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

基本信息

批准号：
10584583
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
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10584583
关键词：
Address Affect Animal Model Biological Biological Markers Biophysics Brain Brain Diseases Brain region Characteristics Clinical Communities Complex Computer Models Computing Methodologies 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 Parameter Estimation 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 conflict resolution improved in vivo multiscale data neural new therapeutic target novel optimal treatments patient population prevent spatiotemporal statistics surgery outcome treatment strategy voltage

项目摘要

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.

项目概要癫痫是世界上最严重的脑部疾病，影响着全世界近 5000 万人。尽管采取了最大程度的医疗管理，但估计其中 30% 的患者癫痫发作仍控制不佳，带来巨大的财务成本以及对健康和生活质量的影响。癫痫的治疗需要更详细地了解驱动癫痫的时空动态描述这些动态特征尤其困难，因为像许多大脑功能一样，过程跨越空间和时间尺度，从小型神经群体的快速活动到缓慢的活动某一尺度的大脑信号如何与其他尺度的问题是一个重要且知之甚少的问题，而癫痫动物模型却提供了这一点。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，这些人类癫痫模型尚不清楚。癫痫动物模型的替代方法是研究。然而，人类患者体内自发发生的癫痫发作是典型的体内临床。在这个项目中，记录仅提供了癫痫发作多尺度动态的有限视图。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，研究小组将进行分析。人类患者癫痫发作期间的同步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，团队将开发并应用新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。研究将代表着在更深入地了解人类癫痫发作、新方法方面取得的重大进展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，新从脑电压记录中估计模型参数和变量的方法以及新的候选方法癫痫手术治疗的目标。