Xu, L., Johnson, Timothy D., Nichols, Thomas E. and Nee, Derek E.. (2009) Modeling inter-subject variability in fMRI activation location: a Bayesian hierarchical spatial model. Biometrics, Vol.65 (No.4). pp. 1041-1051. ISSN 0006-341X

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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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Statistics Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Magnetic resonance imaging -- Mathematical models, Brain mapping
Journal or Publication Title:	Biometrics
Publisher:	Wiley-Blackwell Publishing Ltd.
ISSN:	0006-341X
Date:	December 2009
Volume:	Vol.65
Number:	No.4
Number of Pages:	11
Page Range:	pp. 1041-1051
Identification Number:	10.1111/j.1541-0420.2008.01190.x
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	National Institutes of Health (U.S.) (NIH)
Grant number:	PO1 CA087684, RO1 MH069326 (NIH)
URI:	http://wrap.warwick.ac.uk/id/eprint/38193

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