Abstract
BACKGROUND: Schizophrenia (SZ) is a serious chronic mental disorder of unknown cause, and its core pathological mechanism is closely related to abnormal brain functional network connections. Resting-state functional magnetic resonance imaging (rs-fMRI) has become a core technology for exploring brain functional networks due to its advantages of being non-invasive and having high spatiotemporal resolution. Graph theory algorithms can quantify the topological properties of brain networks, providing objective indicators for revealing abnormal organizational patterns of brain networks. Although existing research has insufficient systematic analysis of the topological properties of the whole brain network, it is difficult to comprehensively explain its pathophysiological mechanism. Therefore, the study aims to combine rs-fMRI with graph theory algorithms to systematically explore the changing characteristics of topological properties of brain functional networks in SZ patients, providing imaging evidence for the pathological mechanism analysis of schizophrenia. METHODS: Sixty patients with a clear diagnosis of SZ were selected, and 60 healthy controls matched by gender and age were also selected. None of the research subjects had a history of severe physical diseases, brain trauma, or other neuropsychiatric disorders. Both groups received rs-fMRI data acquisition, and the core topological attribute indicators were calculated based on the graph theory algorithm: small-world index σ, clustering coefficient C, feature path length L, global efficiency Eglob and node degree K. The data were expressed as mean ± standard deviation. The independent sample t-test was used to compare the differences in topological attributes between the two groups. Pearson correlation analysis was used to explore the correlation between abnormal topological indicators in the patient group and the Positive and Negative Syndrome Scale (PANSS) score. RESULTS: Compared with the healthy control group, the index σ of the SZ patient group was significantly decreased (t = 7.63, p<.001), the coefficient C was significantly decreased (t = 8.21, p<.001), the characteristic pathway length L was significantly prolonged (t = 9.35, p<.001), and the Eglob was significantly decreased (t = 8.79, p<.001). Node degree analysis showed that the node degree K of the default mode network and key nodes of the edge system in the patient group was significantly reduced (p<.01). The results of the relevant analysis showed that σ in the patient group was significantly negatively correlated with the total PANSS score (r = -0.58, p<.001), the clustering coefficient C was significantly negatively correlated with the negative symptom score (r = -0.53, p<.001), and the characteristic path length L was significantly positively correlated with the positive symptom score (r = 0.49, p<.01). DISCUSSION: The research results show that there are significant topological property changes in the brain functional networks of patients with schizophrenia, and these abnormal indicators are related to the severity of the patients’ clinical symptoms. This study provides direct imaging evidence for the pathological mechanism of “abnormal brain network connections” in schizophrenia. In the future, the sample size can be expanded to include first-episode untreated patients, and the stability of topological attribute changes can be verified through longitudinal tracking design, further revealing the deep mechanism of abnormal brain networks and providing new ideas for the development of targeted brain regulation intervention strategies.