Dynamic Network Connectivity Analysis on Stereo-Electroencephalography

【作者】 Mao Jun-Wei； Ye Xiao-Lai； Li Yong-Hua； Liang Pei-Ji； Xu Ji-Wen； 张溥明；

【Author】 Mao Jun-Wei;Ye Xiao-Lai;Li Yong-Hua;Liang Pei-Ji;Xu Ji-Wen;Zhang Pu-Ming;School of Biomedical Engineering, Shanghai Jiao Tong University;Department of Functional Neurosurgery, Renji Hospital, Shanghai Jiao Tong University;

【机构】 School of Biomedical Engineering, Shanghai Jiao Tong University； Department of Functional Neurosurgery, Renji Hospital, Shanghai Jiao Tong University；

【摘要】 Accurate localization of the epileptogenic zone(EZ) is essential for the successful surgical treatment of the refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography(SEEG) signals is effective in localizing the EZ.SEEG data were recorded from seven patients underwent presurgical evaluation for the treatment of refractory focal epilepsy, and the subsequent resective surgery gave the patients good outcome. The time-variant multivariate autoregressive model was constructed by Kalman filter and the timevariant partial directed coherence was computed, which was then used to construct the dynamic directed network of the epileptic brain. Three graph measures, in-degree, out-degree and betweenness centrality, were used to analyze the characteristic of the dynamic network and to find the important nodes in it. In all seven patients, the indicative EZ localized by in-degree and betweenness centrality were highly consistent to the clinical diagnosed EZ. However, the out-degree did not indicate significant difference between nodes in the network. In this work, the method based on ictal SEEG signals and effective connectivity analysis localized the EZs accurately. It suggested that in-degree and betweenness centrality may be better network characteristics to localize the EZs than out-degree.更多还原

【Abstract】 Accurate localization of the epileptogenic zone(EZ) is essential for the successful surgical treatment of the refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography(SEEG) signals is effective in localizing the EZ.SEEG data were recorded from seven patients underwent presurgical evaluation for the treatment of refractory focal epilepsy, and the subsequent resective surgery gave the patients good outcome. The time-variant multivariate autoregressive model was constructed by Kalman filter and the timevariant partial directed coherence was computed, which was then used to construct the dynamic directed network of the epileptic brain. Three graph measures, in-degree, out-degree and betweenness centrality, were used to analyze the characteristic of the dynamic network and to find the important nodes in it. In all seven patients, the indicative EZ localized by in-degree and betweenness centrality were highly consistent to the clinical diagnosed EZ. However, the out-degree did not indicate significant difference between nodes in the network. In this work, the method based on ictal SEEG signals and effective connectivity analysis localized the EZs accurately. It suggested that in-degree and betweenness centrality may be better network characteristics to localize the EZs than out-degree.更多还原