Literature DB >> 28338845

Computational Psychiatry and the Challenge of Schizophrenia.

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


  13 in total

Review 1.  Toward a neurobiology of delusions.

Authors:  P R Corlett; J R Taylor; X-J Wang; P C Fletcher; J H Krystal
Journal:  Prog Neurobiol       Date:  2010-06-15       Impact factor: 11.685

2.  Research agenda. Promoting convergence in biomedical science.

Authors:  Phillip A Sharp; Robert Langer
Journal:  Science       Date:  2011-07-29       Impact factor: 47.728

Review 3.  Computational neuroscience.

Authors:  T J Sejnowski; C Koch; P S Churchland
Journal:  Science       Date:  1988-09-09       Impact factor: 47.728

Review 4.  Computational psychiatry as a bridge from neuroscience to clinical applications.

Authors:  Quentin J M Huys; Tiago V Maia; Michael J Frank
Journal:  Nat Neurosci       Date:  2016-03       Impact factor: 24.884

Review 5.  Computational approaches to schizophrenia: A perspective on negative symptoms.

Authors:  Lorenz Deserno; Andreas Heinz; Florian Schlagenhauf
Journal:  Schizophr Res       Date:  2016-12-13       Impact factor: 4.939

6.  Probabilistic Reinforcement Learning in Patients With Schizophrenia: Relationships to Anhedonia and Avolition.

Authors:  Erin C Dowd; Michael J Frank; Anne Collins; James M Gold; Deanna M Barch
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2016-09

7.  Computational psychiatry.

Authors:  P Read Montague; Raymond J Dolan; Karl J Friston; Peter Dayan
Journal:  Trends Cogn Sci       Date:  2011-12-14       Impact factor: 20.229

Review 8.  Linking microcircuit dysfunction to cognitive impairment: effects of disinhibition associated with schizophrenia in a cortical working memory model.

Authors:  John D Murray; Alan Anticevic; Mark Gancsos; Megan Ichinose; Philip R Corlett; John H Krystal; Xiao-Jing Wang
Journal:  Cereb Cortex       Date:  2012-11-29       Impact factor: 5.357

9.  Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Authors:  Adam Mourad Chekroud; Ryan Joseph Zotti; Zarrar Shehzad; Ralitza Gueorguieva; Marcia K Johnson; Madhukar H Trivedi; Tyrone D Cannon; John Harrison Krystal; Philip Robert Corlett
Journal:  Lancet Psychiatry       Date:  2016-01-21       Impact factor: 27.083

10.  Preliminary evidence of attenuation of the disruptive effects of the NMDA glutamate receptor antagonist, ketamine, on working memory by pretreatment with the group II metabotropic glutamate receptor agonist, LY354740, in healthy human subjects.

Authors:  John H Krystal; Walid Abi-Saab; Edward Perry; D Cyril D'Souza; Nianjin Liu; Ralitza Gueorguieva; Lisa McDougall; Tracy Hunsberger; Aysenil Belger; Louise Levine; Alan Breier
Journal:  Psychopharmacology (Berl)       Date:  2004-08-10       Impact factor: 4.530

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  15 in total

1.  Will Machine Learning Enable Us to Finally Cut the Gordian Knot of Schizophrenia.

Authors:  Neeraj Tandon; Rajiv Tandon
Journal:  Schizophr Bull       Date:  2018-08-20       Impact factor: 9.306

2.  Development and testing of a web-based battery to remotely assess cognitive health in individuals with schizophrenia.

Authors:  Bruno Biagianti; Melissa Fisher; Benjamin Brandrett; Danielle Schlosser; Rachel Loewy; Mor Nahum; Sophia Vinogradov
Journal:  Schizophr Res       Date:  2019-02-04       Impact factor: 4.939

3.  Editorial Board Changes for 2018.

Authors:  William T Carpenter; Paul D Shepard; Laura M Rowland; Janet L Smith
Journal:  Schizophr Bull       Date:  2018-01-13       Impact factor: 9.306

4.  Computational Psychiatry: Embracing Uncertainty and Focusing on Individuals, Not Averages.

Authors:  Adam M Chekroud; Chadrick E Lane; David A Ross
Journal:  Biol Psychiatry       Date:  2017-09-15       Impact factor: 13.382

5.  Nonsocial and social cognition in schizophrenia: current evidence and future directions.

Authors:  Michael F Green; William P Horan; Junghee Lee
Journal:  World Psychiatry       Date:  2019-06       Impact factor: 49.548

6.  Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Authors:  Matthew Bracher-Smith; Karen Crawford; Valentina Escott-Price
Journal:  Mol Psychiatry       Date:  2020-06-26       Impact factor: 15.992

7.  Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

Authors:  Robert Suchting; Joshua L Gowin; Charles E Green; Consuelo Walss-Bass; Scott D Lane
Journal:  Front Behav Neurosci       Date:  2018-05-07       Impact factor: 3.558

8.  Differential Valuation and Learning From Social and Nonsocial Cues in Borderline Personality Disorder.

Authors:  Sarah K Fineberg; Jacob Leavitt; Dylan S Stahl; Sharif Kronemer; Christopher D Landry; Aaron Alexander-Bloch; Laurence T Hunt; Philip R Corlett
Journal:  Biol Psychiatry       Date:  2018-06-05       Impact factor: 13.382

Review 9.  What is mood? A computational perspective.

Authors:  James E Clark; Stuart Watson; Karl J Friston
Journal:  Psychol Med       Date:  2018-02-26       Impact factor: 7.723

10.  Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.

Authors:  Paolo Fusar-Poli; Dominic Stringer; Alice M S Durieux; Grazia Rutigliano; Ilaria Bonoldi; Andrea De Micheli; Daniel Stahl
Journal:  Transl Psychiatry       Date:  2019-10-17       Impact factor: 6.222

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