Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, United States of America.
Cleveland Clinic Lerner College of Medicine, Case Western Reserve University School of Medicine, 10900 Euclid Avenue, Cleveland, OH 44106, United States of America.
J Neural Eng. 2024 Aug 29;21(4). doi: 10.1088/1741-2552/ad705f.
For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial electrode recordings to identify brain networks participating in early seizure organization and propagation (i.e. the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high-dimensional sEEG data. We apply here an unsupervised data-driven algorithm, dynamic mode decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free.DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ('modes') defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce dynamic modal maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the higher-frequency mode-based norm index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery.DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ.This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.
对于药物难治性癫痫患者，立体脑电图（sEEG）是一种使用颅内电极记录来识别参与早期癫痫发作组织和传播的脑网络的手术方法（即癫痫发作区，EZ）。如果确定，通过切除、消融或神经调节治疗手术 EZ 可以导致无癫痫发作。迄今为止，sEEG 数据的量化，包括其可视化和解释，仍然是临床和计算方面的挑战。鉴于物理定律的难以捉摸或控制方程对复杂大脑动力学的建模，数据科学为识别高维 sEEG 数据中的未知模式提供了独特的见解。我们在这里应用一种无监督的数据驱动算法，动态模式分解（DMD），对来自五名局灶性癫痫患者（三名颞叶癫痫患者和两名扣带回癫痫患者）的 sEEG 记录进行分析，这些患者随后接受了切除或消融手术并成为无癫痫发作。DMD 获得了非线性数据动力学的线性近似，生成了定义重要信号特征的相干结构（“模式”），用于提取频率、增长率和空间结构。DMD 被改编为在频带子带中生成动态模态图（DMM），以捕获 sEEG 数据中癫痫样动力学的发作和演变。此外，我们开发了一种用于评估 EZ 定位电极接触的静态估计方法，称为基于高频模式的范数指数（MNI）。代表性患者癫痫发作的 DMM 和 MNI 图与临床 sEEG 结果和手术后无癫痫发作的结果进行了验证。DMD 在较高频率（即伽马（包括高伽马）和β频带）最具信息性，成功识别了 EZ 接触点。DMM/MNI 图的联合解释最好地识别了特定模式网络变化的时空演变，与所有五名患者的 sEEG 结果和结局具有很强的一致性。该方法还确定了 EZ 以外的其他接触点的网络衰减。这是 DMD 首次应用于 sEEG 数据分析，支持将神经工程、数学和机器学习方法集成到传统的 sEEG 审查和癫痫手术决策工作流程中。
