Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Filters applied. Clear all
Multicenter Study
. 2018 Sep 1;75(9):960-968.
doi: 10.1001/jamapsychiatry.2018.1543.

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

Affiliations
Free PMC article
Multicenter Study

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

Yoonho Chung et al. JAMA Psychiatry. .
Free PMC article

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.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Cannon reported serving as a consultant to Boehringer Ingelheim Pharmaceuticals and Lundbeck A/S and is a coinventor (with the other North American Prodrome Longitudinal Study [NAPLS] investigators) on a pending patent of a blood-based predictive biomarker for psychosis. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Model of Neurodevelopmental Trajectories of Cortical Synaptic Density in Association With Onset of Psychosis
Possible paths to schizophrenia, with the gradation of colors from yellow to orange representing the increasing severity of psychotic symptoms.,,,
Figure 2.
Figure 2.. Brain Age Model Building, Validation, and Application
A, Cross-validation results within the Pediatric Imaging, Neurocognition, and Genetics (PING) study sample with bias adjustment. Estimated brain age is plotted as a function of observed chronological age at baseline magnetic resonance imaging scan. The solid diagonal line indicates linear fit, and the dashed diagonal lines indicate the 95% prediction interval. Colors correspond to different scanners (R2 = 0.84; mean absolute error [MAE] = 1.69 years; mean error = –0.001 year). B, Optimized brain age model trained with the PING sample was applied without modification to the healthy control (HC), clinically high-risk with no conversion to psychosis (CHR-NC), and clinically high-risk with conversion to psychosis (CHR-C) individuals in the North American Prodrome Longitudinal Study 2 (NAPLS 2). A significant CHR group by chronological age interaction was observed (P = .047). Colors correspond to different groups, and the dashed line indicates where predicted and chronological age perfectly meet (R2 = 0.51; MAE = 1.41 years; mean error = –0.06 year). C, Bar plots of mean (SE) brain age gap by diagnostic groups among younger adolescents (<17 years of age). Both CHR-C individuals and patients with a first episode of psychosis (FE) showed an increased brain age gap compared with HCs (CHR-C, P = .02; FE = 0.02). The dashed line indicates MAE for brain age model performance in NAPLS 2 HCs. D, Bar plots of mean (SE) brain age gap by diagnostic groups among older adolescents and young adults (17-21 years of age). There were no significant differences of brain age gap by group (P > .10). The dashed line indicates MAE for brain age model performance in NAPLS 2 HCs. All error bars indicate SE. aP < .05, corrected.
Figure 3.
Figure 3.. Course of Functioning From Baseline to 12-Month Follow-up by Brain Age Gap
Global Assessment of Functioning Scale (GAF) scores at baseline and 12-month follow-up among clinically high-risk individuals according to whether their brain age gap scores were in the expected range vs underestimated or overestimated using the mean absolute error of the brain age model as a threshold (underestimated: brain age gap < −1.5; expected: −1.5 ≤ brain age gap ≤1.5 years; and overestimated: 1.5 < brain age gap). Mean GAF scores improved from baseline to follow-up when the brain age gap was within the mean absolute error range (P < .001) but did not improve for individuals with an underestimated or overestimated brain age gap. Clinically high-risk individuals who converted to psychosis were disproportionately represented among those with an overestimated brain age gap (underestimated, n = 0; expected, n = 8; overestimated, n = 9), and a similar pattern was observed even when converters were excluded (eFigure 6 in the Supplement). aP < .001, corrected.
Figure 4.
Figure 4.. Annualized Rate of Percentage Change
A, Annualized rate of percentage change in the right superior frontal thickness, plotted as a function of age at baseline. B, Annualized rate of percentage change in third ventricle volume, plotted as a function of age at baseline. Locally weighted smoothing curve with 95% CI (colored area) is shown for each diagnostic group. With the use of a generalized linear model, a significant group (clinically high risk with conversion to psychosis [CHR-C] vs healthy control [HC]) by chronological age interaction was observed (right superior frontal thickness: P = .03; third ventricle: P = .001). Outliers defined by studentized deleted residuals exceeding ±3 were excluded (2 HCs, 4 individuals at clinically high risk with no conversion to psychosis [CHR-NC], and 2 individuals at CHR-C). Further details about demographics, image processing steps, and principal findings are reported in prior publications.,,

Similar articles

See all similar articles

Cited by 6 articles

See all "Cited by" articles

Publication types

MeSH terms

Feedback