Current Projects
The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Project
Some traumatic brain injury patients secondarily develop epilepsy. EpiBioS4Rx is studying a preclinical rodent model of induced brain injury and human brain injury patients in order to understand the mechanism of epileptogenesis. We are applying data science and machine learning to imaging and electrophysiology to identify translational biomarkers that will help predict epilepsy after brain injury. Currently, we work with scalp and depth EEG, resting-state fMRI, diffusion-weighted imaging, and structural data.
Data Archive for the BRAIN Initiative (DABI)
An archive and analysis sandbox for human invasive neurophysiology data. The platform supports data deposition and de-identification, protocol detection, quality assessment, mapping of data attributes into a common schema, query, analytics, and visualization.
COVID-19 Data Archive (COVID-ARC)
A data archive and resource platform for multimodal (i.e., demographics, clinical outcomes, imaging scans) and longitudinal data related to COVID-19.
The Virtual Brain Segmenter (VBS) is a tool that utilizes virtual reality to transform how manually correcting segmentation errors is accomplished. VBS allows users to walk into the brain and visualize features that are challenging to see on a computer. Visualizing the 3-dimensional structure of the brain using simulated virtual reality rather than the 2-dimensional interface makes understanding neuroanatomy more intuitive to non-experts.
BRAIN Integrated Resource for Human Anatomy and Intracranial Neurophysiology (BRAIN^2)
Despite significant advances in human neurosciences emerging from direct intracranial recordings of brain activity in human volunteers, it can be difficult to fully interpret these signals because of an incomplete understanding of the factors that affect their quality and patterns. This is further complicated by the relative rarity of these signals, which makes it challenging to conduct the large-scale studies that are needed to study the factors that may contribute to signal variability, such as underlying brain anatomy. In this project, we use the power of numbers derived from the Data Archive for the BRAIN Initiative to create a large integrated multimodal dataset to enable large-scale analyses of these brain signals and their significance, especially as it pertains to the influence of brain anatomy on these neural signals.