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Abstract #1140

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

1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

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.