Computational modelling for understanding mechanisms of Alzheimer’s disease progression 

Now Closed

Computational modelling for understanding mechanisms of Alzheimer’s disease progression 

Primary Supervisor: Dr Neil Oxtoby, UCL Centre for Medical Image Computing
Secondary Supervisor: Dr Marc Busche, UK Dementia Research Institute at UCL

Project Summary:
· 4-year MPhil/PhD studentship funded by the Department of Computer Science, via Dr Neil Oxtoby’s UKRI Future Leaders Fellowship
· Unique and exciting opportunity to perform research at the intersection of computer science, medical imaging, machine learning, and neuroscience.
· Funding includes a stipend of £17,285 per annum and UK/EU resident tuition fees

The appointee will join the Progression Of Neurological Disease (POND) team within the Centre for Medical Image Computing (CMIC) and will enjoy the additional benefits of joining the i4health CDT.

Background:
Alzheimer’s disease (AD) is the leading cause of dementia, which is one of the biggest challenges of modern medicine and healthcare. Many hundreds of clinical trials over decades have produced zero disease-modifying interventions/drugs!

The POND group in UCL’s Centre for Medical Image Computing (CMIC) are pioneers and world-leaders in computational modelling of neurological disease progression, including leading the highly successful EU Horizon 2020 EuroPOND project (europond.eu) and the new EU JPND E-DADS project (e-dads.github.io).

These data-driven computational methods have drawn on the increasing availability of large clinical and neuroimaging datasets. The models themselves provide new utility and key insights into disease, including fine-grained patient assessment (and clinical trial recruitment!) and improved understanding of disease mechanisms.

However, important open questions, challenges, and opportunities remain.

Research Aims:
This studentship is associated with Dr. Neil Oxtoby’s UKRI Future Leaders Fellowship: “I-AIM: Individualised Artificial Intelligence for Medicine”. I-AIM is an ambitious project to advance the state of the art in computational modelling of neurological disease progression, both in terms of individualised/personalised medicine and in understanding disease mechanisms — including exploration of the role of AI in these endeavours.

The primary application area of interest is clinical trials in the elusive quest for a disease-modifying drug for AD. The computational “supermodels” to be developed in this project include both phenomenological models for describing disease trajectories in terms of biomarkers (from imaging and other modalities), and mechanistic models exploring the underlying biological mechanisms of neurological disease progression.

In this project, data-driven image-based modelling will draw on large clinical/neuroimaging datasets as well as data originating from mouse models of AD (acquired through a collaboration with the Busche Lab in the Dementia Research Institute at UCL). Translational data will consist of high-resolution imaging (e.g. multiphoton microscopy) readouts of brain cell function and amyloid beta and tau pathology, the two neuropathological hallmarks of AD, as well as outputs from ultrasensitive assays for blood-based biomarkers.

This is a unique and exciting opportunity to perform research at the intersection of computer science, medical imaging, machine learning, and neuroscience.

Person Specification:
· The ideal applicant will have a strong mathematical and/or computational background and a keen interest in effecting meaningful change in neurology and/or neuroscience.
· Candidates should have experience and interest in developing machine learning methods (statistical models) for data analysis.
· It is desirable, although not essential, that the candidate has experience with (big) clinical data and longitudinal statistical modelling.
· Applications are sought to support the I-AIM work stream on understanding Alzheimer’s disease mechanisms, with a close link to phenomenological modelling of disease progression.
· 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.

To Apply:
Please send a CV (including names of two referees) and cover letter expressing your interest and motivation to Dr Neil Oxtoby: n.oxtoby@ucl.ac.uk
Nominal closing date: 31 August 2020. Applications will remain open until the position is filled and will be assessed continuously, so we recommend submitting ASAP.