Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis

In this study, we present a method for estimating the mutual information for a localized pattern of fMRI data. We show that taking a multivariate information approach to voxel selection leads to a decoding accuracy that surpasses an univariate inforamtion approach and other standard voxel selection methods. Furthermore,we extend the multivariate mutual information theory to measure the functional connectivity between distributed brain regions. By jointly estimating the information shared by two sets of voxels we can reliably map out the connectivities in the human brain during experiment conditions. We validated our approach on a 6-way scene categorization fMRI experiment. The multivariate information analysis is able to find strong information flow between PPA and RSC, which confirms existing neuroscience studies on scenes. Furthermore, by exploring over the whole brain, our method identifies other interesting ROIs that share information with the PPA, RSC scene network,suggesting interesting future work for neuroscientists.