December 2023
Volume 64, Issue 15
Open Access
Visual Neuroscience  |   December 2023
The Association of Retinal Microvasculature With Gray Matter Changes and Structural Covariance Network: A Voxel-Based Morphometry Study
Author Affiliations & Notes
  • Junfeng Liu
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Wendan Tao
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Xiaonan Guo
    School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
  • William Robert Kwapong
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Chen Ye
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Anmo Wang
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Xinmao Wu
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Zhetao Wang
    Department of Radiology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Ming Liu
    Department of Neurology, West China Hospital, Sichuan University China, Chengdu, Sichuan Province, China
  • Correspondence: Ming Liu, Centre of Cerebrovascular Diseases, Department of Neurology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Chengdu 610041, China; wyplmh@hotmail.com
  • Xiaonan Guo, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066104, China; guoxiaonan@ysu.edu.cn
  • Footnotes
     JL and WT contributed equally to this work.
Investigative Ophthalmology & Visual Science December 2023, Vol.64, 40. doi:https://doi.org/10.1167/iovs.64.15.40
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      Junfeng Liu, Wendan Tao, Xiaonan Guo, William Robert Kwapong, Chen Ye, Anmo Wang, Xinmao Wu, Zhetao Wang, Ming Liu; The Association of Retinal Microvasculature With Gray Matter Changes and Structural Covariance Network: A Voxel-Based Morphometry Study. Invest. Ophthalmol. Vis. Sci. 2023;64(15):40. https://doi.org/10.1167/iovs.64.15.40.

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Abstract

Purpose: Increasing evidence suggests that retinal microvasculature may reflect global cerebral atrophy. However, little is known about the relation of retinal microvasculature with specific brain regions and brain networks. Therefore, we aimed to unravel the association of retinal microvasculature with gray matter changes and structural covariance network using a voxel-based morphometry (VBM) analysis.

Methods: One hundred and forty-four volunteers without previously known neurological diseases were recruited from West China Hospital, Sichuan University between April 1, 2021, and December 31, 2021. Retinal microvasculature of superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) were measured by optical coherence tomography angiography using an automatic segmentation. The VBM and structural covariance network analyses were applied to process brain magnetic resonance imaging (MRI) images. The associations of retinal microvasculature with voxel-wise gray matter volumes and structural covariance network were assessed by linear regression models.

Results: In the study, 137 participants (mean age = 59.72 years, 37.2% men) were included for the final analysis. Reduced perfusion in SVP was significantly associated with reduced voxel-wise gray matter volumes of the brain regions including the insula, putamen, occipital, frontal, and temporal lobes, all of which were located in the anterior part of the brain supplied by internal carotid artery, except the occipital lobe. In addition, these regions were also involved in visual processing and cognitive impairment (such as left inferior occipital gyrus, left lingual gyrus, and right parahippocampal gyrus). In regard to the structural covariance, the perfusions in SVP were positively related to the structural covariance of the left lingual gyrus seed with the left middle occipital gyrus, the right middle occipital gyrus, and the left middle frontal gyrus.

Conclusions: Poor perfusion in SVP was correlated with reduced voxel-wise gray matter volumes and structural covariance networks in regions related to visual processing and cognitive impairment. It suggests that retinal microvasculature may offer a window to identify aging related cerebral alterations.

Due to the increased life expectancy, aging-related cerebral diseases, such as dementia and Parkinson’s disease, are becoming the foremost challenge in the 21st century. However, the underlying mechanism of these diseases is not well understood, and effective treatments are still limited. Recent studies suggested that cerebral small vessel disease may contribute to the development of these diseases.1,2 It implicated that vascular alterations, especially microvascular pathology, may play an important role in the development of aging-related cerebral alterations. Given the shared embryologic origin and microvascular features between the retina and brain,3 accumulating evidence suggests that the retinal structure reflects the microstructure of the brain,4 whereas the retinal microvasculature reflects the cerebral microvasculature.5 
Currently, the association of retinal structures evaluated by optical coherence tomography with brain neurodegenerative alterations has been well studied.69 For example, thinner retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) were associated with a higher risk of Alzheimer's disease,1012 and a reduced global gray matter volume and white matter atrophy.69 However, previous studies mainly focused on the association between retinal structure and global brain alterations, whereas very little is known about the association between the retinal microvasculature and brain changes, especially regional brain change.13,14 
In the past decades, retinal microvasculature assessed by fundus photography were linked with cognitive impairment,15 cerebral white matter lesions,16 and cerebral atrophy.17 However, due to the low resolution of the retinal photography the superficial microvasculature (arterioles and venules) can be visualized and imaged; this imaging modality cannot image the deeper retinal microvascular network (capillaries) in detail.18 Optical coherence tomography angiography (OCTA) is an imaging tool that noninvasively and easily shows a high resolution of the retinal microvasculature in different retinal layers.19 Previous studies2022 using the OCTA suggested that it has the potential to be used as a screening tool to detect brain structural changes. Our recent study23 also found a relationship between the retinal microvasculature and cerebral small vessel disease markers, indicating that macular microvascular changes may reflect brain frailty in aging individuals. However, most of these studies have exclusively focused on global brain changes, and it remains unclear whether retinal changes in non-diseased individuals reflects changes in specific brain regions. 
Following the advent of voxel-based morphometry (VBM),24 it allows us to study the association of retinal microvasculature with brain structure alterations on the smallest regional level, the voxel level using magnetic resonance imaging (MRI). Previous studies using optical coherence tomography (OCT) and VBM demonstrated an association between RNFL thickness and microstructural integrity in the visual pathway, that is, the optic nerve, tract, and radiation.13,14 We aimed to investigate whether retinal microvascular perfusion can also reflect the brain gray matter damage, even beyond the visual pathway in a combined OCTA/VBM study. 
In addition, the neural correlates of retinal microvasculature changes have not yet been studied on a neuro-circuitry level. Structural covariance analysis enables to explore the anatomic brain networks based on the covariation of morphometry (e.g. volume or cortical thickness) of different brain areas.25 The idea is that brain regions that grow together are highly correlated in size, suggesting maturational coupling or structural covariance between them, due to brain connectivity.26 A previous study suggested that structural covariance networks are partially similar to known functional networks,27 although their biological meaning has not yet been clarified. Albeit that an association between structural covariance networks and retinal microvasculature has never been investigated in individuals. 
Therefore, we aimed to investigate the association of the retinal microvasculature measured by OCTA with gray matter volume using VBM analysis. We also evaluated the relationship between structural covariance networks and microvasculature in the retina. We hypothesized that the retinal microvasculature may relate to microstructural and volume alterations in the brain, especially in the visual related regions and also in regions that are particularly vulnerable to the neurodegenerative processes occurring in dementia and/or cognitive impairment. Our findings may make a significant contribution to shed more light on the underlying pathophysiology of how the microvascular changes play a role in aging-related cerebral alterations. 
Methods
Study Participants
A volunteer cohort without previously known neurological diseases was recruited from West China Hospital, Sichuan University, Chengdu, China since April 1, 2021. The study was approved by the Biomedical Research Ethics Committee of West China Hospital, Sichuan University (2020[104]). The study protocol followed the principles of the Declaration of Helsinki, and all the participants signed informed consents. The inclusion criteria of our participants were as follows: (1) participants had to be age 50 years or more; (2) they could have no previously diagnosed neurological and mental diseases; (3) they could have no ocular abnormalities with a normal fundus and visual acuity; (4) there could be no major memory concerns or Montreal Cognitive Assessment score ≥23; and (5) there could be no contraindications for brain MRI. In the present study, 144 participants enrolled between April 1, 2021, and December 31, 2021, were included. Participants were excluded if the quality of brain scans was poor, making the analysis of VBM difficult, or if the OCTA images had signal quality less than 6.28 
Data Collection
Clinical Characteristics
Using a standardized form, demographic characteristics (age and sex), vascular risk factors (hypertension, diabetes mellitus, hyperlipidemia, smoking, and drinking), and education were collected. Years of education were assessed for each individual from the first grade onward. Visual acuity was measured when performing OCT. 
Swept-Source Optical Coherence Tomography Angiography Imaging
The swept-source optical coherence tomography angiography imaging (SS-OCTA) examinations were performed by an examiner who had 5 years of experience with this device (author W.R.K.). The OCTA protocol is described in greater detail by our previous study.29 In brief, images of the fundus were obtained using SS-OCTA (VG 200; SVision Imaging Limited, Luoyang China). En face angiograms of the superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) were generated by automatic segmentation in a 6 mm area centered on the fovea. The SVP was defined as the microvasculature between the base of the retinal nerve fiber layer (RNFL) to the junction between the inner plexiform layer (IPL) and inner nuclear layer (INL); the ICP was defined as the microvasculature between the IPL/INL junction to the junction between the INL and the outer plexiform layer (OPL); the DCP was defined as the microvasculature between the INL/OPL junction to 25 µm below the OPL (Fig. 1). Microvascular perfusion was used to assess the microvasculature of the macular plexuses and was defined as the length of microvessels of the perfused microvasculature per unit area in millimeters squared (mm2) in the analyzed region (6 × 6 mm2 around the fovea). 
Figure 1.
 
Segmentation of the retinal microvasculature using the SS-OCTA. SVP, superficial vascular plexus; ICP, intermediate capillary plexus; DCP, deep capillary plexus.
Figure 1.
 
Segmentation of the retinal microvasculature using the SS-OCTA. SVP, superficial vascular plexus; ICP, intermediate capillary plexus; DCP, deep capillary plexus.
MRI Acquisition
The MRI examinations were performed using a standard 3T scanner (Siemens Skyra) equipped with a 32-channel head coil in Wuhou Health Management Center, West China Hospital. The sequences of MRI include T1 and T2-weighted imaging, fluid-attenuated inversion recovery (FLAIR) and susceptibility weighted imaging (SWI). For each participant, anatomic high-resolution MRI volumes were acquired in a transverse plane using a 3-dimensional (3-D) magnetization-prepared rapid gradient echo (MPRAGE) T1-weighted sequence. Imaging parameters were TR = 1900 ms; TE = 2.4 ms; FA = 9 degrees; FOV = 250 mm; 256 × 192 matrix; 191 slices; and voxel dimension =1.0 × 1.0 × 1.0 mm. 
Cerebral Small Vessel Disease MRI Markers
Four cerebral small vessel disease (CSVD) markers, including lacunes, white matter hyperintensity (WMH), cerebral microbleeds (CMBs), and enlarged perivascular spaces (EPVS) were rated by one experienced neurologist (Wendan Tao). The definitions of lacunes, WMH, CMBs, and EPVS were according to the standards for reporting vascular changes on neuroimaging (STRIVE) consensus criteria.30 The severity of WMHs was graded with a Fazekas score from 0 to 3,31 separately located in the periventricular or deep regions, with the sum of the scores providing a total WMH score. EPVS were classified as located either in basal ganglia (BG) or centrum semiovale (CS) regions, and were rated according to a 3-category ordinal scale (0–10, 10–25, and >25).32 Another experienced neurologist (author C.Y.) evaluated a random sample of 20 participants to assess inter-rater agreement. The inter-rater kappa coefficient was 0.83 for lacunes, 0.70 for the severity of WMH, 0.85 for CMBs, 0.75 for BG-EPVS, and 0.65 for CS-EPVS. 
Voxel-Based Morphometry Analysis
Structural T1-weighted images were processed using the Computational Anatomy Toolbox 12 (CAT12, http://dbm.neuro.uni-jena.de/cat/) based on Statistical Parametric Mapping 12 (SPM12, https://www.fil.ion.ucl.ac.uk/spm/). First, all images were visually checked for artifacts and manually reoriented to set the image origin at the anterior commissure. Subsequently, the reoriented images were spatially registration to Montreal Neurologic Institute space; segmented into gray matter, white matter, and cerebrospinal fluid maps; and modulated using the Jacobian modulation to obtain voxel-wise gray-matter volume maps. Finally, gray matter maps were smoothed with an isotropic Gaussian kernel (full width at half maximum = 8 mm). 
Relationships Between Gray Matter Volumes and SS-OCTA Parameters
Brain-retina relationships were identified using the general linear model between the dependent variable (voxel-wise gray matter volume) and independent variables (microvascular perfusion in SVP, ICP, or DCP). Age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes were taken as covariates in the model. In order to correct the problem of multiple comparisons and control the false positive rate, an independent two-tails one-sample t-test with accompanying false-discovery rate (FDR) correction (P < 0.01) was used to identify significant OCT-related changes in voxel-wise gray matter volume. 
Structural Covariance Network Analysis
The region that showed the greatest OCTA-related changes with gray matter volume in the above VBM analysis was denoted as the region of interest (ROI) in structural covariance network analysis. A sphere ROI centered on the peak coordinates with a radius of 6 mm was constructed as the seed region of structural covariance network analysis. The relationships between the dependent variable (voxel-wise gray matter volume of the whole brain) and independent variable (gray matter volume of the seed region) were modeled using the general linear model. Age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes were taken as covariates in the model. Two-tailed one-sample t-test and FDR correction (P < 0.05) were used to identify brain regions that showed significant structural covariance with the seed region. 
Relationships Between Structural Covariance Network and SS-OCTA Parameters
To ascertain the associations between the structural covariance network and retinal microvasculature, we modeled the relationships between the dependent variable (voxel-wise gray matter volume of the whole brain) and independent variable (gray matter volume of the seed region, OCTA parameters, and their interaction term) using the general linear model. A positive interaction effect reflects that increasing OCTA changes with increasing structural covariance. Similarly, a negative interaction effect reflects a negative relationship between the OCTA and structural covariance. Age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes were also taken as covariates in the model. Significant interaction effect was identified using two-tailed one-sample t-test and FDR correction (P < 0.05). 
Results
Demographic and Baseline Characteristics
During the study period, 144 individuals with SS-OCTA data that met the inclusion criteria were registered, 7 of whom were excluded due to incomplete MRI scans. In the end, 137 individuals were included for the final analysis. The demographic and clinical data of the subjects are displayed in Table 1. The mean age was 59.72 years, and 37.2% (n = 51) of the participants were men. 
Table 1.
 
Characteristics of the Study Participants
Table 1.
 
Characteristics of the Study Participants
Retinal Microvasculature and Voxel-Based Gray Matter Volumes
The associations of voxel-based gray matter volumes with microvasculature in SVP, ICP, and DCP were evaluated, respectively. According to the VBM analysis, gray matter volumes of the left fusiform gyrus, left inferior temporal gyrus, right parahippocampal gyrus, left inferior occipital gyrus, left putamen, right calcarine, left lingual gyrus, right insula, right middle temporal gyrus, right superior frontal gyrus, right insula/inferior frontal gyrus, left medial prefrontal cortex/anterior cingulate cortex, right superior temporal gyrus, and left cuneus were significantly associated with microvascular perfusion in SVP (P < 0.01; Fig. 2, Table 2), but no regions were found significantly associated with microvasculature in ICP and DCP. All significant voxels showed a positive association, that is, with a decreased perfusion in SVP, the voxel-wise gray matter volume was also decreased (Fig. 3). Similar brain regions were found to significantly be related to microvascular perfusion in SVP using gaussian random field correction (Supplementary Fig. S1). 
Figure 2.
 
Regions of voxel-wised gray matter volume that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase of gray matter volume) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Figure 2.
 
Regions of voxel-wised gray matter volume that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase of gray matter volume) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Table 2.
 
Superficial Vascular Plexus (SVP) Associated With Voxel-Based Gray Matter Areas
Table 2.
 
Superficial Vascular Plexus (SVP) Associated With Voxel-Based Gray Matter Areas
Figure 3.
 
Association between regions of voxel-wised gray matter volume and microvascular perfusion in superficial vascular plexus (SVP). Each row represents one distinct regression model with dependent variables listed on the left side of the figure. The model was adjusted for age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes. CMB, cerebral microbleed; WMH, white matter hyperintensity; BG, basal ganglia; CS, centrum semiovale; EPVS, enlarged perivascular spaces; Left Inferior Occipital Gyrus*, Brodmann area 17; Left Inferior Occipital Gyrus#, Brodmann area 37/19.
Figure 3.
 
Association between regions of voxel-wised gray matter volume and microvascular perfusion in superficial vascular plexus (SVP). Each row represents one distinct regression model with dependent variables listed on the left side of the figure. The model was adjusted for age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes. CMB, cerebral microbleed; WMH, white matter hyperintensity; BG, basal ganglia; CS, centrum semiovale; EPVS, enlarged perivascular spaces; Left Inferior Occipital Gyrus*, Brodmann area 17; Left Inferior Occipital Gyrus#, Brodmann area 37/19.
Retinal Microvasculature and Structural Covariance Network
The lingual gyrus is part of the secondary visual cortex, which is related to visual processing and cognitive domain of spatial or structural visualization.33,34 In addition, the gray matter volume of the left lingual gyrus (T = 6.03, P < 0.001; see Table 2) showed the greatest SVP-related changes. Therefore, we used the left lingual gyrus as seed-ROI to do the following structural covariance analysis. The gray matter morphometry in the left lingual gyrus had significant structural covariance with a broad set of brain regions, and the details of these regions were shown in Supplementary Figure S2. As displayed in Table 3 and Figure 4, the microvascular perfusion in SVP was positively correlated with the structural covariance of the left lingual gyrus seed with the left middle occipital gyrus (93 voxels, T = 4.87, P = 0.012), the right middle occipital gyrus (122 voxels, T = 4.96, P = 0.012), and the left middle frontal gyrus (48 voxels, T = 4.70, P = 0.012). The positive interaction indicates decreased structural covariance in individuals with poorer microvascular perfusion in SVP. 
Table 3.
 
Relationships Between the Structural Covariance Network (SCN) and Superficial Vascular Plexus (SVP)
Table 3.
 
Relationships Between the Structural Covariance Network (SCN) and Superficial Vascular Plexus (SVP)
Figure 4.
 
Regions according to structural covariance analysis that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase structural covariance) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Figure 4.
 
Regions according to structural covariance analysis that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase structural covariance) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Discussion
In the study, we found that poor microvascular perfusion in SVP is significantly associated with lower gray matter volume in specific brain regions, all of which are located in the occipital, frontal, and temporal lobes, except for the insula and putamen. Additionally, decreased microvasculature in SVP was associated with reduced structural covariance of the left lingual gyrus seed with the left middle occipital gyrus, the right middle occipital gyrus, and the left middle frontal gyrus. Our finding suggests retinal microvascular perfusions are associated with brain microstructural alterations and structural covariances. It supports the hypothesis that vascular impairment resulting in tissue hypoperfusion may contribute to aging related brain alterations. As the window to the brain, monitoring retinal microvasculature may add insights in the pathophysiology of aging related brain diseases, particularly the role of microvascular contribution to neurodegenerative alterations, progression, and even treatment efficacy. 
A previous study reported the associations of the thickness of RNFL with lesions in visual pathways in patients with multiple sclerosis.35 Recently, both the Rotterdam13 and UK biobank studies6 indicated that a thinner RNFL and GCIPL may reflect the lesions in brain's visual pathway, even beyond the context of specific pathologies directly influencing retinal structures. Extending on findings from previous studies, our current study explored the macula microvasculature and showed decreased perfusion in SVP was correlated with reduced gray-matter volume in the occipital lobe, which includes primary visual cortex and higher order visual areas. We also showed that microvascular perfusion in SVP is significantly correlated with gray matter changes in the lingual gyrus, fusiform gyrus, middle temporal gyrus, and inferior temporal gyrus. Although these structures are not directly considered as part of the visual pathway, they are connected to the visual cortex or involved in visual information processing including visual motion and color perception.36,37 
A possible mechanism may help us understand why significant relationship between visual related brain structures and retinal microvasculature was found in SVP. The SVP, primarily found in the ganglion cell layer (GCL) is responsible for the metabolism of the GCL,38 where thinning of the ganglion cells and axons has been observed in most neurodegenerative diseases, such as dementia and Parkinson's disease.39,40 GCIPL thickness is a macular measure which is linked with injury of axons originating in areas serving central vision.41 It is suggested that changes in the brain regions involved in visual processing may result in insult to or interference of connection within the visual tract and thereby may cause retrograde degeneration in the retina.41 Given that the SVP plays a significant role in neurons responsible for visual processing and/or vision, the association between SVP and visual-related brain regions suggests that changes in the brain may reflect SVP impairment. Contrarily, it may be plausible that SVP dysfunction may cause anterograde degeneration, which may lead to thinning of the GCIPL causing changes in the gray matter microstructure involved in vision and/or visual processing.42 
Recent reports suggest small vessel disease are implicated in dementia,2 and retinal microvasculature are surrogate indicators of the cerebral microvasculature in dementia.43 To that end, accumulating evidence has shown the retinal microvascular changes that occur in dementia.44,45 Similarly, our study has shown that the decreased microvascular perfusion in SVP was associated with the reduced gray matter volumes in the parahippocampal gyrus, the anterior cingulate cortex, and the medial temporal gyrus, which are indicators of dementia, suggesting an association between SVP and the cerebral structures associated with cognition.4648 By extension, we have shown that even in people without cognitive dysfunction, retinal microvascular changes in the SVP may reflect subtle changes associated with dementia and cognitive impairment which may not be visually detectable on MRI images. All the above evidence is in favor of common neurodegenerative processes occurring at the retinal and brain level. In addition, it suggests the potential use for the microvascular changes in SVP to reflect brain alterations in regions that are particularly vulnerable in dementia and cognitive decline. 
Previous studies49,50 have suggested that retinal microvasculature may be related to cerebral microcirculation through arterial stiffness and arteriosclerosis. The internal carotid artery supplies the retina and the anterior part of the brain (e.g. frontal cortex). Thus, it is plausible to find the significant association of retinal microvascular perfusion with gray matter volumes in brain locations, such as the insula, putamen, frontal, and temporal lobes in the present study. In addition, similar results were also reported by the AGES-Reykjavik study,51 which found retinal focal arteriolar signs using retinal photography correlated with white matter hyperintensities, particularly those located in the frontal lobe. Further investigation is needed to determine whether retinal microvasculature is associated with the perfusion in these brain regions with reduced gray matter volumes. 
In regard to the structure covariance analysis, the poor microvascular perfusion in SVP was related to reduced structural covariance of the left lingual gyrus seed with the left middle occipital gyrus, the right middle occipital gyrus, and the left middle frontal gyrus. In addition, the main function of the occipital and frontal lobes is to comprehensively process incoming visual information and they also play a pivotal role in the connection between the visual system and other sensory systems. Considering this, these findings might be interpreted as reduced interhemispheric and connectivity of geometrically corresponding brain regions with poor microvasculature in the retina. The interhemispheric connectivity between homotopic regions is a fundamental characteristic of the human brain.52 Altered interhemispheric functional connectivity has been reported in psychiatric and neurogenerative diseases53,54 and normal aging.55 Our finding adds novel evidence that suggests a role for interhemispheric cooperation in the pathophysiology of retinal microvascular alterations in participants without neurological diseases. We additionally showed a positive correlation of SVP with structural covariance between the heterotopic and ipsilateral regions, including the left lingual gyrus, middle occipital gyrus, and middle frontal gyrus. It could also be explained by a combination of region-specific anatomic alterations and modification of connectivity. 
Our current study evaluated the association between retinal microvasculature and the cerebral gray matter changes. We showed the perfusion in SVP correlated with most gray matter structures in the brain whereas the perfusion in ICP and DCP did not. SVP is a network of both large and small vessels located in the GCIPL whereas the ICP and DCP are capillaries are located beneath the SVP,38 as shown in Figure 1. It is suggested that retinal microvascular may reflect cerebral microcirculation,3 and the superficial plexus is more sensitive to cerebral changes (both structural and vascular) than the deeper plexuses because it is the entry point of blood flow into the retina.38,56 This may also explain why a significant relationship between brain structures and retinal microvasculature was only found in SVP (superficial microvasculature) but not in ICP and DCP (deeper microvasculature). 
Some potential limitations should be considered. First, our study is a cross-sectional study with a relatively small sample size, which limits us to draw a conclusion about temporality and causality. Despite that, we chose a lower and stricter threshold (P < 0.01) to analyze the association of OCTA parameters with gray matter volumes, which makes our VBM results more convincing. Further longitudinal studies with a larger sample size are needed to verify our findings. Second, all the participants were Chinese, which may make our findings hard to generalize to other ethnic groups. Third, the changes of the structural covariance network with the retinal microvasculature may be related to the damage of white matter and functional connectivity. Therefore, studying the association of retinal microvasculature with structural covariance network and other multimodal networks within visual pathway in the brain will enhance our findings and provide in-depth meaning. Despite all that, this is the first study to investigate the retinal microvascular changes with brain structures in a neuro-circuitry level. 
Conclusions
In participants without neurological diseases, the microvascular perfusion in SVP was associated with voxel-wise gray matter volumes in specific brain regions, including the insula, putamen, occipital, frontal, and temporal lobes, all of which were located in the anterior part of the brain supplied by the internal carotid artery, except the occipital lobe. In addition, these regions were also involved in visual processing and cognitive impairment (such as the left inferior occipital gyrus, the left lingual gyrus, and the right parahippocampal gyrus). The decreased microvascular perfusions in SVP were also correlated with reduced structural covariance among the homotopic, heterotopic, and ipsilateral regions. These findings enable us to better understand the underlying mechanisms between vascular pathology and the aging process. Furthermore, retinal microvasculature may offer a window to identify aging related alterations in the brain. 
Acknowledgments
The authors are grateful to the participants for their support of the present study. 
Funded by the National Natural Science Foundation of China (82371323), Sichuan Science and Technology Program (2023NSFSC1558), and the Technology Innovation R&D Project of Chengdu Science and Technology Bureau (2021-YF05- 01325-SN). X.G. was supported by the National Natural Science Foundation of China (62303396) and Natural Science Foundation of Hebei Province (No. H2021203002). 
Authors' Contributions: J.L. drafted the manuscript. W.T., C.Y., A.W., X.W., and Z.W. collected the imaging data. W.R.K. collected the OCTA data. X.G. did the voxel-based morphometry analysis. M.L. revised and supervised the paper. 
Availability of Data and Materials: The data sets generated during and analyzed during the current study are not publicly available due to participants’ privacy and ownership issues but are available from the corresponding author on reasonable request. 
Disclosure: J. Liu, None; W. Tao, None; X. Guo, None; W.R. Kwapong, None; C. Ye, None; A. Wang, None; X. Wu, None; Z. Wang, None; M. Liu, None 
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Figure 1.
 
Segmentation of the retinal microvasculature using the SS-OCTA. SVP, superficial vascular plexus; ICP, intermediate capillary plexus; DCP, deep capillary plexus.
Figure 1.
 
Segmentation of the retinal microvasculature using the SS-OCTA. SVP, superficial vascular plexus; ICP, intermediate capillary plexus; DCP, deep capillary plexus.
Figure 2.
 
Regions of voxel-wised gray matter volume that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase of gray matter volume) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Figure 2.
 
Regions of voxel-wised gray matter volume that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase of gray matter volume) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Figure 3.
 
Association between regions of voxel-wised gray matter volume and microvascular perfusion in superficial vascular plexus (SVP). Each row represents one distinct regression model with dependent variables listed on the left side of the figure. The model was adjusted for age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes. CMB, cerebral microbleed; WMH, white matter hyperintensity; BG, basal ganglia; CS, centrum semiovale; EPVS, enlarged perivascular spaces; Left Inferior Occipital Gyrus*, Brodmann area 17; Left Inferior Occipital Gyrus#, Brodmann area 37/19.
Figure 3.
 
Association between regions of voxel-wised gray matter volume and microvascular perfusion in superficial vascular plexus (SVP). Each row represents one distinct regression model with dependent variables listed on the left side of the figure. The model was adjusted for age, sex, years of education, smoking, drinking, diabetes, hypertension, hyperlipidemia, acuity, presence of CMB, presence of lacunes, total WMH score, the severity of BG-EPVS, the severity of CS-EPVS, and total intracranial volumes. CMB, cerebral microbleed; WMH, white matter hyperintensity; BG, basal ganglia; CS, centrum semiovale; EPVS, enlarged perivascular spaces; Left Inferior Occipital Gyrus*, Brodmann area 17; Left Inferior Occipital Gyrus#, Brodmann area 37/19.
Figure 4.
 
Regions according to structural covariance analysis that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase structural covariance) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Figure 4.
 
Regions according to structural covariance analysis that were significantly associated with microvascular perfusion in superficial vascular plexus (SVP; FDR corrected, P < 0.01). Colors correspond to t-values and reflect the direction of the associations from regression models: red for a positive (increase structural covariance) association per standard deviation increase of microvascular perfusion in superficial vascular plexus (SVP). L, left and R, right.
Table 1.
 
Characteristics of the Study Participants
Table 1.
 
Characteristics of the Study Participants
Table 2.
 
Superficial Vascular Plexus (SVP) Associated With Voxel-Based Gray Matter Areas
Table 2.
 
Superficial Vascular Plexus (SVP) Associated With Voxel-Based Gray Matter Areas
Table 3.
 
Relationships Between the Structural Covariance Network (SCN) and Superficial Vascular Plexus (SVP)
Table 3.
 
Relationships Between the Structural Covariance Network (SCN) and Superficial Vascular Plexus (SVP)
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