Computational psychiatry and the challenge of schizophrenia

John H. Krystal, John D. Murray, Adam M. Chekroud, Philip R. Corlett, Genevieve Yang, Xiao Jing Wang, Alan Anticevic

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)473-475
Number of pages3
JournalSchizophrenia bulletin
Volume43
Issue number3
DOIs
StatePublished - 2017

Keywords

  • Computational neuroscience
  • Computational psychiatry
  • Delusions
  • Machine learning
  • Medication selection
  • Schizophrenia
  • Working memory

ASJC Scopus subject areas

  • Psychiatry and Mental health

Fingerprint Dive into the research topics of 'Computational psychiatry and the challenge of schizophrenia'. Together they form a unique fingerprint.

Cite this