Bayesian Non-Central Chi Regression For Neuroimaging

TL;DR

This paper introduces a Bayesian non-central chi regression model for neuroimaging data, improving localization accuracy of brain activity and diffusion metrics over traditional Gaussian models, especially at low SNR.

Contribution

It develops a flexible Bayesian regression framework for NC-chi distributed neuroimaging data with automatic covariate selection and efficient MCMC sampling.

Findings

Rician model outperforms Gaussian in localizing brain activity at low SNR.

The model accurately estimates diffusion metrics, correcting underestimation by Gaussian assumptions.

Bayesian variable selection effectively identifies relevant covariates.

Abstract

We propose a regression model for non-central $\chi$ (NC-$\chi$) distributed functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the NC-$\chi$ distribution to be linked to explanatory variables, with the relevant covariates automatically chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed for simulating from the joint Bayesian posterior distribution of all model parameters and the binary covariate selection indicators. Simulated fMRI data is used to demonstrate that the Rician model is able to localize brain activity much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the theoretically correct Rician model.