Ma Yijia, Qian Jing, Gu Qizhang, Yi Wanyi, Yan Wei, Yuan Jianxuan, Wang Jun
Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Entropy (Basel). 2023 Sep 13;25(9):1330. doi: 10.3390/e25091330.
Depression is a psychiatric disorder characterized by anxiety, pessimism, and suicidal tendencies, which has serious impact on human's life. In this paper, we use Granger causality index based on polynomial kernel as network node connectivity coefficient to construct brain networks from the magnetoencephalogram (MEG) of 5 depressed patients and 11 healthy individuals under positive, neutral, and negative emotional stimuli, respectively. We found that depressed patients had more information exchange between the frontal and occipital regions compared to healthy individuals and less causal connections in the parietal and central regions. We further analyzed the topological properties of the network revealed and found that depressed patients had higher average degrees under negative stimuli ( = 0.008) and lower average clustering coefficients than healthy individuals ( = 0.034). When comparing the average degree and average clustering coefficient of the same sample under different emotional stimuli, we found that depressed patients had a higher average degree and average clustering coefficient under negative stimuli than neutral and positive stimuli. We also found that the characteristic path lengths of patients under negative and neutral stimuli significantly deviated from small-world network. Our results suggest that the analysis of polynomial kernel Granger causality brain networks can effectively characterize the pathology of depression.
抑郁症是一种以焦虑、悲观和自杀倾向为特征的精神疾病，对人类生活有严重影响。在本文中，我们分别使用基于多项式核的格兰杰因果指数作为网络节点连接系数，从5名抑郁症患者和11名健康个体在积极、中性和消极情绪刺激下的脑磁图（MEG）构建脑网络。我们发现，与健康个体相比，抑郁症患者额叶和枕叶区域之间的信息交流更多，而顶叶和中央区域的因果连接更少。我们进一步分析了所揭示网络的拓扑特性，发现抑郁症患者在消极刺激下平均度更高（=0.008），平均聚类系数低于健康个体（=0.034）。在比较同一样本在不同情绪刺激下的平均度和平均聚类系数时，我们发现抑郁症患者在消极刺激下的平均度和平均聚类系数高于中性和积极刺激。我们还发现，患者在消极和中性刺激下的特征路径长度显著偏离小世界网络。我们的结果表明，多项式核格兰杰因果脑网络分析可以有效地表征抑郁症的病理特征。
