A Bayesian hierarchical framework for spatial modeling of fMRI data

F. Du Bois Bowman, Brian Caffo, Susan Spear Bassett, Clinton Kilts

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


Applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric, neurological, and substance abuse disorders and their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. Complementary approaches consider the ensemble of voxels constituting an anatomically defined region of interest (ROI) or summary statistics, such as means or quantiles, of the ROI. In this work, we present a Bayesian extension of voxel-level analyses that offers several notable benefits. Among these, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance matrix for regional mean parameters allows for the study of inter-regional (long-range) correlations, and the model employs an exchangeable correlation structure to capture intra-regional (short-range) correlations. Estimation is performed using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling. We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer's disease.

Original languageEnglish (US)
Pages (from-to)146-156
Number of pages11
Issue number1
StatePublished - Jan 1 2008


  • Bayesian analysis
  • Connectivity
  • Functional neuroimaging
  • MCMC
  • Regions of interest
  • Volumes of interest

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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