Reorganization of Gray Matter Networks in Patients with Moyamoya Disease

Reorganization of Gray Matter Networks in Patients with Moyamoya Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Reorganization of Gray Matter Networks in Patients with Moyamoya Disease Huan Zhu, Peijiong Wang, Wenjie Li, Qihang Zhang, Chenyu Zhu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4461906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed. Compared to HCs, MMD patients exhibited decreased clustering coefficient (Cp) and local efficiency (Eloc). Ischemic patients showed decreased Eloc and increased characteristic path length (Lp) compared to asymptomatic and hemorrhagic patients. MMD patients had significant regional abnormalities, including decreased degree centrality (DC) in the left medial orbital superior frontal gyrus, left orbital inferior frontal gyrus, and right calcarine fissure and surrounding cortex. Increased DC was found in bilateral olfactory regions, with higher betweenness centrality (BC) in the right median cingulate, paracingulate fusiform gyrus, and left pallidum. Ischemic patients had lower BC in the right hippocampus compared to hemorrhagic patients, while hemorrhagic patients had decreased DC in the right triangular part of the inferior frontal gyrus compared to asymptomatic patients. Subnetworks related to MMD and white matter hyperintensity volume were identified. There is significant reorganization of gray matter networks in patients compared to HCs, and among different types of patients. Gray matter networks can effectively detect MMD-related brain structural changes. Moyamoya Disease Gray Matter Network Graph Theory Gray Matter Volume Network-Based Statistic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Moyamoya disease (MMD) is a rare cerebrovascular disorder involving the progressive narrowing of the distal internal carotid arteries and proximal intracranial arteries. Collateral vessels arise from the base of the skull, which appears as a puff of cigarette smoke on digital subtraction angiography (DSA) 1 . Reduced cerebral blood flow in the major vessels can lead to transient ischemic attack, infarction, cerebral hemorrhage, and cognitive impairment 2 . MMD patients without apparent structural brain damage still have subtle changes in brain microstructure caused by chronic hypoperfusion. Neuroimaging studies have revealed remarkable changes in brain structure and networks in MMD. For example, MMD patients showed decreased gray matter volume and damaged white matter fiber integrity compared to healthy controls. Additionally, damage to several brain regions was associated with cognitive impairment 3,4 . Lei et al. 5 found that the network topology in MMD was significantly reorganized based on resting-state functional MRI. Brain functional network connectivity changes result in the brain requiring more energy to accomplish specific tasks 6 . Additionally, patients with MMD who exhibit different symptoms also show variations in collateral compensatory blood vessels, cerebral perfusion, and brain structure indicating that the brain network may be affected 7,8 . The morphological data of the human brain contains a variety of information, and networks constructed based on statistical correlations of morphological indices between cerebral regions reflect developmental coordination or synchronized maturation between brain areas and characterize the brain's structural organization 9 . A previous study found a reorganization of brain structural covariance networks in ischemic MMD compared to healthy controls 10 . However, this method can only be analyzed at the group level and loses individual brain morphology information. Fortunately, new methods have been proposed to construct single-subject morphometric networks, which have proven meaningful and reliable 11,12 . This study analyzed the individual gray matter volume network of MMD patients and proposed the following hypotheses: 1) The network of patients differs from that of healthy controls. 2) Types of symptoms influence the topological properties of individual networks. 3) Individual networks have a particular potential for predicting patients and brain structural lesions. RESULTS Demographics and Group Matching As shown in Table 1 , the age and sex ratio of patients and HCs were well-matched. The gray matter and white matter volumes of the patients were lower than those of the HCs. The difference was still significant after correction for total intracranial volume. Table 1 Demographics and clinical characteristics of MMD patients and healthy controls. HC (99) MMD (136) t/χ² p Age 45.40 ± 8.84 44.19 ± 10.68 0.923 0.357 Gender (male) 40(40.40%) 51(37.50%) 0.204 0.652 Education level Primary 5(5.05%) 12(8.82%) 1.603 0.449 Median 54(54.55%) 66(48.53%) Graduate 40(40.40%) 58(42.65%) Hypertension 30(30.30%) 54(39.71%) 2.205 0.138 Diabete 12(12.12%) 18(13.24%) 0.064 0.801 Hyperlipid 25(25.25%) 25(18.38%) 1.614 0.204 GMV (ml) 633.02 ± 58.10 603.07 ± 61.00 3.742 < 0.001 WMV (ml) 515.43 ± 55.13 488.35 ± 60.84 3.503 < 0.001 GMV/TIV 0.44 ± 0.02 0.43 ± 0.03 3.600 < 0.001 WMV/TIV 0.36 ± 0.02 0.35 ± 0.02 4.397 < 0.001 TIV (ml) 1433.22 ± 135.80 1403.87 ± 11.70 1.631 0.104 GMV, gray matter volume; WMV, white matter volume; TIV, total intracranial volume. Changes in Brain Network Properties Between HC and MMD In the defined threshold range, both groups exhibited a small-world topology as indicated by all γ > 1 with λ ≈ 1 or σ > 1 (Fig. 2 ). Patients showed a significant decrease in aEloc (p = 0.013) and aCp (p = 0.006) compared to HCs. No significant difference was observed in other global measures (Table 2 ). Table 2 Comparisons of global network properties