City Research Online

Abstract

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging especially in Mild Cognitive Impairment (MCI) subjects. Quantitative structural Magnetic Resonance Imaging (MRI) acquisition methods in combination with Computer-Aided Diagnosis (CAD) are currently being used for the assessment AD. These acquisitions methods include: i) Voxel-based Morphometry (VBM), ii) volumetric measurements in specific Regions of Interest (ROIs), iii) cortical thickness measurements, iv) shape analysis and v) texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following 3 groups: Normal Controls (NC), MCI and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the Medial Temporal Lobe (MTL) especially in the entorhinal cortex, whereas with disease progression both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.

Publication Type:	Article
Additional Information:	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Alzheimer’s disease, classification , computer-aided diagnosis, entorhinal cortex, hippocampus, mild cognitive impairment, MCI, prediction, quantitative MRI
Departments:	School of Science & Technology > Engineering School of Science & Technology > Computer Science > giCentre
SWORD Depositor:	Symplectic Administrator

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