Understanding the fast and slow spatiotemporal dynamics of human seizures

了解人类癫痫发作的快慢时空动态

• 批准号: 10361503
• 负责人: SYDNEY S CASH
• 金额: $ 45.69万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10361503
• 关键词: Address; Affect; Animal Model; Biological; Biological Markers; Biophysics; Brain; Brain Diseases; Brain region; Characteristics; Clinical; Communities; Complex; Computer Models; Computing Methodologies; Conflict (Psychology); Data; Data Analyses; Development; Electrodes; Electrophysiology (science); Epilepsy; Evolution; Financial cost; Frequencies; Health; High Frequency Oscillation; Human; Interdisciplinary Study; Link; Medical; Medicine; Methods; Microelectrodes; Modeling; Nature; Neurosciences; Operative Surgical Procedures; Outcome Study; Patient Care; Patients; Pattern; Persons; Population; Process; Quality of life; Reproducibility; Research; Research Personnel; Seizures; Signal Transduction; Surface; Techniques; Testing; Therapeutic; Time; Travel; clinically relevant; improved; in vivo; multiscale data; new therapeutic target; novel; optimal treatments; patient population; prevent; relating to nervous system; spatiotemporal; statistics; surgery outcome; treatment strategy; voltage

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.

项目摘要 癫痫是世界上最突出的严重脑部疾病，影响全球近5000万人。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， 具有显著的经济成本和对健康和生活质量的影响。为了提高治疗效果 癫痫的管理需要更详细地了解时空动力学， 癫痫发作。描述这些动力学是特别困难的，因为，像许多大脑功能一样， 过程跨越空间和时间尺度，从小神经群体的快速活动到缓慢的神经活动。 从癫痫发作到大脑区终止的演变。大脑信号在一个尺度上是如何与 在其他尺度上的变化是一个重要的、但了解甚少的问题。虽然癫痫动物模型提供了 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影响尚不清楚。癫痫动物模型的另一种选择是研究 人类患者群体体内自发发生的癫痫发作。然而，典型的体内临床 记录仅提供了癫痫发作的多尺度动力学的有限视图。在这个项目中， 一个由癫痫学家和临床神经生理学家、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。为此，该团队将分析 同时微电极和宏电极记录人类患者在癫痫发作期间， 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，该团队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。完成建议 研究将代表着对人类癫痫发作更深入了解的重大进展，新方法 为了分析和模拟在复杂的多尺度数据中观察到的癫痫发作的时空动态， 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。 癫痫的治疗方法有哪些