Detection of sparse differential dependent functional brain connectivity

Abstract

Functional brain connectivity analysis is an increasingly important technique in neuroscience, psychiatry, and autism research. Functional connectivity can be measured by considering co-activation of brain regions in resting-state functional magnetic resonance imaging (rs-fMRI). We propose a novel Bayesian model to detect differential connections in cross-correlated functional connectivity between region of interest (ROI) pairs. The proposed sparse clustered neighborhood model induces a lower-dimensional sparsity and clustering based on a nonparametric Bayesian approach to model sparse differentially connected ROI pairs. Second, it induces a structured dependence model for modeling potential dependence among ROI pairs. We demonstrate Bayesian inference and performance of the proposed model in simulation studies and compare with a standard model. We utilize the proposed model to contrast functional connectivities between participants with autism spectrum disorder and neurotypical participants using cross-correlated rs-fMRI data from four sites of the Autism Brain Image Data Exchange.

Keywords: ABIDE data base; Bayesian modeling; Dirichlet process; fMRI data; spatial autoregression.