Tensor-Based Analysis of Genetic Influences on Brain Integrity Using DTI in 100 Twins

Authors:
Agatha D. Lee;Natasha Leporé;Caroline Brun;Yi-Yu Chou;Marina Barysheva;Ming-Chang Chiang;Sarah K. Madsen;Greig I. Zubicaray;Katie L. Mcmahon;Margaret J. Wright;Arthur W. Toga;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;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;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;Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Queensland Institute of Medical Research, Brisbane, Australia;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
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

Information from the full diffusion tensor (DT) was used to compute voxel-wise genetic contributions to brain fiber microstructure. First, we designed a new multivariate intraclass correlation formula in the log-Euclidean framework [1]. We then analyzed used the full multivariate structure of the tensor in a multivariate version of a voxel-wise maximum-likelihood structural equation model (SEM) that computes the variance contributions in the DTs from genetic (A), common environmental (C) and unique environmental (E) factors. Our algorithm was tested on DT images from 25 identical and 25 fraternal twin pairs. After linear and fluid registration to a mean template, we computed the intraclass correlation and Falconer's heritability statistic for several scalar DT-derived measures and for the full multivariate tensors. Covariance matrices were found from the DTs, and inputted into SEM. Analyzing the full DT enhanced the detection of A and C effects. This approach should empower imaging genetics studies that use DTI.