NOW CLOSED:Computational modelling of disease progression & subtype discovery in Alzheimer’s disease

NOW CLOSED: PhD Studentship - Computational modelling of disease progression and subtype discovery in Alzheimer’s disease - Deadline extended to 11th July 2023

Primary Supervisor:  
Prof Daniel C. Alexander, UCL Centre for Medical Image Computing

The project is a 4-year PhD studentship to be based in the UCL Centre for Medical Image Computing (CMIC). The studentships comes with a generous stipend approximately at the Wellcome Trust rates (the maximum possible for PhD studentships in the UK).

The successful candidate will align with the UCL Centre for Doctoral Training (CDT) in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the activities and events organised by the centre.

Project Background:

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:

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

Person specification & requirements:

• The ideal applicant will have a strong mathematical and/or computational background and a keen interest in effecting meaningful change to patients living with chronic diseases such as dementia.
• Candidates should have experience and interest in developing machine learning methods (statistical models)
• Experience with any of image analysis, big data analysis, Bayesian modelling and longitudinal statistical modelling would be advantageous but not essential.
• Candidates must have a UK (or international equivalent) first class or 2:1 honours degree and an MSc in physics, computer science, mathematics, engineering, or a comparable subject.

Eligibility

This studentship is for Home Fee applicants only. Overseas fee payers will not be considered. You can find out more information about Fee Status criteria here

Deadline extended to 11th July 2023

How to Apply

Please complete the following steps to apply.

• Send an expression of interest and current CV to: d.alexander@ucl.ac.uk and cdtadmin@ucl.ac.uk .Please quote Project Code: 23027 in the email subject line.
• Make a formal application to via the UCL application portal. Please select the programme code Medical Imaging TMRMEISING01 and enter Project Code 23027 under ‘Name of Award 1’.

Application Process:

• After the deadline, all applicants that specified Project 23027 and with a Portico application will be considered for interview.
• Candidates will normally be invited for interview within two-weeks of the deadline. If you have not been contacted within this time-period, you have unfortunately not been successful in being shortlisted.
• The interview panel will normally consist of the supervision team on the project and the CDT Director.
• The interview will normally consist of a short presentation (5-10mins) by the candidate followed by questions from a panel.
• The successful candidate will be informed by email and given a week to confirm whether they wish to accept the PhD place and funding.
• Note that applications without specifying the project they are applying for and/or making a formal Portico application will be automatically rejected.
• Once accepted, a formal UCL offer of admission will be sent to the applicant as well as an offer of studentship funding.