Mapping individual differences across brain network structure to function and behavior with connectome embedding.

ABSTRACT: The connectome, a comprehensive map of the brain's anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes "context" is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we ex

SUBMITTER: Levakov G 

PROVIDER: S-EPMC8464439 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature