6533b82bfe1ef96bd128ceb8
RESEARCH PRODUCT
Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.
Jeannie-marie LeoutsakosBrandon WhitcherHakon GrydelandOlivier SalvadoYongxia (Sharon) ZhouKerim MunirPierre PayouxPierrick BourgeatJeffrey LooiKaikai ShenGary CutterLouise RyanEric JouventJens PruessnerBas JasperseJurgen FrippHarald HampelChristian GaserFreimut JuenglingHerve Lemaitresubject
Models AnatomicMaleSupport Vector MachineDatabases FactualNeuropsychological TestsHippocampusFunctional Laterality030218 nuclear medicine & medical imagingLogical addressCorrelation0302 clinical medicineDiscriminative modelAlzheimer Centre [DCN PAC - Perception action and control NCEBP 11][ INFO.INFO-TI ] Computer Science [cs]/Image Processingeducation.field_of_studyBrain MappingPrincipal Component AnalysisVerbal LearningMagnetic Resonance ImagingNeurologyData Interpretation Statistical[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Principal component analysisEducational StatusFemalePsychologyCognitive NeurosciencePopulationFeature selectionVerbal learningStatiscal Shape Model03 medical and health sciencesAlzheimer DiseaseArtificial IntelligenceSupport Vector MachinesHumansAlzheimer Centre [NCEBP 11]educationAgedMemory DisordersNeurology & NeurosurgeryModels Statisticalbusiness.industryPattern recognitionSupport vector machineMental RecallAlzheimerArtificial intelligenceAtrophybusiness030217 neurology & neurosurgerydescription
Item does not contain fulltext 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).
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-01-01 |