The multivariate A/C/E model and the genetics of fiber architecture

Authors:
Agatha D. Lee;Natasha Leporé;Yi-Yu Chou;Caroline Brun;Marina Barysheva;Ming-Chiang Chang;Sarah K. Madsen;Katie L. McMahon;Greig I. de Zubicaray;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:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.