Eigenvector centrality mapping as a new model-free method for analyzing fMRI data

Introduction. 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. Standard methods use either correlations with pre-specified seed regions or independent component analysis, both of which require assumptions about the source (seed-based) or validity (ICA) of a network. In this work, we introduce an alternative assumptionand parameter-free method based on a particular form of node centrality called “eigenvector centrality”. Eigenvector centrality (Bonacich, 2007) 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 so-called betweenness centrality have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements (Bullmore and Sporns 2009). Here, we propose to use eigenvector centrality instead of betweenness centrality. Because of its much greater computational speed it is possible to apply it to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. We tested eigenvector centrality mapping (ECM) on “resting state” data of 35 subjects.