Linking Retinal Structure and Visual Function Using OCT and Virtual Reality

About the Project

The human visual system depends on the precise interplay between retinal structure and neural processing to support high-acuity vision. Understanding this structure–function relationship is central to neuroscience and vision science, yet remains poorly characterised in humans at a population level. Recent advances in optical coherence tomography (OCT) and immersive virtual reality (VR) platforms now make it possible to link high-resolution retinal phenotyping with dynamic measurements of visual behaviour in novel ways.

This project will combine OCT imaging of retinal microarchitecture with VRbased visual function testing to define how subtle variations in retinal structure influence sensory processing. OCT provides micrometre-scale resolution of retinal layers, enabling quantification of foveal pit morphology, photoreceptor packing, and vascular features. These structural metrics will be paired with VRdriven assessments of contrast sensitivity, motion perception, and eye movement behaviour, providing a quantitative framework for linking anatomy to function.

The student will establish and validate new pipelines for multimodal data integration, including automated OCT segmentation, machine learning–based image analysis, and quantitative modelling of visual performance. By leveraging large-scale human datasets as well as prospective data collection, this project will generate novel insights into the biological determinants of visual function.

The work sits within BBSRC remit, addressing fundamental questions in sensory neuroscience and developmental biology, with broader implications for understanding plasticity, neural coding, and visual processing across the lifespan.

Techniques that will be undertaken during the project

The student will gain expertise in advanced retinal imaging using optical coherence tomography (OCT), including image acquisition and automated layer segmentation. They will develop and apply virtual reality (VR) platforms to deliver visual tasks, capture eye movements, and assess sensory performance.

Training will also cover quantitative data analysis, machine learning for image and behavioural datasets, and statistical modelling of structure–function relationships. Where relevant, the student will have the opportunity to engage with large-scale imaging genetics datasets for population-level analysis.

Enquiries

Project Enquiries to mt350@le.ac.uk

To apply please use the application link at the bottom of this web page Psychology and Vision Sciences | Postgraduate research | University of Leicester

Funding Notes

This project is only available on a self funded basis or if you have your own sponsorship.