Understanding Alzheimer’s disease progression through phenotypes discovery using manifold learning techniques

Abstract

Alzheimer’s disease (AD) is clinically highly heterogeneous, varying in terms of rates of progression, test and cognitive symptoms among patients, as well as from a neuroimaging perspective. In the datasets provided by The Alzheimer’s Disease Neuroimaging Initiative (ADNI), researchers collect, validate and utilize data, including MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the disease. Data coming from these datasets allow discovering phenotypes that could help to better understand the disease and provide targeted treatment. The objective of this thesis is to identify data-driven phenotypes using manifold learning and unsupervised clustering on multimodal longitudinal imaging and nonimaging data. First, we apply a novel approach for dimensionality reduction called PHATE that captures both local and global nonlinear structure using an informationgeometric distance between datapoints that would facilitate the discovery of possible AD phenotypes. Over PHATE output space, we performed a multiple-kernel unsupervised clustering to obtain profiles and describe AD phenotypes where features are weighted to construct kernels. Our results show that our approach can reveal AD progression trajectories in a lower dimensionality space, improving the results of the profiling where we obtained 4 possible profile subgroups using MRI cross-sectional baseline data and 8 possible profile subgroups when using longitudinal data. Furthermore, longitudinal data established clearer separation among profiles and higher significance for cognitive tests and general volumetric cerebral values than baseline data. Identifying these profiles could be useful for more personalized treatment of such a heterogeneous disease as AD.