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Updated: Aug 9, 2025

Using Retinal Imaging to Study Dementia
Published on: November 6, 2017
Structural and functional changes in the retina in Parkinson's disease
Jordan N Alves1, Britta U Westner2, Andreas Højlund3
1Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark jordannicolasalves@gmail.com.
Retinal abnormalities, including electrophysiological delays and structural thinning, are early indicators of Parkinson's disease (PD). These visual changes may serve as valuable, cost-effective markers for early PD detection and patient stratification.
Area of Science:
- Neuroscience
- Ophthalmology
- Neurology
Background:
- Parkinson's disease (PD) involves dopaminergic neuron degeneration, causing motor symptoms.
- Visual disorders and retinal abnormalities are frequently observed in PD patients, even at early stages.
- Retinal changes may offer valuable predictive markers for PD, but interpretation is complicated by normal aging.
Purpose of the Study:
- To review retinal dysfunction in Parkinson's disease.
- To evaluate the potential of retinal abnormalities as early diagnostic and prognostic markers for PD.
- To explore the relationship between retinal changes and disease progression.
Main Methods:
- Review of electroretinography (ERG) findings in PD patients and animal models.
- Analysis of optical coherence tomography (OCT) measurements of retinal nerve fiber layer and macula.
- Examination of underlying chemical changes and the impact of levodopa treatment on the retina.
Main Results:
- Electrophysiological studies show delays and decreased amplitude in ERG recordings in PD.
- OCT reveals retinal nerve fiber layer and macular thinning in PD patients.
- Functional retinal measures appear more consistent than structural OCT measurements in PD.
Conclusions:
- Retinal abnormalities, particularly functional measures, are consistent indicators in Parkinson's disease.
- Retinal evaluation offers accessible and cost-effective methods for early PD assessment.
- Retinal markers hold potential for patient stratification and predicting disease outcomes.
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