Yuan Liu, Ma Xiaoqian, Li David, Ouyang Lijun, Fan Lejia, Li Chunwang, He Ying, Chen Xiaogang
Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China.
Schizophrenia (Heidelb). 2022 Nov 4;8(1):91. doi: 10.1038/s41537-022-00305-0.
It is widely accepted that there are some common network patterns in the human brain. However, the existence of stable and strong functional connections in the human brain and whether they change in schizophrenia is still a question. By setting 1% connections with the smallest coefficient of variation, we found a widespread brain functional network (frame network) in healthy people(n = 380, two datasets from public databases). We then explored the alterations in a medicated group (60 subjects with schizophrenia vs 71 matched controls) and a drug-naive first-episode group (68 subjects with schizophrenia vs 45 matched controls). A linear support vector classifier (SVC) was constructed to distinguish patients and controls using the medicated patients' frame network. We found most frame connections of healthy people had high strength, which were symmetrical and connected the left and right hemispheres. Conversely, significant differences in frame connections were observed in both patient groups, which were positively correlated with negative symptoms (mainly language dysfunction). Additionally, patients' frame network were more left-lateralized, concentrating on the left frontal lobe, and was quite accurate at distinguishing medicated patients from controls (classifier accuracy was 78.63%, sensitivity was 86.67%, specificity was 76.06%, and the area under the curve (AUC) was 0.83). Furthermore, the results were repeated in the drug-naive set (accuracy was 84.96%, sensitivity was 85.29%, specificity was 88.89%, and AUC was 0.93). These findings indicate that the abnormal pattern of frame network in subjects with schizophrenia might provide new insights into the dysconnectivity in schizophrenia.
人们普遍认为人类大脑中存在一些常见的网络模式。然而,人类大脑中稳定且强大的功能连接是否存在以及它们在精神分裂症中是否发生变化仍是一个问题。通过设定变异系数最小的1%连接,我们在健康人群(n = 380,来自公共数据库的两个数据集)中发现了一个广泛的脑功能网络(框架网络)。然后,我们在一个药物治疗组(60例精神分裂症患者与71例匹配对照)和一个未用药的首发组(68例精神分裂症患者与45例匹配对照)中探索了变化情况。构建了一个线性支持向量分类器(SVC),使用药物治疗患者的框架网络来区分患者和对照。我们发现健康人的大多数框架连接具有高强度,它们是对称的且连接左右半球。相反,在两个患者组中均观察到框架连接存在显著差异,这些差异与阴性症状(主要是语言功能障碍)呈正相关。此外,患者的框架网络更偏向左侧化,集中在左额叶,并且在区分药物治疗患者与对照方面相当准确(分类器准确率为78.63%,敏感性为86.67%,特异性为76.06%,曲线下面积(AUC)为0.83)。此外,在未用药组中重复了这些结果(准确率为84.96%,敏感性为85.29%,特异性为88.89%,AUC为0.93)。这些发现表明,精神分裂症患者框架网络的异常模式可能为精神分裂症中的连接障碍提供新的见解。