title: Bayesian fMRI time series analysis with spatial priors.
abstract: We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.
authors.affiliation: Wellcome Department of Imaging Neuroscience, UCL, London, UK. wpenny@fil.ion.ucl.ac.uk
authors.forename: William D
authors.initials: WD
authors.lastname: Penny
authors.forename: Nelson J
authors.initials: NJ
authors.lastname: Trujillo-Barreto
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D005145:Face; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016011:Normal Distribution; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014796:Visual Perception