A Biofidelic Model of PTE (Project 1)

点击翻译按钮获取中文摘要

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.