Quantitative interictal networks to localize seizure generators

项目总结 难治性癫痫患者的手术结果一般，很大程度上是由于定位错误。 抽搐产生器。目前，癫痫专家通过手动识别癫痫发作来定位癫痫发作发生器 颅内脑电的起始区。还有大量的发作间歇期脑电数据(癫痫发作之间)，主要是 被忽视是因为，到目前为止，临床医生缺乏严格的方法来解释这些数据。最近的研究工作由Dr。 康拉德、她的合著者和其他人验证了两种分析发作间期脑电的定量方法：发作间歇期 癫痫样放电(IED)和功能连接(FC)。这项工作证明了定量的 对简易爆炸装置和FC的分析揭示了有助于识别癫痫发生器的区域--这里称为中枢 (Conrad等人，Brain，2020；Conrad等人，Network NeuroScience，2020)。这一提议的项目将评估 IED和FC集线器，以阐明它们的机制及其在外科计划中的潜在效用。 这一提议的中心假设是癫痫可测量地改变发作间网络，使 美国将用发作间歇期数据调查癫痫发作发生器。该提案的目标是：确定 IED和FC中枢之间的时间稳定性和空间对应(目标1)，以确定这些中枢是否 定位癫痫发生器(目标2)，并积极探索皮层刺激的发作间网络以验证 假设FC中心(目标3)。这些研究将扩大我们对癫痫网络的理解。如果 发作间期脑电的成功定量分析将加强癫痫发生器的定位，并改善 手术结果。 这个拟议的项目还将为患有癫痫的康拉德医生提供关键的职业发展培训 宾夕法尼亚大学讲师。这项建议是基于康拉德博士在定量脑电方面的背景 癫痫分析与临床。康拉德博士将接受布莱恩·利特的指导，他是一位世界知名的 计算性癫痫；领先的网络理论专家Danielle Bassett；以及领先的专家Eric Marsh 在定量简易爆炸装置分析中。本建议书中的培训计划包括在指导下完成建议的 研究，以及严格的教学、研讨会、实验室会议和与临床直接相关的计划 工作。这些经验加在一起，将提供时间序列统计、机器学习、 受试者登记、大脑皮层刺激和脑电数据采集。该拟议的项目和培训计划将 启动康拉德博士的独立研究生涯，专注于使用多模式方法来评估 以期提高我们对癫痫的认识和治疗。

PROJECT SUMMARY Surgical outcomes for patients with intractable epilepsy are modest, in large part due to mis-localization of seizure generators. Currently, epileptologists localize seizure generators by manually identifying the seizure onset zone on intracranial EEG. There is also abundant interictal EEG data (between seizures) that is largely ignored because, until now, clinicians lacked rigorous approaches to interpret this data. Recent work by Dr. Conrad, her co-authors, and others, validated two quantitative methods to analyze interictal EEG: interictal epileptiform discharges (IEDs) and functional connectivity (FC). This work demonstrates that quantitative analysis of IEDs and FC reveals regions — referred to here as hubs — that help identify seizure generators (Conrad et al., Brain, 2020; Conrad et al., Network Neuroscience, 2020). This proposed project will evaluate IED and FC hubs in order to elucidate their mechanisms and their potential utility in surgical planning. The central hypothesis of this proposal is that epilepsy measurably alters the interictal network, allowing us to probe seizure generators with interictal data. The objectives of the proposal are: to determine the temporal stability and spatial correspondence between IED and FC hubs (Aim 1), to determine if these hubs localize seizure generators (Aim 2), and to actively probe the interictal network with cortical stimulation to verify hypothesized FC hubs (Aim 3). These studies will expand our understanding of epileptic networks. If successful, quantitative analysis of interictal EEG will enhance localization of seizure generators and improve surgical outcomes. This proposed project will also provide critical career development training to Dr. Conrad, an Epilepsy Instructor at University of Pennsylvania. The proposal builds on Dr. Conrad's background in quantitative EEG analysis and clinical epilepsy. Dr. Conrad will be mentored by Brian Litt, a world-renowned expert in computational epilepsy; Danielle Bassett, a leading expert in network theory; and Eric Marsh, a leading expert in quantitative IED analysis. The training plan in this proposal includes mentored completion of the proposed research, as well as a rigorous program of didactics, workshops, lab meetings, and directly relevant clinical work. Together, these experiences will provide essential training in time-series statistics, machine learning, subject enrollment, cortical stimulation, and EEG data acquisition. This proposed project and training plan will launch Dr. Conrad on an independent research career focused on using a multimodal approach to evaluating interictal data in order to improve our understanding and treatment of epilepsy.