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

Tian Hong Zhang; Li Hua Xu; Yan Yan Wei; HuiRu Cui; Xiao Chen Tang; Ye Gang Hu; Hai Chun Liu; Zi Xuan Wang; Tao Chen; Zheng Hui Yi; Chun Bo Li; Ji Jun Wang

doi:10.1016/j.bpsc.2024.09.002

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

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Sep 13:S2451-9022(24)00270-2. doi: 10.1016/j.bpsc.2024.09.002. Online ahead of print.

Authors

Tian Hong Zhang¹, Li Hua Xu², Yan Yan Wei², HuiRu Cui², Xiao Chen Tang², Ye Gang Hu², Hai Chun Liu³, Zi Xuan Wang⁴, Tao Chen⁵, Zheng Hui Yi², Chun Bo Li², Ji Jun Wang⁶

Affiliations

¹ Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China. Electronic address: zhang_tianhong@126.com.
² Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
³ Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.
⁴ Shanghai Xinlianxin Psychological Counseling Center, Shanghai, PR China.
⁵ Big Data Research Lab, University of Waterloo, Ontario, Canada; Labor and Worklife Program, Harvard University, Cambridge, Massachusetts, United States.
⁶ Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China; Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China.

PMID: 39278622
DOI: 10.1016/j.bpsc.2024.09.002

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.

Keywords: Negative symptoms; Neurocognition; Subtype; Transition; Ultra high risk.