Prospective classification of Alzheimer's disease conversion from mild cognitive impairment

Abstract

Alzheimer's disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this reason, it is important to early detect whether the disease converts from mild cognitive impairment (MCI) which is a prodromal phase of AD. With the advance in brain imaging techniques, various machine learning algorithms have become able to predict the conversion from MCI to AD by learning brain atrophy patterns. However, at the time of diagnosis, it is difficult to distinguish between the conversion group and the non-conversion group of subjects because the difference between groups is small, but the within-group variability is large in brain images. After a certain period of time, the subjects of conversion group show significant brain atrophy, whereas subjects of non-conversion group show only subtle changes due to the normal aging effect. This difference on brain atrophy makes the brain images more discriminative for learning. Motivated by this, we propose a method to perform classification by projecting brain images into the future, namely prospective classification. The experiments on the Alzheimer's Disease Neuroimaging Initiative dataset show that the prospective classification outperforms ordinary classification. Moreover, the features of prospective classification indicate the brain regions that significantly influence the conversion from MCI to AD.