Traumatic brain injury (TBI) is a leading cause of death in young adults and a risk factor for acquired epilepsy. Severe TBI, after a period of time, causes numerous neuropsychiatric and neurodegenerative problems with varying comorbidities; and brain homeostasis may never be restored. As a consequence of disrupted equilibrium, neuropathological changes such as circuit remodeling, reorganization of neural networks, changes in structural and functional plasticity, predisposition to synchronized activity, and post-translational modification of synaptic proteins may begin to dominate the brain. These pathological changes, over the course of time, contribute to conditions like Alzheimer disease, dementia, anxiety disorders, and post-traumatic epilepsy (PTE). PTE is one of the most common, devastating complications of TBI; and of those affected by a severe TBI, more than 50% develop PTE. The etiopathology and mechanisms of PTE are either unknown or poorly understood, which makes treatment challenging. Although anti-epileptic drugs (AEDs) are used as preventive strategies to manage TBI, control acute seizures and prevent development of PTE, their efficacy in PTE remains controversial. In this review, we discuss novel mechanisms and risk factors underlying PTE. We also discuss dysfunctions of neurovascular unit, cell-specific neuroinflammatory mediators and immune response factors that are vital for epileptogenesis after TBI. Finally, we describe current and novel treatments and management strategies for preventing PTE.

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain’s abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain’s response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate asses sment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.