Anand Adrish, Magnotti John F, Smith David N, Gadot Ron, Najera Ricardo A, Hegazy Mohamed I R, Gavvala Jay R, Shofty Ben, Sheth Sameer A
1Departments of Neurosurgery and.
2Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
J Neurosurg. 2022 Mar 18;137(5):1237-1247. doi: 10.3171/2022.1.JNS212943. Print 2022 Nov 1.
Magnetoencephalography (MEG) is a useful component of the presurgical evaluation of patients with epilepsy. Due to its high spatiotemporal resolution, MEG often provides additional information to the clinician when forming hypotheses about the epileptogenic zone (EZ). Because of the increasing utilization of stereo-electroencephalography (sEEG), MEG clusters are used to guide sEEG electrode targeting with increasing frequency. However, there are no predefined features of an MEG cluster that predict ictal activity. This study aims to determine which MEG cluster characteristics are predictive of the EZ.
The authors retrospectively analyzed all patients who had an MEG study (2017-2021) and underwent subsequent sEEG evaluation. MEG dipoles and sEEG electrodes were reconstructed in the same coordinate space to calculate overlap among individual contacts on electrodes and MEG clusters. MEG cluster features-including number of dipoles, proximity, angle, density, magnitude, confidence parameters, and brain region-were used to predict ictal activity in sEEG. Logistic regression was used to identify important cluster features and to train a binary classifier to predict ictal activity.
Across 40 included patients, 196 electrodes (42.2%) sampled MEG clusters. Electrodes that sampled MEG clusters had higher rates of ictal and interictal activity than those that did not sample MEG clusters (ictal 68.4% vs 39.8%, p < 0.001; interictal 71.9% vs 44.6%, p < 0.001). Logistic regression revealed that the number of dipoles (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.04-1.14, t = 3.43) and confidence volume (OR 0.02, 95% CI 0.00-0.86, t = -2.032) were predictive of ictal activity. This model was predictive of ictal activity with 77.3% accuracy (sensitivity = 80%, specificity = 74%, C-statistic = 0.81). Using only the number of dipoles had a predictive accuracy of 75%, whereas a threshold between 14 and 17 dipoles in a cluster detected ictal activity with 75.9%-85.2% sensitivity.
MEG clusters with approximately 14 or more dipoles are strong predictors of ictal activity and may be useful in the preoperative planning of sEEG implantation.
脑磁图（MEG）是癫痫患者术前评估的有用组成部分。由于其高时空分辨率，MEG在形成关于癫痫发作起始区（EZ）的假设时，常常能为临床医生提供额外信息。随着立体脑电图（sEEG）的使用日益增加，MEG簇越来越频繁地被用于指导sEEG电极靶向。然而，尚无用于预测发作期活动的MEG簇的预定义特征。本研究旨在确定哪些MEG簇特征可预测EZ。
作者回顾性分析了所有接受MEG检查（2017 - 2021年）并随后接受sEEG评估的患者。将MEG偶极子和sEEG电极重建于同一坐标空间，以计算电极上各个触点与MEG簇之间的重叠情况。MEG簇特征，包括偶极子数量、邻近度、角度、密度、强度、置信参数和脑区，被用于预测sEEG中的发作期活动。采用逻辑回归来识别重要的簇特征，并训练一个二元分类器以预测发作期活动。
在纳入的40例患者中，196个电极（42.2%）采样了MEG簇。采样MEG簇的电极发作期和发作间期活动发生率高于未采样MEG簇的电极（发作期68.4%对39.8%，p < 0.001；发作间期71.9%对44.6%，p < 0.001）。逻辑回归显示，偶极子数量（比值比[OR] 1.09，95%置信区间[CI] 1.04 - 1.14，t = 3.43）和置信体积（OR 0.02，95% CI 0.00 - 0.86，t = -2.032）可预测发作期活动。该模型预测发作期活动的准确率为77.3%（敏感性 = 80%，特异性 = 74%，C统计量 = 0.81）。仅使用偶极子数量的预测准确率为75%，而簇中14至17个偶极子的阈值检测发作期活动的敏感性为75.9% - 85.2%。
具有约14个或更多偶极子的MEG簇是发作期活动的有力预测指标，可能有助于sEEG植入的术前规划。
