Parasuram Harilal, Gopinath Siby, Pillai Ashok, Diwakar Shyam, Kumar Anand
Amrita Advanced Centre for Epilepsy (AACE), Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.
Department of Neurology, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.
Front Neurol. 2021 Nov 3;12:738111. doi: 10.3389/fneur.2021.738111. eCollection 2021.
Precise localization of the epileptogenic zone is very essential for the success of epilepsy surgery. Epileptogenicity index (EI) computationally estimates epileptogenicity of brain structures based on the temporal domain parameters and magnitude of ictal discharges. This method works well in cases of mesial temporal lobe epilepsy but it showed reduced accuracy in neocortical epilepsy. To overcome this scenario, in this study, we propose Epileptogenicity Rank (ER), a modified method of EI for quantifying epileptogenicity, that is based on spatio-temporal properties of Stereo EEG (SEEG). Energy ratio during ictal discharges, the time of involvement and Euclidean distance between brain structures were used to compute the ER. Retrospectively, we localized the EZ for 33 patients (9 for mesial-temporal lobe epilepsy and 24 for neocortical epilepsy) using post op MRI and Engel 1 surgical outcome at a mean of 40.9 months and then optimized the ER in this group. Epileptic network estimation based on ER successfully differentiated brain regions involved in the seizure onset from the propagation network. ER was calculated at multiple thresholds leading to an optimum value that differentiated the seizure onset from the propagation network. We observed that ER < 7.1 could localize the EZ in neocortical epilepsy with a sensitivity of 94.6% and specificity of 98.3% and ER < 7.3 in mesial temporal lobe epilepsy with a sensitivity of 95% and specificity of 98%. In non-seizure-free patients, the EZ localization based on ER pointed to brain area beyond the cortical resections. Methods like ER can improve the accuracy of EZ localization for brain resection and increase the precision of minimally invasive surgery techniques (radio-frequency or laser ablation) by identifying the epileptic hubs where the lesion is extensive or in nonlesional cases. For inclusivity with other clinical applications, this ER method has to be studied in more patients.
癫痫发作起始区的精确定位对于癫痫手术的成功至关重要。癫痫发作起始指数（EI）基于发作期放电的时域参数和幅度，通过计算来估计脑结构的癫痫发作起始性。该方法在颞叶内侧癫痫病例中效果良好，但在新皮质癫痫中准确性有所降低。为克服这一情况，在本研究中，我们提出了癫痫发作起始等级（ER），这是一种基于立体脑电图（SEEG）的时空特性对EI进行量化的改进方法。发作期放电期间的能量比、受累时间以及脑结构之间的欧几里得距离被用于计算ER。回顾性地，我们利用术后MRI和平均40.9个月时的恩格尔1级手术结果，对33例患者（9例为颞叶内侧癫痫，24例为新皮质癫痫）的癫痫发作起始区进行定位，然后在该组中优化ER。基于ER的癫痫网络估计成功地将癫痫发作起始涉及的脑区与传播网络区分开来。在多个阈值下计算ER，得出一个能区分癫痫发作起始和传播网络的最佳值。我们观察到，在新皮质癫痫中，ER<7.1可定位癫痫发作起始区，敏感性为94.6%，特异性为98.3%；在颞叶内侧癫痫中，ER<7.3，敏感性为95%，特异性为98%。在未实现无发作的患者中，基于ER的癫痫发作起始区定位指向了皮质切除范围之外的脑区。像ER这样的方法可以提高癫痫发作起始区定位的准确性，以便进行脑切除，并通过识别病变广泛或无病变情况下的癫痫病灶中心，提高微创手术技术（射频或激光消融）的精确性。为了与其他临床应用兼容，必须在更多患者中研究这种ER方法。
