Topological Data Analysis on Dynamic Brain Networks

Explore a cutting-edge tutorial on Topological Data Analysis (TDA) for dynamic brain networks presented by Moo K. Chung from the University of Wisconsin, Madison. Delve into a data-driven TDA framework designed to uncover state spaces in dynamically changing functional brain networks. Learn fundamental TDA concepts and discover how topological distance can be used to cluster brain networks into distinct states without relying on models. Examine the incorporation of temporal dimensions in brain network data using Wasserstein distance for a more nuanced analysis of network changes over time. Gain hands-on experience with this method and understand its advantages over traditional k-means clustering for estimating state spaces. Investigate the potential of TDA in determining the heritability of state changes. Based on research published in PLOS Computational Biology, this 51-minute tutorial offers valuable insights for researchers and students interested in advanced neuroimaging analysis techniques.

Moo K. Chung - Tutorial: Topological Data Analysis on Dynamic Brain Networks - IPAM at UCLA