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Gyrification network measures may predict later psychosis

Patients who later develop psychosis exhibited disorganized gyrification network properties, indicating that constructing gyrification-based networks may help predict future psychosis, even among those at high risk, according to study findings published in JAMA Psychiatry.

“Predicting psychosis onset in individuals at clinical high risk for psychosis is essential to administer preventive interventions,” Tushar Das, PhD, department of psychiatry, University of Western Ontario, Canada, and colleagues wrote. “However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical assessment. Therefore, research is striving for reliable brain markers to improve the prediction of psychosis onset in these individuals.”

Researchers examined the irregularities in graph-based gyrification network measures in the early stages of psychosis and tested the accuracy of this approach to predict transition to psychosis among high-risk Swiss individuals in a cross-sectional MRI study with follow-up.

Participants were sorted into one of four study groups: healthy controls (n = 44), at-risk mental state individuals without later transition to psychosis (n =63), at-risk mental state individuals with later transition to psychosis (n = 16) and patients with first-episode psychosis who did not take antipsychotics (n = 38). To quantify global integration, segregation and small-worldness, the investigators constructed gyrification-based structural covariance networks known as connectomes, then examined group differences in network properties using functional data analysis across multiple network densities.

Analysis showed small-worldness was reduced among at-risk patients with later transition to psychosis and among patients with first-episode psychosis who did not take antipsychotics compared with the healthy controls and at-risk patients who did not transition to psychosis. The difference between those who later developed psychosis and those who did not showed large effect sizes. Furthermore, small-worldness was linked to reduced integration and increased segregation among at-risk patients with later transition to psychosis and patients with first-episode psychosis compared with the other groups.

When Das and colleagues used the connectome properties as features, they obtained a good classification performance. Gyrification network measures predicted the future outcome of transition to psychosis with an accuracy of 90.49%; a balanced accuracy of 81.34%; a positive predictive value of 84.47%; and a negative predictive value of 92.18%. In addition, sensitivity was 66.11% and specificity was 96.58%.

“Gyrification connectomes can potentially be used to provide sample enrichment among [clinical high risk] individuals to promote prevention of psychosis,” Das and colleagues concluded. “We provide the first report to date that a transition to psychosis is associated with developmental disruptions in the morphogenesis of cortical folding. This observation makes perturbed neurodevelopment directly relevant to the neurobiology of psychosis onset in a sample with clinically defined [at-risk mental state].” – by Savannah Demko

Disclosures: Das reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.