Connectome-based predictive modeling of early and chronic psychosis symptoms.

Abstract

Previous research indicates that early (EP) and chronic (CP) psychosis share brain correlates and symptoms. However, notable clinical differences, such as treatment responses and symptom severity, exist, suggesting the need for further investigation. For example, the brain networks underlying EP and CP symptoms may be distinct, driven by factors like symptom severity and disease-related burden. Differences, if any, in these brain networks are largely unknown because EP and CP have predominantly been studied, characterized, and compared to control populations independently. This study's objective was to directly compare the neural correlates of CP (n = 123) and EP (n = 107) symptoms using connectome-based predictive modeling (CPM) and resting-state functional magnetic resonance imaging. We predicted both samples' positive and negative symptoms from the Positive and Negative Syndrome Scale (PANSS). Prediction effect sizes were higher in CP, and prediction of general psychopathology and total symptoms was only possible in CP. Virtual lesioning analyses revealed the frontoparietal network as a critical component of EP and CP symptom networks. Predictive models were broadly similar between EP and CP. We also generalized the EP positive score model to CP positive symptoms and identified group differences between CP and matched HCs more robustly than EP. Overall, broadly similar networks were found in CP and EP, but larger effects were observed in CP. Our findings provide a foundation for longitudinal studies to track connectivity changes in symptom networks throughout the psychosis lifespan. Similar stage-comparative approaches can enhance understanding of the etiology of early and chronic psychosis symptoms for therapeutic applications.