between HC and MMD HC (99) MMD (136) t p aEg ( \(\times {e}^{-1}\) ) 2.5 ± 0.04 2.49 ± 0.04 1.575 0.117 aEloc ( \(\times {e}^{-1}\) ) 3.66 ± 0.05 3.64 ± 0.04 2.508 0.013 aCp ( \(\times {e}^{-1}\) ) 3.14 ± 0.05 3.12 ± 0.05 2.790 0.006 aLp ( \(\times {e}^{-1}\) ) 10.41 ± 0.16 10.44 ± 0.17 -1.004 0.316 aγ ( \(\times {e}^{-1}\) ) 9.49 ± 0.58 9.47 ± 0.67 0.189 0.85 aλ ( \(\times {e}^{-1}\) ) 5.8 ± 0.07 5.8 ± 0.08 -0.532 0.595 aσ ( \(\times {e}^{-1}\) ) 8.01 ± 0.45 7.99 ± 0.52 0.187 0.852 aEg = AUC of global efficiency; aEloc = AUC of local efficiency; aCp = AUC of clustering coefficient; aLp = AUC of characteristic path length; aγ = AUC of normalized clustering coefficient; aλ = AUC of normalized characteristic path length; aσ = AUC of small-worldness. Figure 3 displays brain regions that showed significant between-group differences in at least one nodal metric in patients compared to HCs (FDR corrected, p < 0.05): nodal DC of bilateral olfactory cortex (OLF) and nodal BC of right median cingulate and paracingulate gyri (DCG.R), fusiform gyrus (FFG. R) and left pallidum (PAL.L) were significantly lower in HCs, whereas MMD patients showed significantly decreased nodal DC in left orbital part of inferior frontal gyrus (ORBinf.L), left medial orbital part of superior frontal gyrus (ORBsupmed.L) and right calcarine fissure and surrounding cortex (CAL.R). Demographic and Clinical Characteristics in Different Types of MMD MMD patients were divided into three groups according to their symptoms. The demographic and clinical characteristics of 136 patients are shown in Table 3 . No significant difference was found among the three groups in gray matter volume, white matter volume, the ratio of white matter hyperintensity volume to total intracranial volume, white matter hyperintensity, and number of lacunes after Bonferroni correction. Table 3 Demographics and clinical characteristics of MMD patients with different symptoms Ischemic (62) Hemorrhagic ( 12 ) Asymptomatic (62) F p Age 44.39 ± 10.75 43.67 ± 12.5 44.1 ± 10.42 0.027 0.973 Gender (male) 24(38.70%) 4(33.30%) 23(37.10%) 0.132 0.966 Education level Primary 6(9.68%) 1(8.33%) 5(8.06%) 1.175 0.882 Median 27(43.55%) 6(50.00%) 33(53.23%) Graduate 29(46.77%) 5(41.67%) 24(28.71%) Hypertension 30(48.39%) 3(25.00%) 21(33.87%) 3.917 0.162 Diabete 9(14.52%) 1(8.33%) 8(12.90%) 0.346 0.938 Hyperlipid 15(24.19%) 0(0.00%) 10(16.13%) 4.308 0.143 MRA stage left 1 3(4.84%) 0(0.00%) 5(8.06%) 6.164 0.360 2 46(74.19%) 8(66.66%) 37(59.68%) 3 10(16.13%) 4(33.33%) 19(30.65%) 4 3(4.84%) 0(0.00%) 1(1.61%) MRA stage right 1 3(4.84%) 0(0.00%) 4(6.45%) 3.634 0.705 2 42(67.74%) 7(58.33%) 39(62.90%) 3 14(22.58%) 4(33.33%) 18(29.03%) 4 3(4.84%) 1(8.33%) 1(1.61%) GMV/TIV 0.43 ± 0.03 0.43 ± 0.02 0.43 ± 0.02 0.82 0.443 WMV/TIV 0.35 ± 0.02 0.34 ± 0.03 0.35 ± 0.02 3.442 0.035 WMH (Lesion Volume) 2.15 ± 3.94 3.57 ± 3.07 1.43 ± 2.77 2.222 0.112 WMH (Number of Lesions) 8 ± 5.02 7.75 ± 5.17 6.85 ± 6.04 0.685 0.506 WMH/TIV ( \(\times {e}^{-2}\) ) 0.15 ± 0.29 0.25 ± 0.21 0.1 ± 0.21 1.999 0.14 GMV (ml) 599.17 ± 60.08 606.53 ± 56.4 607.15 ± 63.39 0.279 0.757 WMV (ml) 484.65 ± 57.51 474.63 ± 58.68 494.7 ± 64.59 0.755 0.472 TIV (ml) 1403.68 ± 133.25 1413.07 ± 111.21 1402.28 ± 145.69 0.031 0.969 Lacune 0.73 ± 1.67 0.33 ± 1.16 0.27 ± 0.79 1.973 0.143 GMV, gray matter volume; WMV, white matter volume; TIV, total intracranial volume; WMH, white matter hyperintensity. Alterations in Brain Network Properties in Different Types of MMD Table 4 shows the global network properties among the three types of MMD patients. The aEg of ischemic MMD patients was significantly lower than that of hemorrhagic and asymptomatic patients (p = 0.001, p = 0.003, respectively, Bonferroni corrected). The aLp showed a significant increase in ischemic patients compared to the other two types (p = 0.004, p = 0.003, respectively, Bonferroni corrected). The aλ of ischemic patients was significantly higher than that of hemorrhagic patients. Table 4 Comparisons of global network properties in MMD patients Ischemic (62) Hemorrhagic ( 12 ) Asymptomatic (62) F p aEg 0.247 ± 0.003 0.252 ± 0.004 0.250 ± 0.004 9.75 < 0.001 aEloc 0.363 ± 0.004 0.365 ± 0.005 0.365 ± 0.005 2.175 0.118 aCp 0.312 ± 0.005 0.313 ± 0.005 0.313 ± 0.005 0.668 0.514 aLp 1.050 ± 0.017 1.033 ± 0.017 1.040 ± 0.016 8.67 < 0.001 aγ 0.958 ± 0.070 0.913 ± 0.088 0.943 ± 0.059 2.603 0.078 aλ 0.582 ± 0.008 0.576 ± 0.010 0.580 ± 0.007 3.611 0.03 aσ 0.806 ± 0.057 0.778 ± 0.067 0.797 ± 0.043 1.611 0.204 aEg = AUC of global efficiency; aEloc = AUC of local efficiency; aCp = AUC of clustering coefficient; aLp = AUC of characteristic path length; aγ = AUC of normalized clustering coefficient; aλ = AUC of normalized characteristic path length; aσ = AUC of small-worldness. For the regional network measures, as shown in Fig. 4 , BC of the right hippocampus (HIP.R) was significantly increased in hemorrhagic patients compared to ischemic patients (p < 0.001, Bonferroni corrected). Moreover, hemorrhagic patients showed a significant decrease in DC of the right triangular part of the inferior frontal gyrus (IFGtriang.R) compared with asymptomatic patients (p < 0.001, Bonferroni corrected). Network-Based Statistic (NBS) Prediction NBS prediction was used to identify MMD patients from HCs based on the gray matter network AUC = 0.631 (95% CI: 0.623, 0.639, p < 0.001). The subnetworks included frontal, temporal, and occipital lobes. NBS-predict regression analysis was also used to predict white matter hyperintensity volume with Pearson’s correlation coefficients of 0.318 (95% CI: 0.286, 0.349, p < 0.001). The results are shown in Fig. 5 . Discussion In this study, the individual brain network was constructed based on gray