This collection in Neuroretina invites high-quality original research, reviews, and clinical reports that explore the interface between ophthalmology, neurology, and data science. We aim to showcase how ocular biomarkers—whether structural, vascular, proteinopathy, functional, or molecular—can aid in the early detection, disease stratification, and progression monitoring of neurodegenerative disorders.

Research focusing on the development of machine learning models, longitudinal imaging studies, comparative biomarker studies, and cross-disciplinary applications is especially encouraged. Submissions may also discuss the integration of oculomics in clinical workflows and the challenges in translating findings from research to bedside.

Topics of Interest include (but are not limited to):

· Retinal and optic nerve biomarkers in Alzheimer’s and Parkinson’s disease

· OCT-based structural changes in neurodegeneration

· Retinal vasculature and microvascular anomalies in dementia risk

· AI and deep learning in ocular image analysis for neurodegenerative disease detection

· Correlation of ocular biomarkers with neuroimaging (MRI, PET) findings, and blood-based biomarkers

· Oculomic approaches in multiple sclerosis and demyelinating disorders

· Role of the retina in disease progression tracking and outcome prediction

· Multimodal oculomics combining imaging, genomics, and clinical data

· Challenges in standardizing ocular biomarkers for clinical application

· Population-based screening strategies using oculomics

· Animal models and experimental research linking retinal changes to brain pathology