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.

项目摘要 癫痫是最常见的严重神经系统疾病之一，约40%的癫痫患者不会 对现有治疗作出反应。在临床上，长期的，难治性癫痫与负面的手术结果， 通常与分布性癫痫发作有关，而不是局部致痫区。了解 癫痫作为一种大规模的脑网络异常，使新的治疗选择的发展， 研究方向。目前，大多数与癫痫网络分析相关的研究都是 集中在发作期，而很少有人致力于癫痫发生的早期阶段的分析 （潜伏期）。研究癫痫发生的大脑网络特性同样重要， 开展抗癫痫干预措施，防治癫痫。在实验初期，我们 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。 它们是由病理性相互连接的神经元簇（PIN簇）产生的，反映了神经元的爆发。 人口激增慢性癫痫动物模型的最新进展证明了空间分布 pHFO事件，这意味着癫痫发生过程中大规模PIN簇网络的发展。是 关键是研究网络拓扑结构和特征的PIN簇形成的癫痫网络， 以进一步了解癫痫发生的潜在机制。 为了填补这一空白，本研究计划使用红藻氨酸（KA）探索基于pHFO的网络。 诱发癫痫发生的癫痫持续状态（SE）模型。我们假设SE后的癫痫发生是 这取决于大规模的PIN簇网络的形成， 和pHFO的时间耦合。结合生物相容的、基于有机材料的神经接口阵列 （NeuroGrid）与多通道硅探针，我们的目标是确定pHFOs的空间和时间分布 （目标1）。利用图论分析和香农熵等先进的计算算法 (SE)，我们建议调查基于pHFO的致癫痫的因果关系和特征， 网络（Aim2）。本研究的结果将评估新的基于网络的记录设计的鲁棒性 和算法开发。它还将确定pHFO导出的网络参数是否是 癫痫发生的可靠生物标志物。未来的计划是将pHFO网络概念转化为 将计算机工具应用于癫痫的临床研究。这种方法可能会开辟一个新的方向 预防癫痫发展和治疗癫痫。

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