matter volume. In comparison with HCs, MMD patients had significant brain atrophy, and the individual brain network of patients had significant changes in both global and local properties. Meanwhile, patients with diverse symptom patterns also have differences in the topological properties of brain networks. The NBS-predict method was also used to analyze the subnetworks in individual structural networks related to both MMD and white matter hyperintensity. These results suggest that individual gray matter volume networks responded to the pathophysiological processes of MMD. Both healthy controls and patients had brain networks with small-world properties, indicating well-established networks. Information can be processed in local regions and the whole network with low wiring costs, which means that networks with small-world topography show high efficiency for functional segregation and integration. However, the global properties, including Cp and Eloc, show differences between patients and HCs. Cp reflects functional integration, while Eloc reflects the efficiency of local communication. Eloc and Cp decreased in patients, indicating that the ability to combine specialized information from distributed brain regions rapidly was impaired in MMD patients, and the network was less error-tolerant than HCs. This evidence points to a transition towards a "weaker small-world" pattern in the individual brain networks of MMD patients, which is supported by other research 5,10 . Similar changes were observed in patients with different symptoms. We observed decreased Eloc and increased aLp in ischemic MMD patients. The Lp represents the average shortest path length between all pairs of nodes in the network. The shorter the Lp, the less energy is consumed when integrating local information. It can be explained by the viewpoint that asymptomatic patients were considered at an early stage of MMD 8 and hemorrhagic MMD patients without apparent brain structural damage had relatively good collateral circulation and higher bran perfusion 13,14 . Thus, compared to ischemic MMD, patients of the other two types may have relatively good brain functional compensation. In addition to changes in global network characteristics, alterations in regional topological measures of several nodes were also observed. Regional DC values of ORBinf.L, ORBsupmed.L, and CAL.R significantly decreased in MMD patients after FDR correlation. The left ORBinf is strongly associated with semantic retrieval and produces sustained activity of semantic representations, thus playing a core role in the semantic attentional system 15 . Hu et al. found lower white matter fibers and functional connectivity between the left supplementary motor area and the left ORBinf in MMD. Additionally, the functional connectivity was correlated with cognitive function 16 . The ORBsupmed.L is a vital node in the default mode network which is involved in cognitive control, learning and memory 17 . The CAL cortex contains most primary visual cortex and associated with visual processing and spatial memory 18 . In mild cognitive impairment patients, both the structural and metabolic status of CAL.R were changed compared to HCs 19 . The altered topological properties of these nodes may account for the severe memory and attention deficits in MMD 20 . Compared to healthy controls, patients with MMD showed increased DC of bilateral OLF and BC of DCG.R, FFG.R, and PAL.L. The OLF has close connections with the insula, hypothalamus, and hippocampus, which are essential for learning and memory 21 . The aberration of the surface area of the OLF has been reported as a biomarker for cognitive dysfunction in patients with Parkinson's disease 22 . The DCG connects various regions in the fronto-parietal network and is also a critical part of the limbic system, which is essential for cognitive integration and emotion processing 23 . The FFG is another important node in the visual ventral stream, which participates in various visual cognitive functions, including face perception, word recognition, and semantic processing 24 . As mentioned above, the importance of CAL was found to be reduced in MMD patients. The study results suggest that MMD patients experience functional remodeling of the visual cortex. However, further investigation is needed to determine the relationship between disease pathophysiology and visual cognition in MMD. As a component of the basal ganglia, the PAL is responsible for controlling muscle contractions and motor processes. The PAL has been reported to receive inputs from regions in the limbic system, such as the prefrontal cortex, hippocampus, and amygdala, which regulate motivation. Moreover, the cortex–striatum–pallidum–thalamus–cortex loop is associated with the reward system and may be involved in reinforcement learning 25 . The increased importance of the above nodes in the grey matter brain networks of MMD may imply compensatory alterations for the maintenance of the corresponding functions, the exact mechanisms of which need to be further explored together with multimodal brain networks and cognitive assessments. Changes in local network properties were observed among MMD patients with different symptomatic phenotypes. Specifically, the BC of HIP.R was decreased in ischemic patients compared to hemorrhagic patients, while the DC of IFGtriang.R was decreased in hemorrhagic patients compared to asymptomatic patients. The HIP encodes, stores, and retrieves memories 26 , while the IFGtriang belongs to the execution control network. Sun et al. found that the IFGtriang is a key node related to acroparesthesia in MMD patients 27 . Hu et al. revealed that hemorrhagic MMD patients have an increase in cortical thickness in several brain regions compared with ischemic patients and HCs. The increased cortical thickness is associated with higher cerebral blood flow and integrity of white matter fiber 7 . Given that asymptomatic MMD may be in the early stages with relatively stable hemodynamic status 8 , and hemorrhagic MMD patients tend to have well compensated cerebral perfusion 13,14 , it is reasonable to infer that the cerebral perfusion is the main reason for the difference in local network properties among MMD patients. The individual brain network of patients with ischemic MMD tended to deteriorate when compared to asymptomatic patients and hemorrhagic patients without significant cerebral structural damage. This phenomenon was hypothesized to be closely linked to variations in cerebral hemodynamic compensation among patients. The load on the collateral vessels in hemorrhagic MMD is often excessive, as frequently observed in patients with abnormally dilated choroidal anastomotic arteries 28,29 . The combination of low overall cerebral blood flow and relatively high local blood flow constitutes a distinct pathophysiological condition in hemorrhagic MMD, leading to unique alterations in the topological properties of the cerebral network in these patients that differ from those observed in ischemic or asymptomatic patients. Future research should include studies of cerebral perfusion, together with a comprehensive investigation of angiographic features and genetic variation, to elucidate the pathophysiological mechanisms underlying different symptomatic types of MMD and their impact on brain networks. NBS-predict is an algorithm developed based on network-based statistics 30 . NBS applies traditional clustering statistics to graph theory analysis to identify connected structures or components in a network, and the statistical validity of this method was enhanced. By combining graph theory with machine learning algorithms, NBS-predict effectively mitigates the curse of dimensionality and makes results more generalizable. The NBS-predict method was utilized to differentiate between MMD patients and HCs. The subnetwork discovered involved the limbic, vision, sensory, and motor modules and the default network, which had a moderate differentiating effect. Additionally, the correlation analysis with white matter hyperintensity volume indicated that most of the work nodes were affected, which is consistent with the diffuse nature of the lesion. Therefore, individual brain networks were found to be associated with MMD-related lesions. The individual brain network constructed based on cortical morphology similarity is consistent with networks constructed using other modalities, such as functional and white matter fiber networks. Intercortical morphology similarity contains anatomical, functional, and genetically relevant information. The similarity in cytoarchitectural categorization may be the anatomical basis for morphology similarity connectivity. The classification of morphologically similar brain regions will likely be the same 11 . Additionally, regions with similar cortical thickness may have more white matter fiber connections and exhibit greater functional coherence and correlation with cognitive performance 31,32 . Thus, individual brain networks may contain more biological information. Abnormalities in individual networks have been shown to be associated with cognitive impairment diseases 11 . Furthermore, individual network abnormalities have been reported in cerebrovascular diseases such as cerebral small-vessel disease and carotid stenosis 33,34 . This study investigated the individual brain networks of patients with MMD, a cerebrovascular disease commonly associated with cognitive impairment. The study provides new information on the reorganization of brain structure and networks in MMD patients. There are several limitations to this study. First, the structural brain images of the MMD patients included in this study were acquired by two MRI scans of the same model and used the same sequence settings. Only gray matter volume was analyzed in this study, and the data were normalized for subsequent analyses, thus minimizing the effects caused by differences in MRI equipment. Second, this study could not explore the factors associated with network reorganization in patients with MMD because of a lack of information on cognitive function and genes. Third, because most patients with hemorrhagic MMD have undergone ventricular puncture and hematoma removal surgery, resulting in significant structural brain damage, the number of patients with hemorrhagic MMD included in this study was relatively small. Hemorrhagic MMD patients are unique in terms of cerebral perfusion, and the course of the disease, and the study of the brain networks of such patients still needs more data. In conclusion, our findings suggest that there is a reorganization of individual brain networks in both HCs and MMD patients, as well as among different types of MMD patients. In addition, it has been observed that individual brain networks are associated with MMD-related brain structure alternation. Methods Participants This study was approved by the ethics committee of Beijing Tiantan Hospital, Capital Medical University (KY2023-275-03) and all methods were performed in accordance with relevant guidelines and regulations. All MMD patients and healthy control participants were volunteers and provided informed consent. The information of 140 patients diagnosed with MMD in our hospital from January 2019 to September 2023 was reviewed. The inclusion criteria were as follows: 1) diagnosed with MMD according to the criteria of the Research Committee on Spontaneous Occlusion of the Circle of Willis 35 ; 2) over 18 years of age; 3) no evidence of brain lesions on T1-weighted images larger than 15 mm in diameter; 4) no cranial surgery prior to recruitment; 5) no history of other diseases with cognitive impairment or use of drugs that may alter cognitive function; 6) no MRI contraindications. 