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

Brun, Caroline, Leporé, Natasha, Pennec, Xavier, Chou, Yi-Yu, Lee, Agatha D., Barysheva, Marina, De Zubicaray, Grieg, Meredith, Matthew, McMahon, Katie, Wright, Margaret J., Toga, Arthur W. and Thompson, Paul M. (2008). A Tensor-Based Morphometry Study of Genetic Influences on Brain Structure Using a New Fluid Registration Method. In: Lecture Notes In Computer Science. Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II. 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, New York, New York, USA, (914-921). 6 - 10 September 2008.


Author Brun, Caroline
Leporé, Natasha
Pennec, Xavier
Chou, Yi-Yu
Lee, Agatha D.
Barysheva, Marina
De Zubicaray, Grieg
Meredith, Matthew
McMahon, Katie
Wright, Margaret J.
Toga, Arthur W.
Thompson, Paul M.
Title of paper A Tensor-Based Morphometry Study of Genetic Influences on Brain Structure Using a New Fluid Registration Method
Conference name 11th International Conference on Medical Image Computing and Computer-Assisted Intervention
Conference location New York, New York, USA
Conference dates 6 - 10 September 2008
Proceedings title Lecture Notes In Computer Science. Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II   Check publisher's open access policy
Journal name Medical Image Computing and Computer-Assisted Intervention - Miccai 2008, Pt Ii, Proceedings   Check publisher's open access policy
Place of Publication Berlin / Heidelberg, Germany
Publisher Springer
Publication Year 2008
Sub-type Fully published paper
DOI 10.1007/978-3-540-85990-1_110
ISBN 978-3-540-85989-5
ISSN 0302-9743
1611-3349
Volume 5242
Start page 914
End page 921
Total pages 8
Language eng
Abstract/Summary 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.
Subjects 09 Engineering
Q-Index Code E1
Q-Index Status Provisional Code

 
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