Abstract
In this article, we use longitudinal morphometry (shape and size) measures of hippocampus in subjects with mild dementia of Alzheimer type (DAT) and nondemented controls in logistic discrimination. The morphometric measures we use are volume and metric distance measures at baseline and follow-up (two years apart from baseline). Morphometric differences with respect to a template hippocampus were measured by the metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm. LDDMM assigns metric distances on the space of anatomical images, thereby allowing for the direct comparison and quantization of morphometric changes. We also apply principal component analysis (PCA) on volume and metric distance measures to obtain principal components that capture some salient aspect of morphometry. We construct classifiers based on logistic regression to distinguish diseased and healthy hippocampi (hence potentially diagnose the mild form of DAT). We consider logistic classifiers based on volume and metric distance change over time (from baseline to follow-up), on the raw volumes and metric distances, and on principal components from various types of PCA analysis. We provide a detailed comparison of the performance of these classifiers and guidelines for their practical use. Moreover, combining the information conveyed by volume and metric distance measures by PCA can provide a better biomarker for detection of dementia compared to volume, metric distance, or both.
Keywords: Computational anatomy, dementia of Alzheimer Type, hippocampus, large deformation diffeomorphic metric mapping (LDDMM), logistic discrimination, morphometry, principal component analysis
Current Alzheimer Research
Title:Metric Distances between Hippocampal Shapes Indicate Different Rates of Change over Time in Nondemented and Demented Subjects
Volume: 9 Issue: 8
Author(s): Elvan Ceyhan, Mirza Faisal Beg, Can Ceritoglu, Lei Wang, John C. Morris, John G. Csernansky, Michael I. Miller and J. Tilak Ratnanather
Affiliation:
Keywords: Computational anatomy, dementia of Alzheimer Type, hippocampus, large deformation diffeomorphic metric mapping (LDDMM), logistic discrimination, morphometry, principal component analysis
Abstract: In this article, we use longitudinal morphometry (shape and size) measures of hippocampus in subjects with mild dementia of Alzheimer type (DAT) and nondemented controls in logistic discrimination. The morphometric measures we use are volume and metric distance measures at baseline and follow-up (two years apart from baseline). Morphometric differences with respect to a template hippocampus were measured by the metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm. LDDMM assigns metric distances on the space of anatomical images, thereby allowing for the direct comparison and quantization of morphometric changes. We also apply principal component analysis (PCA) on volume and metric distance measures to obtain principal components that capture some salient aspect of morphometry. We construct classifiers based on logistic regression to distinguish diseased and healthy hippocampi (hence potentially diagnose the mild form of DAT). We consider logistic classifiers based on volume and metric distance change over time (from baseline to follow-up), on the raw volumes and metric distances, and on principal components from various types of PCA analysis. We provide a detailed comparison of the performance of these classifiers and guidelines for their practical use. Moreover, combining the information conveyed by volume and metric distance measures by PCA can provide a better biomarker for detection of dementia compared to volume, metric distance, or both.
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Cite this article as:
Ceyhan Elvan, Faisal Beg Mirza, Ceritoglu Can, Wang Lei, C. Morris John, G. Csernansky John, I. Miller Michael and Tilak Ratnanather J., Metric Distances between Hippocampal Shapes Indicate Different Rates of Change over Time in Nondemented and Demented Subjects, Current Alzheimer Research 2012; 9 (8) . https://dx.doi.org/10.2174/156720512803251138
DOI https://dx.doi.org/10.2174/156720512803251138 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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