  Standard fMRI connectivity analyses depend on aggregating the time series of
individual voxels within regions of interest (ROIs). In certain cases, this
spatial aggregation implies a loss of valuable functional and anatomical
information about smaller subsets of voxels that drive the ROI level
connectivity. We use two recently published graphical search methods to
identify subsets of voxels that are highly responsible for the connectivity
between larger ROIs. To illustrate the procedure, we apply both methods to
longitudinal high-resolution resting state fMRI data from regions in the medial
temporal lobe from a single individual. Both methods recovered similar subsets
of voxels within larger ROIs of entorhinal cortex and hippocampus subfields
that also show spatial consistency across different scanning sessions and
across hemispheres. In contrast to standard functional connectivity methods,
both algorithms applied here are robust against false positive connections
produced by common causes and indirect paths (in contrast to Pearson's
correlation) and common effect conditioning (in contrast to partial correlation
based approaches). These algorithms allow for identification of subregions of
voxels driving the connectivity between regions of interest, recovering
valuable anatomical and functional information that is lost when ROIs are
aggregated. Both methods are specially suited for voxelwise connectivity
research, given their running times and scalability to big data problems.
