Row Fogo Centre for Research into Ageing and the Brain
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Applications for PhD studentship position with Precision Medicine Doctoral Training Programme are still opened

Apply by 20th January 2022 via Precision Medicine Doctoral Training Programme website.

Project title: Developing explainable Machine Learning approaches for brain health prognosis and patient stratification in a nationwide dataset

Application deadline

20th January 2022




Cerebral small vessel disease (SVD) is estimated to affect up to 1 billion people worldwide. SVD refers to a syndrome of clinical and imaging findings that result from pathologies of the small blood vessels in the brain [1]. SVD causes up to 45% of dementia and accounts for about 20% of all strokes. The cause of the pathology is poorly understood since, until recently, it has been difficult to identify the brain abnormalities in people in vivo and there are no reliable experimental models [2]. There has been a rapid advance in understanding of SVD with the wider availability of magnetic resonance imaging (MRI). This shows overt SVD lesions such as, among others, white matter hyperintensities, lacunes, microbleeds, and perivascular spaces [3]. Although risk factors (hypertension, smoking, and diabetes) increase the risk of SVD, these together explain only a small proportion of variance in SVD lesions [1], indicating that much greater insight into the small vessel abnormalities are needed for patient stratification and prognosis. The supervisory team is world-leading in the development neuroimaging analysis approaches to quantifying SVD lesion burden in Compute Tomography (CT) and MRI scans. However, work to date has occurred primarily in the context of clinical studies and trials. There’s a pressing need to adapt these techniques to routinely collected scans, which suffer from high variability in quality, and design analysis pipelines that can be integrated into routine clinical practice. The Scottish Medical Imaging (SMI) Service, launched in November 2020, has created a national database of deidentified routine clinical images that can be linked to outcome data within the National Safe Haven. The SMI database has been created from a copy of the NHS Scotland Picture Archiving & Communication System dataset covering the years 2010-2017. This is a unique resource worldwide and has potential to lead to a step change in SVD understanding.


The main aim of the study is developing neuroimaging analysis approaches for the automated quantification of SVD lesions in routinely acquired CT/MRI scans and develop predicitive models for patient stratification and prognosis based on these interpretable features. Progress towards this aim will be delivered based on the following objectives: Page 4 of 7

1. Using the SMI service, build a virtual cohort of 5,000 patients (and the same number of matched controls) admitted to hospital with: ischemic stroke or vascular dementia with available MRI structural data (T1, T2, and FLAIR sequences) and/or CT; differentiating them from those without any pathology, neoplastic lesion(s), haemorrhagic stroke, a genetic disease, brain injury, or malformations. Link the imaging database to clinical outcomes in the National Safe Haven.

2. Develop methods for: a) automated quality scoring, b) upsampling and reconstructing (super-resolution) the scans using generative adversarial models (GANs)

3. Based on a subset of the images that have been scored visually for SVD lesions as well as other features, develop deep learning approaches to predict (a regression problem in ML): a) the quantification of individual lesion burden, and b) aggregated measures of SVD disease burden such as the Brain Health Index developed by the supervisory team [4].

4. Investigate associations between these automated measurements and patient outcomes related to brain health in the period 2010-2017. Based on these univariate associations, demographics, and brain health history, develop predicitive models for patient stratification and prognosis.

Training Outcomes

The student will receive state-of-the-art training in the core disciplines of image analysis, computational modelling, statistical methods, and data science while gaining expert knowledge in the context of brain SVD and in neuroscience more generally. This highly interdisciplinary approach is well aligned with the “T-shaped researcher” training requirements identified as key in the DTP.



Dr Miguel O. Bernabeu

Dr Maria del C. Valdés-Hernández

Professor Adam Waldman


Further details

Find more details and how to apply link and instructions on the Precision Medicine Doctoral Training Programme website.

PhD studentship opportunity with Precision Medicine Doctoral Training Programme (external website)


Other PhD Studentship offered by Precision Medicine Doctoral Training Programme

Read about other PhD programmes in Precision Medicine across The University of Edinburgh (22 projects).

Please, check information about individual projects as different application deadlines may apply.

Precision medicine Doctoral Training Programme -list of all PhD Studentship projects


Related links

Read about other PhD opportunities available at the Row Fogo Centre.

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