Machine learning model to characterize seizure development in traumatic brain injury patients

Traumatic brain injury (TBI) occurs in 69 million people annually and many patients go on to develop disabling disorders such as post-traumatic epilepsy (PTE). This work focuses on data modeling and analysis for TBI patients who develop seizures. We investigated and analyzed MRI scans using voxel-based morphometry (VBM) to characterize gray level intensity differences between TBI patients who developed seizures and TBI patients who have not developed seizures. We used MRI scans from the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy, which aims to identify epileptogenic biomarkers through an international project involving multiple species, modalities, and research institutions. Using the VBM approach, statistically significant voxel changes were identified between the two clinical groups in different brain regions. Stochastic modeling and statistical analysis of the data in terms of interesting, confounding factors (age and total intracranial volume) and residual variability applied to each voxel independently, are presented. Statistical inference is used to test hypotheses that are expressed as functions of the General Linear Model estimated regression parameters. In addition, we used significant voxels to train a Neural Network (NN) classifier and evaluate the informative power of the proposed approach. The NN was able to distinguish the two clinical groups with an Area Under the receiver operating characteristics Curve of 62%.