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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.

项目总结