RT Journal Article
SR Electronic
T1 Bayesian non-central chi regression for neuroimaging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 095844
DO 10.1101/095844
A1 Bertil Wegmann
A1 Anders Eklund
A1 Mattias Villani
YR 2016
UL http://biorxiv.org/content/early/2016/12/21/095844.abstract
AB We propose a regression model for non-central χ (NC-χ) 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-χ 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.