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The functional network approach, where fMRI BOLD time series are mapped tonetworks depicting functional relationships between brain areas, has opened newinsights into the function of the human brain. In this approach, the choice ofnetwork nodes is of crucial importance. One option is to consider fMRI voxelsas nodes. This results in a large number of nodes, making network analysis andinterpretation of results challenging. A common alternative is to usepre-defined clusters of anatomically close voxels, Regions of Interest (ROIs).This approach assumes that voxels within ROIs are functionally similar. Becausethese two approaches result in different network structures, it is crucial tounderstand what happens to network connectivity when moving from the voxellevel to the ROI level. We show that the consistency of ROIs, defined as themean Pearson correlation coefficient between the time series of their voxels,varies widely in resting-state experimental data. Therefore the assumption ofsimilar voxel dynamics within each ROI does not generally hold. Further, thetime series of low-consistency ROIs may be highly correlated, resulting inspurious links in ROI-level networks. Based on these results, we recommend thataveraging BOLD signals over anatomically defined ROIs should be carefullyconsidered.

Consistency of Regions of Interest as nodes of functional brain networks measured by fMRI