Young, AL; (2017) Modelling progression and heterogeneity in Alzheimer's disease. Doctoral thesis , UCL (University College London).

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Abstract

Alzheimer’s disease is the most common form of dementia, accounting for approximately 60-80% of cases. Pathologically, the disease is characterised by the accumulation of amyloid plaques and neurofibrillary tangles in brain tissue, which give rise to downstream neurodegeneration and cognitive deficits. Biomarkers, such as volumetric measures of neurodegeneration derived from Magnetic Resonance Imaging, allow the progression of Alzheimer’s disease to be monitored in vivo. Hypothetical models have been proposed that describe a distinct sequence of biomarker changes, but also heterogeneity in this sequence across different population subgroups. However, the quantitative evolution and heterogeneity of these biomarker changes has yet to be determined. This thesis investigates the progression and heterogeneity of Alzheimer’s disease by developing mathematical models of disease progression that characterise the evolution of biomarker measurements from cross-sectional data. Three key contributions are made. First, the application of data-driven models to sporadic and dominantly-inherited Alzheimer’s disease to determine the sequence of biomarker changes in each form of Alzheimer’s disease, and to ascertain the utility of patient staging systems derived from the models. Second, the development of a simulation framework that produces synthetic neurodegenerative disease datasets, allowing the evaluation of the performance of mathematical models of disease progression. Third, the formulation of a data-driven subtyping model that uniquely uncovers population subgroups with distinct biomarker trajectories, enabling the separation of disease subtype from disease stage. Application of this model to sporadic Alzheimer’s disease provides a novel data-driven classification of Alzheimer’s disease into subtypes with distinct patterns of regional volume loss, as well as fine-grained subtyping and staging information. The models proposed in this thesis have wide potential further application to advance disease understanding and to provide precise patient staging information for other diseases and developmental processes.

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