Quantifying the Topology of Coma
Explore a 48-minute lecture on quantifying the topology of coma, presented by Pablo Suárez-Serrato from the National Autonomous University of Mexico (UNAM). Delve into the complex world of network comparison and graph distance measurement, focusing on a new efficient measure based on the marked length spectrum. Discover how this distance relates to structural features like hubs and triangles through non-backtracking matrix eigenvalues. Learn about the topological interpretation of non-backtracking cycles as a homotopical application of topological data analysis in complex networks. Examine a manifold learning application that distinguishes networks inferred from fMRI data of healthy and comatose patients. Gain insights into this cutting-edge research presented at the Deep Learning and Medical Applications 2020 conference, hosted by the Institute for Pure & Applied Mathematics (IPAM) at UCLA.