100 healthy controls (HCs) matched for age, sex, and educational background were recruited using the following criteria: 1) no history of neurological, psychiatric, or cognitive disease; 2) no history of drug use that could alter cognitive function; 3) no MRI contraindications. Participants with incomplete clinical information and MRI data that did not meet quality control standards were excluded. 136 patients with MMD and 99 HCs were finally selected for the study. The MRA stage of MMD was recorded 1 . Lacune was defined as a round or ovoid subcortical cavity of cerebrospinal fluid signal with a diameter of 3 to 15 mm, and the number of lacunes was recorded 36 . Study Design The baseline clinical information of all participants was recorded, including details such as age, gender, and education. T1-MPRAGE, fluid-attenuated inversion recovery (FLAIR), and time-of-flight magnetic resonance angiography (TOF-MRA) images were subsequently examined. Gray matter volume, white matter volume, and total brain volume were derived from T1-MPRAGE images, accompanied by the construction of individual gray matter volume networks. FLAIR was employed to evaluate lacunes and white matter hyperintensity lesions, while MRA served as the diagnostic criteria for MMD and its stage assessment. The study compared alterations in individual networks among patients and investigated sub-networks associated with MMD. The medical history of each patient was collected in detail. Patients with transient limb weakness, hemiparesis, and aphasia that can recover without symptoms, and no cerebral infarction observed on brain MRI, were classified as ischemic type. Hemorrhagic MMD was diagnosed based on clinical presentation, including severe headache and consciousness disturbance, and confirmed by computed tomography (CT) scan, with a time interval between symptom onset and the first CT scan of less than 24 hours. Asymptomatic MMD was defined as patients without a history of cerebrovascular events such as transient ischemic attack, cerebral infarction, intracranial hemorrhage, seizure, or involuntary movement. MRI Data Acquisition MRI data were acquired using an Ingenia 3.0 Tesla scanner (Philip Medical Systems, Best, Netherlands) equipped with a 32-channel head coil. A T1-weighted MPRAGE sequence with the following parameters was used for all scans: repetition time (TR) / echo time (TE) = 6.84/3.09 ms, flip angle = 8°, field of view (FOV) = 240×240 mm 2 , acquisition matrix = 240×240, slice thickness = 1.0 mm, voxel size 1.0×1.0×1.0 mm 3 . A T2-weighted fluid-attenuated inversion recovery sequence with the following parameters was used for all scans: TR/TE/inversion time (TI) = 4800/340/1650 ms, flip angle = 90°, slice thickness = 1 mm, voxel size = 1.0×1.0×1.0 mm 3 . A TOF-MRA sequence with the following parameters was used for all scans: TR/TE = 22/3.5 ms, flip angle = 18°, acquisition matrix = 384×250, slice thickness = 1 mm, voxel size 1.0×1.0×1.0 mm 3 . Structural Image Preprocessing All T1 images were manually checked for scanning artifacts and then subjected to standard preprocessing using voxel-based morphometry (VBM) based on statistical parametric mapping 12 ( http://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ). The VBM analysis steps were: 1) The segmentation tool was first used to segment individual structural data to obtain gray matter images. 2) A study-specific template based on Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) was created, which is based on the gray matter images of all the participants 37 . 3) The gray matter images of each participant were warped into the template and then normalized to the standard Montreal Neurological Institute (MNI) standard space. 4) Voxel values in individual gray matter images were then modulated and resampled to a resolution of 2 mm 3 . 5) Finally, all gray matter images were smoothed with a 15 mm full width at half maximum (FWMH) Gaussian kernel 38 . In addition, the volumes of gray matter, white matter, and total intracranial volume were also calculated for further analysis. Construction of Individual Cortical Thickness Networks The graynet toolbox was used to construct individual networks 39 . Network nodes were defined based on the Automated Anatomical Labeling (AAL) 90 atlas. Histogram-weighted networks (HiWeNet) based on gray matter volume were constructed. For each node according to the AAL90 atlas, the voxel-wise gray matter volume was transformed into a histogram and 5% outliers from the distribution were discarded. The histogram was further binned into uniformly spaced N = 100 bins and the histogram counts were then normalized for k = 1: N. The network edges were calculated as the histogram distance between two nodes (Fig. 1 ). A detailed description of the method can be found in the original articles 12 . The weight of the edge was thus defined as the statistical similarity of the morphological distributions and rescaled to [0, 1] using the min-max scaling. Since the weight of the edge was negatively correlated with the morphological similarity between the two nodes, the network matrix was subtraction normalized, with each edge subtracted by 1 to generate the traditional structural connectivity matrix 33 . White Matter Hyperintensity White matter hyperintensity burden was automatically segmented by the lesion prediction