Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks.

ABSTRACT: Mounting evidence shows that brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the relatively small changes in BOLD (blood-oxygen-level-dependent) signals across tasks, it is difficult to detect the change of cognitive status without requiring prior knowledge of the experimental design. To address this challenge, we present a dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph embeddings, which allows us to use brain networks to disentangle cognitive events more accurately than using raw BOLD signals. The backbone of our method is essentially a representation learning process for projecting BOLD signals into a latent vertex-temporal domain with the greater bi

SUBMITTER: Lin Y 

PROVIDER: S-EPMC8091140 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature