Computational Psychiatry Mathematical Modeling of Mental Illness
Coordonnateurs : Anticevic Alan, Murray John D
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.
Meeting Emerging Challenges and Opportunities in Psychiatry Through Computational Neuroscience Section I. Applying Circuit Modeling to Understand Psychiatric Symptoms 1. Cortical Circuit Models in Psychiatry: Linking Disrupted Excitation-Inhibition Balance to Cognitive Deficits Associated with Schizophrenia 2. Serotonergic Modulation of Cognition in Prefrontal Cortical Circuits in Major Depression 3. Dopaminergic Neurons in the Ventral Tegmental Area and their Dysregulation in Nicotine Addiction Section II. Modeling Neural System Disruptions in Psychiatric Illness 4. Computational Models of Dysconnectivity in Large-Scale Resting-State Networks 5. Dynamic Causal Modelling and its Application to Psychiatric Disorders 6. Systems Level Modeling of Cognitive Control in Psychiatric Disorders: A focus on schizophrenia 7. Computational Psychiatry: Mathematical Modeling of Mental Illness Section III: Characterizing Complex Psychiatric Symptoms via Mathematical Models 8. A Case Study in Computational Psychiatry: Addiction as Failure Modes of the Decision-Making System 9. Modeling Negative Symptoms in Schizophrenia 10. Bayesian Approaches to Learning and Decision Making 11. Computational Phenotypes Revealed by Interactive Economic Games
academics, researchers, advanced students, and clinicians in the fields of computational neuroscience, clinical neuroscience, psychiatry, clinical psychology, neurology, and cognitive neuroscience
John D. Murray, Ph.D., is an Assistant Professor of Psychiatry, Neuroscience, and Physics at Yale University School of Medicine, where he directs a research program in computational neuroscience with a focus on computational models of neuropsychiatric disorders. He received his Ph.D. in Physics from Yale University, and was a postdoctoral researcher at New York University.
- Offers an in-depth overview of the rapidly evolving field of computational psychiatry
- Written for academics, researchers, advanced students and clinicians in the fields of computational neuroscience, clinical neuroscience, psychiatry, clinical psychology, neurology and cognitive neuroscience
- Provides a comprehensive survey of work from leaders in this field and a presentation of a range of computational psychiatry methods and approaches geared towards a broad array of psychiatric problems
Date de parution : 09-2017
Ouvrage de 332 p.
15x22.8 cm
Thèmes de Computational Psychiatry :
Mots-clés :
Acetylcholine; Actor-critic; Addiction; Anhedonia; Anterior cingulate cortex; Approach and avoidance; Bayesian inference; Bayesian modeling; Behavior; Biophysically based models; Cognitive control; Computational modeling; Computational neuroscience; Computational psychiatry; Decision making; Decision-making models; Decision-making; Delusions; Dopamine; DTI; Economic games; EEG; Excitation�inhibition balance; fMRI; Functional connectivity; Gambling; Generative model; Generative modeling; Glutamate; Goal-directed; Habitual; Jumping to conclusions; Model comparison; Model-based; Negative symptoms; Neuroeconomics; Neuroimaging; Neuromodeling; Nicotine; Nicotinic receptor; Pavlovian bias; Predictive coding; Prefrontal cortex; Proactive control; Pruning; Psychiatric disorders; Psychopathology; Psychosis; Q-learning; Reinforcement learning; Resting-state networks; Reward prediction error; Schizophrenia; Selective serotonin reuptake inhibitors (SSRI)Working memory; Substance abuse; Treatments for addiction; Ventral tegmental area; Whole-brain computational modeling; Working memory
