Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy

Abstract

In order to find effective treatments for Alzheimer’s disease, a devastating neurodegenerative disease affecting millions of people worldwide, we need to identify subjects at risk of Alzheimer’s as early as possible. To this end, disease progression models have been recently developed, which not only to perform early diagnosis, but also estimate a unique disease signature that is used to predict the subjects’ disease stages and future evolution. However, these models have not yet been applied to rare neurodegenerative diseases, are not suitable to understand the complex dynamics of biomarkers, work only on large multimodal datasets, and their predictive performance has not been objectively validated. In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimer’s disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing visual deficits. My first contribution is a study on the progression of Posterior Cortical Atrophy, using models already developed: the Event-based Model and the Differential Equation Model. However, these models assume pathology follows a predefined brain parcellation. In order to overcome this, I then developed DIVE, a novel spatio-temporal model of disease progression that estimates fine-grained spatial patterns of pathology, potentially enabling us to understand complex disease mechanisms relating to pathology propagation along brain networks. However, all these models were still challenging to apply to rare neurodegenerative diseases like Posterior Cortical Atrophy due to lack of larger, multimodal and longitudinal data. I thus developed Disease Knowledge Transfer, a novel disease progression model that estimates the multimodal progression of rare neurodegenerative diseases from limited, unimodal datasets, by transferring information from larger, multimodal datasets of typical neurodegenerative diseases. Finally, In order to evaluate the performance of such models as well as standard machine learning methods, I organized the TADPOLE challenge, a competition which aims to identify algorithms and features that best predict the evolution of Alzheimer’s disease.