Bayesian inference of neurodegenerative spreading models

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Project Summary (Please complete the summary in the following format with headings present. Should be 4,000 characters Maximum):1) Brief description of the context of the research including potential impactNeurodegenerative diseases may be the biggest challenge facing 21st Century healthcare. Delaying Alzheimer's onset by five years would relieve untold suffering and save £10B annually in the UK. This may come from either treatments that slow disease (none are yet proven) or avoiding risk associated behaviour. Both require deeper understanding of disease initiation and progression.Neurodegenerative diseases exhibit characteristic patterns of spread over the brain that provide clues to the underlying mechanisms of disease aetiology and propagation. This enables us to implement computational models encoding various hypotheses of disease propagation to predict the evolution of brain pathology using large collections of imaging data.This research reveals key unknowns on the biology of neurodegenerative disease like when and where it starts; how it propagates over the brain; how it varies among diseases, subtypes, and individuals; how risk factors influence mechanism. Thus, it will enhance disease understanding, prevention strategies, etc.2) Aims and Objectives -The specific objectives are to:1: Implement a new Bayesian inference framework for evaluation of candidate models of neurodegenerative disease.2. Work with researchers developing novel connectivity mapping and disease progression modelling to incorporate their models within the Bayesian framework.3. Implement models of neurodegenerative pathology spreading. 4. Use the new framework to evaluate the models against state-of-art imaging data sets. 3) Novelty of Research MethodologyThe primary novelty of the research is a new Bayesian inference framework to deal with current limitations in existing computational models. This will underpin new quantitative results evaluating the ability of models of neurodegenerative disease against measured patient data sets thereby shedding light on which models are likely given evidence and which are not. The new framework enables several further advances in the models themselves as well as new approaches to connectivity mapping and disease progression modelling, as well as new insights into the biology of neurodegenerative disease. The project interacts closely with other projects developing these ideas to ensure the framework is compatible with models and algorithms emerging in those areas and vice versa.4) Alignment to EPSRC's strategies and research areasThe project aligns with EPSRC's Healthcare technologies theme and the following research areas:- Artificial intelligence technologies- Clinical technologies- Image and vision computing- Medical imaging- Statistics and applied probability5) Any companies or collaborators involved. To be updated in due course