Format

Send to

Choose Destination
See comment in PubMed Commons below
Schizophr Bull. 2017 May 1;43(3):473-475. doi: 10.1093/schbul/sbx025.

Computational Psychiatry and the Challenge of Schizophrenia.

Author information

1
Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
2
Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
3
Department of Neuroscience, Yale-New Hospital, New Haven, CT, USA.
4
VA National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA.
5
Department of Psychology, Yale University, New Haven, CT, USA.
6
Department of Psychology, Spring Health, New York, NY, USA.
7
Center for Neural Science, NYU, New York, NY, USA.

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.

KEYWORDS:

computational neuroscience; computational psychiatry; delusions; machine learning; medication selection; schizophrenia; working memory

PMID:
28338845
PMCID:
PMC5464204
[Available on 2018-05-01]
DOI:
10.1093/schbul/sbx025
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems
    Loading ...
    Support Center