Retinal parameters,cortical cerebral microinfarcts,and their interaction with cognitive impairment

Abstract

Background:

Quantitative changes in retinal vessels and thinning of optic nerves have been associated with subclinical (atherosclerosis, inflammation) and clinical age-related brain pathologies (stroke and neurodegeneration). However, data on the association between both retinal vascular and neuronal parameters with cortical cerebral microinfarcts (CMIs) and how these factors jointly influence cognition are lacking.

Aim:

We investigated the association of retinal vascular and neuronal changes with CMIs on 3 T MRI and explored their interaction with cognitive impairment in a memory-clinic population.

Methods:

A total of 538 participants were included. Retinal vascular parameters (caliber, tortuosity, and fractal dimension) were measured from retinal fundus photographs using a semi-automated computer-assisted program. Retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thicknesses were obtained from optical coherence tomography. Cortical CMIs were defined as hypointense on T1-weighted MRI, <5 mm in diameter and restricted to the cortex. Cognition was assessed using Clinical Dementia Rating Sum-of-Boxes (CDR-SoB) score and detailed neuropsychological test. Multivariable regression analysis was conducted adjusting for age, sex, hypertension, hyperlipidemia, diabetes mellitus, smoking, diagnosis, white matter hyperintensities volume, lacunes, and cerebral microbleeds.

Results:

Larger venular caliber (Rate ratios (RR): 1.15, 95% CI: 1.01–1.38, p = 0.014), increased venular fractal dimension (RR: 1.58, 95% CI: 1.31–1.91, p ⩽ 0.001), increased venular tortuosity (RR: 1.54, 95% CI: 1.35–1.75, p ⩽ 0.001), and thinner GC-IPL (RR: 1.24, 95% CI: 1.13–1.36, p ⩽ 0.001) were associated with CMI counts. Among individuals in highest tertile of retinal parameters, a significant interaction was observed between venular tortuosity (RR: 1.12, 95% CI: 1.02–1.22, p-interaction = 0.014) and GC-IPL (RR: 1.05, 95% CI: 1.01–1.11, p-interaction < 0.001) with CMIs on CDR-SoB.

Conclusion:

Retinal vascular and neuronal parameters are associated with cortical CMIs, and persons with both pathologies are likely to have cognitive impairment. Further studies may be warranted to evaluate the clinical utility of retinal parameters and CMI in risk prediction for cognitive dysfunction.

Keywords

Cortical microinfarcts cognition retina

Introduction

In addition to the traditional MRI markers of vascular injury such as lacunar infarcts and white matter hyperintensities (WMH), a novel marker of cerebrovascular disease (CeVD), cortical cerebral microinfarcts (CMIs) have gained increasing attention because of its major role in cognitive impairment and dementia.¹ This is in part due to CMIs recently being made visible in-vivo using conventional imaging techniques, hence enabling study of the impact of these lesions on brain structural and functional damage during life.^2,3

The retina and optic nerve are outgrowths of the embryonic diencephalon and, therefore, share anatomic similarities, and functional and immunological characteristics with the brain.^4,5 The presence of ocular manifestations in neurodegenerative and cerebrovascular pathologies, such as Alzheimer’s disease (AD) and stroke, emphasizes the strong relationship between the eye and the brain.^6–8 Retinal changes in particular serve as a surrogate for cerebral changes in these disorders. Offering a “window to the brain,” the transparent eye enables non-invasive imaging of these changes in retinal structure and vasculature.

Previous studies have shown that quantitative changes in retinal vessel width (narrower arteriolar caliber and wider venules) have been associated with subclinical (lacunes and WMH) and clinical age-related brain diseases.⁹ Furthermore, smaller fractal dimensions reflecting a sparser branching pattern were associated with cerebral microbleeds and lacunar infarcts.^10,11 Similarly, thinning of the optic nerve has been associated with cerebral infarcts.^12,13 However, data on the association between both retinal vascular and neuronal parameters with cortical CMIs are lacking. Given the influence of retinal changes and cortical CMIs on brain vasculature and their common involvement in cognitive impairment and AD, the interaction of these two factors is of interest to better understand how they jointly give rise to cognitive impairment. So far, previous studies have only examined the individual effects of retinal changes and cortical CMIs on other imaging correlates and cognitive dysfunction without examining their possible interaction. We, therefore, investigated the association of retinal vascular and neuronal changes with cortical CMIs on 3 T MRI and explored their interaction in relation to cognitive impairment in a memory-clinic population. We first hypothesize that persons with altered retinal parameters (e.g., wider and more tortuous venules, thinner retinal neuronal layer) are likely to have higher counts of CMIs on MRI scans. Second, persons with both CMI and retinal changes are more likely to have cognitive impairment.

Methods

Study population

This cross-sectional study was conducted as part of an ongoing prospective memory-clinic study which recruits individuals from National University Hospital, Singapore, involving the following diagnoses:¹⁴ (1) No cognitive impairment (NCI): involving no objective cognitive impairment on neuropsychological tests, or functional loss; (2) cognitive impairment no dementia (CIND) with impairment in at least one cognitive domain on a neuropsychological test battery without loss of daily functions; and (3) dementia diagnosed according to DSM-IV criteria.

From August 12, 2010, to August 27, 2019, a total of 654 participants were recruited who were invited to undergo clinical assessment, brain MRI, retinal imaging, and neuropsychological assessment. Of 654 participants, 39 individuals had no or poor quality MRI required for CMI grading and 77 did not have gradable retinal photographs or complete optical coherence tomography (OCT) scanning leaving 538 for final analysis.

Ethics approval for this study was obtained from National Healthcare Group Domain-Specific Review Board. The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained in the preferred language of the participants.

Retinal photography

Retinal fundus photographs were taken of each eye with a non-mydriatic digital camera after dilation of pupils with 1% tropicamide eye drops grading at Singapore Eye Research Institute.^11,15 Optic-disk centered images of a randomly selected eye from each participant were fed through a semi-automated computer-assisted program, Singapore I Vessel Assessment (software version: 3.0). The following retinal vascular parameters were extracted and graded.^11,15

Retinal vascular caliber

The program calculates retinal arteriolar and venular calibers as central retinal artery equivalent and central retinal vein equivalent, based on the revised Knudtson–Parr–Hubbard formula (Supplemental Figure 1(a)).^11,15

Retinal vascular fractal dimension

Retinal vascular fractal dimension was evaluated from the skeletonized vascular network using the box-counting method and represents a “global” measure that summarizes the whole branching pattern of the retinal vascular tree (Supplemental Figure 1(b)).^11,15

Retinal vascular tortuosity

Retinal vascular tortuosity was computed as the integral of the curvature square along the path of the vessel, normalized by the total path length; this measure is dimensionless as it represents a ratio measure (Supplemental Figure 1(c)).^11,15 The estimates were summarized (average) as retinal arteriolar and venular tortuosity.

Spectral domain-optical coherence tomography

After pupil dilation using tropicamide1%, SD-OCT was used to acquire macular scans and optic disk scan using the macular cube 200×200 and optic nerve head cube 200×200 scanning protocols in each eye. The built-in software of Cirrus HD-OCT was used to measure peripapillary RNFL (average and quadrants) and macular GCIPL (average and sectoral) thicknesses automatically (Supplemental Figure 2).^16–18

Neuroimaging

MRI was performed on a 3T Siemens Magnetom Trio Tim scanner, using a 32-channel head coil, at National University of Singapore. Cortical CMIs were graded on T1-, T2-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) sequences and were defined as hypointense lesions on T1-weighted images, <5 mm in diameter, restricted to the cortex, and perpendicular to the cortical surface. The location of a hypointense cortical lesion found on T1 was confirmed on FLAIR and T2-weighted images (Supplemental Figure 3). The lesion was rated as a definite cortical CMI if it was hyperintense or isointense (with the surrounding tissue) on FLAIR and T2.^1,2

Other SVD markers were defined based on STRIVE criteria. (Details on imaging markers, parameters, and interrater reliability are shown in Supplemental Material.)

Cognitive impairment

All participants underwent detailed clinical assessment including administration of the Clinical Dementia Rating (CDR) scale. These assessments were discussed in weekly consensus meetings attended by neurologists, psychologists, and research staff. Change in CDR scores was defined as one unit increase in sum-of-boxes (SoB).

Statistical analyses

To examine the differences between NCI, CIND, and dementia, chi-square test was used for categorical variables, ANOVA for continuous and normally distributed variables and Kruskal–Wallis test for skewed data (CDR). To examine the association between retinal parameters and presence of cortical CMIs, logistic regression analysis was used. Poisson regression models were constructed to determine the association between retinal parameters and cortical CMIs as count data with rate ratios (RR) and 95% confidence interval (CI). All regression analyses were initially adjusted for age and sex (Model I) and subsequently for hypertension, hyperlipidemia, diabetes mellitus, smoking, diagnosis, white matter hyperintensities volume, lacunes, and cerebral microbleeds (Model II). Similar analyses were performed stratified by diagnostic groups (NCI, CIND, and dementia).

We further tested the interaction between retinal parameters and cortical CMIs on CDR-SoB by including the cross-product term of “individual retinal parameter x cortical CMIs counts” with the main effect terms of each variable in the models. These models were adjusted for covariates in a similar fashion as described previously. For all models, p value < 0.05 was considered statistically significant. Correction for multiple comparisons (6 retinal parameters) was performed using the Bonferroni method with a significance level set at 0.05/6~0.0083.

Results

Table 1 shows the characteristics of the study participants according to their diagnostic status. A total of 116 (21.6%) participants were diagnosed with NCI, 233 (43.3%) as CIND and 189 (35.1%) as dementia. Individuals with dementia were likely to be older, and had a higher burden of hypertension and diabetes mellitus followed by CIND. Moreover, individuals with dementia have thinner RNFL and a higher burden of CMIs and worse cognitive functioning.

Table 1.

Characteristics of study population.

Variables	NCI (n = 116)	CIND (n = 233)	Dementia (n = 189)	P value
Demographics and risk factors
Age, years, mean(SD)	68.8 (7.2)	72.9 (7.7)	74.7 (7.5)	0.001
Men, no.(%)	55 (47.4)	112 (48.1)	74 (39.2)	0.152
Hypertension, no.(%)	73 (62.9)	160 (68.7)	152 (80.4)	0.002
Hyperlipidemia, no.(%)	82 (70.7)	180 (77.3)	137 (72.5)	0.338
Diabetes mellitus, no.(%)	24 (20.7)	85 (36.5)	85 (45)	<0.001
Smoking, no.(%)	26 (22.4)	59 (25.3)	49 (25.9)	0.774
Retinal vascular parameters
Caliber arteriolar, µm, mean(SD)	113.5 (13.1)	110.9 (12.7)	109.9 (13.7)	0.790
Caliber venular, µm, mean(SD)	163.9 (15.6)	160.2 (19.6)	159.3 (19.9)	0.764
Fractals arteriolar, mean(SD)	1.16 (0.06)	1.14 (0.09)	1.14 (0.12)	0.881
Fractals venular, mean(SD)	1.17 (0.06)	1.15 (0.06)	1.16 (0.07)	0.874
Tortuosity arteriolar, mean(SD)	1.08 (0.02)	1.07 (0.01)	1.07 (0.02)	0.203
Tortuosity venular, mean(SD)	1.09 (0.02)	1.09 (0.02)	1.09 (0.02)	0.191
Retinal neuronal parameters
Ganglion cell-inner plexiform layer, µm, mean(SD)	77.1 (8.9)	73.8 (12.1)	72.5 (12.8)	0.166
Retinal nerve fiber layer, µm, mean(SD)	41.9 (89)	35.2 (20.2)	33.5 (22.7)	0.025
MRI marker and cognitive function
Cortical cerebral microinfarcts, no.(%)	13 (11.2)	50 (21.5)	57 (30.2)	0.001
CDR Sum-of-Boxes, median(IQR)	0 (0–2)	0.5 (0–4)	6 (1–18)	<0.001

