Meeting Banner
Abstract #2940

Detecting functional connectivity changes in a pig traumatic brain injury model using resting-state fMRI

Wenwu Sun1, Kelly M. Scheulin2, Sydney E. Sneed2, Madison M. Fagan2, Savannah R. Cheek2, Christina B. Welch2, Morgane E. Golan2, Frankin D. West2, and Qun Zhao1
1Department of Physics and Astronomy, University of Georgia, Athens, GA, United States, 2Regenerative Bioscience Center, University of Geogia, Athens, GA, United States

Functional magnetic resonance imaging (fMRI) has great potential to evaluate how networks respond and compensate for network dysfunction caused by traumatic brain injury (TBI). In this study, sparse dictionary learning (sDL) and independent component analysis (ICA) were applied to resting-state fMRI (rs-fMRI) data, collected from a group of piglets 1-day (D1) and 7-days (D7) after TBI. Activation maps were generated using group ICA and group sDL, both with dual regression. Voxel-wise permutation tests were then applied to identify changes to six resting-state networks (RSNs). Consistency was observed through the two methods, indicating functional network activity changes after injury.

This abstract and the presentation materials are available to members only; a login is required.

Join Here