[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 18.209.104.7. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Original Investigation
September 2018

Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk

Author Affiliations
  • 1Department of Psychology, Yale University, New Haven, Connecticut
  • 2Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
  • 3Semel Institute for Neuroscience and Human Behavior, Department of Psychology, University of California, Los Angeles
  • 4Department of Psychiatry, University of California, San Diego, La Jolla
  • 5Department of Psychiatry, Zucker Hillside Hospital, Queens, New York
  • 6Department of Psychiatry, University of California, San Francisco
  • 7Department of Psychiatry, Yale University, New Haven, Connecticut
  • 8Department of Psychiatry, University of North Carolina, Chapel Hill
  • 9Renaissance Computing Institute, University of North Carolina, Chapel Hill
  • 10Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
  • 11Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston
  • 12Department of Psychology, Emory University, Atlanta, Georgia
  • 13Department of Psychiatry and Human Behavior, University of California, Irvine
JAMA Psychiatry. 2018;75(9):960-968. doi:10.1001/jamapsychiatry.2018.1543
Key Points

Question  Are deviations from the normal neuroanatomical maturation pattern associated with future psychosis among clinically high-risk individuals?

Findings  In this longitudinal multicenter study, clinically high-risk individuals between 12 and 17 years of age showed exaggerated deviation in neuroanatomical maturity at the time of baseline evaluation, which in turn was associated with a greater risk for developing psychosis and a pattern of stably poor functioning, while accelerated reduction in cortical thickness among clinically high-risk individuals who developed psychosis was found to apply only to those who were 18 years of age or older.

Meaning  Clinically high-risk individuals expressing prodromal symptoms of psychosis in early adolescence show contemporaneous signs of neuroanatomical vulnerability, which may be useful for estimating onset of psychosis.

Abstract

Importance  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.

Objective  To investigate distinct neuroanatomical markers that can differentiate aberrant neurodevelopmental trajectories among clinically high-risk (CHR) individuals.

Design, Setting, and Participants  In this prospective longitudinal multicenter study, a neuroanatomical-based age prediction model was developed using a supervised machine learning technique with T1-weighted magnetic resonance imaging scans of 953 healthy controls 3 to 21 years of age from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and then applied to scans of 275 CHR individuals (including 39 who developed psychosis) and 109 healthy controls 12 to 21 years of age from the North American Prodrome Longitudinal Study 2 (NAPLS 2) for external validation and clinical application. Scans from NAPLS 2 were collected from January 15, 2010, to April 30, 2012.

Main Outcomes and Measures  Discrepancy between neuroanatomical-based predicted age (hereafter referred to as brain age) and chronological age.

Results  The PING-derived model (460 females and 493 males; age range, 3-21 years) accurately estimated the chronological ages of the 109 healthy controls in the NAPLS 2 (43 females and 66 males; age range, 12-21 years), providing evidence of independent external validation. The 275 CHR individuals in the NAPLS 2 (111 females and 164 males; age range, 12-21 years) showed a significantly greater mean (SD) gap between model-predicted age and chronological age (0.64 [2.16] years) compared with healthy controls (P = .008). This outcome was significantly moderated by chronological age, with brain age systematically overestimating the ages of CHR individuals who developed psychosis at ages 12 to 17 years but not the brain ages of those aged 18 to 21 years. Greater brain age deviation was associated with a higher risk for developing psychosis (F = 3.70; P = .01) and a pattern of stably poor functioning over time, but only among younger CHR adolescents. Previously reported evidence of accelerated reduction in cortical thickness among CHR individuals who developed psychosis was found to apply only to those who were 18 years of age or older.

Conclusions and Relevance  These results are consistent with the view that neuroanatomical markers of schizophrenia may help to explain some of the heterogeneity of this disorder, particularly with respect to early vs later age of onset of psychosis, with younger and older individuals having differing intercepts and trajectories in structural brain parameters as a function of age. The results also suggest that baseline neuroanatomical measures are likely to be useful in estimating onset of psychosis, especially (or only) among CHR individuals with an earlier age of onset of prodromal symptoms.

×