Multi-scale detection of hierarchical community architecture in structural and functional brain networks.

ABSTRACT: Community detection algorithms have been widely used to study the organization of complex networks like the brain. These techniques provide a partition of brain regions (or nodes) into clusters (or communities), where nodes within a community are densely interconnected with one another. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies: clusters embedded within clusters of other clusters. To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neig

SUBMITTER: Ashourvan A 

PROVIDER: S-EPMC6508662 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature