SCH: EXP: Collaborative Research: Exploring Sparsity and Spectral-Temporal Decomposition in Real-Time Network Modulation for Intractable Epilepsy

SCH：EXP：合作研究：探索顽固性癫痫实时网络调制中的稀疏性和频谱-时间分解

• 批准号: 1406447
• 负责人: Behnaam Aazhang
• 金额: $ 30万
• 依托单位: William Marsh Rice University
• 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: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1406447&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; Exploring

Understanding the relationship between brain activity and human behavior is not only one of the most important scientific challenges of our generation but also one of the most important challenges in medicine and public health. This project develops new technology that can address the minute size of the neurons, and the vast amount of data generated by neural activity. This project leverages the collaborative environment between Rice and Texas Medical Center to develop novel electrical stimulation approaches to modulate the seizure network, adaptively and selectively. If successful, the end result would be a reparative therapy that leverages inherent brain plasticity mechanisms and may one day be independent of chronically implanted electronics.This project develops algorithms that capture the dynamic, frequency dependent connectivity of the brain from real-time monitoring of the brain using ECoG (Electrocorticography) and then identifying the "optimal" parameters of the LFS (low-frequency electrical stimulation) to modulate the connectivity of the epilepsy network with temporal and spatial precision. The complexity of modeling such connectivity in real-time is managed by first segmenting neural activity into different epochs and spectral bands and then deriving the sparse connectivity in each of the segments. Effective connectivity in each spectral-temporal segment is estimated using Granger causality. LFS is applied after detecting interictal epileptiform discharges (IEDs) at spatial locations identified from the model. These critical steps lead to the development of a prototype system of real-time stimulation with a natural trade-off of complexity versus accuracy prompting a compromise between battery life and efficacy. The efficacy of spatially-optimized, activity-triggered LFS is evaluated by measuring the irritability of the seizure network and comparing the rate of IEDs detected during pre- and post-treatment periods. These experiments would point the way to treatment of pharmacologically refractory epilepsy without surgical resection of brain tissue and lead to reparative therapies leveraging inherent brain plasticity. The proposed methodology presents the first of its kind reparative, real-time, and selective network modulation to treat a debilitating disease.

了解大脑活动和人类行为之间的关系不仅是我们这一代人最重要的科学挑战之一，也是医学和公共卫生领域最重要的挑战之一。该项目开发的新技术可以解决神经元的微小尺寸以及神经活动产生的大量数据。该项目利用Rice和Texas Medical Center之间的合作环境，开发新的电刺激方法，以适应性和选择性地调节癫痫发作网络。如果成功的话，最终的结果将是一种利用固有的大脑可塑性机制的修复疗法，并且有一天可能独立于长期植入的电子设备。使用ECoG实时监测大脑的频率依赖性连接（皮层电图），然后确定LFS的“最佳”参数（低频电刺激）来以时间和空间精度调制癫痫网络的连接性。实时建模这种连接的复杂性是通过首先将神经活动分割成不同的时期和谱带，然后导出每个片段中的稀疏连接来管理的。使用格兰杰因果关系估计每个频谱时间段中的有效连接性。LFS应用后，检测发作间期癫痫样放电（IED）的空间位置从模型中确定。这些关键步骤导致实时刺激的原型系统的开发，其具有复杂性与准确性的自然权衡，促使电池寿命和功效之间的折衷。通过测量癫痫发作网络的易激性并比较治疗前和治疗后期间检测到的IED的比率来评价空间优化的活动触发LFS的功效。这些实验将为不手术切除脑组织的难治性癫痫的治疗指明方向，并导致利用固有脑可塑性的修复性治疗。所提出的方法提出了第一种修复性，实时和选择性网络调制来治疗衰弱性疾病。