Leveraging edge-centric networks complements existing network-level inference for functional connectomes.

ABSTRACT: The human connectome is modular with distinct brain regions clustering together to form large-scale communities, or networks. This concept has recently been leveraged in novel inferencing procedures by averaging the edge-level statistics within networks to induce more powerful inferencing at the network level. However, these networks are constructed based on the similarity between pairs of nodes. Emerging work has described novel edge-centric networks, which instead use the similarity between pairs of edges to construct networks. In this work, we use these edge-centric networks in a network-level inferencing procedure and compare this novel method to traditional inferential procedures and the network-level procedure using node-centric networks. We use data from the Human Connectome Project

SUBMITTER: Rodriguez RX 

PROVIDER: S-EPMC9838718 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature