Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease.
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.
Chou
Yi-Yu
YY
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225E, Los Angeles, CA, USA.
Leporé
Natasha
N
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225E, Los Angeles, CA, USA.
de Zubicaray
Greig I
GI
Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia.
Carmichael
Owen T
OT
Departments of Neurology and Computer Science, University of California, Davis, CA, USA.
Becker
James T
JT
Department of Neurology and Alzheimer's Disease Research Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Toga
Arthur W
AW
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225E, Los Angeles, CA, USA.
Thompson
Paul M
PM
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225E, Los Angeles, CA, USA. Electronic address: thompson@loni.ucla.edu.
D000368:Aged; D000544:Alzheimer Disease / Q000235:genetics* / Q000473:pathology*; D053327:Apolipoprotein E4 / Q000235:genetics*; D001271:Atlases as Topic; D001931:Brain Mapping / Q000379:methods; D002552:Cerebral Ventricles / Q000473:pathology*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical
D053327:Apolipoprotein E4