Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain.

ABSTRACT: Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular "betweenness centrality" - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector c

SUBMITTER: Lohmann G 

PROVIDER: S-EPMC2860504 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature