Han Ling, Song Xinke, Li Chunsheng
Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Dec 25;39(6):1082-1088. doi: 10.7507/1001-5515.202202022.
Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.
癫痫是一种脑网络连接紊乱的神经疾病。从定向功能连接的角度分析癫痫发作的脑网络机制具有重要意义。本文采用定向传递函数方法，针对癫痫脑电图（EEG）信号在发作间期、发作前期和发作期的不同子频段构建因果脑网络，分析不同条件下脑网络的信息传递路径及动态变化过程。最后，分析了不同节律脑网络特征属性的动态变化。结果表明，在三个阶段中脑网络拓扑结构从随机网络转变为规则网络，全脑网络的节点连接呈现逐渐下降趋势。额叶、颞叶和枕叶区域内部节点之间的通路连接数量增加。病变区域存在大量具有信息流出的枢纽节点。α、β和γ波发作期的全局效率显著高于发作间期和发作前期。发作前期的聚类系数高于发作期，发作期的聚类系数高于发作间期。额叶、颞叶和顶叶的聚类系数显著增加。本研究结果表明，癫痫因果脑网络的拓扑结构和特征属性能够反映癫痫发作的动态过程。未来，本研究在癫痫病灶定位和癫痫发作预测方面具有重要的研究价值。
