Outer Retinal Layer Thickness Changes in White Matter Hyperintensity and Parkinson's Disease

Zhao, Yitian; Zhao, Jinyu; Gu, Yuanyuan; Chen, Bang; Guo, Jiaqi; Xie, Jianyang; Yan, Qifeng; Ma, Yuhui; Wu, Yufei; Zhang, Jiong; Lü, Qianqian; Liu, Jiang

doi:10.3389/fnins.2021.741651

Cited by 4 publications

(5 citation statements)

References 44 publications

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“…Nerve fibers in the nasal quadrant arrive at the optic disk directly. Nerve fibers in the temporal quadrant are bounded by horizontal meridians, which, respectively, curve around the macular fovea and reach the optic disk ( 10 ). Among healthy individuals, the retinal thickness in the nasal quadrant is the thickest and the temporal quadrant is the thinnest ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

“…Optical coherence tomography (OCT) is a non-invasive imaging technique based on low coherence interferometry that makes it possible to view high-resolution images of the retina in vivo . OCT system generates cross-sectional images of the retina that show the reflectivity information captured in depth by means of whiter or darker bands ( 10 ). RNFL is composed of ganglion cell axons that travel in bundles until the optic nerve.…”

Section: Introductionmentioning

confidence: 99%

“…They have the same properties in embryology, physiology, and morphology. First, the retina develops from the diencephalon during the embryonic stage ( 10 ). Next, given that ocular arteries descend from the internal carotid artery, the vessels of the retina have many anatomical and physiological traits in common with the vessels of the brain.…”

Section: Introductionmentioning

confidence: 99%

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Retinal thickness changes in different subfields reflect the volume change of cerebral white matter hyperintensity

Teng²,

Jia³

et al. 2022

Front. Neurol.

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PurposeTo investigate the relationship between the retinal thickness in different subfields and the volume of white matter hyperintensity (WMH), with the hope to provide new evidence for the potential association between the retina and the brain.MethodsA total of 185 participants aged over 40 years were included in our study. Magnetic resonance imaging (MRI) was used to image the WMH, and WMH volume was quantitatively measured by a specific toolbox. The thickness of the total retina, the retinal nerve fiber layer (RNFL), and the ganglion cell and inner plexiform layer (GCIP) was measured by optical coherence tomography (OCT) in nine subfields. The association between retinal thickness and WMH volume was demonstrated using binary logistic regression and Pearson correlation analysis.ResultsParticipants were divided into two groups by the WMH volume (‰, standardized WMH volume) median. In the quartile-stratified binary logistic regression analysis, we found that the risk of higher WMH volume showed a positive linear trend correlation with the thickness of total retina (95% CI: 0.848 to 7.034; P for trend = 0.044)/ GCIP (95% CI: 1.263 to 10.549; P for trend = 0.038) at the central fovea, and a negative linear trend correlation with the thickness of nasal inner RNFL (95% CI: 0.086 to 0.787; P for trend = 0.012), nasal outer RNFL (95% CI: 0.058 to 0.561; P for trend = 0.004), and inferior outer RNFL (95% CI: 0.081 to 0.667; P for trend = 0.004), after adjusting for possible confounders. Correlation analysis results showed that WMH volume had a significant negative correlation with superior outer RNFL thickness (r = −0.171, P = 0.02) and nasal outer RNFL thickness (r = −0.208, P = 0.004).ConclusionIt is suggested that central fovea and outer retina thickness are respectively associated with WMH volume. OCT may be a biological marker for early detection and longitudinal monitoring of WMH.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retinal thickness changes in different subfields reflect the volume change of cerebral white matter hyperintensity

Teng²,

Jia³

et al. 2022

Front. Neurol.

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“…In this study, 31 patients (56 eyes) with WMH, six PD patients (11 eyes), and 32 healthy controls (58 eyes) were included. The results suggested that HFL + ONL, OS, and IZ + RPE layers were potentially associated with brain‐related diseases 77 . The parameters generated by the segmentation of retinal microvasculature and nerve layers offer a promising approach for the analysis of various neurodegenerative diseases.…”

Section: Application Of Ai Using Ocular Images For Systemic Diseases ...mentioning

confidence: 99%

“…The results suggested that HFL + ONL, OS, and IZ + RPE layers were potentially associated with brain-related diseases. 77 The parameters generated by the segmentation of retinal microvasculature and nerve layers offer a promising approach for the analysis of various neurodegenerative diseases. End-to-end DL models based on large datasets are increasingly popular in the analysis of neurological diseases.…”

Section: Neurological Diseasesmentioning

confidence: 99%

Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

Miao,

Zou,

et al. 2024

MedComm – Future Medicine

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The eye serves as a unique window into systemic health, offering clinicians a valuable opportunity for early detection and targeted treatment. Against this backdrop, advancements in artificial intelligence (AI) and ophthalmic imaging are converging to pave the way for more precise and predictive diagnostics. This review aims to elucidate the transformative role of AI in utilizing ophthalmic imaging for the detection and prediction of systemic diseases. We begin by introducing the advantages of the eye as a valuable tool for detecting systemic diseases. We also provide an overview of various ophthalmic imaging techniques that have proven useful in predicting systemic ailments. Then, we summarize two research patterns for analyzing ocular data, followed by the introduction of current AI applications using ophthalmic images that significantly increase diagnostic precision. Despite the promise, challenges such as data heterogeneity and model interpretability persist, which are also covered in this review. We conclude by discussing future directions and the immense potential these AI‐enabled approaches hold for revolutionizing healthcare. As AI technologies advance, their potential integration with ophthalmic imaging offers promising avenues for improving the diagnosis, prediction, and management of various systemic diseases, thereby contributing to the evolving landscape of integrated healthcare.

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Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline

et al. 2023

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PurposeTo evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment.MethodsUsing OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted.ResultsHigher Alzheimer’s disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson’s disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements.Conclusions and relevanceRetinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.

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Outer Retinal Layer Thickness Changes in White Matter Hyperintensity and Parkinson's Disease

Cited by 4 publications

References 44 publications

Retinal thickness changes in different subfields reflect the volume change of cerebral white matter hyperintensity

Retinal thickness changes in different subfields reflect the volume change of cerebral white matter hyperintensity

Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline

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