Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

TL;DR

This paper develops statistical methods to compare Gaussian Graphical Models (GGMs) for brain connectivity, providing confidence intervals for differences in network edges, especially in high-dimensional, low-sample neuroimaging data.

Contribution

It introduces the debiased multi-task fused lasso for joint GGM comparison, enabling accurate confidence intervals for edge differences with structured priors.

Findings

Validated on synthetic data showing accurate confidence intervals.

Applied to autism neuro-imaging data demonstrating practical utility.

Achieved reliable detection of connectivity differences in brain networks.

Abstract

Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance estimators. Comparing functional connectivity of subjects in two populations calls for comparing these estimated GGMs. Our goal is to identify differences in GGMs known to have similar structure. We characterize the uncertainty of differences with confidence intervals obtained using a parametric distribution on parameters of a sparse estimator. Sparse penalties enable statistical guarantees and interpretable models even in high-dimensional and low-sample settings. Characterizing the distributions of sparse models is inherently challenging as the penalties produce a biased estimator. Recent work invokes the sparsity assumptions to effectively remove the bias from a sparse estimator such as the lasso. These distributions can be used to give confidence intervals on edges in GGMs, and by extension their differences. However, in the case of comparing GGMs, these estimators do not make use of any assumed joint structure among the GGMs. Inspired by priors from brain functional connectivity we derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. This leads us to introduce the debiased multi-task fused lasso, whose distribution can be characterized in an efficient manner. We then show how the debiased lasso and multi-task fused lasso can be used to obtain confidence intervals on edge differences in GGMs. We validate the techniques proposed on a set of synthetic examples as well as neuro-imaging dataset created for the study of autism.