Longitudinal Analysis of Retinal Microvascular and Choroidal Imaging Parameters in Parkinson's Disease Compared with Controls

Diagnosing neurodegenerative disorders using retina as an external window: A systematic review of OCT-MRI correlations

BackgroundRecent studies have explored optical coherence tomography (OCT) and OCT-angiography (OCT-A) as biomarkers for Alzheimer's disease (AD). However, correlations between OCT/OCT-A and neurodegeneration metrics remain underexplored.ObjectiveWe performed a systematic review of OCT/OCT-A and structural brain imaging using MRI across various neurodegenerative disorders.MethodsWe searched Medline, Embase, and various other databases from January to June 2023 using keywords regarding neurodegenerative conditions and OCT/OCT-A. Out of 2962 citations. 93 articles were reviewed, and 28 met our inclusion criteria.ResultsLayer-or-region-specific retinal metrics were the most promising for non-vascular neurodegeneration, while vascular retinal parameters had the unique capacity to reflect vascular lesions. Both types of biomarkers correlated with global brain atrophy. Microstructural brain alterations best correlated with layer-specific thinning of retina.ConclusionsA better understanding of associations between retinal and brain lesions could eventually lead to the clinical application of retinal biomarkers for the early diagnosis of neurodegenerative conditions.

The elusive relationship between retinal anatomy and brain amyloid

This scientific commentary refers to 'Retinal microstructure and microvasculature in association with brain amyloid burden', by Egle et al. (https://doi.org/10.1093/braincomms/fcaf013).

Insights Into Retinal Pathologies in Neurological Disorders: A Focus on Parkinson's Disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis, and Alzheimer's Disease

Neurological diseases are central nervous system (CNS) disorders affecting the whole body. Early diagnosis of the diseases is difficult due to the lack of disease-specific tests. Adding new biomarkers external to the CNS facilitates the diagnosis of neurological diseases. In this respect, the retina has a common embryologic origin with the CNS. Retinal imaging technologies including optical coherence tomography (OCT) can be used in the understanding and processual monitoring of neurological diseases. Retinal imaging has been recently recognized as a potential source of biomarkers for neurological diseases, increasing the number of studies in this direction. In this review, the association of retinal abnormalities with Parkinson's disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and Alzheimer's disease (AD) is explained. Structural and functional abnormalities in retina as a predictive marker may facilitate early diagnosis of diseases. Although not all retinal abnormalities are predictive of neurologic diseases, changes in the retinal layers including retinal pigment epithelium and plexiform layers should suggest the risk of PD, MS, ALS, and AD.

Choroidal thickness in the eyes of Parkinson's disease patients measured using optical coherence tomography: A systematic review and meta-analysis

BACKGROUND

Parkinson's disease (PD) presents a complex etiology involving genetics and environmental factors. Non-motor symptoms often precede motor manifestations. Dopaminergic neuron degeneration, oxidative stress, and vascular changes characterize PD. Retinal changes are studied as potential biomarkers, yet choroidal involvement remains unclear. This review aims to clarify choroidal thickness's role in PD progression for diagnostic advancements.

METHODS

We examined PubMed, Scopus, and Embase databases. Depending on the heterogeneity, an appropriate model was used for the meta-analysis. Additionally, meta-regression, publication bias, subgroup analyses, and quality evaluation were carried out.

RESULTS

We evaluated twelve studies involving 442 PD patients and 608 healthy controls. This study found insignificant differences in choroidal thickness between PD patients and healthy controls.

CONCLUSION

Choroidal thickness is influenced by age, axial length, and intraocular pressure, with PD potentially impacting thickness through neurodegenerative mechanisms. However, inconsistencies exist in the findings, warranting further investigation. Future studies should explore the impact of disease severity, medication effects, and other confounding variables on choroidal thickness in PD patients. Additionally, advanced imaging modalities like optical coherence tomography angiography (OCTA) may provide more comprehensive evaluations of choroidal vascular changes in PD.

Levodopa is associated with reduced development of new-onset geographic atrophy in patients with age-related macular degeneration

BACKGROUND

Geographic atrophy (GA) is a significant cause of vision loss in patients with age-related macular degeneration (AMD). Current treatments are limited to anti-complement drugs, which have limited efficacy to delay progression with significant risk of complications. Levodopa (L-DOPA) is a byproduct of melanin synthesis that is associated with reduced development of neovascular AMD. In this study, we determined if L-DOPA was associated with a reduced likelihood of new-onset GA.

