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In neuroimaging data analysis, Gaussian graphical models are often used tomodel statistical dependencies across spatially remote brain regions known asfunctional connectivity. Typically, data is collected across a cohort ofsubjects and the scientific objectives consist of estimating population andsubject-specific graphical models. A third objective that is often overlookedinvolves quantifying inter-subject variability and thus identifying regions orsub-networks that demonstrate heterogeneity across subjects. Such informationis fundamental in order to thoroughly understand the human connectome. Wepropose Mixed Neighborhood Selection in order to simultaneously address thethree aforementioned objectives. By recasting covariance selection as aneighborhood selection problem we are able to efficiently learn the topology ofeach node. We introduce an additional mixed effect component to neighborhoodselection in order to simultaneously estimate a graphical model for thepopulation of subjects as well as for each individual subject. The proposedmethod is validated empirically through a series of simulations and applied toresting state data for healthy subjects taken from the ABIDE consortium.

Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection