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

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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. doi:10.1111/j.1541-0420.2008.01190.x ISSN 0006-341X.

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Official URL: http://dx.doi.org/10.1111/j.1541-0420.2008.01190.x

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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, Engineering and Medicine > Science > Statistics
Faculty of Science, Engineering and Medicine > Engineering > 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
Official Date: December 2009
Dates:
DateEvent
December 2009Published
Volume: Vol.65
Number: No.4
Number of Pages: 11
Page Range: pp. 1041-1051
DOI: 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)

Data sourced from Thomson Reuters' Web of Knowledge

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