Topological Learning and Its Application to Multimodal Brain Network Integration.

ABSTRACT: A long-standing challenge in multimodal brain network analyses is to integrate topologically different brain networks obtained from diffusion and functional MRI in a coherent statistical framework. Existing multimodal frameworks will inevitably destroy the topological difference of the networks. In this paper, we propose a novel topological learning framework that integrates networks of different topology through persistent homology. Such challenging task is made possible through the introduction of a new topological loss that bypasses intrinsic computational bottlenecks and thus enables us to perform various topological computations and optimizations with ease. We validate the topological loss in extensive statistical simulations with ground truth to assess its effectiveness of discrimina

SUBMITTER: Songdechakraiwut T 

PROVIDER: S-EPMC8797159 | biostudies-literature | 2021 Sep-Oct

REPOSITORIES: biostudies-literature