| Literature DB >> 28338845 |
John H Krystal1,2,3,4, John D Murray1, Adam M Chekroud5,6, Philip R Corlett1,5, Genevieve Yang1, Xiao-Jing Wang7, Alan Anticevic1.
Abstract
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. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center 2017.Entities:
Keywords: computational neuroscience; computational psychiatry; delusions; machine learning; medication selection; schizophrenia; working memory
Mesh:
Year: 2017 PMID: 28338845 PMCID: PMC5464204 DOI: 10.1093/schbul/sbx025
Source DB: PubMed Journal: Schizophr Bull ISSN: 0586-7614 Impact factor: 9.306