2 research outputs found
Topological Learning for Brain Networks
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 MRI (fMRI) onto the
template structural brain network obtained through the diffusion MRI (dMRI)