Bayesian fMRI time series analysis with spatial priors

William D Penny, Nelson J Trujillo-Barreto, Karl J Friston

Research output: Contribution to journalArticlepeer-review

188 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)350-362
Number of pages13
Issue number2
Early online date17 Nov 2004
Publication statusPublished - 15 Jan 2005


  • Bayes Theorem
  • Brain
  • Brain Mapping
  • Face
  • Humans
  • Magnetic Resonance Imaging
  • Neurological Models
  • Theoretical Models
  • Multivariate Analysis
  • Normal Distribution
  • Regression Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Visual Perception

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