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

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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 γ (P = 0.0101) and 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 SN and 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 saliency network (SN) , Sensorimotor Network (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. Currently, there is a lack of research on the integration of graph theory analysis and functional magnetic resonance imaging to investigate the changes in brain functional region connectivity in high myopia. In order to improve the diagnosis of high myopia and provide timely prevention of neurological diseases caused by changes in brain function. To provide new perspectives for future research on the pathological and physiological mechanisms of high myopia. high myopia graph theory network-based statistics brain function topological organization Figures Figure 1 Figure 2 Figure 3 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 – 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 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 FC status between brain regions [ 9 , 10 ] . Cheng et al observed a significant reduction in VMHC values in the fusiform gyrus and putamen of HM patients, indicating that abnormal visual experience may affect their visual recognition function [ 11 ] . Wang et al 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 [ 40 ] . Additionally, Zhai et al 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 , 41 ] . The global attributes of the network are the description of the connectivity of the whole-brain network, which mainly include clustering coefficient (Cp) and local efficiency (E loc ) to measure the local information transfer efficiency and fault tolerance, shortest path length (Lp) and global efficiency (E glob ) 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 [ 42 ] . 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 – 17 ] . Wang et al. 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 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 the 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 education background. Subject Requirements: (1) Visual acuity 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 mm 2 , matrix = 416 × 416, echo train length = 11. All participants were informed to close their eyes during the MRI scan, wear earplugs, relax their body, 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 2mm 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 3mm x 3mm x 3mm. 4）Spatial smoothing: spatial smoothing was performed on the standardized imaging data, and the half-height full-width was set to 6mm. 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 0.8Hz) 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). Then, the average time series between all nodes are calculated to define the edges in the network, resulting in a 90x90 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 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 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 (E glob )；7）local efficiency (E loc ). 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 89 HM patients (41 males, 48 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. 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, Figure 1). 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 Amygdala compared to HC (P<0.001, Bonferroni-corrected).(Table 4, Figure 2). 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, Figure 3). 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 is an increase in FC between SN and SMN, and a 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 et al have suggested that small-world properties can achieve optimal balance between the whole and the local in the brain network, providing a basis for efficient information communication [ 20 ] . Cao et al believe 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 – 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 – 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 , 43 ] . 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 [ 44 ] . Shu et al found changes in the caudate nucleus and putamen in blind patients, emphasizing the importance of vision loss in motor control [ 34 ] . Similarly, Zikou et al discovered a decrease in fractional anisotropy (FA) in the caudate nucleus and putamen of glaucoma patients [ 35 ] . Currently, there is no research confirming structural changes in the striatum of HM patients. But, 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 [ 45 , 46 ] . 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 [ 47 ] . 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 [ 36 , 37 ] . Wu et al found a reduction in cortical surface thickness in the right STG of HM patients, indicating a possible association with vision [ 38 ] . Huang et al discovered that HM patients had significantly increased GMV in the right parahippocampal gyrus, which could contribute to memory impairments in HM patients [ 39 ] . DCG and PreCG are part of the frontal lobe. Wang et al 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 Indeed, there are some limitations in our research. First of all, various factors during the MRI-related examination of the subjects may affect the results. Secondly, due to the limited number of subjects finally included, we did not analyze the correlation between HM clinical manifestations and topological brain tissue characteristics. In future studies, we will strive to minimize these issues. 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. Declarations Acknowledgments This is a short text to acknowledge the contributions of specifc colleagues, institutions, or agencies that aided the efforts of the authors. Author 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). 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). Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval 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. Consent to participate Written informed consents were provided by all the participants. Consent for publication If the manuscript is accepted, we agree to publish it. Conflicts of interest The authors declare no competing interests. References Pan CW, Ramamurthy D, Saw SM. Worldwide prevalence and risk factors for myopia. Ophthalmic Physiol Opt. 2012 Jan;32(1):3-16. doi: 10.1111/j.1475-1313.2011.00884.x. PMID: 22150586. Morgan IG, Ohno-Matsui K, Saw SM. Myopia. Lancet. 2012 May 5;379(9827):1739-48. doi: 10.1016/S0140-6736(12)60272-4. PMID: 22559900. 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Tables Table 1 Descriptions of the network metrics examined in this study Attribute Character Description Global metrics Clustering coefficient Cp The extent of local interconnectivity or cliquishness of a network Characteristic path length Lp The extent of overall communication efficiency of a network Gamma γ The deviation of Cp of a network from those of surrogate random networks Lambda λ The deviation of Lp of a network from those of surrogate random networks Sigma σ The small-worldness indicating the extent of a network between randomness and order Global efficiency E glob The ability of a network to transmit information at the local level Local efficiency E loc The ability of a network to transmit information at the global level Nodal metrics NodalEfficiency Ne The ability of a node to propagate information with the other nodes in a network Degree centrality Dc The number of edges linked to a node Betweeness centrality Bc The influence that one node has over the flow of information between all other nodes in the network Nodal Clustering Coefficient NCp The ratio of the actual number of edges in the subnetwork to the maximum possible number of edges. NodalLocalEfficiency NLe The efficiency of local information transfer between nodes. NodalShortestPath NLp The shortest connection between two nodes. Abbreviations: Cp, clustering coefficient; Lp, characteristic path length; γ, normalized clustering coefficient; λ, normalized characteristic path length; σ, scalar smallworldness;E glob , global efficiency; E loc , local efficiency Table 2 Demographic and clinical characteristics of HM and HC groups. Characteristic HM HC Men/women 41/48 24/35 Age (years) 26.23 ± 5.462 25.78 ± 3.102 ALM (OD) 26.67 ± 0.874 23.90 ± 0.971 ALM (OS) 26.58 ± 0.985 23.74 ± 0.693 Abbreviations: HM, high myopia; HC, healthy control; ALM, axial length; OD, oculus dexter; OS, oculus sinister. Table 3 Significant differences in integrated global network parameters between two groups Network parameters HM (mean) HC (mean) t -Values p -Values Cp 0.251 0.254 -1.317 0.1897 Lp 0.836 0.835 1.908 0.8489 γ 1.065 1.096 -2.608 0.0101* λ 0.490 0.490 -2.455 0.8063 σ 0.944 0.968 -2.419 0.0168* E glob 0.263 0.262 5.463 0.5856 E loc 0.337 0.340 -1.754 0.0814 Notes: The small-world parameters and network efficiency parameters comparisons in patients with HM and HCs. Both the HM and HCs exhibited small-world attribute. The HM group showed decreased exhibited increased Lp, E glob , and decreasedCp, E loc γ, σ, and λ. The symbol “*” denotes p<0.05. (two sample t-tests, p<0.05). The significance of bold values indicate the p<0.05 and the corresponding t-values. Abbreviations: Cp, clustering coefficient; Lp,characteristic path length; γ, normalized clustering coefficient; λ, normalized characteristic path length; σ,scalar smallworldness; E glob , global efficiency; E loc , local efficiency; HM, high myopia; HC, health control. Table 4 Between-group differences in nodal characteristics in patients with HM and HC Brain regions Nodal Local Efficiency t-Values p-Values HM<HC Amygdala -3.886 0.0001 Note: Bonferroni correction was applied to each nodal characteristic, the p-value thresholds for nodal characteristics were 0.01.The significance of bold values indicate the p<0.001. Abbreviations: HM, high myopia; HC, health control. Table 5 Significantly altered functional connectivities in HM patients compared with HCs Region 1 Category Region 2 Category t -Values p -Values PHG.R Temporal PoCG.L Parietal 3.407 0.0009 DCG.L Frontal PUT.L Subcortical 3.442 0.0008 PreCG.R Frontal HES.R Temporal 3.615 0.0004 DCG.L Frontal HES.R Temporal 3.639 0.0004 DCG.R Frontal HES.R Temporal 4.389 ＜0.0001 PoCG.L Parietal HES.R Temporal 3.473 0.0007 DCG.L Frontal STG.L Temporal 3.819 0.0002 DCG.R Frontal STG.L Temporal 3.774 0.0002 DCG.L Frontal STG.R Temporal 4.772 ＜0.0001 DCG.R Frontal STG.R Temporal 4.102 0.0001 IFGoperc.L Prefontal CAU.L Subcortical -3.522 0.0006 IFGoperc.R Prefontal CAU.L Subcortical -3.426 0.0008 CAU.R Subcortical PUT.L Subcortical -3.829 0.0002 CAU.R Subcortical PUT.R Subcortical -3.940 0.0001 PUT.L Subcortical PUT.R Subcortical -5.570 ＜0.0001 CAU.L Subcortical PAL.L Subcortical -3.572 0.0005 CAU.R Subcortical PAL.L Subcortical -3.929 0.0001 CAU.L Subcortical PAL.R Subcortical -3.812 0.0002 CAU.R Subcortical PAL.R Subcortical -4.176 0.0001 Note: NBS method identified a significantly altered network (16 nodes and 19 connections) in HM group relative to HCs. (P<0.01). 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;NBS, network-based statistics;HM, high myopia; HC, health control. Additional Declarations No competing interests reported. Supplementary Files BINWEITableS1.docx Supplementary Information Network Construction :Table S1: Abbreviations for the regions in the AAL-atlas. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3974165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274011350,"identity":"13119dbd-d30d-4385-be02-29549a24c93e","order_by":0,"name":"Bin Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wei","suffix":""},{"id":274011351,"identity":"44b4c399-57e2-4bfb-a565-ab13fe20312a","order_by":1,"name":"Xin Huang","email":"","orcid":"","institution":"Jiangxi Provincial 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Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIie3RsQrCMBCA4YuBTCd1bKkPESkEh0AfxKUgZOvu0EEQ4ujaPo4EmqXQVbdMzu0TqB0dpHFzyDffP9wdQBD8J+IGLjGi9Op8E7qpD2qdnNme+yYsxc5I3qNYec3zW/lIGk0xMyAAKrmbTzqbbUfNUBhQDlpVHucSYTW5NxqnxHJyNB6JYZAudYzZiejYL7F68V6fI6eU+SV5105HLjA2jPLCZ5ekVtMrn3l06Uc3VHI++VT8Nh4EQRB88wL9NDwUb9it9AAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China","correspondingAuthor":true,"prefix":"","firstName":"Xiao-Rong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-02-21 01:15:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3974165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3974165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51718818,"identity":"a4b4a5dc-9a0c-4f25-af2e-168df0fddd78","added_by":"auto","created_at":"2024-02-27 21:34:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254764,"visible":true,"origin":"","legend":"\u003cp\u003eMean functional connectivity strengths and global network properties between the HM and HC groups. The figure shows that within the defined sparsity range (0.05\u0026lt;S\u0026lt;0.50), both the HM and HC groups exhibit typical small-world properties (γ=Cp\u003csub\u003ereal\u003c/sub\u003e/Cp\u003csub\u003erand\u003c/sub\u003e\u0026gt;1,λ=Lp\u003csub\u003ereal\u003c/sub\u003e/Lp\u003csub\u003erand\u003c/sub\u003e≈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.\u003c/p\u003e\n\u003cp\u003eThe symbol “*” denotes statistical significance（P\u0026lt;0.05）.\u003c/p\u003e\n\u003cp\u003eAbbreviations: Cp= clustering coefficient; Lp= characteristic path length; λ= normalized shortest path length;γ= normalized clustering coefficient;σ=small-worldness；E\u003csub\u003eglob\u003c/sub\u003e = global efficiency; E\u003csub\u003eloc \u003c/sub\u003e= local efficiency；AUC, area under curve; HM,High myopia; HC,health control.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3974165/v1/f8bc06988a5da7773cb5df0a.jpg"},{"id":51718819,"identity":"0b49fab1-a704-4bce-a985-20057c3b7d0a","added_by":"auto","created_at":"2024-02-27 21:34:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209563,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant nodal characteristics map the differences between two groups.\u003c/p\u003e\n\u003cp\u003eNotes:Blued color indicates decreased nodal characteristics (HM\u0026lt;HC) (P\u0026lt;0.001, Bonferroni-corrected). The HM group had a significant decreased nodal centralities in the AMYG.R.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3974165/v1/6bd0006f0283d60a73423291.jpg"},{"id":51718816,"identity":"6d2c22e7-2050-45a8-8d00-a4a84f0f04d6","added_by":"auto","created_at":"2024-02-27 21:34:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":601910,"visible":true,"origin":"","legend":"\u003cp\u003eGraph 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.\u003c/p\u003e\n\u003cp\u003eNotes: NBS method identified a significantly altered network (16 nodes and 19 connections) in HM group relative to HCs. (P\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u003c/p\u003e\n\u003cp\u003ePHG,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.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3974165/v1/402ee35bfe9be31cef48c20a.jpg"},{"id":52696653,"identity":"eff08026-27b8-42d8-b430-f99ed821741e","added_by":"auto","created_at":"2024-03-14 16:14:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3974165/v1/9849dee6-e6ee-4443-8e18-d9e887e08a1d.pdf"},{"id":51718817,"identity":"f6a6992f-253b-462e-adcb-a38d1cb863ac","added_by":"auto","created_at":"2024-02-27 21:34:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12371,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e\n\u003cp\u003eNetwork Construction :Table S1: Abbreviations for the regions in the AAL-atlas.\u003c/p\u003e","description":"","filename":"BINWEITableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3974165/v1/63440b55a909d7de3e851a48.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAnalyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 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 \u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. 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\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, current research has only provided limited understanding of the topological structure of brain networks in HM patients.\u003c/p\u003e \u003cp\u003eWith 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 FC status between brain regions \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Cheng et al observed a significant reduction in VMHC values in the fusiform gyrus and putamen of HM patients, indicating that abnormal visual experience may affect their visual recognition function \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Wang et al 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 \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Additionally, Zhai et al 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 \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003eThe brain is a complex network that facilitates the separation and integration of information processing \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The global attributes of the network are the description of the connectivity of the whole-brain network, which mainly include clustering coefficient (Cp) and local efficiency (E\u003csub\u003eloc\u003c/sub\u003e) to measure the local information transfer efficiency and fault tolerance, shortest path length (Lp) and global efficiency (E\u003csub\u003eglob\u003c/sub\u003e) 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 \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Wang et al. 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 \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. 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 rs-fMRI combined with graph theoretical analysis methods can comprehensively study the changes in the complex networks of HM at the whole-brain level.\u003c/p\u003e \u003cp\u003eThe aim of our study is to investigate the topological organization differences in brain functional connectivity among HM patients. We hypothesize that the visual impairment, emotional processing, and cognitive dysfunction in HM patients may be related to the disruption of network topology organization.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eParticipants\u003c/p\u003e\n\u003cp\u003eDuring 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 education background.\u003c/p\u003e\n\u003cp\u003eSubject Requirements: (1) Visual acuity 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.\u003c/p\u003e\n\u003cp\u003eExclusion 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.\u003c/p\u003e\n\u003cp\u003eInclusion 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.\u003c/p\u003e\n\u003cp\u003eMRI Acquisition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll 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\u0026deg;,number of sagittal slices = 176, field of view (FOV) = 240 \u0026times; 240 mm2,slice thickness = 1 mm without gap, acquisition matrix = 256 \u0026times; 256.\u003c/p\u003e\n\u003cp\u003eThe 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 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e, matrix = 416 \u0026times; 416, echo train length = 11.\u003c/p\u003e\n\u003cp\u003eAll participants were informed to close their eyes during the MRI scan, wear earplugs, relax their body, and not engage in any thoughts.\u003c/p\u003e\n\u003cp\u003eData Preprocessing\u003c/p\u003e\n\u003cp\u003eThe fMRI data preprocessing was carried out using the Data Processing \u0026amp; 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 2mm or rotation motion of more than 2 degrees were removed.\u0026nbsp;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 3mm x 3mm x 3mm. 4）Spatial smoothing: spatial smoothing was performed on the standardized imaging data, and the half-height full-width was set to 6mm. 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 0.8Hz) was used to extract imaging data within this frequency range, thus eliminating the influence of physiological noise.\u003c/p\u003e\n\u003cp\u003eNetwork Construction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing 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). Then, the average time series between all nodes are calculated to define the edges in the network, resulting in a 90x90 correlation matrix. This matrix is converted to a binary matrix, and Fisher\u0026apos;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 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.\u003c/p\u003e\n\u003cp\u003eNetwork Analysis\u003c/p\u003e\n\u003cp\u003eAfter constructing the corresponding functional network, the global and node topology attribute metrics are calculated.All metrics concepts are listed in Table 1\u003c/p\u003e\n\u003cp\u003eThe global network metrics included：1）\u0026nbsp;clustering coefficient (Cp)；2）characteristic path length (Lp)；3）normalized clustering coefficient (\u0026gamma;)\u0026nbsp;；4）normalized characteristic path length (\u0026lambda;)；5）small-worldness (\u0026sigma;)；6）global efficiency (E\u003csub\u003eglob\u003c/sub\u003e)；7）local efficiency (E\u003csub\u003eloc\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eThe 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).\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eUsing\u0026nbsp;\u0026chi;\u0026sup2; test and\u0026nbsp;independent sample t-test, the demographic and clinical data of two groups of subjects were statistically analyzed with SPSS software version 29.0.\u003c/p\u003e\n\u003cp\u003eIn 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\u0026lt;0.05）and 6 regional node parameters（P\u0026lt;0.05, Bonferroni-corrected）, and calculate the AUC for each indicator for statistical analysis.\u003c/p\u003e\n\u003cp\u003eUsing 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.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBasic Information\u003c/p\u003e\n\u003cp\u003eThis study included a total of 89 HM patients (41 males, 48 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.\u003c/p\u003e\n\u003cp\u003eSmall-world changes in brain functional networks\u003c/p\u003e\n\u003cp\u003eThe 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\u0026gt; 1, λ≈1, σ\u0026gt; \u0026nbsp;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, Figure 1).\u003c/p\u003e\n\u003cp\u003eNodal changes in brain functional networks\u003c/p\u003e\n\u003cp\u003eComparison 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 Amygdala compared to HC (P\u0026lt;0.001, Bonferroni-corrected).(Table 4, Figure 2).\u003c/p\u003e\n\u003cp\u003eGraph theory analysis of HM related functional connection alterations\u003c/p\u003e\n\u003cp\u003eBased 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\u0026lt;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, Figure 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe 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 is an increase in FC between SN and SMN, and a decrease in functional connectivity between regions of the basal ganglia network.\u003c/p\u003e \u003cp\u003eAlterations in global network topology attributes\u003c/p\u003e \u003cp\u003eThe working principle of the brain is to quickly understand and process information through the integration and separation of functions \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Shu et al have suggested that small-world properties can achieve optimal balance between the whole and the local in the brain network, providing a basis for efficient information communication \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Cao et al believe that small-world properties can change accordingly with the occurrence of diseases \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003eAlterations in network node topology attributes.\u003c/p\u003e \u003cp\u003eIn 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 \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Numerous studies consistently demonstrate the strong association between the amygdala and the hippocampus\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Additionally, our previous research has also identified functional connectivity abnormalities in the hippocampus \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003eFunctional connectivity\u003c/p\u003e \u003cp\u003eHM 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 \u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Shu et al found changes in the caudate nucleus and putamen in blind patients, emphasizing the importance of vision loss in motor control \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Similarly, Zikou et al discovered a decrease in fractional anisotropy (FA) in the caudate nucleus and putamen of glaucoma patients \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Currently, there is no research confirming structural changes in the striatum of HM patients. But, we speculate that HM patients may experience cognitive decision-making difficulties and weakness in eye movements.\u003c/p\u003e \u003cp\u003eIn 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 \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. 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 \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Wu et al found a reduction in cortical surface thickness in the right STG of HM patients, indicating a possible association with vision \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Huang et al discovered that HM patients had significantly increased GMV in the right parahippocampal gyrus, which could contribute to memory impairments in HM patients \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. DCG and PreCG are part of the frontal lobe. Wang et al observed significant changes in BC in the dorsal DCG of HM patients, suggesting alterations in processing functions related to memory, vision, and attention \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eIndeed, there are some limitations in our research. First of all, various factors during the MRI-related examination of the subjects may affect the results. Secondly, due to the limited number of subjects finally included, we did not analyze the correlation between HM clinical manifestations and topological brain tissue characteristics. In future studies, we will strive to minimize these issues.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResearch has shown that, compared to the HC group, patients with HM experience abnormal changes in the topological organization of their brain\u0026rsquo;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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a short text to acknowledge the contributions of specifc colleagues, institutions, or agencies that aided the efforts of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consents were provided by all the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf the manuscript is accepted, we agree to publish it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePan CW, Ramamurthy D, Saw SM. Worldwide prevalence and risk factors for myopia. Ophthalmic Physiol Opt. 2012 Jan;32(1):3-16. doi: 10.1111/j.1475-1313.2011.00884.x. PMID: 22150586.\u003c/li\u003e\n\u003cli\u003eMorgan IG, Ohno-Matsui K, Saw SM. Myopia. 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PMID: 27825906.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Descriptions of the network metrics examined in this study\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttribute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eGlobal metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eClustering coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eCp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe extent of local interconnectivity or cliquishness of a network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eCharacteristic path length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eLp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe extent of overall communication efficiency of a network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003e\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe deviation of Cp of a network from those of surrogate random networks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eLambda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003e\u0026lambda;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe deviation of Lp of a network from those of surrogate random networks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003e\u0026sigma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe small-worldness indicating the extent of a network between randomness and order\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eGlobal efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eE\u003csub\u003eglob\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe ability of a network to transmit information at the local level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eLocal efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eE\u003csub\u003eloc\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe ability of a network to transmit information at the global level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eNodal metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eNodalEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eNe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe ability of a node to propagate information with the other nodes in a network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eDegree centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eDc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe number of edges linked to a node\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eBetweeness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eBc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe influence that one node has over the flow of information between all other nodes in the network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eNodal Clustering Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eNCp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe ratio of the actual number of edges in the subnetwork to the maximum possible number of edges.