Table 2 shows the association of retinal vascular parameters with cortical CMIs. Larger venular caliber was associated with the presence of CMIs in age and sex-adjusted model. This association remained after controlling for all the covariates. In addition, larger venular caliber, increased fractal dimension, and increased venular tortuosity were associated with increasing CMI counts in age, sex, vascular risk factors, diagnosis, and other SVD markers model. The association between fractal dimension and venular tortuosity remains significant after correcting for multiple comparisons.

Table 2.

Association of Singapore I vessel assessment parameters with cortical CMIs.

	Presence of CMIOR (95% CI)	CMI countsRR (95% CI)
Model I
Caliber (per SD increase)
Arteriolar	0.75 (0.55–1.01), p = 0.062	0.92 (0.79–1.07), p = 0.276
Venular	1.53 (1.11–2.13), p = 0.010	1.25 (1.07–1.46), p = 0.005
Fractal dimension (per SD increase)
Arteriolar	0.93 (0.72–1.19), p = 0.558	1.19 (1.00–1.41), p = 0.047
Venular	0.94 (0.69–1.28), p = 0.942	1.29 (1.11–1.52), p = 0.001
Tortuosity (per SD increase)
Arteriolar	0.97 (0.78–1.19), p = 0.965	0.95 (0.86–1.06), p = 0.377
Venular	1.03 (0.85–1.25), p = 0.782	1.12 (1.05–1.19), p ⩽ 0.001
Model II
Caliber (per SD increase)
Arteriolar	0.78 (0.54–1.16), p = 0.222	0.97 (0.81–1.16), p = 0.970
Venular	1.51 (1.01–2.26), p = 0.047	1.15 (1.01–1.38), p = 0.014
Fractal dimension (per SD increase)
Arteriolar	1.19 (0.76–1.88), p = 0.451	1.76 (1.43–2.18), p ⩽ 0.001†
Venular	1.11 (0.76–1.63), p = 0.579	1.58 (1.31–1.91), p ⩽ 0.001†
Tortuosity (per SD increase)
Arteriolar	1.04 (0.79–1.36), p = 0.806	1.02 (0.89–1.16), p = 0.782
Venular	1.13 (0.82–1.57), p = 0.450	1.54 (1.35–1.75), p ⩽ 0.001†

Model I: adjusted for age and sex.

Model II: Model I + hypertension, hyperlipidemia, diabetes mellitus, smoking, diagnosis, white matter hyperintensities volume, lacunes, and cerebral microbleeds.

†

Significant after multiple comparison (p < 0.008).

Table 3 shows the association of retinal neuronal parameters measured by OCT with cortical CMIs. Thinner GC-IPL was associated with increasing CMI counts in both age and sex and multivariate-adjusted model (Model II) surviving multiple comparison. No association was observed with RNFL thickness and cortical CMIs. (Results by diagnostic groups are shown in Supplemental Material.)

Table 3.

Association of OCT parameters with cortical CMIs.

	Presence of CMIsOR (95% CI)	CMI countsRR (95% CI)
Model I
GC-IPL, per SD decrease	1.14 (0.93–1.39), p = 0.207	1.25 (1.16–1.37), p ⩽ 0.001
RNFL, per SD decrease	1.09 (0.87–1.36), p = 0.471	1.04 (0.94–1.15), p = 0.487
Model II
GC-IPL, per SD decrease	1.11 (0.87–1.41), p = 0.391	1.24 (1.13–1.36), p ⩽ 0.001†
RNFL, per SD decrease	0.99 (0.79–1.25), p = 0.969	0.95 (0.86–1.04), p = 0.232

Model I: adjusted for age and sex.

Model II: Model I + hypertension, hyperlipidemia, diabetes mellitus, smoking, diagnosis, white matter hyperintensities volume, lacunes, and cerebral microbleeds.

†

Significant after multiple comparison (p < 0.008).

When the cross-product term of each individual retinal parameter and cortical CMI was included in the regression model with CDR-SoB as outcome, the interaction term was significant between venular tortuosity and thinner GC-IPL and cortical CMIs (p < 0.05) (Table 4). However, these associations did not survive multiple comparison. Among individuals with highest tertile of retinal parameters, a significant interaction was still observed between venular tortuosity (RR: 1.12, 95% CI: 1.02–1.22, p-interaction = 0.014) and GC-IPL (RR: 1.05, 95% CI: 1.01–1.11, p-interaction < 0.001) with cortical CMIs on CDR-SoB. These results were independent of all covariates (Supplemental Table 1). No association was observed with global cognition (Table 4) and cognitive domains (Supplemental Table 2).

Table 4.

Association of retinal parameters with CDR with the interaction term retinal parameter*CMIs counts.

	CDR (sum of boxes)RR (95% CI)	Global cognitionβ (95% CI)
Caliber (per SD increase)
Arteriolar	1.03 (0.94–1.14), p = 0.510	0.03 (–0.11; 0.16), p = 0.712
Venular	0.93 (0.85–1.03), p = 0.175	0.05 (–0.07; 0.18), p = 0.386
Fractal dimension (per SD increase)
Arteriolar	0.96 (0.85–1.08), p = 0.459	0.57 (–0.12; 0.21), p = 0.569
Venular	1.07 (0.97–1.18), p = 0.198	–0.00 (–0.07; 0.06), p = 0.907
Tortuosity (per SD increase)
Arteriolar	1.04 (0.98–1.09), p = 0.135	0.09 (–0.01; 0.18), p = 0.073
Venular	1.12 (1.02–1.21)^*, p = 0.012	–0.04 (–0.15; 0.08), p = 0.505
OCT parameters (per SD decrease)
GC-IPL	1.06 (1.00–1.14)^*, p = 0.05	–0.06 (–0.15; 0.03), p = 0.207
RNFL	1.13 (1.04–1.23), p = 0.014	–0.02 (–0.13; 0.09), p = 0.739

p < 0.05 for the interaction term retinal parameters and CMIs.

NOTE: All p values were above 0.0083; hence, no multiple testing was performed.

Discussion

In this study, persons with retinal abnormalities represented by wider venular caliber, increased fractal dimension, more tortuous vascular network, and thinner GC-IPL were more likely to have cortical CMIs on the scans. These effects were more obvious in demented participants compared to CIND and NCI. Furthermore, we also found evidence of interaction between retinal parameters and cortical CMIs on cognitive impairment with stronger associations observed between higher tertiles of venular tortuosity and retinal neuronal layers and cortical CMIs.

It is suggested that structural alterations in the retinal microvasculature and neuronal layers may reflect concomitant microangiopathic and neurodegenerative processes occurring in the brain that predispose people to develop CeVD. Previous studies have shown that retinal venular widening is associated with increased risk of stroke and mortality¹⁹ which was further confirmed in a meta-analysis of 20,798 participants.²⁰ Our results go beyond the previous findings by showing that the retinal venular widening is related to cortical CMIs. The underlying mechanisms may be altered vessel wall stress, cerebral hypoxia,²¹ or venous insufficiency in both the retina and the brain.²⁰

Contrary to our expectation, our results have shown that increased fractal venular dimensions are related to increased CMI counts. We speculate that the increased retinal fractal dimension could be a quantitative biomarker of ischemic cerebral small vessel disease. So far, limited data have shown that increased retinal fractal dimension was more likely to have lacunar than non-lacunar stroke¹⁰ due to their linkage with retinal vasculogenesis and angiogenesis.^10,22 Deviation of retinal fractal dimension from the norm may reflect abnormalities in these vascular growth processes. Arteriovenous differentiation following hypoxic cues during vasculogenesis may increase retinal fractal dimension^23,24 as shown in diabetic retinopathy.^25,26 Therefore, it is possible that increased retinal fractal dimension is a marker of adverse alterations in the retinal, and perhaps cerebral microvascular network in response to hypoxic stimuli (Supplemental Figure 4(a) to (d)).²⁷

We also showed that increased venular tortuous vessels were associated with increased cortical CMIs. Previous data have shown the association between tortuous retinal vascular network with cerebral microbleeds and ischemic stroke.^11,28 It is suggested that elevated vessel tortuosity is a marker of vessel wall dysfunction and blood–retinal barrier damage.²⁹ and is possibly related to cerebral arteriolar blood flow control, and local ischemia.^30–32

Past studies have reported patients with stroke to have thinner RNFL caused by transneuronal retrograde degeneration.¹² Our data, on the contrary, showed that the thinning of GC-IPL is associated with cortical CMIs. Thickness measurements of GC-IPL and RNFL are both markers of retinal ganglion cell structural integrity, with the RNFL being mainly composed of retinal ganglion cell axons, whereas the GC-IPL is composed of both the cell bodies and dendrites of the retinal ganglion cells.³³ Reduction in dendritic complexity and area occurs prior to retinal ganglion cell death and loss,³⁴ suggesting that GC-IPL may be more informative and sensitive to vascular damage compared to the RNFL.

In patients with dementia, we found that wider venular caliber, increased fractal dimensions, and thinner GC-IPL layer were associated with CMIs in line with previous studies, which report patients with dementia to have vascular geometric and neuronal changes in relation to CeVD.^12,15 Our study also reported an interaction between venular fractal dimension, venular tortuosity and thinner retinal neuronal layers and cortical CMIs on CDR suggesting that both retinal and cerebral microvascular and neuronal changes take place concurrently leading to cognitive impairment. Pathological studies have suggested that cerebral blood flow may be reduced in the demented brain by more tortuous cerebral arterioles and collagen deposition in cerebral venules, whereas reduced vascular density may be due to a reduction in angiogenesis caused by vascular endothelial growth factor becoming bound to amyloid-β and sequestered in plaques.¹⁵ Thus, we speculate that the changes in retinal microvasculature may indicate similar pathological changes in its cerebrovascular network. Though no association was observed with detailed cognitive testing, we postulate that the observed association with CDR-SoB might be due to the latter being more sensitive in detecting cognitive and functional impairment compared to cognitive testing alone as CDR takes into account subjective cognitive complaints, objective cognitive impairment, and functional abilities of the person. However, as these results did not survive multiple testing, the findings should be interpreted with caution.

Limitations of the study include the following: the cross-sectional design of the study limits the interpretation of the results with respect to the cause and effect. The results were obtained from a memory-clinic cohort, which limits the generalizability of the data to general population. Moreover, participants with ocular co-morbidities and inability to undergo retinal assessment were excluded which might introduce selection bias. The strengths of this study include objective assessment of retinal fundus images using a validated computer-assisted program and a cohort spanning varying degrees of cognitive impairment status.

In conclusion, retinal vascular and neuronal parameters are associated with cortical CMIs on 3 T MRI and they jointly influenced cognition. Further studies may be warranted to evaluate the clinical utility of retinal parameters and CMI in risk prediction for cognitive dysfunction.

Supplemental Material

sj-docx-6-wso-10.1177_17474930221097737 – Supplemental material for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment

Supplemental material, sj-docx-6-wso-10.1177_17474930221097737 for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment by Saima Hilal, Carol Y Cheung, Tien Yin Wong, Leopold Schmetterer and Christopher Chen in International Journal of Stroke

Supplemental Material

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Supplemental material, sj-tif-2-wso-10.1177_17474930221097737 for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment by Saima Hilal, Carol Y Cheung, Tien Yin Wong, Leopold Schmetterer and Christopher Chen in International Journal of Stroke

Supplemental Material

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Supplemental material, sj-tif-3-wso-10.1177_17474930221097737 for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment by Saima Hilal, Carol Y Cheung, Tien Yin Wong, Leopold Schmetterer and Christopher Chen in International Journal of Stroke

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Supplemental material, sj-tif-4-wso-10.1177_17474930221097737 for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment by Saima Hilal, Carol Y Cheung, Tien Yin Wong, Leopold Schmetterer and Christopher Chen in International Journal of Stroke

Supplemental Material

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Supplemental material, sj-tiff-1-wso-10.1177_17474930221097737 for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment by Saima Hilal, Carol Y Cheung, Tien Yin Wong, Leopold Schmetterer and Christopher Chen in International Journal of Stroke

Supplemental Material

sj-tiff-5-wso-10.1177_17474930221097737 – Supplemental material for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment

Supplemental material, sj-tiff-5-wso-10.1177_17474930221097737 for Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment by Saima Hilal, Carol Y Cheung, Tien Yin Wong, Leopold Schmetterer and Christopher Chen in International Journal of Stroke

Footnotes

Data access statement

The data that support the findings of this study are available from the corresponding author upon request.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Singapore National Medical Research Council (grant nos. NMRC/CG/NUHS/2010 and NMRC/CG/013/2013) and bright focus foundation (reference no: A2018165F) [grant no. R-608-000-248-597].

ORCID iD

Saima Hilal

Supplemental material

Supplemental material for this article is available online.

References

Van Veluw

Hilal

Kuijf

, et al. Cortical microinfarcts on 3T MRI: clinical correlates in memory-clinic patients. Alzheimers Dement 2015; 11: 1500–1509.

Hilal

Sikking

Shaik

, et al. Cortical cerebral microinfarcts on 3T MRI: a novel marker of cerebrovascular disease. Neurology 2016; 87: 1583–1590.

Hilal

Tan

van Veluw

, et al. Cortical cerebral microinfarcts predict cognitive decline in memory clinic patients. J Cereb Blood Flow Metab 2020; 40: 44–53.

London

Benhar

Schwartz

. The retina as a window to the brain-from eye research to CNS disorders. Nat Rev Neurol 2013; 9: 44–53.

De Groef

Cordeiro

. Is the eye an extension of the brain in central nervous system disease? J Ocul Pharmacol Ther 2018; 34: 129–133.

Lim

JKH

, et al. The eye as a biomarker for Alzheimer’s disease. Front Neurosci 2016; 10: 536.

Cheung

CYL

Ikram

Chen

Wong

. Imaging retina to study dementia and stroke. Prog Retin Eye Res 2017; 57: 89–107.

Mahajan

Votruba

. Can the retina be used to diagnose and plot the progression of Alzheimer’s disease? Acta Ophthalmol 2017; 95: 768–777.

Ikram

Ong

Cheung

CYL

Wong

. Retinal vascular caliber measurements: clinical significance, current knowledge and future perspectives. Ophthalmologica 2013; 229: 125–136.

10.

Cheung

Liew

Lindley

, et al. Retinal fractals and acute lacunar stroke. Ann Neurol 2010; 68: 107–111.

11.

Hilal

Ong

Cheung

CYL

, et al. Microvascular network alterations in retina of subjects with cerebral small vessel disease. Neurosci Lett 2014; 577: 95–100.

12.

Mauschitz

Bonnemaijer

PWM

Diers

, et al. Systemic and ocular determinants of peripapillary retinal nerve fiber layer thickness measurements in the European Eye Epidemiology (E3) population. Ophthalmology 2018; 125: 1526–1536.

13.

Park

HYL

Park

Cho

Park

. Transneuronal retrograde degeneration of the retinal ganglion cells in patients with cerebral infarction. Ophthalmology 2013; 120: 1292–1299.

14.

Hilal

Chai

van Veluw

, et al. Association between subclinical cardiac biomarkers and clinically manifest cardiac diseases with cortical cerebral microinfarcts. JAMA Neurol 2017; 74: 403–410.

15.

Cheung

CYL

Ong

Ikram

, et al. Microvascular network alterations in the retina of patients with Alzheimer’s disease. Alzheimers Dement 2014; 10: 135–142.

16.

Tham

Chee

, et al. Retinal nerve fiber layer thickness in a multiethnic normal Asian population: the Singapore epidemiology of eye diseases study. Ophthalmology 2019; 126: 702–711.

17.

Tham

Chee

Dai

, et al. Profiles of ganglion cell-inner plexiform layer thickness in a multi-ethnic Asian population: the Singapore epidemiology of eye diseases study. Ophthalmology 2020; 127: 1064–1076.

18.

Cheung

CYL

Ong

Hilal

, et al. Retinal ganglion cell analysis using high-definition optical coherence tomography in patients with mild cognitive impairment and Alzheimer’s disease. J Alzheimers Dis 2015; 45: 45–56.

19.

Rim

Teo

AWJ

Yang

HHS

Cheung

CYL

Wong

. Retinal vascular signs and cerebrovascular diseases. J Neuroophthalmol 2020; 40: 44–59.

20.

McGeechan

Liew

Macaskill

, et al. Prediction of incident stroke events based on retinal vessel caliber: a systematic review and individual-participant meta-analysis. Am J Epidemiol 2009; 170: 1323–1332.

21.

Sun

Wang

Mackey

Wong

. Retinal vascular caliber: systemic, environmental, and genetic associations. Surv Ophthalmol 2009; 54: 74–95.

22.

Masters

. Fractal analysis of the vascular tree in the human retina. Annu Rev Biomed Eng 2004; 6: 427–452.

23.

Daxer

. Mechanisms in retinal vasculogenesis: an analysis of the spatial branching site correlation. Curr Eye Res 1995; 14: 251–254.

24.

Daxer

. The fractal geometry of proliferative diabetic retinopathy: implications for the diagnosis and the process of retinal vasculogenesis. Curr Eye Res 1993; 12: 1103–1109.

25.

Cheung

Donaghue

Liew

, et al. Quantitative assessment of early diabetic retinopathy using fractal analysis. Diabetes Care 2009; 32: 106–110.

26.

Liew

Wang

Cheung

, et al. The retinal vasculature as a fractal: methodology, reliability, and relationship to blood pressure. Ophthalmology 2008; 115: 1951–1956.

27.

Daxer

. Fractals and retinal vessels. Lancet 1992; 339: 618.

28.

Ong

De Silva

Cheung

CYL

, et al. Microvascular structure and network in the retina of patients with ischemic stroke. Stroke 2013; 44: 2121–2127.

29.

Hammes

Feng

Pfister

Brownlee

. Diabetic retinopathy: targeting vasoregression. Diabetes 2011; 60: 9–16.

30.

Lammie

. Hypertensive cerebral small vessel disease and stroke. Brain Pathol 2002; 12: 358–370.

31.

Benderro

Lamanna

. Hypoxia-induced angiogenesis is delayed in aging mouse brain. Brain Res 2011; 1389: 50–60.

32.

Tomita

Kubis

Calando

Tran Dinh

Méric

Seylaz

Pinard

. Long-term in vivo investigation of mouse cerebral microcirculation by fluorescence confocal microscopy in the area of focal ischemia. J Cereb Blood Flow Metab 2005; 25: 858–867.

33.

Ong

Hilal

Cheung

CYL

, et al. Retinal neurodegeneration on optical coherence tomography and cerebral atrophy. Neurosci Lett 2015; 584: 12–16.

34.

Williams

Thirgood

Oliphant

, et al. Retinal ganglion cell dendritic degeneration in a mouse model of Alzheimer’s disease. Neurobiol Aging 2013; 34: 1799–1806.

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