Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy.
Unit of Non-Linear Physics and Mathematical Modelling, Engineering Department, University Campus Bio-Medico of Rome, Rome, Italy; Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA.
Clin Neurophysiol. 2022 Jul;139:49-57. doi: 10.1016/j.clinph.2022.04.009. Epub 2022 Apr 26.
Delineation of the seizure onset zone (SOZ) is required in children with drug resistant epilepsy (DRE) undergoing neurosurgery. Intracranial EEG (icEEG) serves as gold standard but has limitations. Here, we examine the utility of virtual implantation with electrical source imaging (ESI) on ictal scalp EEG for mapping the SOZ and predict surgical outcome.
We retrospectively analyzed EEG data from 35 children with DRE who underwent surgery and dichotomized into seizure-free (SF) and non-seizure-free (NSF). We estimated virtual sensors (VSs) at brain locations that matched icEEG implantation and compared ictal patterns at VSs vs icEEG. We calculated the agreement between VSs SOZ and clinically defined SOZ and built receiver operating characteristic (ROC) curves to test whether it predicted outcome.
Twenty-one patients were SF after surgery. Moderate agreement between virtual and icEEG patterns was observed (kappa = 0.45, p < 0.001). Virtual SOZ agreement with clinically defined SOZ was higher in SF vs NSF patients (66.6% vs 41.6%, p = 0.01). Anatomical concordance of virtual SOZ with clinically defined SOZ predicted outcome (AUC = 0.73; 95% CI: 0.57-0.89; sensitivity = 66.7%; specificity = 78.6%; accuracy = 71.4%).
Virtual implantation on ictal scalp EEG can approximate the SOZ and predict outcome.
SOZ mapping with VSs may contribute to tailoring icEEG implantation and predict outcome.
在接受神经外科手术的耐药性癫痫（DRE）儿童中，需要描绘发作起始区（SOZ）。颅内脑电图（icEEG）是金标准，但存在局限性。在这里，我们研究了电源成像（ESI）虚拟植入在癫痫发作头皮脑电图上用于绘制 SOZ 和预测手术结果的效用。
我们回顾性分析了 35 名接受手术的 DRE 儿童的 EEG 数据，并将其分为无癫痫发作（SF）和非癫痫发作（NSF）。我们在与 icEEG 植入相匹配的大脑位置估计虚拟传感器（VS），并比较 VS 与 icEEG 的发作模式。我们计算了 VS 的 SOZ 与临床定义的 SOZ 之间的一致性，并构建了接收者操作特征（ROC）曲线来测试其是否预测结果。
手术后 21 名患者为 SF。观察到虚拟和 icEEG 模式之间存在中度一致性（kappa=0.45，p<0.001）。SF 患者的 VS SOZ 与临床定义的 SOZ 的一致性高于 NSF 患者（66.6% vs 41.6%，p=0.01）。虚拟 SOZ 与临床定义的 SOZ 的解剖一致性预测了结果（AUC=0.73；95%CI：0.57-0.89；敏感性=66.7%；特异性=78.6%；准确性=71.4%）。
癫痫发作头皮 EEG 上的虚拟植入可以近似 SOZ 并预测结果。
VS 进行 SOZ 映射可能有助于定制 icEEG 植入并预测结果。
