Computational modelling of disease progression and subtype discovery in Alzheimer's Disease

The motivation for the work is to better understand Alzheimer's disease through advances in disease progression modelling. Disease progression models aim to capture the temporal trajectories of biomarker changes that characterise a particular disease. These models provide crucial insight into disease processes and can be used to inform staging systems for patient stratification. This is particularly important for neurodegenerative diseases, such as Alzheimer's disease, which are extremely heterogenous and whose risk factors are poorly understood.

Previous work has identified subgroups of patients with common patterns of disease progression. However, subgroup discovery is challenging in the presence of confounders, and existing modelling approaches are not well equipped to deal with outliers. Furthermore, existing approaches do not explicitly model the variation of disease trajectories within a subtype, which hinders their ability to make personalised disease predictions.

This project combines disease progression modelling techniques developed by the POND group at UCL, such as the Subtype and Stage Inference model, SuStaIn (Young et al, Nature Communications 2018), with new approaches from the fields of statistical unsupervised learning and outlier detection, to improve the ability of these models to capture the complexity of the subtype landscape. We will focus initially on Alzheimer's disease data sets, as the necessary access to data sets and clinical expertise are readily available at UCL through projects such as EuroPOND and E-DADS.

Research aims:

Incorporate outlier detection methods into existing disease progression modelling approaches, to concurrently estimate subtypes and identify individuals consistent with the model
Used advanced techniques from the field of statistical unsupervised learning to improve the models' ability to identify fine-grained subtypes and within-subtype variability
Apply these models in large clinical datasets to elucidate new insights into disease progression and risk factors that are associated with particular subtypes