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Computational Psychiatry

  • ID: 4080925
  • Book
  • 332 Pages
  • Elsevier Science and Technology
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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.

  • 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

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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

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Anticevic, Alan
Alan Anticevic, Ph.D., is an Assistant Professor of Psychiatry and Psychology at Yale University School of Medicine, where he co-directs a research division and laboratory focused on the integration of cognitive, computational and clinical neuroimaging approaches with the objective of understanding the neurobiology of neuropsychiatric disorders. He received his Ph.D. in Clinical Neuropsychology and Cognitive Neuroscience from Washington University in St. Louis.
Murray, John D
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.
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