Identifying individuals at clinical high risk for psychosis using a battery of tasks sensitive to symptom mechanisms.

The clinical high risk for psychosis (CHR-P) population is important for understanding disease progression and treatment; however, standard approaches to identifying CHR-P individuals are expensive and labor-intensive. Focusing on neurocognitive mechanisms that underlie individual psychosis symptoms (positive, negative, and disorganization) may improve screening and identification. The present study examines whether a behavioral task battery that assays symptom mechanisms can identify CHR-P individuals and predict risk severity. Participants (N = 621) were recruited from clinics and the community as part of the Computerized Assessment of Psychosis Risk (CAPR) consortium study. Structured clinical interviews, a dimensional risk calculator, and behavioral tasks were administered. Clinical interviews identified the following groups: (a) CHR-P (n = 273), (b) non-CHR-P individuals with limited psychosis like experiences (PLEs; n = 120), (c) participants with mental disorders and no PLEs (CLN; n = 82), and (d) healthy controls (HC; n = 146). Multinomial logistic regression indicated that the task battery differentiated groups (p < 0.001), with utility for identifying CHR-P individuals (Sensitivity = 0.87, PPV = 0.51, NPV = 0.77), though with high false positives that varied based on comparison group (Specificity = 0.21-0.43). Tasks also predicted psychosis risk calculator scores (Adjusted R² = 0.12), with the two unique predictors being positive symptom task variables associated with updating beliefs regarding environmental volatility. Overall, symptom mechanism tasks differentiated CHR-P individuals from control groups, suggesting their potential as novel screening tools. Using tasks to more efficiently identify CHR-P individuals (e.g., enrich samples), may lower barriers and identify individuals that may otherwise be missed.