Structural covariance network topology in individuals at clinical high risk for psychosis: the ENIGMA-CHR Study

Brain network architecture is anticipated to influence future grey matter loss in individuals at Clinical High Risk (CHR) for psychosis. However, existing studies on grey matter structural network properties in CHR are scarce and constrained by small sample sizes. Here, we examined network topology differences comparing a) CHR versus healthy controls (HC); b) CHR who transitioned to psychosis (CHR-T) versus those who did not (CHR-NT); and c) different subsyndromes. We included structural scans from 1842 CHR individuals and 1417 HC individuals from 31 sites within the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. At the global level, CHR individuals exhibited lower structural covariance (q < 0.001; Cohen’s d = 0.164) and less optimal structural network configuration than HC (lower global efficiency and clustering coefficient, d = 0.100,0.087, qs <= 0.027). Though no global difference between CHR-T and CHR-NT, network distinctiveness of the frontal and temporal surface area networks was higher in CHR-T than CHR-NT (d = 0.223,0.237) and HC (d = 0.208,0.219) (qs < 0.001). Network distinctiveness of the frontal cortical thickness network was lower in CHR-T (d = 0.218, q < 0.001) than CHR-NT and HC (d = 0.165, q < 0.001). Importantly, higher network distinctiveness was associated with worse positive symptoms in CHR-NT (frontal surface area, q = 0.008, R² = 0.013) and at trend with worse negative symptoms in CHR-T (frontal thickness, q = 0.063, R² = 0.049). Further, the brief intermittent psychotic syndrome subgroup showed more severe network alterations. Together, brain structural networks inform symptoms and the risk of transition to psychosis in CHR individuals.