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Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia

BMC Ophthalmology volume 24, Article number: 315 (2024) Cite this article

Abstract

Aim

Recent imaging studies have found significant abnormalities in the brain’s functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients.

Methods

We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups.

Results

In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01).

Conclusion

HM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

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Introduction

In the past decade or so, due to genetic factors and excessive eye use by adolescents, the myopia rate among teenagers has significantly increased from less than 20% to over 90%. About 20% of people are highly myopic, with a significant surge in myopia incidence [1,2,3,4,5,6]. High myopia (HM), a visual disorder characterized by a refractive error of over 600 degrees and an elongated eyeball, has become increasingly common in China. Although there are many methods available to correct myopia, pathological myopia caused by HM may lead to serious complications and even blindness, and may also affect brain structure [7, 8]. However, current research has only provided a limited understanding of the topological structure of brain networks in HM patients.

With the development of imaging technology, Functional magnetic resonance imaging (fMRI) has been widely used in research on the brain of various diseases, reflecting mainly the activity of brain regions and the functional connectivity (FC) status between brain regions [9, 10]. Cheng observed a significant reduction in Voxel-Mirrored Homotopic Connectivity (VMHC) values in the fusiform gyrus and putamen of HM patients, indicating that abnormal visual experience may affect their visual recognition function [11]. Wang observed that the DKI-derived Kurtosis parameter in HM patients showed a negative correlation with illness duration, indicating microstructural changes in brain regions related to visual and motor conduction functions [39]. Additionally, Zhai used resting-state FC density (FCD) mapping to find a decrease in FCD in areas such as the inferior temporal gyrus, superior marginal gyrus, and lateral prefrontal cortex in HM patients. These findings help to understand the attention deficit of HM [12]. Previously, we also observed abnormal changes in the FC of the hippocampus in HM patients, which may lead to damage in areas such as emotion, behavior, cognitive memory, and related aspects [13]. The findings above indicate that the alterations in FC in the brain regions of HM patients are not limited to a single brain region, but rather involve related brain regions such as those related to emotions, cognition, and behavior at the whole-brain level. This suggests that further exploration of the central nervous mechanism of HM may require a more comprehensive analysis.

The brain is a complex network that facilitates the separation and integration of information processing [19]. Graph theoretical analysis is used to study and analyze the relationships between nodes and edges in a graph, with the aim of understanding and comprehending the working patterns and information transmission characteristics of the brain through quantitative analysis of network features, such as global and node properties [14, 40]. The global attributes of the network are the description of the connectivity of the whole-brain network, which mainly includes clustering coefficient (Cp) and local efficiency (Eloc) to measure the local information transfer efficiency and fault tolerance, shortest path length (Lp) and global efficiency (Eglob) to measure the global information transfer efficiency and integration ability, small worldness (σ). Node properties are used to describe the connectivity between brain regions, including node degree, node efficiency, and betweenness centrality. These parameters can be used to identify hub nodes that play a role as bridges in information transmission in the network [41]. The combination of graph theory and MRI technology has been widely used in recent years for analyzing brain networks, and this method has been applied to various diseases [15,16,17]. Wang discovered that patients with HM have lower values of Eloc and Cp compared to healthy individuals, and there are significant changes in the white matter structural network. The information transmission efficiency in the brain remains relatively unchanged, with the influence of Lp being relatively minor. This suggests that abnormal visual information input may lead to changes in brain structure, resulting in functional reorganization [18]. However, research on HM using fMRI based on graph theoretical analysis techniques is still in its early stages, and it is currently unclear whether HM patients exhibit abnormal node centrality and functional connectivity. Using resting state functional magnetic resonance imaging (rs-fMRI) combined with graph theoretical analysis methods can comprehensively study the changes in the complex networks of HM at the whole-brain level.

The aim of our study is to investigate the topological organization differences in brain functional connectivity among HM patients. We hypothesize that visual impairment, emotional processing, and cognitive dysfunction in HM patients may be related to the disruption of network topology organization.

Materials and methods

Participants

During the period from August to December 2021, the First Affiliated Hospital of Nanchang University recruited a total of 385 participants, of which 254 were excluded. Among the remaining 141 eligible subjects, there were 82 HM patients and 59 healthy controls, who were matched according to their gender, age, and educational background.

