Brain network characteristics separating individuals at clinical high risk for psychosis into normality or psychosis

Abstract

We aimed to separate individuals at clinical high risk for psychosis (CHR) state into subgroups according to neurobiological characteristics using structural and functional network constructs and examine their clinical characteristics. Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in 61 healthy controls (HC), 57 individuals at CHR and 29 patients with schizophrenia (SZ). The main outcome was a likelihood ratio calculated from measures of structural and functional network efficiencies, coupling strength of structural and functional networks, and a disease-specific data analysis, resulting in the most probable classification of CHR into HC or SZ. The likelihood ratios revealed that 33 individuals at CHR were likely similar to HC (CHR-HC), and the remaining 24 CHR individuals were similar to SZ (CHR-SZ). The CHR subgroups were comparable to each other in demographic characteristics and clinical symptoms. However, the verbal and executive functions of CHR-HC were similar to those of HC, and those of CHR-SZ similar to SZ. Additionally, CHR-SZ was more responsive to treatment than CHR-HC during the follow-up period. By combining structural and functional data, we could detect the vulnerable population and provide an active intervention in the early phase of the CHR state.