Modeling cognitive trends in preclinical Alzheimer’s disease (AD) via distributions over permutations

Abstract

This paper presents an algorithm to identify subsets of subjects who share similarities in the context of imaging and clinical measurements within a cohort of cognitively healthy individuals at risk for Alzheimer’s disease (AD). In particular, we wish to evaluate how patterns in the subjects’ cognitive scores or PIB-PET image measurements are associated with a clinical assessment of risk of developing AD, image based measures, and future cognitive decline. The challenge here is that all the participants are asymptomatic, our predictors are noisy and heterogeneous, and the disease specific signal, when present, is weak. As a result, off-the-shelf methods do not work well. We develop a model that uses a probability distribution over the set of permutations to represent the data; this yields a distance measure robust to these issues. We then show that our algorithm produces consistent and meaningful groupings of subjects based on their cognitive scores and that it provides a novel and interesting representation of measurements from PIB-PET images.