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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, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 07 March 2021

Complexity and Heterogeneity in Psychiatric Disorders

Complexity and Heterogeneity in Psychiatric Disorders

Opportunities for Computational Psychiatry

Chapter:
(p.33) 3 Complexity and Heterogeneity in Psychiatric Disorders
Source:
Computational Psychiatry
Author(s):

Nelson Totah

Huda Akil

Quentin J. M. Huys

John H. Krystal

Angus W. MacDonald

Tiago V. Maia

Robert C. Malenka

Wolfgang M. Pauli

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262035422.003.0003

Psychiatry faces numerous challenges: the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. To confront the complexity and heterogeneity intrinsic to brain disorders, psychiatry needs better biological, quantitative, and theoretical grounding. This chapter seeks to identify the sources of complexity and heterogeneity, which include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Computational approaches provide a framework to address complexity and heterogeneity, which cannot be seen as noise to be eliminated from diagnosis and treatment of disorders. Complexity and heterogeneity arise from intrinsic features of brain function, and thus present opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. Challenges to be addressed by a computational framework include: (a) improving the search for risk factors and biomarkers, which can be used toward primary prevention of disease; (b) representing the biological ground truth of psychiatric disorders, which will improve the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine; (c) representing how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.

Keywords:   Strüngmann Forum Report, complexity systems, computational psychiatry, early life development, gene-environment interactions, heterogeneity, machine learning, neurophysiology, reinforcement learning models, stress

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