A Regression Framework for Brain Network Distance Metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Feasible Generalized Least Squares (FGLS), and Permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing Multivariate Distance Matrix Regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data. Highlights Related distances between connection matrices to differences in covariates. Adapted methods for estimation and inference in this framework. Assessment of methods and distance metrics via simulation. Compared our methods to existing MDMR methods via simulation. Analysis of the HCP data with the best approach for each distance metric.