Berlin School of Mind and Brain, Humboldt University, Berlin, Germany.
Center for Neurocognitive Research, MEG Center, MSUPE, Moscow, Russian Federation.
PLoS One. 2022 Oct 25;17(10):e0275063. doi: 10.1371/journal.pone.0275063. eCollection 2022.
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.
可靠地识别刺激性区域（IZ）是对受癫痫影响的医学难治性患者进行正确临床评估的前提。鉴于 MEG 数据的复杂性，对癫痫样神经生理活动进行视觉分析非常耗时，并且可能会遗漏临床相关信息。我们记录并分析了 7 名成功接受癫痫手术（Engel >= II）的癫痫患者（Vectorview Neuromag）的间期活动。我们对特征性癫痫样活动（VIS）进行了视觉标记和定位。我们实施了一个两阶段的管道，用于检测间期棘波和划定 IZ。首先，我们从峰值 ICA 分量中检测候选事件，然后围绕卷积稀疏编码识别的时空模式对事件进行聚类。我们使用聚类事件的平均值创建在幅度峰值（PEAK）和上升斜率的 50%（SLOPE）处计算的 IZ 图。我们通过计算估计的 IZ（VIS、SLOPE 和 PEAK）与手术切除区域（RA）边界之间的距离来验证我们的方法。我们识别了 25 个模仿潜在间期活动的时空模式（每个患者 3.6 个簇）。每个簇的平均峰值为 22.1 [15.0-31.0]个棘波。对于视觉分析，预测的 IZ 图与切除边界的平均距离为 8.4 ± 9.3mm，对于 SLOPE 为 12.0 ± 16.5mm，对于 PEAK 为 22.7 ± 16.4mm。在上升斜率处考虑源传播提供了更接近 RA 的 IZ，并类似于专家观察者的分析。我们在这里验证了一种用于自动检测间期棘波和划定 IZ 的数据驱动方法的性能。该计算框架为癫痫 MEG 记录的可重复且无偏差分析提供了基础。
