Network dynamics of sleep-wake states in epilepsy

癫痫睡眠-觉醒状态的网络动力学

• 批准号: 10591896
• 负责人: Joline Fan
• 金额: $ 21.7万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591896
• 关键词: Address; Anatomy; Antiepileptic Agents; Area; Award; Biometry; Biophysics; Brain; Brain region; Chronic; Clinical; Clinical Trials; Coupling; Data; Development Plans; Diffusion Magnetic Resonance Imaging; Drug resistance; Electrophysiology (science); Epilepsy; Frequencies; Goals; Human; Image; Intractable Epilepsy; Investigation; Knowledge; Light; Location; Machine Learning; Magnetoencephalography; Measures; Medical; Mentorship; Methods; Modeling; Outcome; Partial Epilepsies; Pathologic; Pathway Analysis; Patients; Persons; Pharmaceutical Preparations; Physicians; Physiological; Play; Property; Quality of life; Research; Research Project Grants; Scientist; Seizures; Sleep; Structure; Therapeutic; Time; Training; Translational Research; Variant; Wakefulness; Work; biophysical model; career; career development; connectome; epileptiform; frontal lobe; improved; innovation; insight; multidisciplinary; multimodality; neural; neural patterning; neuroregulation; non rapid eye movement; predictive modeling; programs; rational design; signal processing; skills; therapy design; tool; tractography; treatment effect

PROJECT SUMMARY/ABSTRACT Of 46 million people worldwide with active epilepsy, one third are drug-resistant. Emerging neuromodulation- based therapies have demonstrated great potential to reduce seizure frequency and improve the quality of life in patients with drug-resistant epilepsy over time. The mechanisms underlying such therapies are thought to relate to the progressive restructuring of the epileptogenic network toward dynamics that reduce epileptic activity. Yet, the network properties underlying low and high epileptic potential are poorly understood, and the management of neuromodulatory therapies thus remains largely empiric with variable outcomes. To move toward rational approaches rooted in mechanistic understanding, there is a critical need to first fundamentally understand how network dynamics influence epileptogenic activity. In this proposal, we turn to the rich relationship between sleep and epilepsy, as sleep-wake states offer a robust and systematic way to cycle through a wide range of network dynamics that are strongly associated with different epileptic potentials. By leveraging sleep-wake states as a portal to probing dynamic brain networks, the overall objective of this proposal is to identify salient network features that represent states of variable epileptogenic potential and to determine associated network mechanisms that indicate reconfiguration into epileptogenic states. Using a combination of magnetoencephalography (MEG) imaging and diffusion tensor imaging (DTI)/tractography, I will first identify physiologic network dynamics of sleep-wake states in patients with focal epilepsy (Aim 1). I will then identify state-dependent network predictors and develop biophysical models of pathologic states predictive of interictal epileptiform activity (Aim 2). The expected outcome of this work is to gain a deeper understanding of key network features that augment epileptic potential and insight into their underlying mechanisms. This proposal combines an innovative research project with translational implications and a rigorous training and career development plan, which are highly complementary and together will facilitate my transition into an independent physician-scientist. I have assembled a leading, multidisciplinary mentorship team that has a constellation of expertise aligned with my research and training goals, including in epilepsy, sleep, MEG imaging, structural-function network analysis, neural computation, and biostatistics. In addition, through formal training, coursework, and directed mentorship, I will advance my skills in the areas of signal processing, machine learning, dynamical models, sleep electrophysiology, and clinical trials, which I will continue to use throughout my scientific career. The knowledge and training obtained during this award period will enable me to establish a robust independent research program that leverages multimodal electrophysiology and imaging in humans and insights from the rich relationship between sleep and epilepsy to improve therapeutic tools for patients with medically refractory epilepsy.

项目总结/摘要 在全球4600万活动性癫痫患者中，三分之一是耐药性。新兴的神经调节- 基础疗法在减少癫痫发作频率和改善生活质量方面具有巨大潜力 耐药性癫痫患者的发病率。这些疗法的机制被认为是 与致痫网络朝着减少癫痫发作的动力学进行性重组有关。 活动然而，低和高癫痫电位下的网络特性知之甚少， 因此，神经调节治疗的管理在很大程度上仍然是经验性的，具有可变的结果。移动 对于植根于机械理解的理性方法，首先需要从根本上 了解网络动态如何影响癫痫活动。在这个建议中，我们转向富人， 睡眠和癫痫之间的关系，因为睡眠-觉醒状态提供了一种强大而系统的循环方式， 通过与不同癫痫电位密切相关的广泛的网络动力学。通过 利用睡眠-觉醒状态作为探测动态大脑网络的门户， 一个建议是识别代表可变癫痫电位状态的显著网络特征， 确定指示重新配置为致癫痫状态的相关网络机制。使用 结合脑磁图（MEG）成像和弥散张量成像（DTI）/纤维束成像，我将 首先确定局灶性癫痫患者睡眠-觉醒状态的生理网络动力学（目的1）。我会 然后识别状态依赖的网络预测器，并开发病理状态的生物物理模型， 预测发作间期癫痫样活动（目的2）。这项工作的预期成果是获得更深入的 了解增强癫痫潜能的关键网络特征，并深入了解其潜在的 机制等该提案结合了一个具有翻译影响的创新研究项目和一个 严格的培训和职业发展计划，这是高度互补的，共同将促进我的 转变成一个独立的科学家。我召集了一个领先的多学科导师 团队拥有与我的研究和培训目标一致的专业知识，包括癫痫， 睡眠、MEG成像、结构功能网络分析、神经计算和生物统计学。此外，本发明还提供了一种方法， 通过正式的培训、课程和指导性的指导，我将提高我在信号领域的技能 处理，机器学习，动态模型，睡眠电生理学和临床试验，我将 在我的科学生涯中一直使用。在此获奖期间获得的知识和培训 将使我能够建立一个强大的独立研究计划，利用多模式 人类的电生理学和成像，以及从睡眠和癫痫之间的丰富关系中获得的见解， 为难治性癫痫患者改进治疗工具。