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

Authors
  • Xu, Lei
  • Johnson, Timothy D
  • Nichols, Thomas E
  • Nee, Derek E
Type
Published Article
Journal
Biometrics
Publisher
Wiley (Blackwell Publishing)
Publication Date
Dec 01, 2009
Volume
65
Issue
4
Pages
1041–1051
Identifiers
DOI: 10.1111/j.1541-0420.2008.01190.x
PMID: 19210732
Source
Medline
License
Unknown

Abstract

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.

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