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Dr. Lin Zhang
Assistant Professor
Department of Biostatistics
University of Minnesota School of Public Health

Bi-Level Graphical Modeling of Functional Connectivity Analysis of Resting-State fMRI Data

We consider a novel problem, bi-level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group-level graphical model and inference of both the group- and individual-level graphical models are of interest. We propose a novel random covariance model to learn the group- and individual-level graphical models simultaneously with a new measure of degrees-of-freedom for model complexity that is useful for model selection. We apply the method to our motivating clinical data, a multi-subject resting-state fMRI dataset collected from participants diagnosed with schizophrenia, identifying both individual- and group-level graphical models of functional connectivity.