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 Shanghai, China.
Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai, China.
Front Comput Neurosci. 2016 Oct 27;10:113. doi: 10.3389/fncom.2016.00113. eCollection 2016.
Accurate localization of epileptogenic zones (EZs) is essential for successful surgical treatment of 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 EZs. SEEG data were recorded from seven patients who underwent presurgical evaluation for the treatment of refractory focal epilepsy and for whom the subsequent resective surgery gave a good outcome. A time-variant multivariate autoregressive model was constructed using a Kalman filter, and the time-variant partial directed coherence was computed. This was then used to construct a dynamic directed network model of the epileptic brain. Three graph measures (in-degree, out-degree, and betweenness centrality) were used to analyze the characteristics of the dynamic network and to find the important nodes in it. In all seven patients, the indicative EZs localized by the in-degree and the betweenness centrality were highly consistent with the clinically diagnosed EZs. However, the out-degree did not indicate any significant differences between nodes in the network. In this work, a method based on ictal SEEG signals and effective connectivity analysis localized EZs accurately. The results suggest that the in-degree and betweenness centrality may be better network characteristics to localize EZs than the out-degree.
癫痫源区（EZs）的准确定位对于难治性局灶性癫痫的成功手术治疗至关重要。本研究的目的是调查基于立体脑电图（SEEG）信号的动态网络连通性分析在定位EZs方面是否有效。从7例接受难治性局灶性癫痫术前评估且后续切除手术效果良好的患者中记录SEEG数据。使用卡尔曼滤波器构建时变多元自回归模型，并计算时变偏相干性。然后用其构建癫痫脑的动态有向网络模型。采用三种图测度（入度、出度和介数中心性）分析动态网络的特征并找出其中的重要节点。在所有7例患者中，通过入度和介数中心性定位的指示性EZs与临床诊断的EZs高度一致。然而，出度并未显示网络中各节点之间存在任何显著差异。在这项研究中，一种基于发作期SEEG信号和有效连通性分析的方法能够准确地定位EZs。结果表明，与出度相比，入度和介数中心性可能是定位EZs更好的网络特征。
