Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Epilepsia. 2020 Aug;61(8):1691-1700. doi: 10.1111/epi.16591. Epub 2020 Jul 3.
Seizure recurrence following surgery for temporal lobe (TL) epilepsy may be related to extratemporal epileptogenic foci, so-called temporal-plus (TL+) epilepsy. Here, we sought to leverage whole brain connectomic profiling in magnetoencephalography (MEG) to identify neural networks indicative of TL+ epilepsy in children.
Clinical and MEG data were analyzed for 121 children with TL and TL+ epilepsy spanning 20 years at the Hospital for Sick Children. Resting-state connectomes were derived using the weighted phase lag index from neuromagnetic oscillations. Multidimensional associations between patient connectomes, TL versus TL+ epilepsy, seizure freedom, and clinical covariates were performed using a partial least squares (PLS) analysis. Bootstrap resampling statistics were performed to assess statistical significance.
A single significant latent variable representing 66% of the variance in the data was identified with significant contributions from extent of epilepsy (TL vs TL+), duration of illness, and underlying etiology. This component was associated with significant bitemporal and frontotemporal connectivity in the theta, alpha, and beta bands. By extracting a brain score, representative of the observed connectivity profile, patients with TL epilepsy were dissociated from those with TL+, independent of their postoperative seizure outcome.
By analyzing 121 connectomes derived from MEG data using a PLS approach, we find that connectomic profiling could dissociate TL from TL+ epilepsy. These findings may inform patient selection for resective procedures and guide decisions surrounding invasive monitoring.
颞叶(TL)癫痫手术后的癫痫复发可能与所谓的颞叶加(TL +)癫痫的颞外致痫灶有关。在这里,我们试图利用磁共振脑磁图(MEG)中的全脑连接组学分析来识别儿童 TL + 癫痫的神经网络。
对 SickKids 医院 20 年来 121 例 TL 和 TL +癫痫儿童的临床和 MEG 数据进行了分析。使用来自神经磁振荡的加权相位滞后指数得出静息状态连接组。使用偏最小二乘(PLS)分析对患者连接组、TL 与 TL +癫痫、无癫痫发作和临床协变量之间的多维关联进行了多维关联。通过bootstrap 重采样统计来评估统计显著性。
识别出一个单一的显著潜在变量,代表数据中 66%的方差,其贡献来自癫痫的程度(TL 与 TL +)、疾病持续时间和潜在病因。该成分与θ、α和β频段的双侧颞叶和额颞叶连接显著相关。通过提取大脑评分,代表观察到的连接谱,TL 癫痫患者与 TL +患者区分开来,与他们术后癫痫发作结果无关。
通过使用 PLS 方法分析来自 MEG 数据的 121 个连接组,我们发现连接组学分析可以将 TL 与 TL +癫痫区分开来。这些发现可能为手术治疗提供患者选择,并指导侵袭性监测决策。