With an aging population and well-established links between chronological age, morbidity, and mortality, extensive research in this field is expected. However, diverse aging changes across populations and tissues call for biological aging markers to better represent the aging process and its implications.
Aging significantly impacts brain structure, observed in MRI images’ volume and thickness, and correlates with less favourable mental and physical health. Brain Age, estimated through Deep Learning Models, serves as a biomarker for brain aging. The Brain Age Gap, the difference between brain age and chronological age, correlates with neurodegenerative diseases like Parkinson’s, Alzheimer’s, Schizophrenia, Bipolar Mood Disorders, and Major Depressive Disorder.
The retina, linked to the body and brain due to its shared embryological origin and structure with the central nervous system, is used to study brain disease. Retinal fundus photography, non-invasive and enhanced by deep learning models, calculates a Retinal Age Gap, an aging biomarker. This gap shows associations with overall mortality risk, kidney failure, cardiovascular disease, metabolic syndrome, inflammation, and Parkinson’s.
The study aims to address knowledge gaps concerning associations between retinal age, brain age, and neuropsychiatric disorders. Building on established links between brain age gap and neuropsychiatric conditions, the research explores similar connections with the Retinal Age Gap, leveraging shared embryological origin. The investigation aims to offer insights into the aging process and its implications for neuropsychiatric health.
In psychiatry, heavily reliant on clinical judgment, objective measures for diagnostic assistance and disease monitoring are lacking. Retinal imaging advances, including OCT and fundus photography, offer valuable tools for disease monitoring and therapy response in ophthalmology and neuropsychiatry. Combined with deep learning models, these imaging modalities enable precision medicine approaches, expanding fundus photography’s utility.
Brain Age has been associated with various psychiatric disorders, but costly and time-consuming MRI images limit their use. In contrast, fundus photography and OCT are quick and cost-effective, offering extensive possibilities in medical practice. The potential discovery of a strong association between Brain Age Gap and Retinal Age Gap estimates could revolutionise clinical approaches.
The project, presented as a PhD thesis, aims for completion within three years. By integrating cutting-edge technologies, the study strives to advance neuropsychiatric knowledge and improve diagnostic and therapeutic approaches.