Directed connectivity analysis of resting-state SEEG and DWI to improve lateralization and localization in focal epilepsy

静息态 SEEG 和 DWI 的定向连接分析可改善局灶性癫痫的偏侧化和定位

• 批准号: 10311253
• 负责人: Graham Walter Johnson
• 金额: $ 3.13万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10311253
• 关键词: Directed; connectivity; analysis; resting; state

PROJECT SUMMARY/ABSTRACT Focal epilepsy is the most common form of epilepsy, a debilitating disorder that affects 50 million people worldwide. Approximately 30-40% of patients with focal epilepsy continue to have debilitating seizures despite maximal medical therapy. Epilepsy surgery can eliminate or reduce seizures using resection, ablation, or neurostimulation of regions that generate seizures (“Epileptogenic Zones”, EZs). However, 33-50% of patients that undergo surgery continue to have seizures post-operatively. An important determinate of post-operative outcome is accurate pre-surgical lateralization and localization of EZs. In 50% of patients, lateralization and localization requires invasive intracranial monitoring with stereo-electroencephalography (SEEG) in the hospital for days to weeks to record multiple seizures. This invasive diagnostic process causes significant morbidity to the patient, and interpretation of ictal (seizure) activity from SEEG may sometimes be challenging, inaccurate, and incapable of capturing all the patient’s seizure types. Resting-state (between seizures) SEEG analysis may supplement clinical interpretation by identifying EZs without requiring ictal recordings. Beyond SEEG, diffusion MRI (DWI) and neurostimulation have also been used to attempt EZ lateralization and localization. These studies rely on generating connectivity networks of brain regions and extracting features that predict EZ locations, but EZ lateralization and localization accuracy with these data has been suboptimal. However, few studies have evaluated the directionality of connectivity patterns involving EZs. Therefore, building from previous neurophysiological work that shows tonic inhibition of EZs in focal epilepsy, we hypothesize that electrophysiological resting-state inhibitory inward directed connectivity of EZs will be markedly increased vs. that of Non-EZs, and thus key to predicting epileptogenicity of brain regions. Further, integrating previous work done across the fields of neuroscience and neuropsychology, we also hypothesize specific DWI-derived structural network alterations that are important to lateralize EZs and predict surgical outcome. Our first goal is to develop directed connectivity measures to reliably identify EZs using brief resting-state SEEG recordings and neurostimulation sessions (Aim 1). We then seek to identify noninvasive structural connectivity measures to lateralize EZs and predict surgical outcome using DWI to ultimately reduce the need for invasive intracranial monitoring. We will do this through advanced network analysis of DWI-generated structural connectivity maps (Aim 2). This proposed fellowship will provide research training in a collaborative research atmosphere with expert mentors in translational neuroscience and engineering research. Research training will be conducted in an environment that combines an academic medical center with a level 4 epilepsy center, world class imaging institute, and engineering all on one campus, ensuring an environment uniquely suited to excellent training in all aspects of this proposed work. Studying multiple modalities to characterize epileptic networks and localize EZs has the potential to drastically improve the lives of patients living with this devastating neurological disorder.

项目总结/摘要 局灶性癫痫是最常见的癫痫形式，是一种影响5000万人的衰弱性疾病 国际吧大约30-40%的局灶性癫痫患者继续有衰弱性癫痫发作， 最大限度的药物治疗癫痫手术可以消除或减少癫痫发作通过切除，消融，或 神经刺激产生癫痫发作的区域（“癫痫发生区”，EZ）。33-50%的患者 接受手术的患者在手术后继续癫痫发作。决定术后疗效的重要因素 结果是精确的术前定侧和EZ定位。在50%的患者中， 定位需要在医院用立体脑电图（SEEG）进行侵入性颅内监测 记录多次癫痫发作这种侵入性诊断过程导致显著的发病率， 患者，并且从SEEG对发作（癫痫发作）活动的解释有时可能是具有挑战性的，不准确的， 无法捕捉到病人所有的癫痫类型静息状态（发作之间）SEEG分析可能 通过识别EZ而无需记录发作记录来补充临床解释。除了SEEG，扩散 MRI（DWI）和神经刺激也已用于尝试EZ定侧和定位。这些研究 依赖于生成大脑区域的连接网络，并提取预测EZ位置的特征， 这些数据的EZ偏侧化和定位准确性一直是次优的。然而，很少有研究 评估了涉及EZ的连接模式的方向性。因此，从以前的建筑 神经生理学研究显示局灶性癫痫中EZ的紧张性抑制，我们假设， 电生理静息状态抑制性的EZ向内定向连接将显著增加， 非EZs，因此关键预测癫痫的大脑区域。此外，结合以前的工作， 在神经科学和神经心理学领域，我们还假设特定的DWI衍生的 结构网络的改变对侧化EZ和预测手术结果很重要。我们的首要目标是 开发定向连接措施，使用简短的静息状态SEEG记录可靠地识别EZ， 神经刺激会话（目标1）。然后，我们寻求确定非侵入性的结构连接措施， 使用DWI确定EZ的一侧并预测手术结果，以最终减少侵入性颅内 监测.我们将通过DWI生成的结构连接图的高级网络分析来实现这一点 (Aim 2）的情况。这项拟议的研究金将在合作研究的气氛中提供研究培训， 转化神经科学和工程研究方面的专家导师。研究培训将在 一个环境，结合了学术医疗中心与4级癫痫中心，世界一流的成像 学院，工程都在一个校园，确保环境独特，适合所有优秀的培训 这一拟议工作的各个方面。研究多种模态来表征癫痫网络和定位EZ 有可能极大地改善患有这种毁灭性神经系统疾病的患者的生活。