Multivariate variance-components analysis in DTI

Lee, Agatha D., Leporé, Natasha, de Leeuw, Jan, Brun, Caroline C., Barysheva, Marina, McMahon, Katie L., de Zubicaray, Greig I., Martin, Nicholas G., Wright, Margaret J. and Thompson, Paul M. (2010). Multivariate variance-components analysis in DTI. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, Netherlands, (1157-1160). 14-17 April 2010. doi:10.1109/ISBI.2010.5490199

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Author Lee, Agatha D.
Leporé, Natasha
de Leeuw, Jan
Brun, Caroline C.
Barysheva, Marina
McMahon, Katie L.
de Zubicaray, Greig I.
Martin, Nicholas G.
Wright, Margaret J.
Thompson, Paul M.
Title of paper Multivariate variance-components analysis in DTI
Conference name 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Conference location Rotterdam, Netherlands
Conference dates 14-17 April 2010
Proceedings title 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro   Check publisher's open access policy
Journal name International Symposium on Biomedical Imaging. Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ, U.S.A.
Publisher I E E E
Publication Year 2010
Sub-type Fully published paper
DOI 10.1109/ISBI.2010.5490199
ISBN 9781424441266
ISSN 1945-7928
1945-8452
1945-7936
Start page 1157
End page 1160
Total pages 4
Collection year 2011
Language eng
Abstract/Summary Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, we extend the commonly used Pearson correlation to a more appropriate definition, which uses restricted maximum likelihood methods (REML). We computed proportion of phenotypic variance due to additive (A) genetic factors, common (C) and unique (E) environmental factors using a new definition of the variance components in the diffusion tensor-valued signals. We applied our analysis to a dataset of Diffusion Tensor Images (DTI) from 25 identical and 25 fraternal twin pairs. Differences between the REML and Pearson estimators were plotted for different sample sizes, showing that the REML approach avoids severe biases when samples are smaller. Measures of genetic effects were computed for scalar and multivariate diffusion tensor derived measures including the geodesic anisotropy (tGA) and the full diffusion tensors (DT), revealing voxel-wise genetic contributions to brain fiber microstructure. ©2010 IEEE.
Keyword DTI
Genetics
Multivariate statistics
Twin studies
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Wed, 09 Mar 2011, 16:14:35 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging