current Computational Approaches
125(9)
6.2.1 Negative Symptoms
125(1)
6.2.2 Positive Symptoms
126(6)
6.2.3 Cognitive Symptoms
132(2)
6.3 Case Study Example: Attractor-like Dynamics in Belief Updating in Schizophrenia
134(9)
6.4
Chapter Summary
143(1)
6.5 Further Study
144(1)
7 Depressive Disorders from a Computational Perspective
145(20)
7.1 Introduction
145(1)
7.2 Cognitive Neuroscience of Depression
146(2)
7.3 Past and Current Computational Approaches
148(6)
7.3.1 Connectionist Models
148(2)
7.3.2 Drift-Diffusion Models
150(2)
7.3.3 Reinforcement Learning Models
152(1)
7.3.4 Bayesian Decision Theory
153(1)
7.4 Case Study: How Does Reward Learning Relate to Anhedonia?
154(6)
7.4.1 Signal-Detection Task
155(1)
7.4.2 A Basic Reinforcement Learning Model
156(2)
7.4.3 Including Uncertainty in the Model
158(1)
7.4.4 Testing More Hypotheses
158(1)
7.4.5 Results
159(1)
7.5 Discussion
160(4)
7.6
Chapter Summary
164(1)
7.7 Further Study
164(1)
8 Anxiety Disorders from a Computational Perspective
165(20)
8.1 Introduction
165(2)
8.2 Past and Current Computational Approaches
167(4)
8.3 Case Study Example: Anxious Individuals Have Difficulty in Learning about the Uncertainty Associated with Negative Outcomes (from Browning et al. 2015)
171(9)
8.3.1 Theoretical Background and Expected and Unexpected Uncertainty
171(2)
8.3.2 Learning as a Rational Combination of New and Old Information
173(2)
8.3.3 Effect of Volatility on Human Learning
175(1)
8.3.4 Summary of Browning et al. (201S) Study
176(4)
8.4 Discussion
180(2)
8.5
Chapter Summary
182(1)
8.6 Further Study
182(3)
9 Addiction from a Computational Perspective
185(20)
9.1 Introduction: What Is Addiction?
185(2)
9.2 Past Approaches
187(9)
9.2.1 Economic Models
187(2)
9.2.2 Homeostatic Models
189(2)
9.2.3 Opponent Process Theory
191(1)
9.2.4 Reinforcement Models
191(5)
9.3 Interacting Multisystem Theories
196(3)
9.3.1 How a Question Is Asked Can Change Which System Controls Behavior
197(1)
9.3.2 Damage to One System Can Drive Behavior to Another
197(1)
9.3.3 There Are Multiple Failure Modes of Each of These Systems and Their Interaction
198(1)
9.4 Implications
199(5)
9.4.1 Drug Use and Addiction Are Different Things
199(1)
9.4.2 Failure Modes
199(1)
9.4.3 Behavioral Addictions
200(1)
9.4.4 Using the Multisystem Model to Treat Patients
201(3)
9.5
Chapter Summary
204(1)
9.6 Further Study
204(1)
10 Tourette Syndrome from a Computational Perspective
205(42)
10.1 Introduction
205(11)
10.1.1 Disorder Definition and Clinical Manifestations
205(1)
10.1.2 Pathophysiology
205(9)
10.1.3 Treatment
214(1)
10.1.4 Contributions of Computational Psychiatry
215(1)
10.2 Past and Current Computational Approaches to Tourette Syndrome
216(4)
10.2.1 Reinforcement Learning in Tourette Syndrome
216(1)
10.2.2 Habits in Tourette Syndrome
217(1)
10.2.3 Data-Driven Automated Diagnosis in Tourette Syndrome
218(2)
10.3 Case Study: An Integrative, Theory-Driven Account of Tourette Syndrome
220(20)
10.3.1 Dopaminergic Hyperinnervation as a Parsimonious Explanation for Neurochemical and Pharmacological Data in Tourette Syndrome
221(1)
10.3.2 The Roles of Phasic and Tonic Dopamine in Tourette Syndrome
221(11)
10.3.3 Premonitory Urges and Tics in Tourette Syndrome: Computational Mechanisms and Neural Correlates
232(8)
10.4 Discussion
240(4)
10.4.1 Strengths of the Proposed Theory-Driven Account: A Unified Account That Explains a Wide Range of Findings in Tourette Syndrome
240(1)
10.4.2 Limitations and Extensions
240(4)
10.5
Chapter Summary
244(1)
10.6 Further Study
245(1)
10.7 Acknowledgments
246(1)
11 Perspectives and Further Study In Computational Psychiatry
247(8)
11.1 Processes and Disorders Not Covered in This Book
247(4)
11.1.1 Autistic Spectrum Disorder
248(1)
11.1.2 Bipolar Disorder
249(1)
11.1.3 Obsessive-Compulsive Disorder
249(1)
11.1.4 Attention-Deficit/Hyperactivity Disorder
250(1)
22.1.5 Post-Traumatic Stress Disorder
250(1)
11.1.6 Personality Disorders
251(1)
11.1.7 Eating Disorders
251(1)
11.2 Data-Driven Approaches
251(1)
11.3 Realizing the Potential of Computational Psychiatry
252(2)
11.4
Chapter Summary
254(1)
Notes 255(4)
References 259(62)
Index 321
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