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eNodalLocalEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eNLe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe efficiency of local information transfer between nodes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.521060842433698%\"\u003e\n \u003cp\u003eNodalShortestPath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480499219968799%\"\u003e\n \u003cp\u003eNLp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.9984399375975%\"\u003e\n \u003cp\u003eThe shortest connection between two nodes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eCp, clustering coefficient; Lp, characteristic path length; \u0026gamma;, normalized\u0026nbsp;clustering coefficient; \u0026lambda;, normalized characteristic path length; \u0026sigma;, scalar smallworldness;E\u003csub\u003eglob\u003c/sub\u003e, global efficiency; E\u003csub\u003eloc\u003c/sub\u003e, local efficiency\u003c/p\u003e\n\u003cp\u003eTable 2 Demographic and clinical characteristics of HM and HC groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"347\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMen/women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41/48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24/35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.23 \u0026plusmn; 5.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.78 \u0026plusmn; 3.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALM (OD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.67 \u0026plusmn; 0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.90 \u0026plusmn; 0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALM (OS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.58 \u0026plusmn; 0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.74 \u0026plusmn; 0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eHM, high myopia; HC, healthy control; ALM, axial length; OD, oculus dexter; OS, oculus sinister.\u003c/p\u003e\n\u003cp\u003eTable 3 Significant differences in integrated global network parameters between two groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetwork parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHM (mean)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC (mean)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e-Values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e-Values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003eCp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e-1.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e0.1897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003eLp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e1.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e0.8489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003e\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.608\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0101*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003e\u0026lambda;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e-2.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e0.8063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003e\u0026sigma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.419\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0168*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003eE\u003csub\u003eglob\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e5.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e0.5856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.466898954703833%\"\u003e\n \u003cp\u003eE\u003csub\u003eloc\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.99651567944251%\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.951219512195124%\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e-1.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\"\u003e\n \u003cp\u003e0.0814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e The small-world parameters and network efficiency parameters comparisons in patients with HM and HCs. Both the HM and HCs exhibited small-world attribute. The HM group showed decreased exhibited increased Lp, E\u003csub\u003eglob\u003c/sub\u003e, and decreasedCp,\u0026nbsp;E\u003csub\u003eloc\u003c/sub\u003e \u0026gamma;, \u0026sigma;, and \u0026lambda;. The symbol \u0026ldquo;*\u0026rdquo; denotes p\u0026lt;0.05. (two sample t-tests, p\u0026lt;0.05). The significance of bold values indicate the p\u0026lt;0.05 and the corresponding t-values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eCp, clustering coefficient; Lp,characteristic path length; \u0026gamma;, normalized clustering coefficient; \u0026lambda;, normalized characteristic path length; \u0026sigma;,scalar smallworldness;\u0026nbsp;E\u003csub\u003eglob\u003c/sub\u003e, global efficiency; E\u003csub\u003eloc\u003c/sub\u003e, local efficiency; HM, high myopia; HC, health control.\u003c/p\u003e\n\u003cp\u003eTable 4 Between-group differences in nodal characteristics in patients with HM and HC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"365\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.63013698630137%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.958904109589042%\"\u003e\n \u003cp\u003eBrain regions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.41095890410959%\" colspan=\"2\"\u003e\n \u003cp\u003eNodal Local Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et-Values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-Values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHM\u0026lt;HC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e-3.886\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Bonferroni correction was applied to each nodal characteristic, the p-value thresholds for nodal characteristics were 0.01.The significance of bold values indicate the p\u0026lt;0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e HM, high myopia; HC, health control.