Open AccessUse of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk
Author(s) -
Yoonho Chung,
Jean Addington,
Carrie E. Bearden,
Kristin S. Cadenhead,
Barbara A. Cornblatt,
Daniel H. Mathalon,
Thomas H. McGlashan,
Diana O. Perkins,
Larry J. Seidman,
Ming T. Tsuang,
Elaine F. Walker,
Scott W. Woods,
Sarah McEwen,
Theo G.M. van Erp,
Tyrone D. Can
Publication year - 2018
Publication title -
jama psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.531
H-Index - 365
eISSN - 2168-6238
pISSN - 2168-622X
DOI - 10.1001/jamapsychiatry.2018.1543
Subject(s) - prodrome , psychosis , neurocognitive , schizophrenia (object oriented programming) , neuroimaging , psychology , young adult , longitudinal study , medicine , pediatrics , psychiatry , cognition , developmental psychology , pathology
Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear.
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