Journal
SCHIZOPHRENIA BULLETIN
Volume 43, Issue 3, Pages 473-475Publisher
OXFORD UNIV PRESS
DOI: 10.1093/schbul/sbx025
Keywords
computational psychiatry; computational neuroscience; schizophrenia; delusions; medication selection; working memory; machine learning
Categories
Funding
- National Center for Advancing Translational Science [1UH2TR000960-01]
- National Institute on Alcohol Abuse and Alcoholism [P50AA12870]
- Yale Center for Clinical Investigation grant [UL1 RR024139]
- Department of Veterans Affairs
- National Institutes of Health [DP50D012109-02]
- National Alliance for Research on Schizophrenia and Depression Independent Investigator award
- Blackthorn Therapeutics
- NIMH [R01-MH062349, F30 MH107149]
- NIH [T32GM 007205]
- IMHRO/Janssen Rising Star Translational Research Award [NIMH-R01MH067073]
- CTSA from the National Center for Advancing Translational Science (NCATS), components of the National Institutes of Health (NIH) [UL1 TR000142]
- NIH roadmap for Medical Research
- [R01 MH108590]
Ask authors/readers for more resources
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.
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