Cheng Peng, Liu Zhening, Wang Feiwen, Yang Jun, Sun Fuping, Fan Zebin, Yang Jie, Palaniyappan Lena
Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, PR China.
Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
Int J Clin Health Psychol. 2025 Apr-Jun;25(2):100577. doi: 10.1016/j.ijchp.2025.100577. Epub 2025 May 2.
A notable deficit in working memory (WM) is well established in schizophrenia. Nevertheless, the intricate relationship between various symptoms and WM impairment is still not fully understood. We use three distinct methodologies-symptom network analysis (SNA), Connectome-Based Predictive Modeling (CPM), and brain gene annotation enrichment analysis-to explore the connectome patterns that link WM deficits and symptoms, and their related gene expression.
255 patients with schizophrenia were recruited as two distinct samples. SNA was used to pinpoint the core psychiatric symptoms influenced by WM performance. CPM identified the subnetwork of the functional connectome that was recruited under the 2-back load of the N-back WM task, and predicted the severity of the SNA-based key symptoms. Gene annotation enrichment analysis explored the likely molecular biological processes underlying the symptom-predictive functional WM network.
SNA revealed that disorganized attention (G11 of PANSS) is most closely linked to WM performance in schizophrenia. The WM-based connectome significantly predicted disorganized attention ( = 0.278, = 0.001, permutation- = 0.046), and this model was validated in the second dataset ( = 0.274, = 0.014). The predictive network primarily involved the frontoparietal and frontolimbic networks. Gene enrichment analysis revealed a preferential role for cytoplasmic protein binding, indicating a potential molecular basis for the WM-related, symptom-predictive functional connectivity.
Impaired WM performance in schizophrenia relates to frontoparietal and frontolimbic connectivity and preferentially influences the severity of disorganized attention, a clinically observable phenomenon. The potential role of cytoplasmic protein binding in WM deficits and attentional disorganization in schizophrenia warrants further investigation.
精神分裂症患者工作记忆（WM）存在显著缺陷，这一点已得到充分证实。然而，各种症状与WM损害之间的复杂关系仍未完全明确。我们采用三种不同的方法——症状网络分析（SNA）、基于连接组的预测建模（CPM）和脑基因注释富集分析——来探究连接WM缺陷与症状及其相关基因表达的连接组模式。
招募255名精神分裂症患者作为两个不同的样本。SNA用于确定受WM表现影响的核心精神症状。CPM识别在N-back WM任务的2-back负荷下被激活的功能连接组子网，并预测基于SNA的关键症状的严重程度。基因注释富集分析探究了症状预测性功能性WM网络潜在的分子生物学过程。
SNA显示，在精神分裂症中，紊乱的注意力（PANSS的G11）与WM表现联系最为紧密。基于WM的连接组显著预测了紊乱的注意力（β = 0.278，p = 0.001，置换p = 0.046），并且该模型在第二个数据集中得到了验证（β = 0.274，p = 0.014）。预测网络主要涉及额顶叶和额边缘网络。基因富集分析揭示了细胞质蛋白结合的优先作用，这表明了与WM相关的症状预测性功能连接的潜在分子基础。
精神分裂症患者WM表现受损与额顶叶和额边缘连接有关，并优先影响紊乱注意力的严重程度，这是一种临床可观察到的现象。细胞质蛋白结合在精神分裂症患者WM缺陷和注意力紊乱中的潜在作用值得进一步研究。
