Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion)

Martin Bezener, John Hughes, Galin Jones

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We propose a spatiotemporal Bayesian variable selection model for detecting activation in functional magnetic resonance imaging (fMRI) settings. Following recent research in this area, we use binary indicator variables for classifying active voxels. We assume that the spatial dependence in the images can be accommodated by applying an areal model to parcels of voxels. The use of parcellation and a spatial hierarchical prior (instead of the popular Ising prior) results in a posterior distribution amenable to exploration with an efficient Markov chain Monte Carlo (MCMC) algorithm. We study the properties of our approach by applying it to simulated data and an fMRI data set.

Original languageEnglish (US)
Pages (from-to)1261-1313
Number of pages53
JournalBayesian Analysis
Volume13
Issue number4
DOIs
StatePublished - 2018

Keywords

  • Areal model
  • Bayesian variable selection
  • FMRI
  • MCMC
  • Spatiotemporal

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