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MRC DiMeN Doctoral Training Partnership: Falling into place: a novel approach to phenotyping falls in early Parkinson’s disease


MRC DiMeN Doctoral Training Partnership

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Dr A Yarnall , Dr Y Guan , Dr S Del Din , Prof L Rochester No more applications being accepted Competition Funded PhD Project (Students Worldwide)
Newcastle United Kingdom Artificial Intelligence Bioinformatics Biomedical Engineering Data Science Machine Learning Neurology Neuroscience

About the Project

We are looking for a highly motivated and ambitious PhD student to join our internationally renowned team involved in gait and ageing research. The PhD project utilities data from the ICICLE-PD study, an internationally renowned study with complex, longitudinal data. This project will be the first to utilise a breadth of biomarkers to objectively quantify risk of falls in early Parkinson’s disease (PD), with the ultimate aim to use AI to identify falls phenotypes from which to build a care pathway in the disease.

Background

Between 35 and 90% of individuals with established PD fall, with far reaching physical and psychosocial consequences. The identification and modification of risk factors for falls in PD is therefore a high priority for the NHS.

The neurobiology underlying falls in PD is complex and likely to vary between subjects. Due to the complexity of the pathophysiological processes it is unlikely that a single biomarker will predict conversion to faller status, but taken together, clinical, laboratory, and imaging risk factors may allow clinicians to predict which patients are most likely to progress to this state. This is particularly important in early disease, to allow for targeting of primary risk factors and putative modifiable risks.

Objectives

To utilise a machine learning approach to enable development of tools for classification, precision monitoring and prediction of falls risk in PD using biomarkers collected as part of an established study.

This interdisciplinary project combines expertise in the fields of clinical geriatrics (AY), human movement (LR), engineering applied to wearable technology (SDD) and mathematical and statistical modelling (YG). As part of the project, we will aim to validate our model in an independent cohort, building on European collaborations.

The student will have a variety of opportunities to access relevant and appropriate training including presentation skills and communicating scientific research to the public. They will be integrated within a large multidisciplinary research team, spanning the Brain and Movement team, which has an established research culture and ethos and track record of publications in this area. The team adopts a translational approach to clinical research and thus can provide a supportive environment for the student to embrace this project. A fundamental part of this project will include training in data acquisition and analysis of complex data. The supervisory team and research group has a longstanding track record of high impact research. Our recent work has included:

• Using comprehensive neuropsychology and gait evaluation to distinguish dementia subtypes

• Developing wearable devices and digital technologies for monitoring/ measuring movement

• Linking dynamic imaging methods and behavioural responses to understand neural control of walking

• Developing pharmacological and non-pharmacological interventions for individuals with neurodegenerative conditions

• Investigating falls in neurodegenerative diseases.

The student will be registered with the BRC PhD training programme which will provide the opportunity to attend workshops, seminars and access peer support.

Dr Alison Yarnall: http://bam-ncl.co.uk/team/dr-alison-yarnall/

Dr Silvia Del Din: http://bam-ncl.co.uk/team/dr-silvia-del-din/

Dr Yu Guan: https://www.ncl.ac.uk/computing/people/profile/yuguan.html#background

Professor Lynn Rochester: http://bam-ncl.co.uk/team/prof-lynn-rochester/

Website: https://research.ncl.ac.uk/bam/

Twitter: @BAM_Reseach / @AlisonYarnall

Contact Dr Alison Yarnall ([Email Address Removed]) for more information.

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 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: http://www.dimen.org.uk/overview/student-profiles/flexible-supplement-awards

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

https://bit.ly/3lQXR8A


Funding Notes

Studentships are funded by the Medical Research Council (MRC) for 3.5yrs. Funding will cover UK tuition fees and stipend only. We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.
Please read additional guidance here: https://bit.ly/3kPNjoJ
Studentships commence: 1st October 2021
Good luck!

References

1. Morris R*, Yarnall AJ*, Hunter H, Taylor JP, Baker M, Rochester L (2019). Non-invasive vagus nerve stimulation to target gait impairment in Parkinson’s disease. Movement Disorders, 34(6): 918-919 (*joint first authors).
2. Del-Din S, Galna B, Lord S et al. Falls risk in relation to activity exposure in high-risk older adults. J Gerontol A Biol Sci Med Sci, (2020) 6:1198.
3. GuanY, Li CT, Roli F. On Reducing the Effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method. IEEE Trans. Pattern Analysis and Machine Intelligence (2015) 37:7; 1521-1528.


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