\u003c/p\u003e\n\u003cp\u003eTable 5 Significantly altered functional connectivities in HM patients compared with HCs\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e-Values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e-Values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePHG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePoCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eParietal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.407\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePUT.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.442\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePreCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eHES.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.615\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eHES.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.639\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eHES.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e4.389\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e＜0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePoCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eParietal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eHES.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.473\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eSTG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.819\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eSTG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e3.774\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eSTG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e4.772\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e＜0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eDCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eSTG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\"\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e4.102\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eIFGoperc.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003ePrefontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.522\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eIFGoperc.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\"\u003e\n \u003cp\u003ePrefontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.426\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePUT.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.829\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePUT.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePUT.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePUT.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-5.570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e＜0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePAL.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.572\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePAL.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.929\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePAL.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-3.812\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003eCAU.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.936254980079681%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.733067729083665%\"\u003e\n \u003cp\u003ePAL.R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.127490039840637%\" valign=\"top\"\u003e\n \u003cp\u003eSubcortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.13545816733068%\"\u003e\n \u003cp\u003e-4.176\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eNBS method identified a significantly altered network (16 nodes and 19 connections) in HM group relative to HCs. (P\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003ePHG,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;NBS, network-based statistics;HM, high myopia; HC, health control.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"high myopia, graph theory, network-based statistics, brain function, topological organization","lastPublishedDoi":"10.21203/rs.3.rs-3974165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3974165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 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 γ (P = 0.0101) and σ (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P\u0026lt;0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the SN and SMN in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHM 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 saliency network (SN) , Sensorimotor Network (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.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCurrently, there is a lack of research on the integration of graph theory analysis and functional magnetic resonance imaging to investigate the changes in brain functional region connectivity in high myopia.\u003c/li\u003e\n \u003cli\u003eIn order to improve the diagnosis of high myopia and provide timely prevention of neurological diseases caused by changes in brain function.\u003c/li\u003e\n \u003cli\u003eTo provide new perspectives for future research on the pathological and physiological mechanisms of high myopia.\u003c/li\u003e\n\u003c/ul\u003e","manuscriptTitle":"Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 21:34:25","doi":"10.21203/rs.3.rs-3974165/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2c1f5eb6-bc46-4eba-98ee-7e280f76b2fd","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-14T16:14:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-27 21:34:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3974165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3974165","identity":"rs-3974165","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}