Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

Yi Yu Chou, Natasha Leporé, Greig I. de Zubicaray, Owen T. Carmichael, James T. Becker, Arthur W. Toga, Paul M. Thompson

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.

Original languageEnglish (US)
Pages (from-to)615-630
Number of pages16
JournalNeuroImage
Volume40
Issue number2
DOIs
StatePublished - Apr 1 2008

Fingerprint

Atlases
Alzheimer Disease
Apolipoprotein E4
Magnetic Resonance Imaging
Lateral Ventricles
Brain
Neuroimaging
Genes
Disease Progression
Healthy Volunteers
Pharmaceutical Preparations

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Chou, Y. Y., Leporé, N., de Zubicaray, G. I., Carmichael, O. T., Becker, J. T., Toga, A. W., & Thompson, P. M. (2008). Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease. NeuroImage, 40(2), 615-630. https://doi.org/10.1016/j.neuroimage.2007.11.047

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease. / Chou, Yi Yu; Leporé, Natasha; de Zubicaray, Greig I.; Carmichael, Owen T.; Becker, James T.; Toga, Arthur W.; Thompson, Paul M.

In: NeuroImage, Vol. 40, No. 2, 01.04.2008, p. 615-630.

Research output: Contribution to journalArticle

Chou, YY, Leporé, N, de Zubicaray, GI, Carmichael, OT, Becker, JT, Toga, AW & Thompson, PM 2008, 'Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease', NeuroImage, vol. 40, no. 2, pp. 615-630. https://doi.org/10.1016/j.neuroimage.2007.11.047
Chou, Yi Yu ; Leporé, Natasha ; de Zubicaray, Greig I. ; Carmichael, Owen T. ; Becker, James T. ; Toga, Arthur W. ; Thompson, Paul M. / Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease. In: NeuroImage. 2008 ; Vol. 40, No. 2. pp. 615-630.
@article{cb00227f377343f393f76546e78865d1,
title = "Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease",
abstract = "We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.",
author = "Chou, {Yi Yu} and Natasha Lepor{\'e} and {de Zubicaray}, {Greig I.} and Carmichael, {Owen T.} and Becker, {James T.} and Toga, {Arthur W.} and Thompson, {Paul M.}",
year = "2008",
month = "4",
day = "1",
doi = "10.1016/j.neuroimage.2007.11.047",
language = "English (US)",
volume = "40",
pages = "615--630",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "2",

}

TY - JOUR

T1 - Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

AU - Chou, Yi Yu

AU - Leporé, Natasha

AU - de Zubicaray, Greig I.

AU - Carmichael, Owen T.

AU - Becker, James T.

AU - Toga, Arthur W.

AU - Thompson, Paul M.

PY - 2008/4/1

Y1 - 2008/4/1

N2 - We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.

AB - We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.

UR - http://www.scopus.com/inward/record.url?scp=40649126317&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=40649126317&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2007.11.047

DO - 10.1016/j.neuroimage.2007.11.047

M3 - Article

C2 - 18222096

AN - SCOPUS:40649126317

VL - 40

SP - 615

EP - 630

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 2

ER -