A Biofidelic Model of PTE (Project 1)

PTE 的 Biofidelic 模型（项目 1）

• 批准号: 10713244
• 负责人: Beth A Costine-Bartell
• 金额: $ 26.1万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713244
• 关键词: Affect; Algorithms; Animal Model; Animals; Antiepileptic Agents; Behavior; Bilateral; Biological Markers; Blood - brain barrier anatomy; Brain; Categories; Cells; Chronic; Complex; Contusions; Convulsions; Development; Electroencephalography; Epilepsy; Epileptogenesis; Extracellular Matrix; Family suidae; Frequencies; Functional disorder; Goals; Human; Imaging technology; Immune; Inflammation; Injury; Investigation; Label; Lesion; Machine Learning; Mediating; Methods; Modeling; Morbidity - disease rate; Neocortex; Neuroglia; Noise; Pathogenesis; Peripheral; Phase; Plasma; Post-Traumatic Epilepsy; Refractory; Rodent; Seizures; Serum; Signal Transduction; Site; Synapses; TBI Patients; Technology; Testing; Therapeutic; Training; Traumatic Brain Injury; Traumatic Brain Injury recovery; base; biomarker identification; black hole; cellular imaging; field study; healing; injury and repair; innovation; insight; machine learning pipeline; neocortical; neuroblast; neurovascular unit; new therapeutic target; porcine model; prevent; specific biomarkers; targeted treatment; therapy development; tool

Project 1 Summary Post-traumatic epilepsy (PTE) affects 15-40% of those with traumatic brain injury (TBI) causing significant morbidity even after initial recovery from the TBI. Major limitations in understanding the pathophysiology of PTE are a lack of biofidelic modeling of PTE in the gyrencephalic brain and biomarkers that identify or predict PTE. Our long-term goal is to understand the pathophysiology the leads to PTE in order to develop therapies that halt or inhibit the progression of pathogenesis that leads to post-traumatic epilepsy. The overall objective is to identify biomarkers of PTE in a gyrencephalic brain and the ionic basis of PTE. Our central hypotheses are that long-term ionic changes alters GABAergic signaling leading to PTE and biomarkers of PTE can be identified with machine learning boosting algorithms. The rationale is that a toolkit of automated methods of quantifying biomarkers in a biofidelic model of PTE along with insight into the ionic changes that mediate GABAergic signaling will allow identification of new therapeutic targets and allow efficient testing of those targets to prevent the development of PTE in patients with TBI. We will test our central hypotheses with two Specific Aims: Aim 1: We will train previously established algorithms to identify the best mode, or combination of modes, to identify seizure candidates in these models of PTE and maybe to even predict PTE. We hypothesize that we can use existing algorithms to rapidly analyze behavior and ECoG, identify seizure correlates, and together with peripheral serum biomarkers, use existing boosting algorithms to predict which subjects will develop PTE. Aim 2: We will determine if chronic changes in the ionic basis of GABAergic signaling and in the neocortical network activity are indicated by IED’s and biomarkers in the latent period. We hypothesize that those pigs that develop PTE will have greater peripheral plasma biomarkers associated with inflammation and blood brain barrier opening, greater changes in Cl-o and Cl-I, and faster development of hypersynchronous local and global network activity (EEG). The is approach is innovative in that, we bring cell imaging technology developed in rodent and organotypic culture models and newly developed algorithms to the gyrencephalic brain merging the fields of high technology and large animal models. Our contribution is significant as tools and biomarkers will enable wider use of gyrencephalic models of PTE and prediction of PTE will open up large fields of study not yet possible. Understanding the extracellular-matrix-induced changes that alter the ionic bases of GABAergic signaling and local and global network changes in the same subjects during the course of epileptogenesis may identify mechanisms of epileptogenesis that serve as targets for therapies that may prevent the development of PTE.

项目1摘要 创伤后癫痫（PTE）影响15-40%的创伤性脑损伤（TBI）患者， 即使在从TBI中初步恢复后也存在显著的发病率。理解的主要局限性 PTE的病理生理学缺乏脑回中PTE的生物化学建模， 识别或预测PTE的生物标志物。我们的长期目标是了解其病理生理学 目的是开发治疗方法，阻止或抑制导致PTE的发病机制的进展， 创伤后癫痫总体目标是确定脑回畸形患者PTE的生物标志物。 脑和PTE的离子基础。我们的中心假设是，长期的离子变化改变了 可以通过机器学习识别导致PTE的GABA能信号和PTE的生物标志物 提升算法其基本原理是，一个量化生物标志物的自动化方法工具包 PTE的生物代谢模型沿着对介导GABA能信号传导的离子变化的深入了解， 允许识别新的治疗靶点，并允许有效检测这些靶点，以防止 TBI患者的PTE发展。 我们将通过两个具体目标来测试我们的中心假设：目标1：我们将训练先前建立的 识别最佳模式或模式组合的算法，以识别这些模式中的癫痫发作候选者。 PTE的模型，甚至可以预测PTE。我们假设我们可以使用现有的算法， 快速分析行为和ECoG，识别癫痫发作相关性，并与外周血清 生物标志物，使用现有的增强算法来预测哪些受试者将发展PTE。目标2：我们 确定GABA能信号传导的离子基础和新皮层网络的慢性变化 活性由IED和潜伏期的生物标志物指示。我们假设这些猪 发生PTE的患者将具有更高的与炎症和血液相关的外周血浆生物标志物 脑屏障开放，Cl-o和Cl-I的变化更大，超同步性发展更快 本地和全球网络活动（EEG）。 这种方法是创新的，我们带来了在啮齿动物中开发的细胞成像技术， 器官型培养模型和新开发的算法，以脑回融合， 高科技领域和大型动物模型。我们的贡献是重要的工具， 生物标志物将使PTE的脑回模型得到更广泛的应用， 大的研究领域还不可能。理解细胞外基质诱导的改变 GABA能信号传导的离子基础以及同一受试者的局部和全局网络变化 在癫痫发生的过程中，可以识别作为靶点的癫痫发生机制 用于预防PTE发展的治疗。