Subject Requirements: (1) Myopia of both eyes greater than 600 degrees; (2) Corrected visual acuity greater than 1.0 (3) No special findings on ophthalmic examinations such as fundus photography, B-scan ultrasound, optical coherence tomography (4) Ability to tolerate magnetic resonance imaging (MRI) (5) No other special illnesses.

Exclusion criteria: (1) history of significant eye diseases, such as eye trauma, retinal detachment, diabetic retinopathy, glaucoma, etc.; (2) history of previous eye surgery; (3) presence of brain diseases, such as brain hemorrhage.

Inclusion criteria for HCs: (1) visual acuity measured by eye examination greater than 1.0; (2) tolerance to MRI examination (3) absence of any eye or systemic diseases.

MRI acquisition

All participants underwent scanning using the 3-TeslaTrio MRI scanner system (Trio Tim, Siemens Healthineers, Erlangen, Germany) at the First Affiliated Hospital of Nanchang University in China.T1 weighted imaging scanning parameters: repetition time (TR) = 1900 ms, echo time (TE) = 2.26 ms, flip angle = 12°, number of sagittal slices = 176, field of view (FOV) = 240 × 240 mm2, slice thickness = 1 mm without gap, acquisition matrix = 256 × 256.

The Turbo spin-echo sequence is used for T2WI scanning, with the following parameters: TR = 5100 ms, TE = 117 ms, number of axial slices = 22, slice thickness = 6.5 mm, FOV = 240 × 240 mm2, matrix = 416 × 416, echo train length = 11.

All participants were informed to close their eyes during the MRI scan, wear earplugs, relax their bodies, and not engage in any thoughts.

Data preprocessing

The fMRI data preprocessing was carried out using the Data Processing & Analysis of Brain Imaging toolbox, which is based on SPM8 implemented in MATLAB R2013a. Follow these steps: (1) Convert data from DICOM format to NIFTI format, and remove data from the first 10 time points to avoid the influence of factors such as machine instability during initial startup. (2) The remaining images were time-corrected, volume analysis of function, and head motion correction. Data that have undergone calibration and still have a translation motion of more than 2 mm or rotation motion of more than 2 degrees were removed. (3) Spatial normalization: T1 structural co-registration segmentation and alignment method was used to align the data, and the obtained resting-state data were resampled to a voxel size of 3 mm x 3 mm x 3 mm. (4) Spatial smoothing: spatial smoothing was performed on the standardized imaging data, and the half-height full-width was set to 6 mm. (5) De-linear drift: The linear regression method is used to remove the linear drift phenomenon caused by thermal noise, such as the heat generated by the MRI scanner. (6) Bandpass filtering (from 0.1 to 0.8 Hz) was used to extract imaging data within this frequency range, thus eliminating the influence of physiological noise.

Network construction

Using the graph theory analysis tool GRETNA(http://www.nitrc.org/projects/gretna/), individual brain regions are usually referred to as nodes, and connections between these regions are referred to as edges. Based on the automatic anatomical labeling (AAL) atlas (Table S1), the brain is symmetrically divided into 90 regions of interest (ROI) [46]. Then, the average time series between all nodes is calculated to define the edges in the network, resulting in a 90 × 90 correlation matrix. This matrix is converted to a binary matrix, and Fisher’s r-to-z transformation is applied to each matrix to improve the data distribution for parametric statistical analysis. The method of selecting based on a sparse degree threshold is used, and the range of sparse degree is set to 0.05 ~ 0.50, with a step size of 0.01, to estimate the sparse characteristics of the small world and the number of false edges as little as possible. We calculate the Area Under the Curve (AUC) of network global and node topological attribute indicators within this sparsity range to perform statistical descriptions to reduce the impact of potential biases from any single threshold value.

Network analysis

After constructing the corresponding functional network, the global and node topology attribute metrics are calculated. All metrics concepts are listed in Table 1.

Table 1 Descriptions of the network metrics examined in this study
Full size table

The global network metrics included: (1) clustering coefficient (Cp); (2) characteristic path length (Lp) (3) normalized clustering coefficient (γ) (4) normalized characteristic path length (λ) (5) small-worldness (σ) (6) global efficiency (Eglob) (7) local efficiency (Eloc).

The nodal network metrics included: (1) Nodal Efficiency (Ne); (2) Degree centrality (Dc) (3) Betweeness centrality (Bc) (4) Nodal Clustering Coefficient (NCp) (5) Nodal Local Efficiency (NLe) (6) Nodal Shortest Path (NLp).

Statistical analysis

Using χ² test and independent sample t-test, the demographic and clinical data of two groups of subjects were statistically analyzed with SPSS software version 29.0.

In the range of 0.05–0.50 for sparsity, with a step size of 0.01, we used a two-sample t-test to compare differences between two groups for 7 global network indicators(P < 0.05)and 6 regional node parameters(P < 0.05, Bonferroni-corrected), and calculate the AUC for each indicator for statistical analysis.

Using a network-based statistics (NBS) method(http://www.nitrc.org/projects/nbs/) to analyze the brain functional connectivity regions that display nodal characteristics differences between groups, and calculated the significance of each region using independent two-sample t-tests and nonparametric permutation methods with 10,000 permutations.

Results

Basic information

This study included a total of 82 HM patients (37 males, 45 females, with a mean age of 26.23 ± 5.462 years) and 59 HCs (24 males, 35 females, with a mean age of 25.78 ± 3.102 years). Demographic and clinical characteristics are shown in Table 2.

Table 2 Demographic and clinical characteristics of HM and HC groups
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Small-world changes in brain functional networks.

The thresholds between defined as 0.05 and 0.50 were used, with a step size of 0.01, and both the HM group and HC group demonstrated small-world network properties (γ > 1, λ ≈ 1, σ > 1). There were significant reductions in the γ(P = 0.0101) and σ(P = 0.0168) in HM patients compared to the HC group, with no significant differences observed between the two groups for the remaining topological small-world parameters. (Table 3; Fig. 1).

Table 3 Significant differences in integrated global network parameters between two groups
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Fig. 1
figure 1

Mean functional connectivity strengths and global network properties between the HM and HC groups. The figure shows that within the defined sparsity range (0.05 < S < 0.50), both the HM and HC groups exhibit typical small-world properties (γ = Cpreal/Cprand>1,λ = Lpreal/Lprand≈1). The circles represent the average values of HM and HC, and the error bars represent the standard error of each group’s states.(A) The mean Pearson correlation matrices of the HC and HM group.(B-F) Small-world network architecture and AUC value.(G)AUC value of network efficiency . Abbreviations: Cp= clustering coefficient; Lp= characteristic path length; λ= normalized shortest path length;γ= normalized clustering coefficient;σ=small-worldness; Eglob = global efficiency; Eloc = local efficiency AUC, area under curve; HM, High myopia; HC, health control

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Nodal changes in brain functional networks.

Comparison between two groups on five nodal metrics showed no statistically significant difference except for Nodal Local Efficiency. HM patients showed significantly lower nodal centralities in the right Amygdala compared to HC (P < 0.001, Bonferroni-corrected). (Table 4; Fig. 2).

Table 4 Between-group differences in nodal characteristics in patients with HM and HC
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Fig. 2
figure 2

Significant nodal characteristics map the differences between two groups Notes:A (Left Sagittal View from Midline): The brain’s left hemisphere viewed from the midline, split along the sagittal plane, looking outward. B (Right Sagittal View from Midline): The brain’s right hemisphere viewed from the midline, split along the sagittal plane, looking outward. C (Left Lateral Sagittal View): The brain’s left hemisphere viewed from the outside, split along the sagittal plane, looking inward. D (Right Lateral Sagittal View): The brain’s right hemisphere viewed from the outside, split along the sagittal plane, looking inward Blued color indicates decreased nodal characteristics (HM < HC) (P < 0.001, Bonferroni-corrected). The HM group had a significant decreased nodal centralities in the AMYG.R

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Graph theory analysis of HM related functional connection alterations.

Based on the NBS research method, it was found that there were significant changes in 16 nodes and 19 functional connections in the HM group compared to the HC group (P < 0.01).HM patients displayed significantly higher FC values in the regions of Lenticular nucleus-putamen(PUT), Parahippocampal gyrus(PHG), Median cingulate and paracingulate gyri(DCG), Superior temporal gyrus(STG), Heschl gyrus(HES), Postcentral gyrus(PoCG)and Precental gyrus(PreCG) compared to HC, while there were significant reductions in FC values in the regions of Lenticular nucleus-putamen(PUT), Inferior frontal gyrus- opercular part(IFGoperc), Caudate nucleus(CAU)and Lenticular nucleus-pallidum(PAL). (Table 5; Fig. 3).

Table 5 Significantly altered functional connectivities in HM patients compared with HCs
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Fig. 3
figure 3

Graph theory analysis of alterations in brain functional connectivity. (A)Compared to the HC group, the increased brain functional connectivity in patients with HM. (B)Compared to HC group, patients with HM have reduced brain functional connectivity. Notes NBS method identified a significantly altered network (16 nodes and 19 connections) in HM group relative to HCs. (P < 0.01). The thickness of the line represents the strength of the functional connection. Abbreviations: PHG, Parahippocampal gyrus; DCG, Median cingulate and paracingulate gyri; PreCG, Precental gyrus; PoCG, Postcentral gyrus; IFGoperc, Inferior frontal gyrus, opercular part; CAU, Caudate nucleus; PUT, Lenticular nucleus, putamen; HES, Heschl gyrus; STG, Superior temporal gyrus; PAL, Lenticular nucleus, pallidum ; R, right; L, left

Full size image

Discussion

We constructed a network matrix and used graph theory analysis to explore the topological attributes of abnormal resting-state brain functional networks in HM patients. The research results showed that compared with healthy controls: (1) γ and σ decreased in HM patients; (2) the nodal centralities of the right amygdala decreased; and (3) there was an increase in FC between SN and SMN and an decrease in functional connectivity between regions of the basal ganglia network.

Alterations in global network topology attributes

The working principle of the brain is to quickly understand and process information through the integration and separation of functions [19]. Shu has suggested that small-world properties can achieve the optimal balance between the whole and the local in the brain network, providing a basis for efficient information communication [20]. Cao believes that small-world properties can change accordingly with the occurrence of diseases [21]. Our findings coincide with this notion. In this study, we found that both HM patients and healthy controls exhibit small-world properties within the range of sparsity from 0.05 to 0.50. However, the between-group comparison of global topological indicators, measured by the AUC value, showed that HM patients had significantly decreased σ and γ values. γ is related to the brain network’s ability to separate functions [22, 23]. Therefore, lower γ and σ values may indicate impaired small-world properties of the brain network, disrupted functional segregation, and compromised dynamic balance of the brain network.

Alterations in network node topology attributes

In addition to global topological indicators, certain nodes within the brain network have shown changes, indicating abnormalities in the transmission and integration functions of these nodes. These alterations may result in changes to the associated functional networks [27]. By comparing the AUC values of node indicators Ne, NLe, NCp, NLp, Bc, and Dc in two groups of subjects, this study discovered a significant reduction in NLe values specifically within the right amygdala of patients with HM. The amygdala, located in the posterior and medial temporal lobe, is an important component of the SN, a region responsible for generating and processing emotions. It is involved in bottom-up attention towards emotional stimuli and plays a crucial role in cognitive, memory, learning, and decision-making processes [24,25,26]. Numerous studies consistently demonstrate the strong association between the amygdala and the hippocampus [28]. Additionally, our previous research has also identified functional connectivity abnormalities in the hippocampus [13]. Hence, it is hypothesized that high myopia patients may exhibit impairments in cognitive function, emotion regulation, and memory, attributable to the reduced connectivity within the SN.

Functional connectivity

HM patients exhibited varying degrees of functional connectivity changes in 16 nodes and 19 connections. We observed a significant decrease in connectivity within the prefrontal lobe (IFGoperc) and subcortical regions (CAU, PUT, and PAL). The IFG belongs to the DMN and is closely related to the detection, regulation, and cognitive control of emotions. The changes in its activity are related to the abnormal visual regulation mechanism of HM patients [29,30,31]. The tail nucleus, shell nucleus, and putamen belong to the basal ganglia network, which plays a crucial role in visually guided decision-making and eye movement control. The damage to the basal ganglia network is also the basis for various movement disorders [32, 33, 41]. A study has found a significant relationship between DMN connectivity and regions within the basal ganglia, indicating that these systems jointly engage in associative learning and memory based on rewards, and may represent crucial connections necessary for adaptive cognition [42]. Shu found changes in the caudate nucleus and putamen in blind patients, emphasizing the importance of vision loss in motor control [20]. Similarly, Zikou discovered a decrease in fractional anisotropy (FA) in the caudate nucleus and putamen of glaucoma patients [34]. Currently, there is no research confirming structural changes in the striatum of HM patients. However, we speculate that HM patients may experience cognitive decision-making difficulties and weakness in eye movements.

In addition, there was an increase in FC between the PUT, PHG, DCG, STG, HES, PoCG, and PreCG groups in the HM group, mainly concentrated in the SN and SMN. The SMN primarily synchronizes the processing of sensory input to form a shared sensory experience, whereas the SN plays an indispensable role in the processing of sensorimotor information, overall cognition, and coordination between emotions, pain, and bodily actions [43, 44]. A study found that the density of FC in the SN of individuals with schizophrenia has decreased, while the dynamic increase in connectivity within the SN indicates that Sn dysfunction may be due to a reduction in the stability of internal connections within the network [45]. PHG, STG, and HES are part of the temporal lobe, which is primarily responsible for language function and auditory perception, as well as being involved in long-term memory and emotions [35, 36]. Wu found a reduction in cortical surface thickness in the right STG of HM patients, indicating a possible association with vision [37]. Huang discovered that HM patients had significantly increased GMV in the right parahippocampal gyrus, which could contribute to memory impairments in HM patients [38]. DCG and PreCG are part of the frontal lobe. Wang observed significant changes in BC in the dorsal DCG of HM patients, suggesting alterations in processing functions related to memory, vision, and attention [18]. Our research found that there are widespread connectivity changes in the SMN and SN of the HM group, which may lead to further motor dysfunction and cognitive-emotional deficits.

Limitations

In fact, our study does have some limitations. Firstly, various factors that participants encountered during MRI-related examinations may have influenced the results. Secondly, due to the limited number of participants ultimately included, we did not analyze the correlation between the clinical manifestations of HM and the topological brain tissue characteristics. Additionally, we acknowledge that the impact of the duration of high myopia on brain function changes, which we failed to delve deeply into, may differ from our current findings. When we included demographic variables, we did not include many important variables due to missing data, such as blood pressure, height and BMI, which are all important factors that may affect our results. Therefore, in future studies, we will try to minimize these problems.

Conclusion

Research has shown that, compared to the HC group, patients with HM experience abnormal changes in the topological organization of their brain’s functional connectivity. This mainly manifests in the patients with HM exhibiting reduced levels of γ and σ, a significant decrease in the centrality of the right amygdala node, and abnormal functional connectivity between SN, SMN, and basal ganglia networks. This also relates to a decline in vision and changes in emotional cognition in HM patients. These findings may also provide new perspectives for studying the pathological and physiological mechanisms of HM.

Data availability

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

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Acknowledgements

We would like to express our gratitude to Director Zeng of the Radiology Department and Dr. Wang Yuanyuan from The First Affiliated Hospital of Nanchang University for their significant contributions to this experiment. Their support ensured the smooth progress of our study and provided a solid foundation for future research.

Funding

This study received support from the National Natural Science Foundation of China (Grant No. 82160207), the Technology Plan of Jiangxi Provincial Health and Health Commission (202130156), and the Science and Key Projects of Jiangxi Youth Science Fund (No. 20202ACBL216008).

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Author notes

  1. Bin Wei and Xin Huang contributed equally to this work.

Authors and Affiliations

  1. Department of Ophthalmology, Jiangxi Medical College, Nanchang University, The 1st Affiliated Hospital, Nanchang, Jiangxi, People’s Republic of China

    Bin Wei, Yu Ji, Wen-Wen Fu, Qi Cheng, Ben-Liang Shu, Qin-Yi Huang, Hua Chai, Lin Zhou, Hao-Yu Yuan & Xiao-rong Wu

  2. Department of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China

    Xin Huang

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  1. Bin Wei
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Contributions

Author contributions included conception and study design (B.W, X.W and X.H), data collection or acquisition (Y.J, W.F and Q.C), statistical analysis (B.W, X.H, B.S and Q.H), interpretation of results (L.Z, H.Y, H.C and X.W), drafting the manuscript work or revising it critically for important intellectual content (B.W, X.H, Y.J, Q.C and X.W) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Corresponding author

Correspondence to Xiao-rong Wu.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was approved by the Medical Research Ethics Committee of The First Affliated Hospital of Nanchang University. All data generated or analyzed during this study from patients are included in this published article. All Patients and guardians were provided consent to publish these pictures.

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Written informed consents were provided by all the participants.

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Wei, B., Huang, X., Ji, Y. et al. Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia. BMC Ophthalmol 24, 315 (2024). https://doi.org/10.1186/s12886-024-03592-6

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BMC Ophthalmology

ISSN: 1471-2415

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