Understanding the Fast and Slow Spatiotemporal Dynamics of Human Seizures

项目总结 癫痫是世界上最突出的严重脑部疾病，全世界有近5000万人受到影响。为 据估计，这些患者中有30%，尽管进行了最大限度的医疗管理，但癫痫发作仍然控制不好， 并对健康和生活质量产生巨大的经济成本和影响。为了推进治疗 癫痫的治疗需要对驱动癫痫的时空动力学有更详细的了解 癫痫发作。描述这些动力学特别困难，因为像许多大脑功能一样， 过程跨越空间和时间尺度，从小神经元群体的快速活动到缓慢的 从癫痫发作到大面积脑区终止的演变。一个尺度上的大脑信号如何与 其他规模的问题是一个重大且鲜为人知的问题。虽然癫痫的动物模型提供了 强大的技术来研究空间尺度内和空间尺度之间的详细神经活动， 这些模型对人类癫痫的作用尚不清楚。癫痫动物模型的另一种替代方法是研究 从一群人类患者体内自发地发生癫痫发作。然而，典型的活体临床 对于癫痫发作的多尺度动态，录音只能提供有限的视角。在这个项目中，一个跨学科的 由癫痫专家和临床神经生理学家组成的研究小组，一名统计学家和一名 数学家将研究人类癫痫发作的时空动力学。为了做到这一点，该团队将分析 人类患者在癫痫发作期间的同时微电极和大电极记录， 特别关注癫痫中常见的有组织的时空模式和高频振荡。 为了理解这些数据，研究小组将开发和应用新的方法来表征这些模式， 并将这些活动与计算模型中的候选机制联系起来。完成拟议的 研究将代表着对人类癫痫发作、新方法的更深入了解的重大进展 为了分析和模拟在复杂多尺度数据中观察到的癫痫发作的时空动力学，新的 从脑电压记录估计模型参数和变量的方法，以及新的候选方法 癫痫外科治疗的靶点。

PROJECT SUMMARY Epilepsy is the world’s most prominent serious brain disorder, affecting nearly 50 million people worldwide. For an estimated 30% of these patients, seizures remain poorly controlled despite maximal medical management, with significant financial costs and effects on health and quality of life. To advance the therapeutic management of epilepsy requires a more detailed understanding of the spatiotemporal dynamics that drive seizures. Characterizing these dynamics is especially difficult because, like many brain functions, the processes span spatial and temporal scales, from the fast activity of small neural populations to the slow evolution from seizure onset to termination of large brain regions. How brain signals at one scale relate to those at other scales is a significant and poorly understood issue. While animal models of epilepsy provide powerful techniques to investigate detailed neural activity within and between spatial scales, the relationship of these models to human epilepsy is unclear. An alternative to animal models of epilepsy is to study spontaneously occurring seizures in vivo from a population of human patients. However, typical in vivo clinical recordings provide only a limited view of a seizure’s multiscale dynamics. In this project, an interdisciplinary research group consisting of epileptologists and clinical neurophysiologists, a statistician, and a mathematician will study the spatiotemporal dynamics of human seizures. To do so, the team will analyze simultaneous microelectrode and macroelectrode recordings from human patients during seizures, with a particular focus on the organized spatiotemporal patterns and high frequency oscillations common in epilepsy. To make sense of these data, the team will develop and apply new methods to characterize these patterns, and link these activities to candidate mechanisms in computational models. Completion of the proposed research will represent significant progress towards a deeper understanding of human seizures, new methods to analyze and model the spatiotemporal dynamics of seizures observed in complex multiscale data, new methods to estimate model parameters and variables from brain voltage recordings, and new candidate targets for surgical treatment of epilepsy.