基于立体定向脑电图的颞叶致痫网络独立有效相干分析

Isolated effective coherence analysis of epileptogenic networks in temporal lobe epilepsy using stereo-electroencephalography

临床上,立体定向脑电图(SEEG)广泛应用于记录患者颅内的电活动,其中基于SEEG构建的致痫网络能更好地描述癫痫发作的起源与传播过程,是神经外科确定致痫区的重要手段。本文以5例难治性颞叶癫痫和1例颞叶外癫痫患者的SEEG数据为基础,结合手术切除区域,分析了致痫网络中信息流出(出度值)、流入(入度值)节点与致痫区的相对关系。本文首先在对SEEG数据进行双极导联变换的基础上,对发作初期、中期和后期的SEEG以替代数据法和独立有效相干方法(iCoh)建立致痫网络,然后在δ、θ、α、β和γ频段上分别计算了网络节点的出度值和入度值。最后对患者致痫区内外节点的网络特征进行K-均值(K-means)聚类算法分析,将均值高的分类与致痫区通道进行比较,计算分类准确率。最终结果表明,出度值在δ、α和β频段下对颞叶癫痫的平均分类准确率分别为0.90、0.88和0.89,而网络节点的入度值无区分性。与之相比,颞叶外癫痫患者出度值在颞叶外区域高于颞叶区域。本文研究结果体现出颞叶癫痫患者的低频致痫网络出度值具有很高的分类准确率,今后或可为临床判断患者是否为颞叶癫痫提供一种量化参考指标。

Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on outdegree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.