New approach for identification pHFO networks to predict epileptogenesis

识别 pHFO 网络以预测癫痫发生的新方法

• 批准号: 10665791
• 负责人: Lin Li
• 金额: $ 18万
• 依托单位: UNIVERSITY OF NORTH TEXAS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665791
• 关键词: Aftercare; Animal Model; Animals; Antiepileptogenic; Area; Biological Markers; Brain; Brain Diseases; Characteristics; Chronic; Clinical; Clinical Research; Computational algorithm; Coupling; Data; Development; Early Diagnosis; Effectiveness; Electrophysiology (science); Elements; Entropy; Epilepsy; Epileptogenesis; Event; Exhibits; Future; Goals; Graph; High Frequency Oscillation; Intervention; Intractable Epilepsy; Investigation; Kainic Acid; Knowledge; Lesion; Measures; Methods; Modeling; Neocortex; Network-based; Neurons; Outcome Study; Pathologic; Pathology; Patients; Phenotype; Population; Prevention; Probability; Process; Property; Rattus; Research; Resolution; Seizures; Signal Transduction; Silicon; Spatial Distribution; Status Epilepticus; Techniques; Temporal Lobe Epilepsy; Testing; Training; Translating; Update; algorithm development; biomaterial compatibility; computerized tools; design; effectiveness evaluation; experimental study; graph theory; neocortical; nervous system disorder; network architecture; neural; novel; novel strategies; prevent; preventable epilepsy; software development; support vector machine; surgery outcome

PROJECT SUMMARY Epilepsy is among the most common serious neurological disorders, and about 40% of epilepsy patients do not respond to existing treatment. Clinically, the prolonged, refractory epilepsy with negative surgical outcomes is often associated with distributed epilepsy onset rather than a local epileptogenic zone. Understanding the epilepsy as a large-scale brain network abnormality enables the development of new treatment options and research directions. At present, the majority of research related to analysis of the epileptic network has been focused on the ictal period, while few have been devoted to the analysis of the earlier stages of epileptogenesis (latent period). Investigating the brain network properties of epileptogenesis is as important and can help develop antiepileptogenic interventions for epilepsy prevention and cure. Early in our experiments, we discovered pathological high-frequency oscillations (pHFOs), which are reliable biomarkers of epileptogenesis. They are generated by clusters of pathologically interconnected neurons (PIN-clusters) and reflect bursts of population spikes. Recent updates in the animal models of chronic epilepsy evidenced the spatially distributed pHFO events, which implies the development of large-scale PIN-cluster networks during epileptogenesis. It is critical to study the network topology and characteristics of PIN-cluster-formed epileptogenic networks in order to further understand the underlying mechanisms of epileptogenesis. To fulfill this gap, the present study plan is to explore pHFO-based networks using the Kainic Acid (KA)- induced status epilepticus (SE) model of epileptogenesis. We hypothesize that epileptogenesis after SE is dependent upon the formation of large-scale PIN-cluster networks that is expressed by the spatial occurrence and temporal coupling of pHFOs. Combining the biocompatible, organic–material based neural interface array (NeuroGrid) with multichannel silicon probes, we aim to identify the spatial and temporal profiles of pHFOs (Aim1). Using the advanced computational algorithms such as graph theory analysis and Shannon Entropy (SE), we propose to investigate the causal relationship and characteristics of the pHFO-based epileptogenic networks (Aim2). The outcome of this study will assess the robustness of novel network-based recording design and algorithm development. It will also determine whether the pHFO-derived network parameters are a reliable biomarker of epileptogenesis. The future plans are to translate the pHFO-network concept and computational tools into the clinical study of epilepsy. This approach may open a new direction to the prevention of epilepsy development and cure epilepsy.

项目概要 癫痫是最常见的严重神经系统疾病之一，约 40% 的癫痫患者不患有癫痫症。 临床上，手术结果阴性的长期难治性癫痫是有效的。 通常与分布式癫痫发作相关，而不是局部致癫痫区域。 癫痫作为一种大规模的脑网络异常使得新的治疗方案的开发成为可能 目前，大部分研究都与癫痫网络的分析有关。 重点关注发作期，而很少致力于分析癫痫发生的早期阶段 （潜伏期）研究癫痫发生的大脑网络特性同样重要并且可以提供帮助。 在我们的实验早期，我们开发了用于癫痫预防和治疗的抗癫痫干预措施。 发现了病理性高频振荡（pHFO），这是癫痫发生的可靠生物标志物。 它们是由病理性互连的神经元簇（PIN 簇）生成的，反映了 慢性癫痫动物模型的最新更新证明了其空间分布。 pHFO 事件，这意味着癫痫发生期间大规模 PIN 簇网络的发展。 对于研究 PIN 簇形成的致癫痫网络的网络拓扑和特征至关重要 为了进一步了解癫痫发生的潜在机制。 为了填补这一空白，目前的研究计划是利用红藻氨酸 (KA) 探索基于 pHFO 的网络 - 癫痫发生的诱发性癫痫持续状态（SE）模型我们捕获了 SE 后的癫痫发生。 依赖于大规模 PIN 簇网络的形成，该网络由空间发生表示 和 pHFO 的时间耦合结合生物相容性、有机材料的神经接口阵列。 (NeuroGrid) 使用多通道硅探针，我们的目标是识别 pHFO 的空间和时间分布 （目标1）。使用图论分析和香农熵等先进计算算法。 (SE)，我们建议研究基于 pHFO 的致痫的因果关系和特征 本研究的结果将评估基于网络的新型记录设计的稳健性。 它还将确定 pHFO 导出的网络参数是否是一个。 癫痫发生的可靠生物标志物。未来的计划是转化 pHFO 网络概念和 这种方法可能会为癫痫的临床研究开辟一个新的方向。 预防癫痫的发展和治疗癫痫。

项目成果

Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy.

• DOI: 10.3389/fneur.2023.1077702
• 发表时间: 2023
• 期刊: Frontiers in neurology
• 影响因子: 3.4