Computational Psychiatry and the Challenge of Schizophrenia

Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and c...

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Bibliographic Details
Published in:Schizophrenia bulletin 2017-05, Vol.43 (3), p.473-475
Main Authors: Krystal, John H, Murray, John D, Chekroud, Adam M, Corlett, Philip R, Yang, Genevieve, Wang, Xiao-Jing, Anticevic, Alan
Format: Article
Language:English
Subjects:
Computational Biology - methods
Humans
Neurosciences - methods
Psychiatry - methods
Schizophrenia
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Summary:Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and complex datasets and to promote the development and refinement of formal models for features of this disorder. This presentation introduces the reader to the notion of computational psychiatry and describes discovery-oriented and theory-driven applications to schizophrenia involving machine learning, reinforcement learning theory, and biophysically-informed neural circuit models.
ISSN:0586-7614
1745-1701
DOI:10.1093/schbul/sbx025