False discovery rate control in magnetic resonance imaging studies via Markov random fields

Nguyen, Hien D., McLachlan, Geoffrey J., Cherbuin, Nicolas and Janke, Andrew L. (2014) False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33 8: 1735-1748. doi:10.1109/TMI.2014.2322369

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Author Nguyen, Hien D.
McLachlan, Geoffrey J.
Cherbuin, Nicolas
Janke, Andrew L.
Title False discovery rate control in magnetic resonance imaging studies via Markov random fields
Journal name IEEE Transactions on Medical Imaging   Check publisher's open access policy
ISSN 0278-0062
Publication date 2014-04-07
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TMI.2014.2322369
Open Access Status File (Author Post-print)
Volume 33
Issue 8
Start page 1735
End page 1748
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract Magnetic resonance imaging (MRI) is widely used to study population effects of factors on brain morphometry. Inference from such studies often require the simultaneous testing of millions of statistical hypotheses. Such scale of inference is known to lead to large numbers of false positive results. Control of the false discovery rate (FDR) is commonly employed to mitigate against such outcomes. However, current methodologies in FDR control only account for the marginal significance of hypotheses, and are not able to explicitly account for spatial relationships, such as those between MRI voxels. In this article, we present novel methods that incorporate spatial dependencies into the process of controlling FDR through the use of Markov random fields. Our method is able to automatically estimate the relationships between spatially dependent hypotheses by means of maximum pseudo-likelihood estimation and the pseudo-likelihood information criterion. We show that our methods have desirable statistical properties with regards to FDR control and are able to outperform noncontexual methods in simulations of dependent hypothesis scenarios. Our method is applied to investigate the effects of aging on brain morphometry using data from the PATH study. Evidence of whole brain and component level effects that correspond to similar findings in the literature is found in our investigation.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 7 May 2014.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
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Created: Thu, 22 May 2014, 02:08:01 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging