Eigenvector Centrality Mapping as a New Model-Free Method for Analyzing FMRI Data

Gabriele Lohmann¹, Daniel S. Margulies¹, Dirk Goldhahn¹, Annette Horstmann¹, Burkhard Pleger¹, Joeran Lepsien¹, Arno Villringer¹, Robert Turner¹

We introduce a new assumption- and parameter-free method for the analysis of fMRI resting state data based on 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. We tested eigenvector centrality mapping (ECM) on two resting state scans of 35 subjects, and found a network of hubs including precuneus, thalamus and sensorimotor areas of the marginal ramus of the cingulate and mid-cingulate cortex.