  Recent studies proposed the use of Total Correlation to describe functional
connectivity among brain regions as a multivariate alternative to conventional
pair-wise measures such as correlation or mutual information. In this work we
build on this idea to infer a large scale (whole brain) connectivity network
based on Total Correlation and show the possibility of using this kind of
networks as biomarkers of brain alterations. In particular, this work uses
Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove
that CorEx estimates of total correlation and clustering results are trustable
compared to ground truth values. Second, the inferred large scale connectivity
network extracted from the more extensive open fMRI datasets is consistent with
existing neuroscience studies but, interestingly, can estimate additional
relations beyond pair-wise regions. And finally, we show how the connectivity
graphs based on Total Correlation can also be an effective tool to aid in the
discovery of brain diseases.
