Brain Age Differences From Chronological Age Can Predict Early-Onset Psychosis in Adolescents
This longitudinal, multicenter study examined magnetic resonance imaging scans to assess cortical thickness and grey matter to determine brain age.
In younger, clinically high-risk adolescents, the difference between chronological age and neuroanatomical age of the brain can be used to estimate onset of psychosis, according to a study published in the JAMA Psychiatry.
This longitudinal, multicenter study examined magnetic resonance imaging scans to assess cortical thickness and grey matter to determine brain age. The adolescent's brain age gap was calculated by using their brain age minus their chronological age. The Pediatric Imaging, Neurocognition, and Genetics cohort consisted of typical developing adolescences (n=953) and were used to create the typical brain age model. This model was validated using healthy adolescent controls from the North American Prodrome Longitudinal Study 2 (n=109), and then used to estimate the brain age of clinically high-risk adolescents (n=275).
There was a significant increase in the brain age gap in the clinically high-risk adolescents group, with the largest overestimate in the younger adolescents (≤17 years old). The younger clinically high-risk adolescents who developed psychosis (n=39) and the younger clinically high-risk adolescents who did not (n=236) had a brain age gap significantly greater than 0. Younger adolescents had a significant relationship between brain age gap and severity of psychotic illness. Older brain age was associated with thinning cortical gray matter, and neuroanatomical thinning is associated with early-onset psychosis.
In conclusion, this study created a validated model for determining brain age and brain age gap, which then can be used to predict early-onset psychosis in younger clinically high-risk adolescents who exhibit prodromal symptoms. A large brain age gap “is associated with a higher risk for conversion to psychosis and a pattern of stably poor functioning.”
Chung Y, Addington J, Bearden CE, et al; for the North American Prodrome Longitudinal Study (NAPLS) Consortium and the Pediatric Imaging, Neurocognition, and Genetics (PING) Study Consortium. Use of machine learning to determine deviance in neuroanatomical maturity associated with future psychosis in youths at clinically high risk [published online July 3, 2018]. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2018.1543