Mon, Feb 10, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Andrei Irimia, Gerontology, Biomedical Engineering, & Neuroscience at USC
Talk Title: Multimodal Imaging, Machine Learning and Electrophysiology for Connectome Mapping in Traumatic Brain Injury and Alzheimer's Disease
Series: Medical Imaging Seminar Series
Abstract: Mapping brain circuitry and its changes after traumatic brain injury (TBI) benefits substantially from the integration of multimodal neuroimaging techniques to quantify and monitor brain pathology, plasticity and degeneration. We have integrated fMRI and network theory with EEG and other approaches to perform supervised learning of connectome data and to study functional trajectories after mild TBI (mTBI). Our results show that geriatric mTBI is associated with fronto-hippocampo-limbic alterations in the brain's default mode network (DMN), and that many of these alterations are statistically indistinguishable from those observed in AD. By leveraging machine learning, we have shown that AD-like degradation of functional circuits can be predicted by acute cognitive deficits after geriatric mTBI. In addition to establishing a statistical association between brain injury, cognition and AD-like DMN degradation, these findings advance the important goal of acutely forecasting mTBI patients' chronic alterations of brain connectivity along AD-like functional trajectories.
Biography: Andrei Irimia is Assistant Professor of Gerontology, Biomedical Engineering and Neuroscience at USC. He holds a PhD in biophysics from Vanderbilt University and has done postdoctoral research at UCSD and UCLA prior to joining USC. His research leverages structural MRI, fMRI, DTI and EEG to study the relationship between traumatic brain injury and Alzheimer's disease. His laboratory in the Davis School of Gerontology is funded by the NIH and DoD.
Host: Richard Leahy, email@example.com
Audiences: Everyone Is Invited
Contact: Talyia White