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

Chou, Y. Y., Leporé, N., de Zubicaray, Greig 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), pp. 615-630.

View at publisher

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. © 2007 Elsevier Inc. All rights reserved.

Impact and interest:

54 citations in Scopus
Search Google Scholar™
49 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 85711
Item Type: Journal Article
Refereed: Yes
DOI: 10.1016/j.neuroimage.2007.11.047
ISSN: 1095-9572
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2007 Elsevier
Deposited On: 01 Sep 2015 01:30
Last Modified: 04 Sep 2015 01:14

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page