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

Chou, Y-Y, Lepore, N., De Zubicaray, G. I., Carmichael, O., Becker, J.T., Toga, A.W. and 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. doi:10.1016/j.neuroimage.2007.11.047


Author Chou, Y-Y
Lepore, N.
De Zubicaray, G. I.
Carmichael, O.
Becker, J.T.
Toga, A.W.
Thompson, P.M.
Title Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease
Journal name NeuroImage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2008-01-01
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2007.11.047
Open Access Status DOI
Volume 40
Issue 2
Start page 615
End page 630
Total pages 16
Editor Mazziotta, J.C.
Place of publication USA
Publisher Elsevier
Language eng
Subject C1
1109 Neurosciences
920112 Neurodegenerative Disorders Related to Ageing
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.
Keyword Cognitive Neuroscience
Neurology
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID K01 AG030514
R01 AG021431-01
AG016570
P50 AG005133-24
R21 RR019771
R01 AG021431-02
EB01651
K01 AG030514-01A1
R01 AG021431-04
RR019771
P50 AG016570
P50 AG005133
P41 RR013642
P41 RR13642
AG021431
R01 AG021431-03
P41 RR013642-11
R01 AG021431
P50 AG016570-070004
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: 2009 Higher Education Research Data Collection
 
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Created: Wed, 01 Apr 2009, 00:04:55 EST by Lesley-Jayne Jerrard on behalf of Centre For Magnetic Resonance