Neuroimaging Biomarkers of Post-Traumatic Epileptogenesis
Abstract
Background: Post-traumatic epilepsy (PTE) is the near-ideal condition to evaluate therapies that can prevent epilepsy, as it is one of the most common forms of epilepsy and there is a relatively short latency period prior to onset of recurrent seizures. During this latency period, the brain is thought to undergo changes that increase the propensity for seizures, called epileptogenesis. Successful clinical trials of epilepsy prevention therapies require a better understanding of the process of epileptogenesis. Both TBI and epilepsy are disorders of brain connectivity (i.e., the way the brain network is wired), but the relationship between TBI, brain connectivity abnormalities, and epileptogenesis represents a critical knowledge gap in the field. Brain connectivity or network abnormalities after TBI are associated with chronic physical and neurological symptoms such as memory problems. In epilepsy, connectivity abnormalities can help map the location of seizure onset and spread. The widespread abnormalities in brain networks represent a shared trait between TBI and epilepsy and warrant investigation as a tool for epilepsy prediction after TBI. Objectives/Hypotheses: The overall objectives of this proposal are to assess alterations in brain connectivity from 2 weeks to 6 months after TBI and use these measurements as features to develop predictive models to classify patients who will develop PTE. The central hypotheses are: (1) individuals who develop PTE have abnormal network profiles distinct from those seen with TBI of comparable injury severity without epilepsy, and (2) network profiles can accurately classify whether a patient will develop PTE. Specific Aims: Aim 1: To characterize the longitudinal profile of structural network measures inferred from diffusion magnetic resonance imaging (MRI) in a sample of uninjured controls. Using orthopedic and uninjured friend control data with an interscan interval of 6 months, we will establish the control distribution for structural network measures. Aim 2: To characterize structural network measures inferred from diffusion MRI obtained 2 weeks and 6 months after TBI in patients with and without PTE. Specifically, we will examine differences in network measures both cross-sectionally at each time point as well as the difference in the longitudinal profile between patients with and without PTE. Aim 3: To develop a robust machine learning framework to accurately classify patients who will develop PTE. Specifically, we will utilize structural network measures obtained at 2 weeks and 6 months after injury alongside traditional clinical risk factors for PTE to train models to accurately classify whether a patient will develop PTE. Research Strategy: This study will utilize data from one of the largest, well-characterized samples of patients with TBI enrolled in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Epileptogenesis Project (TRACK-TBI EPI) where PTE diagnosis will be determined by a neurologist who specializes in the care of patients with epilepsy and confirmed by a centralized panel of experts. Patients in TRACK-TBI have diffusion MRI scans, a powerful noninvasive tool for mapping brain connectivity, at both 2 weeks and 6 months after injury. We will utilize linear mixed effects models to compare structural network measures from these diffusion MRI scans in those with and without PTE. We will use these network abnormalities as features alongside traditional clinical risk factors for PTE to train robust machine learning models to accurately classify whether a patient will develop PTE. Innovation and Impact: The proposed research is directly aligned with the ERP’s mission and vision and will use an innovative method of analysis of diffusion MRI to determine if individuals with TBI are at risk of developing PTE. The proposed work has the potential to generate innovative deliverables that will have high impact on the million
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210593
Entities
People
- James Gugger
Organizations
- United States Army
- University of Pennsylvania