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Large efforts are currently under way to systematically map functionalconnectivity between all pairs of millimeter-scale brain regions using bigvolumes of neuroimaging data. Functional magnetic resonance imaging (fMRI) canproduce these functional connectomes, however, large amounts of data andlengthy computation times add important overhead to this task. Previous workhas demonstrated that fMRI data admits a sparse representation in the form adiscrete point-process containing sufficient information for the efficientestimation of functional connectivity between all pairs of voxels. In this workwe validate this method, by replicating results obtained with standardwhole-brain voxel-wise linear correlation matrices in two datasets. In thefirst one (n=71) we study the changes in node strength (a measure of networkcentrality) during deep sleep. The second is a large database (n=1147) ofsubjects in which we look at the age-related reorganization of the voxel-wisenetwork of functional connections. In both cases it is shown that the proposedmethod compares well with standard techniques, despite requiring of the orderof 1 % of the original fMRI time series. Overall, these results demonstratethat the proposed approach allows efficient fMRI data compression and asubsequent reduction of computation times.

A few points suffice: Efficient large-scale computation of brain voxel-wise functional connectomes from a sparse spatio-temporal point-process