METHODS

We performed a retrospective analysis in the Vestrum Health Retina Database. We included eyes with non-neovascular AMD without GA and 1-5 years of follow-up. Eyes were divided into two groups. Exposed to L-DOPA before or on the date of non-neovascular AMD without GA diagnosis, and eyes not exposed to L-DOPA. We extracted age, sex, AREDS2 status, dry AMD stage, smoking history, and conversion rate to GA at years 1 through 5. Propensity score matching was used to match L-DOPA and control groups. Cox proportional hazard regression, adjusting for age, sex, AMD severity, AREDS2 use, smoking status, and L-DOPA use was employed to calculate hazard ratios for new-onset GA detection.

RESULTS

We identified 112,089 control and 844 L-DOPA exposed eyes with non-neovascular AMD without GA. After propensity score matching, 2532 control and 844 L-DOPA exposed eyes remained that were well-matched for age, sex, AMD severity, AREDS2 use, and smoking status. We found that L-DOPA exposure was associated with a significantly reduced likelihood (HR = 0.68, 95% CI: 0.48-0.95, P = 0.025) of new-onset GA detection.

CONCLUSION

L-DOPA use was associated with reduced detection of new-onset GA.

Techniques for imaging the choroid and choroidal blood flow in vivo

The choroid, which is a highly vascularized layer between the retina and sclera, is essential for supplying oxygen and nutrients to the outer retina. Choroidal vascular dysfunction has been implicated in numerous ocular diseases, including age-related macular degeneration, central serous chorioretinopathy, polypoidal choroidal vasculopathy, and myopia. Traditionally, the in vivo assessment of choroidal blood flow relies on techniques such as laser Doppler flowmetry, laser speckle flowgraphy, pneumotonometry, laser interferometry, and ultrasonic color Doppler imaging. While the aforementioned methods have provided valuable insights into choroidal blood flow regulation, their clinical applications have been limited. Recent advancements in optical coherence tomography and optical coherence tomography angiography have expanded our understanding of the choroid, allowing detailed visualization of the larger choroidal vessels and choriocapillaris, respectively. This review provides an overview of the available techniques that can investigate the choroid and its blood flow in vivo. Future research should combine these techniques to comprehensively image the entire choroidal microcirculation and develop robust methods to quantify choroidal blood flow. The potential findings will provide a better picture of choroidal hemodynamics and its effect on ocular health and disease.

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network

Purpose

Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group.

Methods

We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values.

Results

In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory.

Conclusions

Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms.

Translational Relevance

Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.

Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography

Purpose

To develop Choroidalyzer, an open-source, end-to-end pipeline for segmenting the choroid region, vessels, and fovea, and deriving choroidal thickness, area, and vascular index.

Methods

We used 5600 OCT B-scans (233 subjects, six systemic disease cohorts, three device types, two manufacturers). To generate region and vessel ground-truths, we used state-of-the-art automatic methods following manual correction of inaccurate segmentations, with foveal positions manually annotated. We trained a U-Net deep learning model to detect the region, vessels, and fovea to calculate choroid thickness, area, and vascular index in a fovea-centered region of interest. We analyzed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error [MAE]) in internal and external test sets. We compared Choroidalyzer to two manual graders on a small subset of external test images and examined cases of high error.

Results

Choroidalyzer took 0.299 seconds per image on a standard laptop and achieved excellent region (Dice: internal 0.9789, external 0.9749), very good vessel segmentation performance (Dice: internal 0.8817, external 0.8703), and excellent fovea location prediction (MAE: internal 3.9 pixels, external 3.4 pixels). For thickness, area, and vascular index, Pearson correlations were 0.9754, 0.9815, and 0.8285 (internal)/0.9831, 0.9779, 0.7948 (external), respectively (all P < 0.0001). Choroidalyzer's agreement with graders was comparable to the intergrader agreement across all metrics.

Conclusions

Choroidalyzer is an open-source, end-to-end pipeline that accurately segments the choroid and reliably extracts thickness, area, and vascular index. Especially choroidal vessel segmentation is a difficult and subjective task, and fully automatic methods like Choroidalyzer could provide objectivity and standardization.

Structural changes in the retina as a potential biomarker in Parkinson's disease: an approach from optical coherence tomography

Introduction

Parkinson's disease (PD) presents challenges in early diagnosis and follow-up due to the lack of characteristic findings. Recent studies suggest retinal changes in PD are possibly indicative of neurodegeneration. We explored these changes using optical coherence tomography (OCT) to assess retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) thickness.

Methods

Thirty PD and non-PD patients were matched according to demographic characteristics and OCT and clinical evaluations to rule out other neurodegenerative and visual diseases.

Results

We observed a significant thinning of the RNFL in patients diagnosed with PD compared to non-PD patients (p = 0.015). Additionally, this reduction in RNFL thickness was found to correlate with the severity of the disease (p = 0.04).

Conclusion

The OCT serves as a tool for quantifying neurodegeneration in PD, showing a significant correlation with disease severity. These findings suggest that OCT could play a crucial role as a potential biomarker in the diagnosis and monitoring of PD.