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In this study we focus on the problem of joint learning of multipledifferential networks with function Magnetic Resonance Imaging (fMRI) data setsfrom multiple research centers. As the research centers may use differentscanners and imaging parameters, joint learning of differential networks withfMRI data from different centers may reflect the underlying mechanism ofneurological diseases from different perspectives while capturing the commonstructures. We transform the task as a penalized logistic regression problem,and exploit sparse group Minimax Concave Penalty (gMCP) to induce commonstructures among multiple differential networks and the sparse structures ofeach differential network. To further enhance the empirical performance, wedevelop an ensemble-learning procedure. We conduct thorough simulation study toassess the finite-sample performance of the proposed method and compare withstate-of-the-art alternatives. We apply the proposed method to analyze fMRIdatasets related with Attention Deficit Hyperactivity Disorder from variousresearch centers. The identified common hub nodes and differential interactionpatterns coincides with the existing experimental studies.

Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection