Functional MRI provides a unique perspective of neuronal organization; however, these
data include many complex sources of spatiotemporal variability, which require spatial …

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging
data. Spatial models and Bayesian approaches for inference in such models have …

Bayesian learning methods are the basis of many powerful analysis techniques in
neuroimaging, permitting probabilistic inference on hierarchical, generative models of data …

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that
provides an indirect measure of neuronal activity by detecting blood flow changes, has …

In this paper we propose BVAR-connect, a variational inference approach to a Bayesian
multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity …

When modelling FMRI and other MRI time-series data, a Bayesian approach based on
adaptive spatial smoothness priors is a compelling alternative to using a standard …

In this paper we present a novel wavelet-based Bayesian nonparametric regression model
for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide …

Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-
dimensional spatial smoothing priors has been shown to produce state-of-the-art activity …

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in
popularity versus traditional volumetric fMRI. In addition to offering better whole-brain …

In previous work (Penny et al.,[2005]: Neuroimage 24: 350–362) we have developed a
spatially regularised General Linear Model for the analysis of functional magnetic resonance …