UCL EPSRC Centre for Doctoral Training in Intelligent Integrated Imaging in Healthcare


Neuroimaging and neurochemical ageing biomarkers for optimising prognosis in motor neurone disease

4 year funded PhD studentship. Application Deadline 3rd June 2021 - Now Closed -

Neuroimaging and neurochemical ageing biomarkers for optimising prognosis in motor neurone disease

21 April 2021

Primary Supervisor: Dr James Cole
Secondary Supervisor: Prof Andrea Malaspina (UCL ION)

A four-year funded PhD studentship is available in the UCL Centre for Medical Image Computing (CMIC), joining the MANIFOLD Lab. This interdisciplinary PhD will involve working closely between CMIC and the UCL Queen Square Motor Neurone Disease Centre. Funding will be at least the UCL minimum. Stipend details can be found here.
The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the activities and events organised by the centre

Project Background
Motor neurone disease (or amyotrophic lateral sclerosis: ALS) is an aggressive and incurable neurodegenerative disease, resulting in rapid widespread loss of brain tissue. Progression rates vary greatly between people with ALS; some patients progress from first symptoms to end-stage disease in less than a year while others survive more than a decade. Rapid progression of ALS is more commonly seen in older people, suggesting an interaction with the ageing process. In normal ageing, magnetic resonance imaging (MRI) shows that brain volume reduces gradually. People who showed greater age-related brain atrophy may be at risk of poorer outcomes from neurodegenerative diseases like ALS. Machine learning has been used to develop a sensitive index of brain health, so-called “brain-age”, which helps predict outcomes in neurodegenerative diseases.

Alongside this, it is now possible to measure neurofilaments from blood samples. Neurofilaments normally sit alongside brain cells and are released into the cerebrospinal fluid and the blood when brain cells die. Neurofilaments levels in blood increase with age, reflecting normal, age-related atrophy and have also previously been associated with ALS. While brain MRI and blood neurofilament have independently shown prognostic value in ALS, they have yet to be analysed together.

Research Aims
This project will combine both brain MRI and neurofilament measurements to develop new ways of predicting disease progression in ALS. The student will use computational models (e.g., statistical methods, machine learning) that can detect accelerated age-related changes to brain structure from MRI alongside blood measures of neurofilaments to distinguish fast and slow progressing ALS patients, using data from the ALS Biomarkers Study. This research will generate a multi-modality (brain and blood) analysis approach for use with individual ALS patients, with the eventual goal of enabling targeted treatments and faster and cheaper clinical trials of potential ALS interventions.

Person specification & requirements
Candidates must have a UK (or international equivalent) first class or 2:1 honours degree and ideally an MSc in neuroscience, psychology, computer science, mathematics, engineering, or a comparable subject.

The ideal applicant will have a background in neuroscience/psychology or mathematics/computing. The ability to work with complex datasets and operate in a command-line environment are essential. The student is expected to have the desire to work in an interdisciplinary environment as well as a keen interest in positively impacting the treatment for patients living with motor neurone disease. Experience with any of neuroimaging, clinical data analysis, advanced statistical modelling or machine learning would be advantageous but not essential.

A full studentship is available for home applicants. Overseas (including EU) fee payers will be considered but may be required to cover the fee difference between home and overseas fee. UCL’s fee eligibility criteria can a be found by following this link


How to apply:
Please complete the following two steps to apply.

1. Send a succinct cover letter outlining your relevant skills and experience (including names of two referees) along with a current CV to: james.cole@ucl.ac.uk and cdtadmin@ucl.ac.uk.
Please quote Project Code: 21001 in the email subject line.


2. Make a formal application to Medical Imaging TMRMEISING01 via the UCL application portal https://www.ucl.ac.uk/prospective-students/graduate/apply
Please enter Project Code 21001 under ‘Name of Award 1

Deadline for Applications: 3rd June 2021. - NOW CLOSED-