Changing gradients: Predicting cognition and wellbeing following stroke using whole-brain intrinsic connectivity gradients and machine learning

Stroke is a leading cause of cognitive impairment, which often leads to difficulties in mobility, communication, attention and working memory, alongside poor wellbeing. While research has investigated the functional consequences of brain injury, it is hard to predict post-stroke recovery, since patients with similar lesions can have very different outcomes. This might be because stroke disrupts whole-brain patterns of connectivity in addition to its local effects. 

This PhD project will use resting-state fMRI to characterise the impact of stroke on whole-brain connectivity patterns (referred to as “gradients”), providing a more wholistic way of understanding post-stroke recovery than an analysis of specific brain sites or networks. Resting-state fMRI provides a measure of the intrinsic functional connectivity of the brain by investigating which brain regions show correlated increases and decreases in blood flow over time. We will extract intrinsic connectivity between all pairs of cortical regions outside the area affected by stroke and then apply a relatively novel gradient decomposition approach (Margulies et al., 2016), which has not yet been used to explore functional recovery following stroke. This approach captures components of variation in connectivity patterns across the cortex to reveal key effects of each stroke. We will also use a novel white-box machine learning approach (Lacy et al., 2015) to identify where connectivity changes have the greatest predictive value. We will consider (i) how stroke disrupts dimensions of connectivity both locally and at a distance from the lesion, (ii) how changes in connectivity gradients predict cognition and wellbeing following stroke and (iii) the extent to which a structural scan, delineating a patient’s lesion, is sufficient for predicting changes in connectivity gradients (important since resting-state data is often not available in a clinical context).

The project is timely since age is a major risk factor for stroke and the burden of this disease is increasing as people live to older age. The project is also expected to have high scientific impact, given the growing profile of gradient decomposition methods and machine learning. It may allow better prediction of outcomes and more effective deployment of therapy following stroke.

You will have the chance to learn diverse skills in this highly interdisciplinary PhD, including neuroimaging analysis, cognitive assessment, coding, advanced statistics, and machine learning. You will be based in a very supportive and productive group based in the Department of Psychology at York, with a co-supervisor in the School of Physics, Engineering and Technology, and you’ll be joining a team of other PhD students and postdocs working on related topics, including how the brain processes words and concepts in a flexible fashion, and how this ability is disrupted by stroke. The PhD position would particularly suit someone with strong quantitative skills and an interest in neuroscience and cognition, as well as a desire to advance understanding for patient benefit. You will have the chance to analyse several large stroke datasets and there are opportunities to tailor the project to focus on aspects of cognition or groups of patients, based on your interests. 

Benefits of being in the DiMeN DTP:

This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle, York and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.

We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.

Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: https://www.dimen.org.uk/blog 

Further information on the programme and how to apply can be found on our website:

https://www.dimen.org.uk/how-to-apply