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Please join us for a talk on Friday, March 1 where Moo K. Chung, associate professor, University of Wisconsin-Madison, will present a seminar titled, “Persistent homology of large-scale brain networks.”
Persistent homology of large-scale brain networks
Presented by Moo K. Chung, associate professor, University of Wisconsin-Madison
Friday, March 1, 2019
Brickyard (BYENG), room 210, Tempe campus [map]
Persistent homology provides a coherent mathematical framework for quantifying brain networks. Instead of looking at networks at a fixed scale, persistent homology charts the changes in topological network features over multiple resolutions and scales. In doing so, it reveals the most persistent topological features, i.e., those that are robust to noise. This scale robustness is crucial since most brain networks are parameter and scale dependent. In this talk, the basic concepts, computational issues and recent advances on the persistent homology-based brain network analysis will be introduced. The talk is, in part, based on a recent review paper on the state-of-the-art brain network models (Solo et al., 2018, IEEE Transactions on Medical Imaging 37:1537-1550).
Moo K. Chung is an associate professor in the department of biostatistics and medical informatics at the University of Wisconsin-Madison. He is also affiliated with the Waisman Laboratory for Brain Imaging and Behavior. Chung’s research focuses on computational neuro-imaging and brain network analysis. His research concentrates on the methodological development required for quantifying and contrasting functional, anatomical shape and network variations in both normal and clinical populations using various mathematical, statistical and computational techniques. He has published two books on neuro-image computation and the third book titled, “Brain Network Analysis” will be published through Cambridge University Press in 2019.