TOPOLOGICAL LEARNING FOR BRAIN NETWORKS

• Published in: The annals of applied statistics Vol. 17; no. 1; p. 403
• Main Authors: Songdechakraiwut, Tananun, Chung, Moo K
• Format: Journal Article
• Language: English
• Published: United States 01.03.2023
• Subjects: topological learning; Wasserstein distance; twin brain imaging study; Topological data analysis; birth-death decomposition; persistent homology
• ISSN: 1932-6157
• DOI: 10.1214/22-aoas1633

This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology. Such challenging task is made possible through the introduction of a computationally efficient topological loss. The use of the proposed loss bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations to assess its effectiveness when discriminating networks with different topology. The method is further demonstrated in a twin brain imaging study where we determine if brain networks are genetically heritable. The challenge here is due to the difficulty of overlaying the topologically different functional brain networks obtained from resting-state functional MRI onto the template structural brain network obtained through diffusion MRI.