Relationships between multimodal ocular imaging and white matter hyperintensity volume

Introduction

White matter hyperintensities (WMH), presumed to have a vascular etiology, are frequently observed in asymptomatic elderly individuals and represent a hallmark of cerebral small vessel disease (CSVD) (1). The underlying pathology of WMHs remains unclear. Emerging evidence suggests associations with white matter (WM) demyelination, axonal loss, and gliosis, driven by mechanisms including blood-brain barrier disruption, reduced cerebral perfusion, endothelial dysfunction, inflammatory responses, oxidative stress, and venous drainage abnormalities (2,3). WMHs have been linked to compromised brain and cardiovascular health, serving as predictors of stroke, depression, cognitive decline, and mortality (4,5). Presently, the clinical assessment of WMH largely relies on magnetic resonance imaging (MRI), which visualizes WM lesions as bilateral, symmetrical hyperintensities on T2-weighted images (T2WI) (6). Nevertheless, MRI is costly, time-consuming, and often challenging to conduct with the requirement of patient cooperation. Therefore, there is a need for more cost-effective methods for screening WMH.

The retina and optic nerve originate from the neuroectoderm at approximately 23 days of the gestation period, when they are formed by invagination from the diencephalon (7). As a result, the retina has a shared cellular lineage with brain tissue and is classified as a component of the central nervous system. The retinal microvasculature exhibits similar anatomical and physiological characteristics and regulatory functions in circulation to cerebral small vessels, including features of the blood-retina barrier and blood-brain barrier (8,9). Consequently, abnormalities observed in the eye may serve as indicators of diseases affecting the central nervous system. Optical coherence tomography (OCT) enables quantification of axonal loss via retinal nerve fiber layer (RNFL) thickness and neuronal damage via ganglion cell layer (GCL) or GCL-inner plexiform layer (GCIPL) thickness (10). Fundus photography provides a direct visualization of retinal vessels, offering a noninvasive approach to assess early changes in cerebral circulation (11). These modalities position retinal imaging as a potential tool for diagnosing and monitoring neurological disorders.

It has been established that retinal vessels are the only ones that can be seen in vivo without intrusive methods (12). Previous studies have associated WMH with retinal parameters such as vessel diameter (13), retinopathy (14), and vascular bifurcation patterns (15) as assessed through fundus photography. These findings imply that damage to the microvasculature may be a contributing factor to the development of WMH. Quantitative retinal structural alterations have been linked to WMH volume, with associations strengthening as WMH severity increases (16-18). However, prior studies only utilized a single method of retinal examination to investigate the relationship between ocular biomarkers and WMH volume. Few studies (19,20) have employed OCT to evaluate retinal structure and fundus photography to assess retinal microvasculature, examining the relationship between ocular biomarkers and WMH from the perspective of retinal structure-microcirculation correlation. In addition, existing research predominantly focuses on total WMH volume and employs rudimentary MRI indices (e.g., Fazekas scale) to characterize WMH severity (17), whereas lobe-specific WMH volume analysis may offer deeper insights into regional brain-eye correlations.

In the KaiLuan Study, our aim was to explore the link between ocular biomarkers and the volume of WMH in both the total brain and specific lobes among a community-based cohort. This research is intended to shed light on the potential of easily accessible and cost-effective eye measurements to serve as indicators for neurodegenerative processes. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2840/rc).

Methods

Study population

This study was part of the KaiLuan Study, a prospective occupational cohort study investigating common diseases among residents of Tangshan City aged 18 years and older. Relevant examinations were conducted from December 2020 to December 2023. This research was carried out in accordance with the principles outlined in the Declaration of Helsinki and its subsequent amendments and received approval from the Medical Ethics Committee at Kailuan General Hospital (IRB No. 2021002) (21). Informed consent was obtained from all participants or their legal representatives prior to enrollment in the study. The overall study protocol (https://bmjopen.bmj.com/content/13/2/e067283.long) is available online. In summary, participants completed an extensive questionnaire that collected data on various aspects, including demographics, socioeconomic status, a structured medical history interview, and lifestyle habits. In addition to the questionnaire, physical measurements such as blood pressure, height, and weight were also taken (Figure 1).

Figure 1 Flowchart of subjects included in the research. MRI, magnetic resonance imaging; OCT, optical coherence tomography.

Fundus photography

Bilateral fundus photography images were captured using a fundus camera (Topcon CX-1, Topcon Corporation, Tokyo, Japan). The quality assurance standards included (22): (I) absence of significant artifacts or blurring; (II) appropriate brightness, neither too dark nor too light; and (III) ensuring that the image field encompassed the entire optic disc and macula. Poor-quality images were excluded from the study. If an image couldn’t be accurately measured, the corresponding image from the other eye was utilized. Participants with poor image quality in both eyes were excluded from the study. In this study, experienced ophthalmologists evaluated the fundus images of volunteers to exclude ocular diseases or abnormalities such as macular degeneration, glaucoma, and high myopia that might affect the measurement of ocular biomarkers.

Retinal vessel segmentation employed NFN+, a novel deep learning technique (23). The NFN+ model introduces innovation through its cascaded design and inter-network skip connections. It consists of two identical backbones, a front network followed by a subsequent network, connected via inter-network skip connections. The front network generates primary vascular probability images by processing input image segments. The following network utilizes the prime vessel probability maps generated by the front network as input to produce vascular segmentation results (24). After applying the vascular segmentation mask from the deep learning model, two experienced retinal specialists manually categorized the arteries and veins. The segmented masks were then partitioned into a range of squares with varying lengths of sides, aligned with the centerlines of the quantitative analysis of retinal vessels employing the box-counting method. The fractal dimension (FD) of the retinal vasculature was subsequently calculated as the slope of the logarithmic relationship concerning the count and sizes of these boxes. Apart from assessing the blood vessel geometry, we also measured the diameters of all arterioles and venules within a region ranging from disc-sized areas (0.50–0.75) surrounding the optic disc. The calibers of the ocular arteries and venules, termed central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE), were calculated with the Knudtson-Hubbard equation. The CRAE to CRVE ratio, known as the arteriole/venular ratio (AVR), was then calculated. Additionally, the global vessel width (both arteriolar and venular) represents the mean diameter of all vascular branches (artery or vein) observed in the respective participants’ eyes (Figure 2).

Figure 2 Examples of segmented retinal vessel images: (A,B) represent a subject with lower WMH volume, (C,D) represent a subject with medium WMH volume, and (E,F) represent a subject with higher WMH volume. (A,C,E) Original fundus image. (B,D,F) Segmented arteries and veins: arteries shown in red, and veins in blue. WMH, white matter hyperintensities.

OCT

OCT images are captured using the 12-line 9 mm radial macula pattern and optic disc pattern on the Topcon Deep Range Imaging OCT Triton device, which is manufactured by Topcon Corporation in Tokyo, Japan. We utilized an ocular-tracking system in tracking mode to ensure that measurements were taken at roughly the same position across various subjects. To guarantee the precision of retinal thickness measurement, we have excluded OCT scans that are of poor quality, in accordance with the OSCAR-IB standard (25).

The segmentation of retinal layers in OCT images was carried out by employing an automated algorithm that was developed by the Retinal Image Analysis Laboratory (26). The retinal structures were categorized as follows: macular retinal nerve fiber layer (mRNFL), macular ganglion cell inner plexiform layer (mGCIPL), macular inner nuclear layer (mINL), and total macular thickness. The thickness of the mGCIPL encompasses both the macular GCL (mGCL) and macular inner plexiform layer (mIPL), while mGCC comprises both mRNFL and mGCIPL. We adhered to the Advised Protocol for OCT Study Terminology and Elements (APOSTEL) guidelines (27), except for total macular thickness, defined as the distance from the inner limiting membrane to the retinal pigment epithelium. In macular OCT images, the algorithm also calculates the average retinal thickness across these layers in nine regions, adhering to the Early Treatment Diabetic Retinopathy Study (ETDRS) grid guidelines (28). We assessed macular OCT images by averaging retinal thickness across six distinct regions. These regions encompassed the foveal subfield, along with both inner and outer rings. This approach enabled a thorough examination of different macular areas, revealing variations in retinal thickness across regions. To signify macular measurements, we’ve introduced the prefix “m” to the sublayer abbreviations, recognizing that certain OCT metrics can be extrapolated from images centered on the optic disc (Figure 3).

Figure 3 Optical coherence tomography of the retinal imaging. (A) OCT of the macula showing normal retinal layer architecture; (B) layer segmentation of the macular OCT image highlighting specific retinal layers, including the RNFL, INL, and GCIPL. RNFL, retinal nerve fiber layer; GCIPL, ganglion cell and inner plexiform layer; INL, inner nuclear layer; OCT, optical coherence tomography.

Brain image acquisition and image analysis

A 3.0 T scanner (GE 750W; General Electric Medical Systems, Milwaukee, Wisconsin, USA) with an eight-channel array coil was used to get the brain MRI. Data acquisition includes the following sequences: T2WI, three-dimensional (3D) brain volume for high-resolution T1-weighted image (T1WI), and 3D fluid-attenuated inversion recovery (FLAIR). The essential acquisition parameters for each modality are outlined in a previous publication (21).

The WMH volume was segmented by nnU-Net, proposed by researchers from the German Cancer Research Center, Heidelberg University, and Heidelberg University Hospital, is a medical image segmentation framework that adapts to any new dataset (29). This framework automatically adjusts all hyperparameters based on the attributes of the given dataset, eliminating the need for manual intervention. In this study, our research team utilized the nnU-Net model for segmenting WMH and developed an automatic segmentation model for WMH extraction based on feedback validation from experienced radiologists on the segmentation results of 200 cases. This achievement represents the automatic extraction of WMH. WMH volume in the frontal, parietal, occipital, temporal and cerebellum were summed from both the right and left volumes. The total WMH was defined as the sum of WMH in the frontal, parietal, occipital, temporal and cerebellar lobes. Total WMH volume and WMH in the frontal, parietal, occipital, temporal lobes and cerebellum were normalized for head size by multiplying the WMH parameters by the volumetric scaling from T1 head images to standard space.

Using RadiAnt’s multi-planar reconstruction (MPR) on 3D FLAIR brain MRI, we first display the data in axial, sagittal and coronal views and adjust the orthogonal crosshairs to intersect at the globe’s approximate center. We then zoom the axial view to clearly delineate the corneal apex (anterior boundary) and the deepest point of the sclera (posterior boundary). Finally, we use the line-measurement tool on the axial plane to record the maximum axial length of the eye (30,31).

Inclusion and exclusion criteria

The inclusion criteria were as follows: (I) aged 18 years and older; (II) able to cooperate and complete fundus photography and OCT examinations; (III) completed brain MRI to evaluate WMH volume. The exclusion criteria included: (I) history of a blood dyscrasias, heart attacks, cancers, and autoimmune conditions; (II) disorders of the nervous system, including sudden and extensive cerebral infarction, mental disorders, neoplasms, seizures, pathologies with central nervous system myelin damage, or injuries; (III) self-reported or history of ocular surgery; (IV) ocular disorders such macular edema and age-related macular degeneration that could affect ocular biomarkers measurement. Ultimately, the study enrolled a total of 716 eligible participants.

Statistical analysis

The research conducted was based on cross-sectional data. For this study, OCT and fundus photography images of both eyes were included and analyzed. If both eyes of a subject were eligible for inclusion, one eye was randomly selected.

Continuous variables were reported either as mean with standard deviation or as median with interquartile range, depending on their distribution. Categorical variables were presented as frequencies and percentages. We examined the association of ocular biomarkers with WMH volume using general linear models: Model 1 adjusted for age, sex and education; and Model 2 additionally adjusted for diabetes mellitus, total cholesterol, body mass index (BMI), mean arterial blood pressure and smoking status. In the sensitivity analysis, when we detected a significant correlation between ocular biomarkers and WMH volume, we further conducted stratified analysis by sex. All analyses were performed using SPSS version 20.0 software (SPSS Inc., Chicago, IL, US).

Results

Characteristics of the study participants

Table 1 displays the demographics of the study population. The WMH volume was used to separate individuals into three groups. In total, 761 individuals were assigned to the lower WMH volume group {WMH volume ≤97.88 mm³, age [mean ± standard deviation (SD)]= 47.76±11.04 years, female =33.1%}; 251 volunteers were categorized as the medium group [WMH volume 97.88–1,458.00 mm³, age (mean ± SD)=68.14±10.05 years, male =50.0%]; and 259 participants were grouped as the higher WMH volume group [WMH volume ≥1,458.00 mm³, age (mean ± SD)=62.09±9.23 years, female =79.2%].

Associations of OCT with WMH volume

After adjusting for age, sex, and education, thinner mGCIPL and mGCC were significantly associated with higher WMH volume in the whole brain as well as in different brain lobes (temporal and occipital) (P<0.05, Table 2). Reduced mINL was obviously associated with increased WMH volume in the whole brain as well as in various brain lobes (P<0.05, Table 2). These associations remained statistically significant even after further controlling for total cholesterol, mean arterial blood pressure, BMI, smoking status, and diabetes mellitus (P<0.05, Table 2). No significant correlation between the mRNFL or the total macular thickness and WMH volume in the total brain or specific lobes (P>0.05, Table 2).

Associations of fundus photography with WMH volume

Table 3 shows the multivariable analysis of fundus photography parameters with total WMH volume and WMH volume in various lobes. After controlling for age, sex, and education, smaller AVR, narrower CRAE, and wider CRVE were significantly associated with larger WMH volume in the occipital lobe (P<0.05). These associations remained significant after additional adjustment for total cholesterol, mean arterial blood pressure, BMI, smoking status, and diabetes mellitus (P<0.05). Additionally, lower FD complexity and wider global vein width were significantly correlated with increased WMH volume in the entire brain and in different brain lobes, including the frontal, occipital, and temporal lobes (P<0.05). No significant associations were observed between fundus photography parameters and WMH volume in the parietal lobe (P>0.05).

Sensitivity analysis of ocular biomarkers with sex on WMH volume

Significant associations were observed between ocular biomarkers and WMH volume in sex-stratified analyses (P<0.05; Figure S1). Among females (but not males), thinner mGCIPL and mINL, as well as wider global venous width, were significantly associated with greater total WMH volume and frontal lobe WMH (P<0.05; Figure S1). Ocular biomarkers (mGCIPL, mINL, mGCC, AVR, CRAE, CRVE, global vein width) were obviously related to greater occipital WMH volume only among females, not in males (P<0.05; Figure S1). Additionally, reduced mGCIPL, mGCC, and wider global venous width were exclusively linked to higher temporal lobe WMH volume in female participants. (P<0.05; Figure S1).

Discussion

In this research, we found that thinner mGCIPL, mINL, and mGCC were associated with higher total WMH volume and lobe-specific WMH volumes (occipital and temporal). In addition, fundus photography parameters (FD, global vein width) were associated with larger total WMH volume and WMH volume in different lobes (frontal, occipital and temporal). These findings suggest that ocular biomarkers provide valuable insights into brain WM damage and also aid in lesion localization.

WMHs are frequently found in approximately 50% of individuals in their fifth decade and in as many as 95% of individuals by the age of 90 (32). Initially thought to be clinically insignificant, recent research indicates that these lesions reflect compromised brain and cardiovascular health, increasing the risk of stroke, depression in later life, cognitive deterioration, impaired gait, and mortality (33,34). Our study has shown that thinning of the mGCIPL, mINL, and mGCC correlates with increased total volume of WMH and WMH volume in different brain lobes (occipital and temporal). These results suggest concurrent neuronal damage in both the retina and distributed brain regions. In line with our observations, Lv et al. (16) suggested that mIPL thickness was inversely associated with WMH volume. The association between reduced mGCC thickness and WMH volume may arise from neuronal loss, alterations in cerebral blood flow, and shared pathological mechanisms such as inflammation and oxidative stress. However, our study did not observe a correlation between the mRNFL and WMH volume, contrasting with the findings of Lv et al. (16). This discrepancy might be attributed to an inherent anatomical difference: the mRNFL is substantially thinner than the peripapillary RNFL (pRNFL), as macular ganglion cell axon bundles converge toward the optic disc (35). Recently, a study on patients with progressive supranuclear palsy (PSP) (36) demonstrated that reduced inner retinal layer (IRL) and GCL thickness was significantly negatively correlated with age-related WM hyperintensity score (ARWMC), while choroidal thickness showed no difference. Aligns with our OCT-WMH findings, supporting retina as a brain health window across diseases.

Our results demonstrate a significant correlation between thinning of the mGCIPL, mINL, and mGCC and a prominent increase in the volume of WMH in the temporal and occipital regions. Similarly, Chua et al. (37) demonstrated that reduced mGCIPL, mGCC, and total macular thickness were associated with smaller normalized grey matter volumes in the occipital pole. It’s well known that occipital lobe atrophy leads to thinning of the internal retinal layer through trans-synaptic retrograde degeneration (38). Regarding the underlying mechanisms, it’s conceivable that localized or widespread brain damage can cause thinning of the retina. Specifically, impairment to brain areas responsible for visual processing could lead to compromised connections within the visual pathway, potentially triggering retrograde degeneration of the optic nerve (39). Indeed, individuals with traumatic brain injury frequently report subjective visual disturbances, suggesting alterations in retinal structures and the existence of axonal injury along the visual pathways (40). In Alzheimer’s disease (AD), accumulation of amyloid-β and tau aggregates in the visual cortex and along the optic radiations has been shown to trigger trans-synaptic retrograde degeneration, leading to thinning of IRLs (mGCIPL, mGCC) detectable by OCT (41). These results demonstrate that the thinning of the mGCIPL and mGCC, which reflect ganglion cell and inner plexiform layer integrity, may mirror neuronal loss in the brain’s visual pathways.

Currently, research on WMH primarily focuses on WMH volume, but this is a rather crude indicator. Recent studies have introduced additional WMH markers, like WMH type and shape, as promising novel indicators that could offer a more comprehensive characterization of WMH beyond volumetric assessment alone (42,43). However, there are few reports on the clinical significance of quantitative WMH volume in different brain regions. Kandiah et al. (44) enrolled 91 patients with Parkinson’s disease and quantitatively analyzed the volumes of WMH in the frontal, temporal, parietal, occipital lobes, and basal ganglia. They found that patients with Parkinson’s disease accompanied by mild cognitive impairment exhibited significantly greater WMH volumes in the frontal, parietal, and temporal lobes compared to cognitively intact counterparts. Our study extends these findings by demonstrating that thinning of OCT-derived retinal parameters may serve as a sensitive marker for lobe-specific WMH volume elevation, providing empirical support for precision clinical management strategies.

The retinal vasculature shares structural and functional homologies with the cerebral microvasculature, making quantitative alterations in retinal vessels potential surrogates for cerebrovascular neurodegenerative processes. Dumitrascu et al. (14) reported a positive correlation between the severity of WM lesions and retinal microvascular abnormalities. Previous studies by Ji et al. (13) indicated that a decrease in CRAE and an increase in CRVE were associated with an increased risk of moderate to severe deep WM lesions, which partially aligns with the results of this study. Our findings suggested that CRAE and CRVE correlated significantly only with WMH in the occipital lobe, and showed no significant correlation with other brain lobes. Potential explanations for these discrepancies include: first, previous studies assessed the severity of WMHs using the Fazekas scale, while our study employed the nnU-Net model for segmenting brain WMH volume; second, the study populations differed; the previous study was based on a hospital-based cross-sectional design, with participants mainly consisting of high-risk individuals with cerebrovascular disease risk factors, whereas this study was based on a large community cohort, with participants primarily being community-dwelling individuals without overt neurological disorders or clinical manifestations of cerebrovascular disease. Normal tension glaucoma (NTG) features progressive optic nerve degeneration at normal intraocular pressure, linked to vascular dysregulation and impaired optic nerve perfusion (45). Microvascular disturbances (endothelial dysfunction, barrier compromise) underlie retinal ganglion cell loss and WMH via hypoperfusion. Our findings of wider CRVE, lower AVR with occipital WMH align with NTG models (46), highlighting global ocular-cerebral small vessel vulnerability.

In addition, this study employed computer-assisted automated measurement methods, which not only measured CRAE and CRVE but also further calculated retinal vascular FD and global vascular width using NFN+ software (47). Our findings showed that lower FD complexity and wider global vein width were significantly associated with larger volumes of WMH in both the entire brain and various brain lobes, such as the frontal, occipital, and temporal lobes. These results suggest that FD and global vein width serve as sensitive indicators for monitoring changes in brain WM microstructure. In line with our findings, a prior study reported associations between retinal arteriolar/venular FD and cognitive impairment (48). It is well known that WMH are associated with the occurrence of dementia and cognitive decline (49). The global vascular width, an innovative metric for assessing the retinal vascular network, is computed by the NFN+ tool, representing the mean width across all retinal artery or vein branches (24). We assume that these metrics enable a more comprehensive evaluation of the retinal vascular network, thus rendering them more sensitive compared to the traditional retinal vascular parameters (50).

Stratified analysis by sex revealed significantly stronger associations between ocular biomarkers and WMH burden in females. These findings align with prior research, including that of Lohner et al. (51), which documented sex-specific increases in WMH volume linked to menopausal status and vascular stiffness. The observed differences may reflect postmenopausal hormonal influences on endothelial function and blood-brain barrier integrity, providing a potential biological basis for gender-specific neurovascular vulnerability. These results underscore the need for further investigation into sex-dependent mechanisms underlying WMH burden and ocular biomarker expression.

The foremost strength of our research is that we applied OCT and fundus photography to analyze the relationship between retinal biomarkers and WMH. By analyzing combined neurovascular parameters rather than isolated biomarkers (52,53), we provide clinically valuable indicators for monitoring WMH, which could enhance sensitivity and specificity for the asymptomatic patients with WM microstructural damage. Additionally, we calculated the volume of WMH in different brain regions and made a more refined division of the WMH volume. These lesions, once dismissed as mere age-related changes (54), are now recognized as correlates of stroke, cognitive decline, and other neurological disorders (55). Therefore, we believe that further understanding of WMH is still needed, and this study lays the foundation for subsequent WMH research.

Several limitations should be considered when interpreting the results of our study. Firstly, the cross-sectional design of our study restricts our ability to establish temporal sequence and causality. Second, the KaiLuan Study comprises a volunteer cohort, potentially representing individuals with a healthier status compared to the general population. Third, concentration and cooperation from individuals are necessary for the fundus photography and OCT imaging procedure. However, some images obtained may be unsuitable for analysis due to factors such as frequent eye blinking, head movement, and eye movement during imaging, all of which can introduce artifacts that may impact the data.

Conclusions

In conclusion, we found that in a community-based cohort of adults, ocular biomarkers (OCT and fundus photography) were associated with larger total WMH volume and WMH volume in different lobes. This research indicates that multimodal ocular biomarkers could offer insights into WMH, and it implies that disruptions in WM microstructure might occur concurrently in both the retina and across the brain. Further studies are needed to explore the potential of multimodal ocular biomarkers as cost-effective and non-invasive methods for investigating WMH.

Acknowledgments

We thank all the participants and project staff who participated in the KaiLuan Study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2840/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2840/dss

Funding: This research was supported by the Beijing key Clinical Discipline Funding (No. 2021-135), and National Natural Science Foundation of China (No. 82272072).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2840/coif). H.S., B.L., Y.N. and G.X. are employees of Ping An Healthcare Technology. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study protocol was approved by the Medical Ethics Committee of Kailuan General Hospital (IRB No. 2021002), and informed consent was obtained from all participants or their legal representatives prior to enrollment.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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