algorithm as implemented in the Lesion Segmentation Tool (LST) version 3.0.0 ( www.statistical-modelling.de/lst.html ) for SPM 40 . The total number and size (ml) of white matter hyperintensity were generated.. Network Properties All network properties of each brain network were calculated using the GRETNA toolbox in MATLAB 41 . According to a previous study 34 , the sparsity range was set from 10–60% with an interval of 5%. The global and nodal network metrics were calculated at each sparsity level, and the area under the curve (AUC) for each property across the entire sparsity range was used for statistical analysis. Global network properties, including small worldness and network efficiency, were analyzed. Five metrics: clustering coefficient (Cp), characteristic path length (Lp), normalized clustering coefficient (γ), normalized characteristic path length (λ), and small worldness (σ) indicate the degree of small-world organization. Cp is the average clustering coefficient of all nodes and reflects the degree of local interconnectivity. Lp is the average shortest path length between any two nodes in a network. γ and λ refer to normalized Cp and normalized Lp, respectively. σ is calculated as aCp/aLp. A brain network with γ > 1 and λ ≈ 1 or σ > 1 has a small-world property, reflecting an optimal balance of integration and segregation for a network. Network efficiency includes global efficiency (Eglob) and local efficiency (Eloc). Eglob measures the efficiency of parallel information transfer in the network. Eloc reflects the communication efficiency in the neighborhood of the nodes, which reflects the fault tolerance level of the network. Node degree centrality (DC) and betweenness centrality (BC) were chosen for local topological characteristics. DC reflects the ability to communicate information, and BC captures the importance of a given node in the flow of information. Detailed definitions of the above metrics and the formulas can be found in Rubinov's review 42 . Network-Based Statistic (NBS) Prediction The NBS prediction method was used to test the effect of MMD and white matter hyperintensity on the individual gray matter volume networks 30 . Parameters were selected as recommended: 40-fold, 50 repeated cross-validation (CV) procedures, hyperparameters with the grid search algorithm, and auto-optimization for classification and regression algorithms. The initial p value was 0.01, and the edge weight threshold was 0.8 to visualize a subnetwork with relevant edges. Statistical Analysis Analysis of demographic and clinical data was performed using IBM SPSS Statistics 25.0. Continuous variables underwent comparison using two-tailed independent samples t-test and analysis of variance (ANOVA), with Bonferroni’s correction applied for post hoc comparisons. Categorical variables were assessed using the Chi-squared test. Network metrics were statistically examined through the GRETNA toolbox ( https://www.nitrc.org/projects/gretna/ ) at a significance threshold of p < 0.05. Age, gender, and education level were adjusted for as confounding variables. The False Discovery Rate (FDR) was employed to adjust for multiple comparisons. Classification algorithms such as logistic regression, support vector classification, decision tree classification, and linear discriminant analysis were employed for identifying MMD-related subnetworks. Regression algorithms including linear regression, support vector regression, and decision tree regression were utilized to identify the subnetwork linked to white matter hyperintensity. This part of the statistical analysis was based on the NBS-Predict toolbox ( https://www.nitrc.org/projects/nbspredict/ ), with the most effective algorithms automatically selected. Declarations Acknowledgements This work was supported by the National Natural Science Foundation of China (Contract grant number: 82371915). Author contributions H.Z. and P.W. conceived and designed the research. H.Z. performed statistical analysis and drafted the manuscript. W.L., Q.Z., C.Z. and T.L. enrolled patients and acquired brain magnetic resonance imaging. T.Y., X.L., Q.Z., J.Z. and Y.Z. critically revised the manuscript. Data availability statement The raw data that support the findings of this study are available from the corresponding author on reasonable request. Additional Information The authors declare that they have no competing interests. References Research Committee on the Pathology and Treatment of Spontaneous Occlusion of the Circle of Willis & Health Labour Sciences Research Grant for Research on Measures for Infractable Diseases. Guidelines for diagnosis and treatment of moyamoya disease (spontaneous occlusion of the circle of Willis). Neurol Med Chir (Tokyo) 52 , 245–266 (2012). Karzmark, P., Zeifert, P. D., Bell-Stephens, T. E., Steinberg, G. K. & Dorfman, L. J. Neurocognitive impairment in adults with moyamoya disease without stroke. Neurosurgery 70 , 634–638 (2012). Kazumata, K. et al. Combined structural and diffusion tensor imaging detection of ischemic injury in moyamoya disease: relation to disease advancement and cerebral hypoperfusion. J Neurosurg 134 , 1155–1164 (2020). Zuo, Z. et al. Atrophy in subcortical gray matter in adult patients with moyamoya disease. Neurol Sci 44 , 1709–1717 (2023). Lei, Y. et al. Reconfigured functional network dynamics in adult moyamoya disease: a resting-state fMRI study. Brain Imaging Behav 14 , 715–727 (2020). Zheng, G. et al. Changes in Brain Functional Network Connectivity in Adult Moyamoya Diseases. Cogn Neurodyn 15 , 861–872 (2021). Hu, J. et al. Moyamoya Disease With Initial Ischemic or Hemorrhagic Attack Shows Different Brain Structural and Functional Features: A Pilot Study. Front Neurol 13 , 871421 (2022). Sun, H. et al. Angiographic and Hemodynamic Features in Asymptomatic Hemispheres of Patients With Moyamoya Disease. Stroke 53 , 210–217 (2022). Alexander-Bloch, A., Giedd, J. N. & Bullmore, E. Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14 , 322–336 (2013). Wang, P. et al. Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis. Front Aging Neurosci 14 , 788661 (2022). Cai, M. et al. Individual-level brain morphological similarity networks: Current methodologies and applications. CNS Neurosci Ther 29 , 3713–3724 (2023). Pr, R. & Sc, S. Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight. Brain structure & function 225 , (2020). Yin, H. et al. A Novel Staging System to Evaluate Cerebral Hypoperfusion in Patients With Moyamoya Disease. Stroke 49 , 2837–2843 (2018). Liu, Z.-W. et al. Collateral Circulation in Moyamoya Disease: A New Grading System. Stroke 50 , 2708–2715 (2019). Matchin, W. G. A neuronal retuning hypothesis of sentence-specificity in Broca’s area. Psychon Bull Rev 25 , 1682–1694 (2018). Hu, J. et al. Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease. BMC Neurol 22 , 179 (2022). Buckner, R. L. & DiNicola, L. M. The brain’s default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci 20 , 593–608 (2019). Yuan, Z. et al. Altered functional connectivity of the right caudate nucleus in chronic migraine: a resting-state fMRI study. J Headache Pain 23 , 154 (2022). Wei, C. et al. A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment. Front Aging Neurosci 13 , 774607 (2021). Shi, Z., Wen, Y.-J., Huang, Z., Yu, L.-B. & Zhang, D. Different aspects of cognitive function in adult patients with moyamoya disease and its clinical subtypes. Stroke Vasc Neurol 5 , 86–96 (2020). Bothwell, A. R., Resnick, S. M., Ferrucci, L. & Tian, Q. Associations of olfactory function with brain structural and functional outcomes. A systematic review. Ageing Res Rev 92 , 102095 (2023). Li, Y. et al. Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson’s disease. NPJ Parkinsons Dis 8 , 176 (2022). Huang, X., Zhang, M., Li, B., Shang, H. & Yang, J. Structural and functional brain abnormalities in idiopathic cervical dystonia: A multimodal meta-analysis. Parkinsonism & Related Disorders 103 , 153–165 (2022). Zhang, W. et al. Functional organization of the fusiform gyrus revealed with connectivity profiles. Hum Brain Mapp 37 , 3003–3016 (2016). Tachibana, Y. & Hikosaka, O. The primate ventral pallidum encodes expected reward value and regulates motor action. Neuron 76 , 826–837 (2012). Lisman, J. et al. Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nat Neurosci 20 , 1434–1447 (2017). Sun, R. et al. White matter structural and network topological changes in moyamoya disease with limb paresthesia: A study based on diffusion kurtosis imaging. Front Neurosci 16 , 1029388 (2022). Fujimura, M. et al. Intrinsic development of choroidal and thalamic collaterals in hemorrhagic-onset moyamoya disease: case-control study of the Japan Adult Moyamoya Trial. J Neurosurg 130 , 1453–1459 (2019). Zhang, Q. et al. Hemorrhagic patterns and their risk factors in patients with moyamoya disease. Eur J Neurol 27 , 2499–2507 (2020). Serin, E., Zalesky, A., Matory, A., Walter, H. & Kruschwitz, J. D. NBS-Predict: A prediction-based extension of the network-based statistic. Neuroimage 244 , 118625 (2021). Sun, L. et al. Structural insight into the individual variability architecture of the functional brain connectome. Neuroimage 259 , 119387 (2022). Gong, G., He, Y., Chen, Z. J. & Evans, A. C. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage 59 , 1239–1248 (2012). Ren, J. et al. Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks. Front. Aging Neurosci. 14 , 1091829 (2023). Gao, Y. et al. Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease. Brain Sci 13 , 1359 (2023). KURODA, S. et al. Diagnostic Criteria for Moyamoya Disease - 2021 Revised Version. Neurol Med Chir (Tokyo) 62 , 307–312 (2022). Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12 , 822–838 (2013). Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38 , 95–113 (2007). Li, Y. et al. Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. NeuroImage 235 , 118018 (2021). Reddy Raamana, P. & C. Strother, S. graynet: single-subject morphometric networks for neuroscience connectivity applications. JOSS 3 , 924 (2018). Schmidt, P. Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging. (Ludwig-Maximilians-Universität München, 2017). Wang, J. et al. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front. Hum. Neurosci. 9 , (2015). Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52 , 1059–1069 (2010). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviewers agreed at journal 17 Sep, 2024 Reviews received at journal 22 Jul, 2024 Reviewers agreed at journal 06 Jul, 2024 Reviewers invited by journal 12 Jun, 2024 Editor assigned by journal 12 Jun, 2024 Editor invited by journal 24 May, 2024 Submission checks completed at journal 24 May, 2024 First submitted to journal 22 May, 2024 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-4461906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310464827,"identity":"509fe958-076e-4652-975e-cad65e368ea4","order_by":0,"name":"Huan Zhu","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Zhu","suffix":""},{"id":310464828,"identity":"9903377a-802e-4b4d-b2d2-bcf14a8202ef","order_by":1,"name":"Peijiong Wang","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Peijiong","middleName":"","lastName":"Wang","suffix":""},{"id":310464829,"identity":"71fd6faa-8d86-499a-a269-03006d287b21","order_by":2,"name":"Wenjie