A Bayesian heteroscedastic GLM with application to fMRI data with motion spikes

Abstract

We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a… (More)
DOI: 10.1016/j.neuroimage.2017.04.069

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