Cheng Peng, Liu Zhening, Wang Feiwen, Yang Jun, Yang Jie
Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.
Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.
Asian J Psychiatr. 2025 May;107:104454. doi: 10.1016/j.ajp.2025.104454. Epub 2025 Mar 22.
First-episode schizophrenia represents a critical period for intervention in the treatment of schizophrenia. Understanding the intricate relationships between psychiatric symptoms and cognitive functions is vital for early precise intervention and predicting illness outcomes. Previous research has largely overlooked this issue, and traditional analytical methods based on pre-established theoretical assumptions are insufficient. This study aims to address this gap by utilizing graph theory-based network analysis.
The study employed the Positive and Negative Syndrome Scale (PANSS) to assess psychiatric symptoms. Cognitive functions were evaluated using the Digit Symbol and Information subtests from the Wechsler Adult Intelligence Scale (WAIS), which measure information processing efficiency and general knowledge, respectively. A network of psychiatric symptoms and cognitive functions was constructed based on these assessments.
The network analysis revealed that negative symptom nodes were central. Notably, node N1 (Blunted affect) showed a negative correlation with the Digit Symbol node, being the only psychiatric symptom node linked to cognitive functions. Community detection analysis indicated that cognitive, positive symptom, and negative symptom nodes tended to cluster within their respective categories, while general psychopathology nodes showed a tendency to cluster with various types of nodes. Some general psychopathology nodes were isolated, reflecting the concealed nature of certain psychiatric symptoms in first-episode schizophrenia patients.
This study innovatively applies network analysis to explore the characteristics of the psychiatric symptom-cognitive function network in Chinese patients with first-episode schizophrenia. The findings provide valuable theoretical insights for targeted symptom-based interventions and for predicting disease outcomes in first-episode schizophrenia.
首发精神分裂症是精神分裂症治疗干预的关键时期。了解精神症状与认知功能之间的复杂关系对于早期精准干预和预测疾病转归至关重要。以往研究很大程度上忽视了这一问题，基于预先设定理论假设的传统分析方法也不够充分。本研究旨在通过基于图论的网络分析来填补这一空白。
本研究采用阳性和阴性症状量表（PANSS）评估精神症状。认知功能通过韦氏成人智力量表（WAIS）中的数字符号和知识分测验进行评估，分别测量信息处理效率和常识。基于这些评估构建了精神症状与认知功能的网络。
网络分析显示阴性症状节点处于中心位置。值得注意的是，节点N1（情感迟钝）与数字符号节点呈负相关，是唯一与认知功能相关的精神症状节点。社区检测分析表明，认知、阳性症状和阴性症状节点倾向于在各自类别内聚类，而一般精神病理节点则倾向于与各类节点聚类。一些一般精神病理节点是孤立的，反映了首发精神分裂症患者某些精神症状的隐蔽性。
本研究创新性地应用网络分析来探索中国首发精神分裂症患者精神症状-认知功能网络的特征。研究结果为基于症状的靶向干预和首发精神分裂症疾病转归的预测提供了有价值的理论见解。
