Schedule

MINDS SEMINAR

MINDS Seminar Series | Moo K. Chung (University of Wisconsin) - Topological Inference and Learning for Brain Networks

MINDS SEMINAR
period : 2021-04-06 ~ 2021-04-06
time : 16:00:00 ~ 17:00:00
개최 장소 : Online streaming (Zoom)
Topic : Topological Inference and Learning for Brain Networks
개요
Date 2021-04-06 ~ 2021-04-06 Time 16:00:00 ~ 17:00:00
Speaker Moo K. Chung Affiliation University of Wisconsin
Place Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : letmein
Topic Topological Inference and Learning for Brain Networks
Contents Advances in functional magnetic resonance imaging (fMRI) enable us to measure spontaneous fluctuations of neural signals in the human brain in higher spatial and temporal resolutions than before. Many previous studies on fMRI based brain network analysis have mainly focused analyzing graph theory features that are often parameter dependendent. Persistent homology provides a more coherent mathematical framework that is invariant to the choice of parameters. Instead of looking at networks at a fixed scale, persistent homology charts the topological changes of networks over every possible scale. In doing so, it reveals the most persistent topological features that are robust to scale changes. In this talk, new topological inference and lerning framework will be presented on graph filtration, a concept our group popularized as a baseline filtration in network modeling. This talk is based on Chung et al. 2019 (Network Neuroscience 3:674-694) and a joint work with a PhD student Tananun Songdechakraiwut (arXiv: 2012.00675)
Shortbio
Moo K. Chung, Ph.D. is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison (http://www.stat.wisc.edu/~mchung). He is also affiliated with the Waisman Laboratory for Brain Imaging and Behavior and Department of Statistics. He participated in the World Class University Project at Seoul Nationional Univeristy as a faulty between 2009-2013. Dr. Chung’s research focuses on topological data analysis, computational neuroimaging 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 three books on neuroimage computation including Brain Network Analysis that was published through Cambridge University Press in 2019.
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