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

Kai-kai Shen*, Jurgen Fripp, Fabrice Mériaudeau, Gaël Chételat, Olivier Salvado, Pierrick Bourgeat, Alzheimer's Disease Neuroimaging Initiative

*Corresponding author for this work

Research output: Contribution to journalArticle

63 Citations (Scopus)

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 T 2 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).

Original languageEnglish
Pages (from-to)2155-2166
Number of pages12
JournalNeuroImage
Volume59
Issue number3
DOIs
Publication statusPublished - 1 Feb 2012
Externally publishedYes

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