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：癫痫影响着全球6000万反复发作的患者，其中40%的患者对任何药物治疗均无反应。这些患者将极大地受益于闭环神经刺激治疗以抑制癫痫发作，但这种治疗的疗效关键取决于刺激是否靠近癫痫发作起源（致痫区，EZ）以及在癫痫发作之前或发作时立即给予。该计划开发了新的计算工具，用于从多通道颅内EEG（iEEG）记录中进行有效的EZ定位和癫痫发作检测。智力优势：这些工具是通过（i）分析癫痫发作时大脑网络的动力学和（ii）开发一个基于模型的框架，该框架结合了多变量统计，贝叶斯估计和最优控制。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结构，以及（2）识别癫痫发作状态特有的拓扑特征并唯一定位EZ。从连续iEEG测量中检测这些特征的规则是自适应的，并且通过最小化检测延迟和假阳性概率的成本函数来优化特异性和灵敏度之间的权衡。更广泛的影响：工程学和神经科学之间的界面将产生多重转化影响。首先，所提出的工具将允许更准确的EZ定位和切除，更有效地审查iEEG信号，以及更有效的癫痫抑制治疗（更有效地放置刺激电极和更有效的神经刺激设备）。总的来说，这些结果将减少住院时间，并可能避免癫痫患者的致命事故，挽救生命，延长预期寿命，并改善药物管理。此外，该计划将引入一个变革性的检测范式，该范式适用于涉及与各种学科相关的隐藏状态转换检测的任何应用（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的多元信号处理和统计建模课程的开发，以及将激励巴尔的摩大都市地区的高中生（特别是少数民族学生）从事工程职业的推广活动。