Ji Shanling, An Wei, Zhang Jing, Zhou Cong, Liu Chuanxin, Yu Hao
Institute of Mental Health, Jining Medical University, Jining, 272056, Shandong, China.
Medical Imaging Department, Shandong Daizhuang Hospital, Shandong, China.
Brain Imaging Behav. 2025 Feb;19(1):111-123. doi: 10.1007/s11682-024-00923-5. Epub 2024 Nov 13.
In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.
近年来，脑信号复杂性作为脑健康的指标以及疾病和功能障碍的预测指标受到了关注。脑熵量化了这种复杂性。对功能网络中心性和连通性的评估表明，信息交流在某些脑区诱发神经信号振荡。然而，它们之间的关系尚不确定。这项研究调查了健康个体和抑郁症患者的脑信号复杂性、网络中心性和连通性。当前的研究包括124名首次发作、未服用过药物的重度抑郁症（MDD）患者和105名健康对照者（HC）的样本。使用静息态功能磁共振成像为每个人创建了六个功能网络。针对每个网络，计算了熵、中心性和连通性。本研究使用结构方程模型检验了脑网络熵、中心性和连通性之间的关联。研究结果表明，在健康对照者中，熵与中心性和连通性均存在显著相关性，而在重度抑郁症患者中，这些相关模式被破坏。与健康对照者相比，重度抑郁症患者在四个网络中表现出更高的熵，并且在所有网络中中心性都发生了变化。结构方程模型表明，网络中心性、连通性和抑郁严重程度对脑熵有影响。然而，未观察到相反方向的影响。这项研究表明，脑信号的复杂性不仅受脑不同区域之间相互作用的影响，还受抑郁严重程度的影响。这些发现增强了我们对脑熵与其影响因素之间关联的理解。
