Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA.
Brain. 2022 Jun 30;145(6):1949-1961. doi: 10.1093/brain/awab480.
Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.
为药物难治性癫痫患者进行手术规划通常需要使用颅内脑电图记录癫痫发作。从发作间期颅内脑电图中得出的定量指标为指导这些手术程序提供了有吸引力的生物标志物；然而，由于电极植入的稀疏性以及时空变化的神经活动和连通性的正常混杂因素，其效用受到限制。我们提出，将颅内脑电图记录与颅内脑电图活动和连通性的规范图谱进行比较，可以可靠地绘制异常区域，确定侵入性治疗的靶点，并增加我们对人类癫痫的理解。我们合并了宾夕法尼亚癫痫中心和蒙特利尔神经学研究所公共数据库的数据，回顾性地汇总了 166 名受试者的发作间期颅内脑电图数据，这些受试者的颅内脑电图记录超过 5000 个通道。对于每个通道，我们计算了每个典型频带的归一化谱功率和相干性。我们通过在大脑中绘制每个特征的分布来构建颅内脑电图图谱，并通过为每个通道生成 z 分数来将图谱与新患者的数据进行比较。我们证明，对于内侧颞叶的癫痫发作起始区，连通性异常的测量值比异常神经活动的单变量测量值具有更大的鉴别价值。我们还发现，癫痫诊断时间较长的患者的连通性异常更大。通过整合单通道活动和区域间功能连通性的测量值，我们发现与正常大脑相比（曲线下面积为 0.77），预测癫痫发作起始区的准确性更高，而不是仅与这两组特征中的任何一组相比。我们提出，将癫痫中心的规范颅内脑电图数据汇总到规范图谱中，为绘制癫痫网络和指导侵入性治疗提供了一种严格、定量的方法。我们公开分享我们的数据、基础设施和方法，并提出了一个利用大数据进行难治性癫痫手术规划的国际框架。
