  Large efforts are currently under way to systematically map functional
connectivity between all pairs of millimeter-scale brain regions using big
volumes of neuroimaging data. Functional magnetic resonance imaging (fMRI) can
produce these functional connectomes, however, large amounts of data and
lengthy computation times add important overhead to this task. Previous work
has demonstrated that fMRI data admits a sparse representation in the form a
discrete point-process containing sufficient information for the efficient
estimation of functional connectivity between all pairs of voxels. In this work
we validate this method, by replicating results obtained with standard
whole-brain voxel-wise linear correlation matrices in two datasets. In the
first one (n=71) we study the changes in node strength (a measure of network
centrality) during deep sleep. The second is a large database (n=1147) of
subjects in which we look at the age-related reorganization of the voxel-wise
network of functional connections. In both cases it is shown that the proposed
method compares well with standard techniques, despite requiring of the order
of 1 % of the original fMRI time series. Overall, these results demonstrate
that the proposed approach allows efficient fMRI data compression and a
subsequent reduction of computation times.
