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.

准确识别刺激区（Irritative Zone, IZ）是对药物难治性癫痫患者开展精准临床评估的必要前提。鉴于脑磁图（Magnetoencephalography, MEG）数据的复杂性，对癫痫样神经生理活动进行视觉分析不仅耗时极长，还可能遗漏具有临床价值的相关信息。本研究对7例成功接受癫痫手术且预后良好（Engel分级≥Ⅱ级）的药物难治性癫痫患者的发作间期脑电活动进行了记录与分析，所用设备为Vectorview Neuromag脑磁图系统。研究人员通过视觉识别法（Visual Identification, VIS）对特征性癫痫样活动进行标记与定位。本研究构建了两阶段分析流水线，用于检测发作间期棘波并划定刺激区：首先从峰值化独立成分分析（Independent Component Analysis, ICA）成分中提取候选事件，随后基于卷积稀疏编码识别的时空模式对事件进行聚类。研究人员以聚类事件的平均值分别生成两种刺激区图谱：一种基于幅值峰值计算（PEAK），另一种基于峰值上升沿50%斜率计算（SLOPE）。本研究通过计算三种方法（VIS、SLOPE、PEAK）所得刺激区与手术切除区（Resected Area, RA）边界的距离，对所提方法进行了验证。本研究共识别出25种可模拟发作间期活动的时空模式，平均每位患者对应3.6个聚类簇；每个聚类簇平均包含22.1个棘波，四分位距为15.0~31.0。三种方法所得刺激区图谱与手术切除缘的平均距离分别为：视觉识别法8.4±9.3 mm，斜率法12.0±16.5 mm，峰值法22.7±16.4 mm。考虑源信号在上升沿的分布后所得的刺激区更接近手术切除区，其结果与专业癫痫医师的视觉分析结果更为相似。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刺激区的方法的性能。该计算框架可为癫痫患者脑磁图记录的可重复、无偏倚分析提供坚实基础。