AI Health Virtual Seminar: Using Multimodal Data and AI to study Post-Traumatic Epilepsy
Dominique Duncan, PhD, USC Stevens Neuroimaging and Informatics Institute, University of Southern California with host Nicoleta J Economou, PhD; Duke AI Health
The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-site, international collaboration that aims to investigate the multifactorial processes underlying the development of epilepsy after traumatic brain injury (TBI). The study involves a parallel investigation in both humans and animal models and includes the collection of multimodal data such as MRI, EEG, and blood samples. The development of epilepsy after TBI is a complex phenomenon that spans multiple modalities, and a comprehensive understanding of its underlying biological mechanisms is essential for the development of effective treatments. To this end, we have established a centralized data archive that standardizes data and provides tools for searching, viewing, annotating, and analyzing them. This archive includes data generated from multicenter preclinical trials, clinical sites, and various laboratories in different formats. We use machine learning (ML) and artificial intelligence (AI) techniques to analyze large-scale, multimodal datasets and identify complex patterns that may not be apparent through traditional statistical methods. By integrating these cutting-edge analytical approaches with our data archive, we aim to accelerate progress towards the development of effective treatments for post-traumatic epilepsy. In addition to this EpiBioS4Rx archive, we have also developed the Data Archive for the BRAIN Initiative (DABI) and the COVID-19 Data Archive (COVID-ARC) to facilitate research in related areas and foster collaboration among researchers worldwide.