RESUMEN
BACKGROUND: Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning. AIMS: We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample. METHOD: Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD). RESULTS: Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD. CONCLUSIONS: Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.
RESUMEN
BACKGROUND: Jumping-to-conclusions (JTC) is a prominent reasoning bias in schizophrenia (SCZ). While it has been linked to not only psychopathological abnormalities (delusions and impulsive decision-making) but also unstable belief formation, its origin remains unclear. We here directly test to which extend JTC is associated with delusional ideation, impulsive decision-making, and unstable belief formation. METHODS: In total, 45 SCZ patients were compared with matched samples of 45 patients with major depressive disorder (MDD) and 45 healthy controls (HC) as delusions and JTC also occur in other mental disorders and the general population. Participants performed a probabilistic beads task. To test the association of JTC with measures of delusions (Positive and Negative Syndrome Scale [PANSS]positive, PANSSpositive-factor, and Peter Delusions Inventory [PDI]), Bayesian linear regressions were computed. For the link between JTC and impulsive decision-making and unstable beliefs, we conducted between-group comparisons of "draws to decision" (DTD), "decision times" (DT), and "disconfirmatory evidence scores" (DES). RESULTS: Bayesian regression obtained no robust relationship between PDI and DTD (all |R2adj| ≤ .057, all P ≥ .022, all Bayes Factors [BF01] ≤ 0.046; αâadj = .00833). Compared with MDD and HC, patients with SCZ needed more time to decide (significantly higher DT in ambiguous trials: all P ≤ .005, r2 ≥ .216; numerically higher DT in other trials). Further, SCZ had unstable beliefs about the correct source jar whenever unexpected changes in bead sequences (disconfirmatory evidence) occurred (compared with MDD: all P ≤ .004 and all r2 ≥ .232; compared with HC: numerically higher DES). No significant correlation was observed between DT and DTD (all P ≥ .050). CONCLUSIONS: Our findings point toward a relationship of JTC with unstable belief formation and do not support the assumption that JTC is associated with impulsive decision-making.
