Estimating Covariate Effects on Functional Connectivity using Voxel-Level fMRI Data

TL;DR

This paper introduces a novel statistical framework for analyzing covariate effects on functional connectivity in fMRI data, addressing high dimensionality and dependence among voxels with a new covariance modeling approach.

Contribution

It develops a new covariance function and estimation procedure for voxel-wise correlations, improving inference accuracy over traditional ROI averaging methods.

Findings

Autism spectrum disorder linked to altered FC between attention-related ROIs.

Proposed method provides calibrated uncertainty and valid inference.

Framework is computationally efficient for large-scale neuroimaging data.

Abstract

Functional connectivity (FC) analysis of resting-state fMRI data provides a framework for characterizing brain networks and their association with participant-level covariates. Due to the high dimensionality of neuroimaging data, standard approaches often average signals within regions of interest (ROIs), which ignores the underlying spatiotemporal dependence among voxels and can lead to biased or inefficient inference. We propose to use a summary statistic -- the empirical voxel-wise correlations between ROIs -- and, crucially, model the complex covariance structure among these correlations through a new positive definite covariance function. Building on this foundation, we develop a computationally efficient two-step estimation procedure that enables statistical inference on covariate effects on region-level connectivity. Simulation studies show calibrated uncertainty quantification, and substantial gains in validity of the statistical inference over the standard averaging method. With data from the Autism Brain Imaging Data Exchange, we show that autism spectrum disorder is associated with altered FC between attention-related ROIs after adjusting for age and gender. The proposed framework offers an interpretable and statistically rigorous approach to estimation of covariate effects on FC suitable for large-scale neuroimaging studies.