Data-driven approach for the delineation of the irritative zone in epilepsy in MEG

The reliable identification of the irritative zone (IZ) is a prerequisite for the correct clinical evaluation of medically refractory patients affected by epilepsy. Given the complexity of MEG data, visual analysis of epileptiform neurophysiological activity is highly time consuming and might leave clinically relevant information undetected. We recorded and analyzed the interictal activity from seven patients affected by epilepsy (Vectorview Neuromag), who successfully underwent epilepsy surgery (Engel >= II). We visually marked and localized characteristic epileptiform activity (VIS). We implemented a two-stage pipeline for the detection of interictal spikes and the delineation of the IZ. First, we detected candidate events from peaky ICA components, and then clustered events around spatio-temporal patterns identified by convolutional sparse coding. We used the average of clustered events to create IZ maps computed at the amplitude peak (PEAK), and at the 50% of the peak ascending slope (SLOPE). We validated our approach by computing the distance of the estimated IZ (VIS, SLOPE and PEAK) from the border of the surgically resected area (RA). We identified 25 spatiotemporal patterns mimicking the underlying interictal activity (3.6 clusters/patient). Each cluster was populated on average by 22.1 [15.0-31.0] spikes. The predicted IZ maps had an average distance from the resection margin of 8.4 ± 9.3 mm for visual analysis, 12.0 ± 16.5 mm for SLOPE and 22.7 ±. 16.4 mm for PEAK. The consideration of the source spread at the ascending slope provided an IZ closer to RA and resembled the analysis of an expert observer. We validated here the performance of a data-driven approach for the automated detection of interictal spikes and delineation of the IZ. This computational framework provides the basis for reproducible and bias-free analysis of MEG recordings in epilepsy.可靠识别致痫区（irritative zone, IZ）是对药物难治性癫痫患者开展正确临床评估的前提。鉴于脑磁图（magnetoencephalography, MEG）数据的复杂性，视觉分析癫痫样神经生理活动不仅耗时极久，还可能遗漏具有临床价值的相关信息。本研究使用Vectorview Neuromag设备，记录并分析了7例成功接受癫痫手术且术后预后良好（Engel分级≥II级）的癫痫患者的发作间期活动。我们对特征性癫痫样活动进行视觉标记与定位（VIS）。我们构建了一套两阶段处理流程，用于检测发作间期棘波并划定致痫区：首先从峰值独立成分分析（independent component analysis, ICA）组件中检测候选事件，随后围绕卷积稀疏编码识别的时空模式对事件进行聚类。我们以聚类事件的平均值生成了两类致痫区图谱，分别基于振幅峰值（PEAK）以及峰值上升沿50%斜率处（SLOPE）。我们通过计算三种估计致痫区（VIS、SLOPE与PEAK）与手术切除区域（resected area, RA）边界的距离，验证了本方法的有效性。本研究共识别出25种可模拟发作间期活动的时空模式，每位患者平均对应3.6个聚类；每个聚类平均包含22.1[15.0-31.0]个棘波。三类方法预测的致痫区图谱与手术切除边缘的平均距离分别为：视觉分析组8.4±9.3 mm，SLOPE组12.0±16.5 mm，PEAK组22.7±16.4 mm。考虑上升沿处的源扩散可得到更贴近手术切除区域的致痫区，其结果与专家观察者的视觉分析结果相近。本研究验证了一种数据驱动的自动化检测发作间期棘波并划定致痫区的方法的性能。该计算框架可为癫痫患者脑磁图记录的可重复、无偏倚分析提供基础。