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 (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 * These two authors share the senior authorship.

Adversarial variational Bayes (AVB) can infer the parameters of a generative model from the data using approximate maximum likelihood. The likelihood of deep generative models model is intractable. However, it can be approximated by a lower bound obtained in terms of an approximate posterior distribution of the latent variables of the data q. The closer q is to the actual posterior, the tighter the lower bound is. Therefore, by maximizing the lower bound one should expect to also maximize the likelihood. Traditionally, the approximate distribution q is Gaussian. AVB relaxes this limitation and allows for flexible distributions that may lack a closed-form probability density function. Implicit distributions obtained by letting a source of Gaussian noise go through a deep neural network are examples of these distributions. Here, we combine AVB with the importance weighted autoencoder, a technique that has been shown to provide a tighter lower bound on the marginal likelihood. This is expected to lead to a more accurate parameter estimation of the generative model via approximate maximum likelihood. We have evaluated the proposed method on three datasets, MNIST, Fashion MNIST, and Omniglot. The experiments show that the proposed method improves the test log-likelihood of a generative model trained using AVB.