Active Brain & Nervous System Psychology & Behaviour

From Eye to Brain: EEG and Retinal Biomarker Profiling for Early Diagnosis and Prognosis of Alzheimer’s Disease

In plain English

AI plain-English summary

A simple eye test and a scalp cap that records brain waves could help doctors spot Alzheimer’s disease years earlier than current methods allow. Today, diagnosing Alzheimer’s reliably requires expensive PET brain scans or a lumbar puncture to sample spinal fluid—procedures that are invasive, costly, and unavailable to most people. This project tackles that gap by testing whether combining two cheap, non-invasive tools—retinal imaging and electroencephalography (EEG)—can detect the disease just as accurately. The researchers will link changes in the retina’s structure to how the brain functions during thinking tasks, using deep learning to analyse data from multiple groups of older adults. They will also check whether this combined approach is practical and cost-effective for routine clinical use. If it works, the impact could be straightforward: a quick, painless, low-cost screening that any GP surgery or high-street optician could offer. That would make early diagnosis far more accessible, giving people more time to plan care and access treatments that slow the disease. It would also reduce pressure on expensive hospital-based diagnostic services, quietly improving how the NHS manages the growing number of dementia cases.

View original technical description
Alzheimer’s disease remains the leading cause of dementia globally, with early detection critical for effective intervention to delay disease progression and the onset of cognitive symptoms. Traditional biomarkers, such as PET imaging and cerebrospinal fluid (CSF) markers are invasive, expensive, and not widely accessible. Non-invasive alternatives including retinal imaging and electroencephalography (EEG) offer promising and accessible avenues for early detection. Research integrating retinal imaging and EEG has shown retinal structural and functional alterations in Alzheimer’s disease. However, the neural mechanisms associating retinal structural changes to whole-brain cognitive dysfunction remain unclear. This project investigates whether retinal structural alterations can serve as indicators of brain function during cognitive processing in Alzheimer’s disease. Retinal and EEG measures will be associated in individuals with disease pathology, validated by PET and CSF. Using deep learning across multiple datasets spanning later adulthood, we will enhance cross-modal analysis of disease progression. Further, we will evaluate the clinical feasibility and cost-effectiveness of an integrated diagnostic approach using retinal imaging and EEG, both independently and in combination. By elucidating the neural mechanisms associating retinal structures to cognitive function, and evaluating the practical utility of these non-invasive modalities, this work aims to advance early diagnosis and prognosis of Alzheimer’s disease.

View the original record at the funder ↗

Researchers

Melody Sequeira (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Developing advanced graph filtering methods and models for robust assessment of transient EEG functional connectivity
Data Science approaches to investigating the vascular footprint of Alzheimer's disease for early disease detection
Multi-modal retinal biomarkers for vascular dementia: developing enabling image analysis tools
Retinal bioimaging for neurodegenerative and cardiovascular diseases
In-home screening of cognitive ageing to enable improved patient outcomes

Original classification

PhD Studentship (Basic)

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.