DETECTION OF LONGITUDINAL BRAIN ATROPHY PATTERNS CONSISTENT WITH PROGRESSION TOWARDS ALZHEIMER’S DISEASE

This thesis develops and applies statistical methodologies to model brain atrophy in humans among multiple brain regions and how this may change over time. Throughout this work, Bayesian multilevel models are progressively developed for single and multiple regions at a given time point as well as modelling how connectivity between multiple regions evolves over time in conjunction with region level estimates. The application of these models provide insight into the detection of longitudinal brain atrophy patterns consistent with healthy ageing or progression towards Alzheimer's disease, and should be of interest to biostatisticians and researchers who deal with neurological spatial data.