Explore a cutting-edge approach to integrating multimodal brain networks in this 49-minute conference talk by Moo K. Chung. Delve into a novel topological learning framework that utilizes persistent homology to integrate networks with different topologies obtained from diffusion and functional MRI. Learn about the Wasserstein distance-based topological loss on graph filtrations, which enables efficient topological computations and optimizations. Discover the application of this framework in a twin brain imaging study to determine the genetic heritability of brain networks. Gain insights into the methodology, including network data analysis, graph mutation, decomposition, and matching techniques. Access the related paper and Matlab codes for hands-on understanding of the concepts presented.