Topological Learning for Brain Networks

• Published in: bioRxiv
• Main Authors: Songdechakraiwut, Tananun, Chung, Moo K
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 02.12.2020; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Bioinformatics; Brain mapping; Computational neuroscience; Functional magnetic resonance imaging; Homology; Neuroimaging; topological learning; resting-state brain networks; twin imaging study; Topological data analysis; persistent homology
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2020.11.30.404665

Abstract This paper proposes a novel topological learning framework that can integrate networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task. The use of the proposed loss function bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations with ground truth to assess the effectiveness of the topological loss in discriminating networks with different topology. The method is further applied to a twin brain imaging study in determining if the brain network is genetically heritable. The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional magnetic resonance imaging (fMRI) onto the template structural brain network obtained through the diffusion tensor imaging (DTI). Competing Interest Statement The authors have declared no competing interest.