Spatial false discovery rate control for magnetic resonance imaging studies

Nguyen, Hien D., McLachlan, Geoffrey J., Janke, Andrew L., Cherbuin, Nicolas, Sachdev, Perminder and Anstey, Kaarin J. (2013). Spatial false discovery rate control for magnetic resonance imaging studies. In: Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, (290-297). 26 - 28 November 2013. doi:10.1109/DICTA.2013.6691531


Author Nguyen, Hien D.
McLachlan, Geoffrey J.
Janke, Andrew L.
Cherbuin, Nicolas
Sachdev, Perminder
Anstey, Kaarin J.
Title of paper Spatial false discovery rate control for magnetic resonance imaging studies
Conference name International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013
Conference location Hobart, TAS
Conference dates 26 - 28 November 2013
Proceedings title Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on
Journal name 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/DICTA.2013.6691531
Open Access Status
ISBN 9781479921263
Start page 290
End page 297
Total pages 8
Collection year 2014
Language eng
Abstract/Summary Magnetic resonance imaging (MRI) is widely used to study the population effects of covariates on brain morphometry. Inferences from these studies often require the simultaneous testing of millions of statistical hypotheses. Such scale of simultaneous testing is known to lead to large numbers of false positive results. False discovery rate (FDR) controlling procedures are commonly employed to mitigate against false positives. However, current methodologies in FDR control only account for the marginal significance of hypotheses and are not able to take into account spatial relationships, such as in MRI studies. In this article, we present a novel method for incorporating spatial dependencies in the control of FDR through the use of Markov random fields. Our method is able to automatically estimate the relationship between spatially dependent hypotheses by means of pseudo-likelihood techniques. We show that the our spatial FDR control method is able to outperform marginal methods in simulations of spatially dependent hypotheses. Our method is then applied to investigate the effect of aging on brain morphometry using data from the PATH study. The results of our investigation were found to be in correspondence with the brain aging literature.
Subjects 1704 Computer Graphics and Computer-Aided Design
1706 Computer Science Applications
Keyword False discovery rate
Magnetic resonance imaging
Markov random field
Pseudo likelihood
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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