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Precision Medicine DTP - Data Science approaches to investigating the vascular footprint of Alzheimer’s disease for early disease detection


Project Description

Background

Changes in the brain that lead to Alzheimer’s disease (AD) are thought to start decades before cognitive symptoms emerge. If biomarkers for these early stages could be identified, it would contribute to a more accurate estimation of an individual’s risk of developing disease and enable the monitoring of high-risk (presymptomatic) persons as well as providing the means for assessing the efficacy of new interventions. The retina links to the visual processing and cognitive centers of the brain, but it is also an extension of the brain sharing embryological origins as well as a blood supply and nerve tissue. It therefore has huge potential as a site for biomarker investigation through easy, noninvasive imaging and computational image analysis to reveal valuable information about microvascular health, deposition, and neurodegenerative damage.

Optical Coherence Tomography Angiography (OCTA) is a non-invasive, dyeless technique for evaluating physiologic components of the retina [1]. OCTA can resolve the microvasculature of the eye with a resolution comparable to histological studies and provides a unique opportunity for AD study. Close collaborators at Duke University recently demonstrated in a small pilot study a reduction in vascular density and regions of impaired flow in the superficial layer of the retina in AD patients compared to controls using OCTA imaging [2]. However, how this information can be leveraged to predict progression to disease is still unknown.

Furthermore, due to the recent adoption of OCTA technology, reproducible retinal microvascular phenotyping based on OCTA remains an open problem in the field. Future multi- centre studies will require robust and validated protocols and automated software tools. The team in Edinburgh has over 10 years’ experience developing the software package VAMPIRE for retinal image analysis used in research centres worldwide [3] and have investigated OCTA image analysis for over two years with funding from Medical Research Council, British Heart Foundation, and The Alan Turing Institute.

Aims

In this project, the student will bring together cutting-edge image processing and computational modelling methods in order to discover early changes in retinal microvascular structure associated with Alzheimer’s disease. The project will take advantage of existing OCTA datasets in Edinburgh and through collaboration with Duke University, which are world- leading. The main objective is to investigate clinically relevant Alzheimer’s disease biomarkers and design the computational pipelines and protocols necessary to facilitate their use in future large-scale clinical studies.

The specific aims are:

Develop deep learning approaches to vessel segmentation in OCTA images that preserve network connectivity for topological network characterisation.
Investigate biomarkers across the whole depth of the retina (superficial capillary plexus, deep capillary plexus, choriocapillaris).
Develop multi-modality retinal vascular phenotyping based on OCTA and the current standard of care fundus camera and OCT.
Establish whether the approach contributes predictive biomarkers indicative of the risk of developing AD in asymptomatic individuals or prognostic biomarkers to supplement or even replace existing clinical trial enrolment criteria currently based on expensive (e.g. PET) or invasive (e.g. CSF sampling) biomarkers.
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 Alzheimer’s disease. This highly interdisciplinary approach is well aligned with the “T-shaped researcher” training requirements identified as key in the DTP in Precision Medicine. The student will develop the essential soft and domain-specific skills necessary to design and implement novel quantitative and computational methods that could solve challenging problems across the entire spectrum of cardiovascular medicine both in academic and industrial settings.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

References

[1] Kashani AH, Chen CL, Gahm JK, Zheng F, Richter GM, Rosenfeld PJ, Shi Y, Wang RK. Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications. Prog Retin Eye Res. 2017 Sep;60:66-100.

[2] Yoon SP, Grewal DS, Thompson AC, Polascik BW, Dunn C, Burke JR, Fekrat S. Retinal Microvascular and Neurodegenerative Changes in Alzheimer's Disease and Mild Cognitive Impairment Compared with Control Participants. Ophthalmol Retina. 2019 Jun;3(6):489-499.

[3] E. Trucco, A. Giachetti, L. Ballerini, D. Relan, A. Cavinato, T. MacGillivray, Morphometric Measurements of the Retinal Vasculature in Fundus Images with VAMPIRE, in Biomedical Image Understanding: Methods and Applications, J. Lim, S. Ong, W. Xiong, Eds., John Wiley & Sons, 2015.

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