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Alzheimer's disease (AD) is a progressive neurological disorder in which the death of brain cells causes memory loss and cognitive decline. The identification of at-risk subjects yet showing no dementia symptoms but who will later convert to AD can be crucial for the effective treatment of AD. For this, Magnetic Resonance Imaging (MRI) is expected to play a crucial role. During recent years, several Machine Learning (ML) approaches to AD-conversion prediction have been proposed using different types of MRI features. However, few studies comparing these different feature representations exist, and the existing ones do not allow to make definite conclusions. We evaluated the performance of various types of MRI features for the conversion prediction: voxel-based features extracted based on voxel-based morphometry, hippocampus volumes, volumes of the entorhinal cortex, and a set of regional volumetric, surface area, and cortical thickness measures across the brain. Regional features consistently yielded the best performance over two classifiers (Support Vector Machines and Regularized Logistic Regression), and two datasets studied. However, the performance difference to other features was not statistically significant. There was a consistent trend of age correction improving the classification performance, but the improvement reached statistical significance only rarely.
alzheimer's disease; magnetic resonance imaging; brain; machine learning; feature representations