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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
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 | ||||
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| Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| 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: |
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| 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) |
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