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Computational PsychiatryNew Perspectives on Mental Illness$
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A. David Redish and Joshua A. Gordon

Print publication date: 2016

Print ISBN-13: 9780262035422

Published to MIT Press Scholarship Online: May 2017

DOI: 10.7551/mitpress/9780262035422.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 25 June 2022

Candidate Examples for a Computational Approach to Address Practical Problems in Psychiatry

Candidate Examples for a Computational Approach to Address Practical Problems in Psychiatry

(p.223) 12 Candidate Examples for a Computational Approach to Address Practical Problems in Psychiatry
Computational Psychiatry

Rosalyn Moran

Klaas Enno Stephan

Matthew Botvinick

Michael Breakspear

Cameron S. Carter

Peter W. Kalivas

P. Read Montague

Martin P. Paulus

Frederike Petzschner

The MIT Press

Scientists and clinicians can utilize a model-based framework to develop computational approaches to psychiatric practice and bring scientific discoveries to a clinical interface. This chapter describes a general modeling perspective, which complements those derived in previous chapters, and provides distinct examples to highlight the scientific and preclinical research that can evolve out of a computational framework to offer new tools for clinical practice. It begins by reviewing areas of theoretical and modeling studies that have reached a critical mass and outlines the pathophysiological insights that have been revealed. The phasic dopamine temporal difference model shows how neurophysiological and neuroanatomical research, incorporated into a learning circuit model, provides a constrained hypothesis testing framework, related to the likely multiple mechanisms contributing to addiction. A potential application of generative models of neuroimaging measurements (dynamic causal models of EEG data) is described to predict individual treatment responses in patients with schizophrenia. The third example offers a novel approach to quantifying patient outcomes under a “recovery model” of psychiatric illness. In conclusion, consideration is given to the community efforts needed to support the validation of these and future applications.

Keywords:   Strüngmann Forum Report, biophysical models, modeling allostasis, HPA axis, addiction, dopamine, glutamate, schizophrenia

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