Antipsychotic (AP) medication in individuals at clinical high risk for psychosis (CHR-P) is not routinely recommended by clinical guidelines but is commonly prescribed. Since little is known about the predictors of AP inception in CHR-P, we analyzed data from two observational cohorts.

To avoid baseline predictors being confounded by previous treatment, participants were selected for analysis from the 764 participants at CHR-P enrolled in NAPLS-2 and the 710 enrolled in NAPLS-3 by excluding those with lifetime histories of AP use. Baseline clinical variables available in both studies were employed as predictors of subsequent AP inception over the next 6 months in univariable and multivariable analyses.

Preliminary analyses indicated no important effects of sample. The final combined population included 79 AP inception participants and 580 participants who did not have AP inception. The AP medications most commonly prescribed were risperidone, aripiprazole, and quetiapine. Univariable analyses identified seven significant predictors of AP inception. The final logistic regression model including these variables was highly significant (χ2 = 36.53, df = 7, p = <0.001). Three variables (current major depression, fewer education years, and current benzodiazepine use) emerged as significant independent predictors of AP inception.

This study is the first to determine baseline characteristics that predict subsequent AP initiation in CHR-P. Some AP use in CHR-P appears to be intended as augmentation of antidepressant treatment for comorbid major depression. Some prescribers may not have detected the attenuated positive symptoms characteristic of CHR-P since their severity did not significantly predict AP inception.

Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.