Multivariate variance-components analysis in DTI

Authors:
Agatha D. Lee;Natasha Leporé;Jan de Leeuw;Caroline C. Brun;Marina Barysheva;Katie L. McMahon;Greig I. de Zubicaray;Nicholas G. Martin;Margaret J. Wrighl;Paul M. Thompson
Affiliations:
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA and Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los ...;Department of Statistics, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Department of Statistics, UCLA, Los Angeles, CA;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Queensland Institute of Medical Research, Brisbane, Australia;Queensland Institute of Medical Research, Brisbane, Australia;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 3
Cited 1

Brain Differences Visualized in the Blind Using Tensor Manifold Statistics and Diffusion Tensor Imaging

FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Multivariate statistics of the jacobian matrices in tensor based morphometry and their application to HIV/AIDS

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fast and simple calculus on tensors in the log-euclidean framework

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Heritability of white matter fiber tract shapes: a HARDI study of 198 twins

MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 (DTl) 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.