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). Chung is affiliated with the Waisman Laboratory for Brain Imaging and Behavior and the Department of Statistics. Chung received PhD from McGill University under Keith Wolseley and trained at the Montreal Neurological Institute. Chung’s research focuses on computational neuroanatomy, spectral geometry, and topological data analysis. Chung mainly concentrates on the methodological development required for quantifying and contrasting brain 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 published through Cambridge University Press in 2019. Currently started writing a new book on Topological Data Analysis for Brain Imaging.

Persistent homology summarizes the changes of topological structures in data through over multiple scales called filtrations. Doing so detect hidden topological signals that persist over different scales. However, a key obstacle of applying persistent homology to brain networks has been the lack of robust statistical inference framework. To address this problem, we present a new topological inference procedure based on the Wasserstein distance. Our approach has no explicit models and statistical distributional assumptions. The inference is performed in a completely data driven fashion. Our metric-based inference significantly differs from traditional feature-based topological data analysis (TDA). The method is applied to the resting-state functional magnetic resonance images (rs-fMRI) of the temporal lobe epilepsy patients and able to localize brain regions that contribute the most to topological differences. We made computer code available at Github . The talk is based on Anand and Chung 2023, IEEE Transactions on Medical Imaging (arXiv:2110.14599) and Songdechakraiwut and Chung 2023 Annals of Applied Statistics (arXiv:2012.00675).