Conditional mutual information maps as descriptors of net connectivity levels in the brain

There is a growing interest in finding ways to summarize the local connectivity properties of the brain through single brain maps. Here we propose a method based on the conditional mutual information (CMI) in the frequency domain. CMI maps quantify the amount of non-redundant covariability between each site and all others in the rest of the brain, partialling out the joint variability due to gross physiological noise. Average maps from a sample of 45 healthy individuals scanned in the resting state show a clear and symmetric pattern of connectivity maxima in several regions of cortex, including prefrontal, orbitofrontal, lateral-parietal, and midline default mode network components; and in subcortical nuclei, including the amygdala, thalamus, and basal ganglia. Such cortical and subcortical hotspots of functional connectivity were more clearly evident at lower frequencies (0.02-0.1 Hz) than at higher frequencies (0.1-0.2 Hz) of endogenous oscillation. CMI mapping can also be easily applied to perform group analyses. This is exemplified by exploring effects of normal aging on CMI in a sample of healthy controls and by investigating correlations between CMI and positive psychotic symptom scores in a sample of 40 schizophrenic patients. Both the normative aging and schizophrenia studies reveal functional connectivity trends that converge with reported findings from other studies, thus giving further support to the validity of the proposed method.