Computational Psychiatry: Mathematical Modeling of Mental Illness is the first systematic effort to bring together leading scholars in the fields of psychiatry and computational neuroscience who have conducted the most impactful research and scholarship in this area. It includes an introduction outlining the challenges and opportunities facing the field of psychiatry that is followed by a detailed treatment of computational methods used in the service of understanding neuropsychiatric symptoms, improving diagnosis and guiding treatments.
This book provides a vital resource for the clinical neuroscience community with an in-depth treatment of various computational neuroscience approaches geared towards understanding psychiatric phenomena. Its most valuable feature is a comprehensive survey of work from leaders in this field.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Contributors - p. ix 6. Preface - p. xi 7. Meeting Emerging Challenges and Opportunities in Psychiatry Through Computational Neuroscience - p. xiii 8. Section I. Applying Circuit Modeling to Understand Psychiatric Symptoms - p. xxxi 9. Chapter 1. Cortical Circuit Models in Psychiatry: Linking Disrupted Excitation–Inhibition Balance to Cognitive Deficits Associated With Schizophrenia - p. 3 10. Chapter 2. Serotonergic Modulation of Cognition in Prefrontal Cortical Circuits in Major Depression - p. 27 11. Chapter 3. Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction - p. 47 12. Section II. Modeling Neural System Disruptions in Psychiatric Illness - p. 84 13. Chapter 4. Computational Models of Dysconnectivity in Large-Scale Resting-State Networks - p. 87 14. Chapter 5. Dynamic Causal Modeling and Its Application to Psychiatric Disorders - p. 117 15. Chapter 6. Systems Level Modeling of Cognitive Control in Psychiatric Disorders: A Focus on Schizophrenia - p. 145 16. Chapter 7. Bayesian Inference, Predictive Coding, and Computational Models of Psychosis - p. 175 17. Section III. Characterizing Complex Psychiatric Symptoms via Mathematical Models - p. 195 18. Chapter 8. A Case Study in Computational Psychiatry: Addiction as Failure Modes of the Decision-Making System - p. 199 19. Chapter 9. Modeling Negative Symptoms in Schizophrenia - p. 219 20. Chapter 10. Bayesian Approaches to Learning and Decision-Making - p. 247 21. Chapter 11. Computational Phenotypes Revealed by Interactive Economic Games - p. 273 22. Index - p. 293
Computational Psychiatry: Mathematical Modeling of Mental Illness is the first systematic effort to bring together leading scholars in the fields of psychiatry and computational neuroscience who have conducted the most impactful research and scholarship in this area. It includes an introduction outlining the challenges and opportunities facing the field of psychiatry that is followed by a detailed treatment of computational methods used in the service of understanding neuropsychiatric symptoms, improving diagnosis and guiding treatments.
This book provides a vital resource for the clinical neuroscience community with an in-depth treatment of various computational neuroscience approaches geared towards understanding psychiatric phenomena. Its most valuable feature is a comprehensive survey of work from leaders in this field.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Contributors - p. ix 6. Preface - p. xi 7. Meeting Emerging Challenges and Opportunities in Psychiatry Through Computational Neuroscience - p. xiii 8. Section I. Applying Circuit Modeling to Understand Psychiatric Symptoms - p. xxxi 9. Chapter 1. Cortical Circuit Models in Psychiatry: Linking Disrupted Excitation–Inhibition Balance to Cognitive Deficits Associated With Schizophrenia - p. 3 10. Chapter 2. Serotonergic Modulation of Cognition in Prefrontal Cortical Circuits in Major Depression - p. 27 11. Chapter 3. Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction - p. 47 12. Section II. Modeling Neural System Disruptions in Psychiatric Illness - p. 84 13. Chapter 4. Computational Models of Dysconnectivity in Large-Scale Resting-State Networks - p. 87 14. Chapter 5. Dynamic Causal Modeling and Its Application to Psychiatric Disorders - p. 117 15. Chapter 6. Systems Level Modeling of Cognitive Control in Psychiatric Disorders: A Focus on Schizophrenia - p. 145 16. Chapter 7. Bayesian Inference, Predictive Coding, and Computational Models of Psychosis - p. 175 17. Section III. Characterizing Complex Psychiatric Symptoms via Mathematical Models - p. 195 18. Chapter 8. A Case Study in Computational Psychiatry: Addiction as Failure Modes of the Decision-Making System - p. 199 19. Chapter 9. Modeling Negative Symptoms in Schizophrenia - p. 219 20. Chapter 10. Bayesian Approaches to Learning and Decision-Making - p. 247 21. Chapter 11. Computational Phenotypes Revealed by Interactive Economic Games - p. 273 22. Index - p. 293
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