Kong Mingjun, Chen Tian, Gao Shuzhan, Ni Sulin, Ming Yidan, Chai Xintong, Ling Chenxi, Xu Xijia
Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China.
Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China.
Front Neurosci. 2022 Jul 25;16:921547. doi: 10.3389/fnins.2022.921547. eCollection 2022.
Schizophrenia is a severe mental disorder affecting around 0.5-1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients' age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.
精神分裂症是一种严重的精神障碍，影响着全球约0.5%-1%的人口。一些研究表明，精神分裂症患者的默认模式网络（DMN）存在功能断开。然而，研究结果仍存在差异。在本研究中，我们使用同质性网络（NH）比较了57例首次诊断未用药的精神分裂症患者与50例健康对照（HC）的DMN内在网络组织，并探讨了DMN与精神分裂症患者临床特征的关系。应用接受者操作特征（ROC）曲线分析和支持向量机（SVM）分析来计算区分精神分裂症患者和HC的准确性。我们的结果表明，与HC相比，患者在左侧额上内侧回（SMFG）、右侧小脑脚I/小脑脚II的NH值显著更高，而在右侧颞下回（ITG）和双侧后扣带回皮质（PCC）的NH值显著更低。此外，右侧小脑脚I/小脑脚II的异常NH值与一般精神病理学评分之间、左侧SMFG的NH值与阴性症状评分之间以及右侧ITG的NH值与处理速度之间呈负相关。而且，患者的年龄以及右侧小脑脚I/小脑脚II和右侧ITG的NH值是社会认知测试表现的预测因素。ROC曲线分析和SVM分析表明，左侧SMFG、右侧ITG和右侧小脑脚I/小脑脚II的NH值组合能够高精度地区分精神分裂症患者和HC。结果强调了DMN在精神分裂症潜在神经病理机制中的重要作用。
