AustralieFrV1, Main, Exploration, bibRecord, 008903

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

Identifieur interne : 008903 ( Main/Exploration ); précédent : 008902; suivant : 008904

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

Auteurs : Caroline Brun [États-Unis] ; Natasha Leporé [États-Unis] ; Xavier Pennec [France] ; Yi-Yu Chou [États-Unis] ; Agatha D. Lee [États-Unis] ; Marina Barysheva [États-Unis] ; Grieg De Zubicaray [Australie] ; Matthew Meredith [Australie] ; Katie Mcmahon [Australie] ; Margaret J. Wright [Australie] ; Arthur W. Toga [États-Unis] ; Paul M. Thompson [États-Unis]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.

RBID : ISTEX:E39042F91CF91217EAE96A4D41AB36255C6393A5

English descriptors

Abstract

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.

Url:

https://api.istex.fr/document/E39042F91CF91217EAE96A4D41AB36255C6393A5/fulltext/pdf

DOI: 10.1007/978-3-540-85990-1_110

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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A Tensor-Based Morphometry Study of Genetic Influences on Brain Structure Using a New Fluid Registration Method

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

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri