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.

了解大脑活动与人类行为之间的关系不仅是我们这一代最重要的科学挑战之一，而且是医学和公共卫生中最重要的挑战之一。该项目开发了可以解决神经元的微小大小以及神经活动产生的大量数据的新技术。该项目利用赖斯和德克萨斯医学中心之间的协作环境来开发新型的电刺激方法，以适应性和有选择地调节癫痫发作网络。 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具有时间和空间精度的癫痫网络的连通性。通过将神经活动首先分割为不同的时期和光谱带，然后在每个段中得出稀疏的连通性来管理这种实时建模这种连通性的复杂性。使用Granger因果关系估算每个光谱时期段中的有效连通性。在从模型中确定的空间位置检测出断发间癫痫放电（IED）后，使用LFS。这些关键步骤导致了实时刺激的原型系统的发展，其复杂性的自然权衡与准确性促使电池寿命和功效之间的妥协。通过测量癫痫发作网络的易怒和比较在处理前和治疗后检测到的IED速率，评估了空间优化，活动触发的LF的功效。这些实验将指向治疗药理学上难治性癫痫的方法，而无需手术切除脑组织，并导致利用固有脑可塑性的修复疗法。提出的方法介绍了同类的第一个修复，实时和选择性网络调节，以治疗令人衰弱的疾病。