Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis.

Abstract

Objective: Understanding the intricate relationship between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high-risk(CHR) for psychosis. This study delves into this complex interplay within a CHR sample and aims to predict the conversion to psychosis.

Methods: A comprehensive cognitive assessment was performed among 744 CHR individuals. The study included a three-year follow-up period to assess conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms(NS), positive symptoms-stressful(PS-S), and positive symptoms-odd(PS-O). The factor scores were used to define three dominant symptom groups. Latent class analysis(LCA) and factor mixture model(FMM) were employed to identify discrete clusters based on symptom patterns. The three-class solution was chosen for the LCA and FMM analysis.

Results: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance in the Brief Visuospatial Memory Test-Revised(Odd ratio, OR=0.702, p=0.001) and Neuropsychological Assessment Battery mazes(OR=0.776, p=0.024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and PS-S affected different cognitive domains. LCA- and FMM-Cluster 1, characterized by severe NS and PS-O, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among LCA and FMM clusters.

Conclusions: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.