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Automated Analysis of Digital Biomarkers and Treatment Response Evaluation from MRI images for People with Bipolar Disorder (Advert Reference: RDF22-R/EE/CIS/ALSHABRAWY)

   Faculty of Engineering and Environment

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  Dr Ossama Alshabrawy  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Bipolar disorder is a severe and highly recurrent mental illness characterised by periods of mania and depression. Bipolar disorder is associated with a substantial morbidity and mortality, significantly reducing lifespan with high rates of suicides and medical comorbidity, such cardiovascular disease. This project identifies this important healthcare need that could be addressed through the development of novel digital biomarkers that will be extracted from MRI images. This helps the clinician to make better diagnosis and treatment. Markers capable of discriminating bipolar disorder and potentially predictive of response to lithium treatment are the focus of this project. The analysis of MRI includes structural imaging, diffusion weighted imaging, and quantitative T1 and T2. The images will be pre-processed using some image processing methods, then the pre-processed images will be used to train the machine learning/deep learning model to extract digital markers for bipolar disorder and then to discriminate between people with bipolar who are following Lithium treatment and who are following other treatments. Finally, this project will analyse the treatment response of lithium over periods of time. This project will make use of data from the Bipolar Lithium Imaging and Spectroscopy Study (BLISS) and the Response to Lithium Network Study (R-LiNK) datasets. It will apply established and novel methods using multiple digital data sources and machine learning/deep Learning models to derive predictive biomarkers of bipolar disorder and response to Lithium treatment, driving forward the goal of personalised medicine in psychiatry.

The Principal Supervisor for this project is Dr. Ossama Alshabrawy.

Eligibility and How to Apply:

Please note eligibility requirement:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.

For further details of how to apply, entry requirements and the application form, see 

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF22-R/…) will not be considered.

Deadline for applications: 20 June 2022

Start Date: 1 October 2022

Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.

Funding Notes:

Each studentship supports a full stipend, paid for three years at RCUK rates (for 2022/23 full-time study this is £16,602 per year) and full tuition fees. Only UK candidates may apply.

Studentships are available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £9,961 per year and full tuition fees) in combination with work or personal responsibilities.

Please note: to be classed as a Home student, candidates must meet the following criteria:

• Be a UK National (meeting residency requirements), or

• have settled status, or

• have pre-settled status (meeting residency requirements), or

• have indefinite leave to remain or enter.


1. Nunes, A., Schnack, H.G., Ching, C.R.K. et al. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry 25, 2130–2143 (2020).
2. Mirjam Quaak, Laurens van de Mortel, Rajat Mani Thomas, Guido van Wingen, “Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis”, NeuroImage: Clinical, Volume 30, 2021, ISSN 2213-1582.

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