Bayesian fMRI time series analysis with spatial priors

Neuroimage. 2005 Jan 15;24(2):350-62. doi: 10.1016/j.neuroimage.2004.08.034.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Brain / anatomy & histology
  • Brain / physiology*
  • Brain Mapping / methods
  • Face
  • Humans
  • Magnetic Resonance Imaging / methods*
  • Models, Neurological
  • Models, Theoretical
  • Multivariate Analysis
  • Normal Distribution
  • Regression Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Visual Perception