Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models

Shen, Kai-kai, Fripp, Jurgen, Meriaudeau, Fabrice, Chetelat, Gael, Salvado, Olivier, Bourgeat, Pierrick and The Alzheimer's Disease Neuroimaging Initiative (2012) Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models. Neuroimage, 59 3: 2155-2166. doi:10.1016/j.neuroimage.2011.10.014

Author Shen, Kai-kai
Fripp, Jurgen
Meriaudeau, Fabrice
Chetelat, Gael
Salvado, Olivier
Bourgeat, Pierrick
The Alzheimer's Disease Neuroimaging Initiative
Title Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models
Journal name Neuroimage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2012-02-01
Year available 2011
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2011.10.014
Volume 59
Issue 3
Start page 2155
End page 2166
Total pages 12
Place of publication Maryland Heights, MO, United States
Publisher Academic Press
Collection year 2012
Language eng
Formatted abstract
The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS).
Keyword Mild Cognitive Impairment
Neuronal Loss
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Available online 14 October 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
Centre for Advanced Imaging Publications
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