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Advanced MRI to improve outcomes of deep brain stimulation

   Faculty of Biology, Medicine and Health

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  Dr L Parkes, Mr Julian Evans, Dr M Silverdale  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

Manchester United Kingdom Biomedical Engineering Biophysics Data Analysis Machine Learning Medical Physics Neurology Neuroscience Physics

About the Project

Approximately 20 patients per year undergo neurosurgery for Parkinson’s disease in Salford Royal hospital. The surgery implants electrodes into the sub-thalamic nucleus (STN) to allow delivery of deep brain stimulation to control motor symptoms such as tremor. The principal aim of this PhD project is to improve the localisation of the STN through the use of advanced MRI techniques such as quantitative susceptibility mapping (QSM) (1).

Advanced MRI may offer a number of advantages over the current use of more conventional images: i) reduced acquisition time offering the possibility of imaging without anaesthetic with associated benefits in cost and safety ii) increased confidence in the placement of electrodes and reduced planning time and iii) improved patient outcomes.

The student will evaluate a number of MRI acquisition approaches for imaging the STN in the healthy human brain. The most promising techniques will be added onto the conventional imaging protocol used in patients and evaluated in terms of confidence in and time required for neurosurgical planning. Software will be developed to input processed images (e.g. QSM images) into the neurosurgical planning software.

A database of existing images will be used to train neural networks to identify the STN and their utility in decision support will be evaluated.

Measurements of the structural and functional connectivity of the STN are associated with clinical outcome (2). We will test whether these measurements can also inform on the best stimulation parameters so reducing the time required to programme the stimulator post-surgery. The association between functional and structural network measurements and parameters such as optimum stimulation frequency will be tested.

The exciting PhD project provides an opportunity to make a direct contribution to solving an important healthcare challenge. The work will be a first step in the translation of advanced MRI methods developed at the University of Manchester into clinical research and practice. 

Entry Requirements:

Applicants are expected to hold (or about to obtain) a minimum upper second class undergraduate honours degree (or equivalent) in physics or engineering with a keen interest in biology and the brain. Research experience in imaging is desirable. 

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website ( Informal enquiries may be made directly to the primary supervisor. You MUST also submit an online application form - choose PhD Biomedical Imaging Sciences.

Equality, Diversity & Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website 

Funding Notes

This project is jointly funded by the University of Manchester and Philips Healthcare. Studentship funding is for a duration of three years to commence in September 2021 and covers UK tuition fees and a UKRI stipend (£15609 per annum 21/22). The University of Manchester aims to support the most outstanding applicants from outside the UK. We are able to offer a scholarship that will enable full studentship to be awarded to international applicants. This full studentship will only be awarded to exceptional quality candidates, due to the competitive nature of this funding.


1. Yu et al, ‘Visualisation of deep brain stimulation targets in Parkinson’s Disease via 3 T QSM’, Acta Neurochirurgica (2021) 163:1335–1345,
2. Horn A et al, ‘Connectivity predicts deep brain stimulation outcome in Parkinson Disease’, Annals of Neurology (2017) 82:67–78,
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