Shi, RiyiMufti, Shatha2026-04-292026https://hdl.handle.net/20.500.14154/78811Traumatic brain injury (TBI) is a leading cause of death and long-term disability worldwide, frequently resulting in complex secondary injuries and chronic neurological conditions such as post-traumatic epilepsy (PTE). Despite the high prevalence of TBI, identifying effective treatments for the subsequent sequalae has been challenging due to the unclear mechanisms of how TBI leads to conditions like PTE. My work addresses this gap by utilizing an in vitro TBI-on-a-chip model to simulate concussive impacts on primary cortical networks and conduct targeted mechanistic investigations into the biochemical and electrophysiological mechanisms driving TBI-induced pathologies like PTE. In the first study, a combination of electrophysiological microelectrode array (MEA) recordings and immunocytochemistry are used to investigate the role of the toxic reactive aldehyde acrolein in promoting injury-induced pathologies. The results identify acrolein as a primary driver of post-TBI astrocyte reactivity and neuronal network hyperexcitability and show that sequestering acrolein pharmacologically with hydralazine effectively mitigates these pathological changes. Furthermore, a novel machine learning (ML) framework using convolutional neural networks and Grad-CAM was employed to analyze astrocyte structural remodeling and pinpoint specific morphological features associated with injury and treatment conditions. In the second study, MEA recordings capturing changes in firing and bursting dynamics after impact are analyzed to examine the mechanisms underlying the transition of injured networks into seizure-like activity (SLA) using custom algorithms to quantify cross-correlogram shapes and identify bursts in network firing. The findings show that injury not only increases the synchronization of neuronal firing, which is a hallmark of SLA, but also reorganizes firing hierarchies and alters leader-follower relationships among neurons. Examining multiple dimensions of network activity simultaneously provides a more comprehensive understanding of how TBI alters neuronal communication and network dynamics and promotes SLA. In the third study, further investigations into altered neuronal interactions that lead to SLA are performed by applying a multilayered ML pipeline, which includes LSTM autoencoders, UMAP clustering, and deep Granger causality to MEA recordings of neuronal networks treated with bicuculline, a standardized experimental model of SLA. The results reveal that distinct neuronal subpopulations with unique activity profiles emerge during SLA, even in states of global network synchronization. Collectively, the findings in this thesis demonstrate that post-traumatic pathologies, especially PTE, are driven by identifiable molecular targets and structured network reorganization, suggesting a variety of options for therapeutic targeting. Furthermore, this work highlights the TBI-on-a-chip system as a versatile platform for investigating the pathophysiology of both TBI and PTE, enabling mechanistic studies into injury progression, the identification of novel therapeutic targets and biomarkers, the screening of new drugs, and the development of promising diagnostic tools.244en-USTraumatic Brain InjuryPost-traumatic seizureneuronal networkshyperexcitable networksastrocyte reactivitymachine learningNetwork Synchronizationacroleinhydralazineelectrophysiological recordingmicroelectrode arrays (MEAs)Thesis