Bayesian Inference of Spatially Varying Correlations via the Thresholded Correlation Gaussian Process

TL;DR

This paper introduces a Bayesian nonparametric model using the thresholded correlation Gaussian process to infer spatially varying brain region associations in neuroimaging, accommodating low SNR and limited data.

Contribution

It proposes a novel TCGP-based model for spatial correlation inference, with theoretical guarantees and efficient computation, applicable to neuroimaging data.

Findings

Model demonstrates accurate identification of correlated brain regions.

Theoretical properties such as posterior consistency are established.

Method performs well in simulations and real fMRI data analysis.

Abstract

A central question in multimodal neuroimaging analysis is to understand the association between two imaging modalities and to identify brain regions where such an association is statistically significant. In this article, we propose a Bayesian nonparametric spatially varying correlation model to make inference of such regions. We build our model based on the thresholded correlation Gaussian process (TCGP). It ensures piecewise smoothness, sparsity, and jump discontinuity of spatially varying correlations, and is well applicable even when the number of subjects is limited or the signal-to-noise ratio is low. We study the identifiability of our model, establish the large support property, and derive the posterior consistency and selection consistency. We also develop a highly efficient Gibbs sampler and its variant to compute the posterior distribution. We illustrate the method with both simulations and an analysis of functional magnetic resonance imaging data from the Human Connectome Project.