Paired Test of Matrix Graphs and Brain Connectivity Analysis

TL;DR

This paper introduces a novel paired test for brain connectivity networks that accounts for correlations in repeated measures, improving inference accuracy in neuroscience studies.

Contribution

It develops a bias- and variance-corrected test statistic for paired graph inference, with a multiple testing procedure controlling false discovery rate in correlated samples.

Findings

Test achieves small estimation error rate.

Method controls false discovery rate asymptotically.

Validated with simulations and Alzheimer's dataset.

Abstract

Inferring brain connectivity network and quantifying the significance of interactions between brain regions are of paramount importance in neuroscience. Although there have recently emerged some tests for graph inference based on independent samples, there is no readily available solution to test the change of brain network for paired and correlated samples. In this article, we develop a paired test of matrix graphs to infer brain connectivity network when the groups of samples are correlated. The proposed test statistic is both bias corrected and variance corrected, and achieves a small estimation error rate. The subsequent multiple testing procedure built on this test statistic is guaranteed to asymptotically control the false discovery rate at the pre-specified level. Both the methodology and theory of the new test are considerably different from the two independent samples framework, owing to the strong correlations of measurements on the same subjects before and after the stimulus activity. We illustrate the efficacy of our proposal through simulations and an analysis of an Alzheimer's Disease Neuroimaing Initiative dataset.