Modeling inter-subject variability in FMRI activation location: a Bayesian hierarchical spatial model

Biometrics. 2009 Dec;65(4):1041-51. doi: 10.1111/j.1541-0420.2008.01190.x.


The aim of this article is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this article, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bayes Theorem*
  • Biometry / methods*
  • Brain Mapping / statistics & numerical data
  • Humans
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Markov Chains
  • Models, Statistical*
  • Monte Carlo Method