2025-26 Project (Barrick & Isaacs & Howe)

Prediction of dementia from population-wide neuroimaging data using novel brain age techniques

SUPERVISORY TEAM

Supervisor

Dr Tom Barrick at City St George’s
Email: tbarrick@sgul.ac.uk

Co-Supervisor

Dr Jeremy Isaacs at City St George’s
Email: jisaacs@sgul.ac.uk

Co-Supervisor

Professor Franklyn Howe at City St George’s
Email: howefa@sgul.ac.uk

PROJECT SUMMARY

Project Summary

This is an exciting opportunity to join a multidisciplinary translational magnetic resonance imaging research group.   

Predicting individuals at risk of progression to dementia remains an unmet clinical challenge. Reliable well-validated biomarkers are required to predict long-term outcomes, monitor disease progression and objectively identify disease subtypes. You will develop and apply quantitative imaging biomarkers to determine brain age from neuroimaging data and predict future incidence of dementia.    

Novel quantitative tissue microstructural imaging techniques such as Quasi-Diffusion Imaging will be applied to UK Biobank data from which biomarkers sensitive to ageing and specific to dementia will be obtained. Structural, microstructural and functional biomarkers will be used to develop multivariate machine learning and artificial intelligence algorithms to quantify differences between brain and chronological age, and individuals’ risk of dementia. Progression to mild cognitive impairment and dementia sub-types such as Alzheimer’s disease, vascular dementia and frontotemporal dementia will be investigated.

Project Key Words

Neuroimaging, dementia, MRI, diffusion, artificial intelligence

MRC LID Themes

  1. Health Data Science

Skills

MRC Core Skills

  1. Quantitative skills
  2. Interdisciplinary skills

Skills we expect a student to develop/acquire whilst pursuing this project:

• Develop understanding of Alzheimer’s disease, vascular dementia and fronto-temporal dementia, and how neuroimaging may influence patient care and clinical decisions. 
• Develop understanding of the physics of MRI, with emphasis on structural, diffusion and functional MRI techniques, and how neuroimaging may be used in general to influence patient care and clinical decisions. 
• Develop novel imaging machine learning and artificial intelligence methods and apply statistical modelling methods to data. 
• Gain experience in processing and analysing large multimodal datasets that include clinical, imaging, demographic and biological data. 
• Develop understanding of the quasi-diffusion model of diffusion dynamics and quantitative tissue microstructural imaging biomarkers. 
• Presentation of findings at clinical and academic conferences, in peer review publications and through public engagement. 
• Understand challenges and opportunities of using patient data in health data science research.

Routes

Which route/s are available with this project?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • City St George’s – MSc Clinical Neuroscience Practice
  • City St George’s – MSc Clinical, Social and Cognitive Neuroscience
  • City St George’s – MSc Computer Science
  • City St George’s – MSc Computer Science with Artificial Intelligence
  • City St George’s – MSc Computer Science with Data Analytics

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? No

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution, meeting – at the minimum – the institutional research degree regulations and expectations. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training (for research degree study). Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Primary location for duration of this research degree: City St George’s, London

Travel requirements for this project: Attendance of training courses and conferences (including both National and International).

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum City St George’s institutional eligibility criteria for doctoral study.
  • Minimum 2:1 honours degree. The ideal candidate will have studied at BSc or MSc level in one of: Computer Science, Physics, Engineering or Mathematics/Statistics or have a background in Magnetic Resonance Imaging or Neuroimaging.

Other useful information

  • Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

1. Background   
Predicting individuals at risk of progression to dementia remains an unmet clinical challenge. Reliable well-validated biomarkers are required to predict long term outcomes, monitor disease progression and objectively identify disease subtypes. MRI methods can quantify the apparent brain age of individuals and the difference with chronological age, termed δ [e.g. 1,2,3,4].

Greater δ corresponds to accelerated brain aging and is a potential marker for brain degenerative diseases [1,2,5].   

Structural and tissue microstructural imaging techniques (using Diffusional Kurtosis Imaging , DKI) offer high sensitivity and specificity to identifying disease pathology in mild cognitive impairment (MCI) and dementia [6,7,8]. We have developed Quasi-Diffusion Imaging (QDI) [9,10,11] a technique that overcomes DKI methodological limitations to provide improved sensitivity and specificity to patterns of grey and white matter tissue microstructure in ageing and disease. QDI has not been previously used in δ quantification.    

2. Project Objectives   
The project will use neuroimaging data from the UK Biobank. We hypothesise that:
(a) Increased δ will be associated with risk of MCI with greater δ corresponding to greater risk of dementia.  
(b) δ enables prediction of progression to MCI and dementia.  
(c) δ enables prediction of dementia subtype (Alzheimer’s Disease, AD, Vascular Dementia, VD and fronto-temporal dementia (FTD)).   

3. Techniques to be used and available data   
δ will be quantified from neuroimaging data using:  
(a) Statistical techniques (i.e. Gaussian process regression [1,2,3,4]),  
(b) Machine learning techniques (i.e.[1,4] 

(c) Artificial intelligence (AI) (i.e. neural networks [1,4]).  These will be trained on quantitative neuroimaging features within anatomical regions (e.g. gyral, sulcal, deep grey structures and white matter bundles) including: (i) anatomical volumes (including vascular white matter damage), (ii) QDI tissue microstructural measures, (iii) functional MRI properties within cortical anatomy. Model development and training will be performed using 10-fold cross validation and modelling to reduce age-based biases [5]. Hypothesis (a) will investigate relationships between δ and clinical and biological variables.   

Techniques will be developed to predict individuals progressing to MCI and dementia subtypes in hypotheses (b) and (c). Sensitivity and specificity will identify the best techniques and neuroimaging features associated with increased dementia risk.   

UK Biobank population-wide data (https://www.ukbiobank.ac.uk/, n=500000 participants, primary care data in n=230000) consists of neuroimaging, clinical and biological data at cross-sectional baseline (n=100000) and 2 year follow-up (n=10000) for which  individuals progressing to different types of dementia have been identified.   

4. Data availability statement    
UK Biobank data is available.   

5. Potential risks to project and plans for mitigation    
To ensure software development is achieved within the first two years the QDI biomarkers will be computed within the first three months using software that Dr Barrick has developed for this purpose.   

6. References   
[1] Franke & Gaser, Frontiers in Neurology, 10(2019). 
[2] Cole et al., Molecular Psychiatry, 23(2018). 
[3] Zhu et al., Translational Psychiatry (2023). 
[4] Dörfel et al., bioRxiv, (2023). 
[5] Smith et al., Neuroimage, 200(2019). 
[6] Risacher et al., Current Alzheimer Research, 6(2009). 
[7] Tu et al., Human Brain Mapping, (2021). 
[8] Raja et al., J Neurosci Methods, 335(2020). 
[9] Barrick et al., Neuroimage, 211(2020). 
[10] Barrick et al., Mathematics, 9(15) (2021). 
[11] Spilling et al., Magnetic Resonance in Medicine, 88(6)  (2022).

Further reading

Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

Additional optional pre-application materials:

  • 10.1002/mrm.29420. Epub 2022 Aug 31.

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Barrick-Isaacs-Howe recording

MRC LID LINKS

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