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

• Published in: NeuroImage (Orlando, Fla.) Vol. 59; no. 3; pp. 2155 - 2166
• Main Authors: Shen, Kai-kai, Fripp, Jurgen, Mériaudeau, Fabrice, Chételat, Gaël, Salvado, Olivier, Bourgeat, Pierrick
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-02-2012; Elsevier Limited; Elsevier
• Subjects: Aged; Alzheimer Disease - classification; Alzheimer Disease - pathology; Alzheimer Disease - psychology; Alzheimer's disease; Artificial Intelligence; Atrophy; Brain Mapping; Computer Science; Data Interpretation, Statistical; Databases, Factual; Educational Status; Female; Functional Laterality - physiology; Hippocampus - pathology; Humans; Image Processing; Magnetic Resonance Imaging - methods; Male; Medical imaging; Memory; Memory Disorders - etiology; Memory Disorders - pathology; Mental Recall - physiology; Models, Anatomic; Models, Statistical; Neuropsychological Tests; Pathology; Pharmaceutical industry; Principal Component Analysis; Principal components analysis; Studies; Support Vector Machine; Verbal Learning - physiology; Statiscal Shape Model; Alzheimer
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.10.014

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).
► Hippocampal statistical shape models for Alzheimer's disease patients and controls. ► Identification of hippocampal subregions affected by the atrophy in AD. ► Localized shape analysis on the atrophy-affected regions. ► Extra discrimination power in disease classification from localized shape analysis. ► Stronger correlation between the local hippocampal shape change and memory decline.