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AI-enabled Imaging

Discovering early biomarkers of Alzheimer’s disease using genetic and physics-informed networks

Project ID: 2023_009

1st Supervisor: Dr Emma Robinson, King’s College London
2nd Supervisor: Dr Jaques-Donald Tournier, King’s College London
Clinical Supervisor: Prof Alexander Hammers, King’s College London


Aims of the Project:

  • To derive novel biomarkers of Alzheimer’s disease derived cortical diffusion and PET
  • To use these to constrain physics-informed models of disease progression
  • To compare against regional gene-expression through precision mapping to the Allen Brain Atlas


Lay Summary:

Detecting early biomarkers of neurodegeneration is a highly challenging problem due to the complex organisational structure and high degree of variation of the human brain. Approximately 50% of dementia sufferers are thought to go undiagnosed in early stages. This limits treatment options and presents significant challenges for patient screening for clinical trials.

Recent studies have indicated that measures of cortical microstructure may present effective, non-invasive markers of early neurodegeneration [1,2]. However, so far these measures have been reported as summary measures averaged across the brain, when it is well known that cellular organisation varies significantly across the cortex, and that the presentation of dementia varies across individuals.

At the same time, recent work in mouse models has shown that the progression of tau pathology through the brain is extremely well constrained by neuronal connectivity, and that deviations from simple models of disease progression can be well explained by gene expression [3]. Similarly inspired models, trained on humans, have been constrained using positron emission tomography (PET) data from the Alzheimer’s Disease Neuroimaging Iniative (ADNI) open dataset [4]. However, thus far these have been limited to global average models of brain organisation, not considering individual variability.

The goal of this project will therefore be to build precision models of the microstructural organisation of individual human brains [5-10], and to use these to constrain geometric deep learning [7, 11, 12] and biophysically-informed neural networks [4,13,14] models of Alzheimer’s disease progression. Findings would be compared against gene expression, to inform mechanistic understanding of the disease, and improve early diagnosis.

The project would best suit students with a background in computing and mathematical modelling.



1 Torso, M. et al. Hum Brain Mapp 42, 967–977 (2020)
2 McKavanagh, R. et al. Hum Brain Mapp 40, 4417–4431 (2019)
3 Cornblath, E. J. et al. Sci Adv 7, eabg6677 (2021)
4 Schäfer, A. et al. Frontiers in Physiology 12, (2021)
5 Glasser, M. F. et al. Nature 536, 171–178 (2016)
6 Robinson, E. C. et al. Neuroimage 100, 414–426 (2014)
7 Fawaz, A. et al. MIUA 26, 469–481 (2022)
8 Tournier, J.-D. et al. NeuroImage 202, 116137 (2019)
9 Raffelt, D. A. et al. NeuroImage 144, 58–73 (2017)
10 Smith, R. E. et al. NeuroImage 119, 338–351 (2015)
11 Fawaz, A. et al. (2022)
12. Dahan, S. et al. arXiv:2203.16414 (2022)
13 Linka, K. et al. arXiv:2205.08304 (2022)
14 da Silva, M. et al. arXiv:2012.07596 (2020)

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