Bayesian fMRI data analysis with sparse spatial basis function priors

Guillaume Flandin, William D Penny

Research output: Contribution to journalArticlepeer-review

87 Citations (Scopus)


In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). These are defined via a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment.

Original languageEnglish
Pages (from-to)1108-1125
Number of pages18
Issue number3
Early online date5 Dec 2006
Publication statusPublished - 1 Feb 2007


  • Algorithms
  • Artificial Intelligence
  • Bayes Theorem
  • Brain
  • Brain Mapping
  • Computer Simulation
  • Evoked Potentials
  • Humans
  • Image Enhancement
  • Computer-Assisted Image Interpretation
  • Magnetic Resonance Imaging
  • Neurological Models
  • Statistical Models
  • Automated Pattern Recognition
  • Evaluation Studies

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