Multivariate variance-components analysis in DTI
Lee, A. D., Leporé, N., De Leeuw, J., Brun, C. C., Barysheva, M., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., & Thompson, P. M. (2010) Multivariate variance-components analysis in DTI. In 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, IEEE, Rotterdam, The Netherlands, pp. 1157-1160.
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.
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|Item Type:||Conference Paper|
|Keywords:||DTI, Genetics, Multivariate statistics, Twin studies|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||Copyright 2010 IEEE|
|Deposited On:||09 Oct 2015 05:45|
|Last Modified:||21 Oct 2015 04:04|
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