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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: 01 August 2021

Clinical Heterogeneity Arising from Categorical and Dimensional Features of the Neurobiology of Psychiatric Diagnoses

Clinical Heterogeneity Arising from Categorical and Dimensional Features of the Neurobiology of Psychiatric Diagnoses

Insights from Neuroimaging and Computational Neuroscience

Chapter:
(p.293) 16 Clinical Heterogeneity Arising from Categorical and Dimensional Features of the Neurobiology of Psychiatric Diagnoses
Source:
Computational Psychiatry
Author(s):

John H. Krystal

Alan Anticevic

John D. Murray

David Glahn

Naomi Driesen

Genevieve Yang

Xiao-Jing Wang

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262035422.003.0016

Clinical heterogeneity presents important challenges to optimizing psychiatric diagnoses and treatments. Patients clustered within current diagnostic schema vary widely on many features of their illness, including their responses to treatments. As outlined by the American Psychiatric Association Diagnostic and Statistical Manual (DSM), psychiatric diagnoses have been refined since DSM was introduced in 1952. These diagnoses serve as the targets for current treatments and supported the emergence of psychiatric genomics. However, the Research Domain Criteria highlight DSM’s shortcomings, including its limited ability to encompass dimensional features linking patients across diagnoses. This chapter considers elements of the dimensional and categorical features of psychiatric diagnoses, with a particular focus on schizophrenia. It highlights ways that computational neuroscience approaches have shed light on both dimensional and categorical features of the biology of schizophrenia. It also considers opportunities and challenges associated with attempts to reduce clinical heterogeneity through categorical and dimensional approaches to clustering patients. Finally, discussion will consider ways that one might work with both approaches in parallel or sequentially, as well as diagnostic schema that might integrate both perspectives.

Keywords:   Strüngmann Forum Report, computational neuroscience, diagnostic schema, DSM, heterogeneity, neuroimaging, RDoC

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