EAGER: Modeling Network Dynamics in the Epileptic Brain to Develop Translational Tools for Seizure Localization and Detection

EAGER：对癫痫大脑中的网络动力学进行建模，以开发用于癫痫定位和检测的转化工具

• 批准号: 1518672
• 负责人: Sabato Santaniello
• 金额: $ 7.24万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-16 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1518672&HistoricalAwards=false
• 关键词: EAGER; Modeling; Network; Dynamics; Epileptic

Objective: Epilepsy affects 60 million people worldwide who suffer from recurrent seizures, and 40% of patients do not respond to any drug therapy. These patients would greatly benefit from closed-loop neuro-stimulation therapy to suppress seizures, but the efficacy of such therapy critically depends on whether the stimulus is administered close to the seizure origin (epileptogenic zone, EZ) and immediately prior to or at seizure onset. This program develops novel computational tools for effective EZ localization and seizure onset detection from multi-channel intracranial EEG (iEEG) recordings. Intellectual Merit: The tools are derived by (i) analyzing the dynamics of the brain network as a seizure approaches and (ii) developing a model-based framework that combines multivariate statistics, Bayesian estimation, and optimal control. The tools use iEEG recordings to (1) reconstruct and track the topology of the brain network over time, and (2) identify topological signatures that are specific of the seizure state and uniquely localize the EZ. The rule that detects these signatures from sequential iEEG measurements is adaptive and optimizes the trade-off between specificity and sensitivity by minimizing a cost function of both the detection delay and the probability of false positives. Broader Impacts: Multiple translational impacts will occur at the interface between engineering and neuroscience. First, the proposed tools will allow more accurate EZ localization and resection, more efficient review of iEEG signals, and more effective treatments for seizure suppression (more effective placement of the stimulation electrodes and more efficient neuro-stimulation devices). Overall, these outcomes will reduce the hospitalization time and potentially avoid fatal accidents to epilepsy patients, save lives, extend life-expectancy, and improve the administration of drugs. Also, this program will introduce a transformative detection paradigm that generalizes to any application involving hidden state transition detection relevant to a wide array of disciplines (e.g., early earthquake detection or threats detection). Finally, this program will support the development of courses in multivariate signal processing and statistical modeling at Johns Hopkins University and of outreach activities that will inspire high school students (especially from minorities) from the Baltimore metropolitan area to pursue a career in engineering.

目的：癫痫会影响全球6000万人复发性癫痫发作的人，40％的患者对任何药物治疗没有反应。这些患者将大大受益于闭环神经刺激疗法以抑制癫痫发作，但是这种疗法的功效在很大程度上取决于刺激是在接近癫痫发作（癫痫发作区，EZ）和癫痫发作之前的癫痫发作附近给予的刺激。 。该程序开发了新型计算工具，可从多渠道内脑电图（IEEG）记录中有效地定位和癫痫发作检测。智力优点：这些工具是通过（i）分析脑网络的动力学来得出的，并且（ii）开发一个基于模型的框架，该框架结合了多元统计，贝叶斯估计和最佳控制。这些工具使用IEEG记录来（1）随着时间的推移重建和跟踪大脑网络的拓扑，（2）确定对癫痫发作状态的特定拓扑特征并独特地定位EZ。从顺序IEEG测量中检测这些签名的规则是自适应的，并通过最大程度地降低检测延迟的成本函数和假阳性的概率来优化特异性和灵敏度之间的权衡。更广泛的影响：工程和神经科学之间的接口将发生多种翻译影响。首先，提出的工具将允许更准确的EZ定位和切除，对IEEG信号的更有效审查以及更有效的癫痫发作抑制处理方法（更有效地放置了刺激电极和更有效的神经刺激设备）。总体而言，这些结果将减少住院时间，并有可能避免癫痫患者的致命事故，挽救生命，延长生命的预期并改善药物的给药。此外，该计划将引入一个变革性检测范式，该范式将概括为涉及与各种学科有关的隐藏状态过渡检测的任何应用（例如，早期地震检测或威胁检测）。最后，该计划将支持约翰·霍普金斯大学（Johns Hopkins University）的多元信号处理和统计建模课程的开发以及将激发巴尔的摩大都会地区的高中生（尤其是来自少数民族）的外展活动，从而从事工程职业。