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

TL;DR

This paper introduces a novel joint learning approach for multiple differential networks using fMRI data from various centers, capturing common and unique brain connectivity patterns related to neurological diseases.

Contribution

It proposes a penalized logistic regression framework with sparse group Minimax Concave Penalty for joint analysis, enhanced by an ensemble-learning procedure, and demonstrates superior performance through simulations and real data application.

Findings

Identified common hub nodes in ADHD fMRI data consistent with experimental studies.

Enhanced detection of differential brain connectivity patterns across multiple research centers.

Outperformed existing methods in simulation studies.

Abstract

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