| Autor: |
Lee AD; Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA., Leporé N; Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA.; Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA., de Leeuw J; Department of Statistics, UCLA, Los Angeles, CA, USA., Brun CC; Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA., Barysheva M; Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA., McMahon KL; Department of Statistics, UCLA, Los Angeles, CA, USA., de Zubicaray GI; Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia., Martin NG; Queensland Institute of Medical Research, Brisbane, Australia., Wright MJ; Queensland Institute of Medical Research, Brisbane, Australia., Thompson PM; Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA. |
| Abstrakt: |
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. |
