Title: Persistent homology on functional brain network
Authors: Moo K Chung - University of Wisconsin-Madison (United States) [presenting]
Abstract: Advances in functional magnetic resonance imaging (fMRI) techniques enabled us to measure spontaneous fluctuations of neural signals in the brain. Many previous studies on resting-state fMRI have mainly focused on the topological characterization of static graph theory features that will not fluctuate over time. We present a simple but very effective data-driven approach to assess the dynamic pattern of resting state functional connectivity using persistent homology. Persistent homology has been successfully applied to various static brain networks by building graph filtrations by sequentially thresholding edge weights. The recently proposed exact combinatorial inference procedure for static network was adapted for statistically quantifying dynamic brain networks.