title: Variational Bayesian inference for fMRI time series.
abstract: We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidelity of this approximation is verified via Gibbs sampling. The VB approach provides a natural extension to previous Bayesian analyses which have used Empirical Bayes. VB has the advantage of taking into account the variability of hyperparameter estimates with little additional computational effort. Further, VB allows for automatic selection of the order of the AR process. Results are shown on simulated data and on data from an event-related fMRI experiment.
authors.affiliation: Wellcome Department of Imaging Neuroscience, University College, London WC1N 3BG, UK. wpenny,karl@fil.ion.ucl.ac.uk
authors.forename: Will
authors.initials: W
authors.lastname: Penny
authors.forename: Stefan
authors.initials: S
authors.lastname: Kiebel
authors.forename: Karl
authors.initials: K
authors.lastname: Friston
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping; D005145:Face; D005163:Factor Analysis, Statistical; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D013997:Time Factors