Bayesian Analysis

Bayesian Spatiotemporal Modeling Using Hierarchical Spatial Priors, with Applications to Functional Magnetic Resonance Imaging

Martin Bezener, John Hughes, and Galin Jones

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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.

Article information

Source
Bayesian Anal., Advance publication (2018), 26 pages.

Dates
First available in Project Euclid: 8 May 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1525766415

Digital Object Identifier
doi:10.1214/18-BA1108

Keywords
Bayesian variable selection fMRI MCMC spatiotemporal areal model

Rights
Creative Commons Attribution 4.0 International License.

Citation

Bezener, Martin; Hughes, John; Jones, Galin. Bayesian Spatiotemporal Modeling Using Hierarchical Spatial Priors, with Applications to Functional Magnetic Resonance Imaging. Bayesian Anal., advance publication, 8 May 2018. doi:10.1214/18-BA1108. https://projecteuclid.org/euclid.ba/1525766415


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