A Tensor-Based Morphometry Study of Genetic Influences on Brain Structure Using a New Fluid Registration Method

Authors:
Caroline Brun;Natasha Leporé;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Grieg Zubicaray;Matthew Meredith;Katie Mcmahon;Margaret J. Wright;Arthur W. Toga;Paul M. Thompson
Affiliations:
Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095;Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095;Asclepios Research Project, INRIA, Sophia-Antipolis Cedex, France 06902;Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095;Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095;Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095;Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia;Genetic Epidemiology Lab, QIMR, Brisbane, Australia;Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095;Laboratory of Neuro Imaging, UCLA, Los Angeles, USA CA 90095
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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Abstract

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.