Zhang TianHong, Xu LiHua, Wei YanYan, Cui HuiRu, Tang XiaoChen, Hu YeGang, Liu HaiChun, Wang ZiXuan, Chen Tao, Yi ZhengHui, Li ChunBo, Wang JiJun
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, China.
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, China.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Jun;10(6):646-655. doi: 10.1016/j.bpsc.2024.09.002. Epub 2024 Sep 13.
Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis.
A comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of 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, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis.
Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test-Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters.
These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.
了解症状维度、症状群与认知障碍之间的复杂关系对于早期发现及干预临床高危精神病个体至关重要。本研究深入探讨临床高危样本中的这种复杂相互作用，旨在预测向精神病的转化。
对744名临床高危个体进行了全面的认知评估。该研究包括一个3年的随访期，以便评估向精神病的转化情况。使用前驱综合征结构化访谈确定症状概况。通过应用因子分析，症状维度被分类为显性阴性症状（NS）、阳性症状-应激性和阳性症状-怪异型。因子得分用于定义3个主要症状组。采用潜在类别分析（LCA）和因子混合模型（FMM）根据症状模式识别离散症状群。LCA和FMM分析选择3类解决方案。
显性NS组个体向精神病的转化率显著高于其他组。特定的认知变量，包括修订版简短视觉空间记忆测试的表现（优势比=0.702，p=0.001）和神经心理评估电池迷宫测试（优势比=0.776，p=0.024），显著预测向精神病的转化。值得注意的是，与NS组和阳性症状-应激性组相关的认知障碍影响不同的认知领域。以严重NS和阳性症状-怪异型为特征的LCA和FMM第1组在认知领域的损伤比其他组更多。LCA和FMM各症状群之间的转化率未观察到显著差异。
这些发现突出了NS在精神病发展中的重要性，并提示了受症状维度影响的特定认知领域。
