Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Epilepsia. 2020 Jun;61(6):1221-1233. doi: 10.1111/epi.16540. Epub 2020 May 26.
Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood.
We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques.
Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1 year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76 ± 4% accuracy, outperforming classifiers operating on clinical and structural imaging features.
Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.
颞叶癫痫(TLE)是成人中最常见的耐药性癫痫。尽管它通常与海马病理学有关,但越来越多的证据表明,除了颞叶中部之外,还有结构变化。然而,功能异常及其与潜在结构改变的联系仍不完全清楚。
我们使用多模态磁共振成像(MRI)分析研究了 30 名耐药性 TLE 患者和 57 名健康对照者。所有患者均经组织学证实为海马硬化,并进行术后影像学检查以描绘其手术切除范围。我们的分析利用了一种新的静息状态功能 MRI 框架,该框架参数化了功能连接距离,整合了宏观脑网络的拓扑和物理特性。功能发现与形态和微观结构指标相结合,并使用机器学习技术评估了对手术结果的预测能力。
与对照组相比,TLE 患者在颞岛叶和前额叶网络中表现出连接距离减小,表明功能网络的拓扑分离。在测试形态和微观结构关联时,我们观察到功能连接收缩与 TLE 相关的皮质萎缩无关,但与潜在白质中的微观结构变化有关。在我们的影像学研究之后,所有患者都接受了前颞叶切除术作为治疗其癫痫发作的手段,并在手术后至少 1 年进行了随访以确定术后癫痫发作的结果。使用具有五重交叉验证的正则化监督机器学习范例,我们证明了特定于患者的功能异常以 76±4%的准确率预测了术后癫痫发作结果,优于基于临床和结构成像特征的分类器。
我们的研究结果表明,连接距离收缩是 TLE 的一个宏观基质。功能拓扑隔离可能代表一种微观结构介导的网络机制,该机制使受影响网络向癫痫发生倾斜,并可能有助于针对特定患者的手术预后预测。