Li","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Li","suffix":""},{"id":310464830,"identity":"8fd62541-4dbf-4209-ac80-e553be87b288","order_by":3,"name":"Qihang Zhang","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Qihang","middleName":"","lastName":"Zhang","suffix":""},{"id":310464831,"identity":"eac47f75-a2bb-42ed-b1aa-ed6a0c3f6f9c","order_by":4,"name":"Chenyu Zhu","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Zhu","suffix":""},{"id":310464832,"identity":"a60c8e54-8366-4e54-a151-8822e49cdd0d","order_by":5,"name":"Tong Liu","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Liu","suffix":""},{"id":310464833,"identity":"1798ad86-b736-43f9-b1d3-0ae475b785af","order_by":6,"name":"Tao Yu","email":"","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Yu","suffix":""},{"id":310464834,"identity":"c103638a-1552-4c86-b958-25a4040a56c5","order_by":7,"name":"Xingju Liu","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xingju","middleName":"","lastName":"Liu","suffix":""},{"id":310464835,"identity":"07f34bbf-9147-4c84-99cc-c6bbe044cf4e","order_by":8,"name":"Qian Zhang","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhang","suffix":""},{"id":310464836,"identity":"d6bf96bd-607c-4987-923a-2bc4a9cfcc5e","order_by":9,"name":"Jizong Zhao","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jizong","middleName":"","lastName":"Zhao","suffix":""},{"id":310464837,"identity":"959951ca-137a-43e9-a0b1-a4741059e9ab","order_by":10,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJACZhDBxt588EFCRQ0JWvh4jiUbPDhzjAQtchI+apIPW5gJKzc4fvbw54KaO3ZtEjxsFYkNbAz87d0J+LWcyUswnnHsWXKbdO+xG4k7ZBgkzpzdgF/LgRyDZB62w8lsMufSbiSeYWMwkMgloOX8G4PDPP+AWiRyzAoS25iJ0HIjx7CZt+2wHUgLA1FaJG+8MWbm7TucwAYMZImEM8d4CPqF73yO8Weeb4ft5dubD378UVEjx9/ei1+LwgEIndgAFeDBqxwE5KFK7QmqHAWjYBSMgpELAODZTFF3zRJiAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Neurosurgical Institute","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-05-22 15:18:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4461906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4461906/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-86553-3","type":"published","date":"2025-01-22T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58143796,"identity":"ad7e2242-4bd4-4630-8142-74fb6db60244","added_by":"auto","created_at":"2024-06-11 18:22:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":549505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart for the construction of gray matter morphological networks using T1-weighted MRI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Gray matter volume maps created using a routine VBM procedure were smoothed with a 15 mm FWHM Gaussian kernel. (b) The gray matter volume maps were divided into 90 regions based on the AAL atlases. (c) Individual gray matter volume connectivity was constructed using the Graynet software. (d) The histogram (hi) distance between each pair of nodes was calculated, resulting in a morphological similarity. (e) The network properties of gray matter morphological networks were finally calculated at both the global and nodal levels.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4461906/v1/7c86803743e3104a06387468.png"},{"id":58143799,"identity":"524c61db-23bf-488f-8e89-edd7b8c78b66","added_by":"auto","created_at":"2024-06-11 18:22:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmall-worldness properties of MMD and HC in the defined sparsity.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) normalized clustering coefficients were larger than 1, (B) normalized characteristic path lengths were approximately equal to 1, and (C) small worldness coefficients were bigger than 1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4461906/v1/6ca050f4543109cf8bd5973f.png"},{"id":58143800,"identity":"bd8a5437-ed79-4f66-afd5-7353c1af0980","added_by":"auto","created_at":"2024-06-11 18:22:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegions of significant differences in nodal degree centrality or betweenness centrality of gray matter morphological networks between MMD patients and healthy controls, showing both increases (red) and decreases (blue) in MMD compared to HC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOLF.L, left olfactory cortex; OLF.R, right olfactory cortex; DCG.R, right median cingulate and paracingulate gyri; FFG.R, fusiform gyrus; PAL.L, left pallidum; ORBinf.L, left orbital part of inferior frontal gyrus; ORBsupmed.L, left medial orbital superior frontal gyrus; CAL.R, right calcarine fissure and surrounding cortex.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4461906/v1/b20ba9c4bfca63519938a849.png"},{"id":58143797,"identity":"099c38d2-fb70-4272-900f-d6f869435e8c","added_by":"auto","created_at":"2024-06-11 18:22:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegions exhibiting altered nodal degree centrality or betweenness centrality among MMD patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIFGtriang.R, right triangular part of inferior frontal gyrus; HIP.R, right hippocampus; DC, degree centrality; BC, betweenness centrality.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4461906/v1/4194df60a0f1b3ec695669aa.png"},{"id":58145254,"identity":"cf2ccc83-5217-4fe5-b12c-1191af277ba3","added_by":"auto","created_at":"2024-06-11 18:30:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":857465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubnetworks identified using NBS prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) Subnetworks with significant classification between MMD and HC. (B) NBS-based regression on volume of white matter hyperintensity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4461906/v1/66c673df272b0ce8933b71ac.png"},{"id":74858461,"identity":"861265ba-35ab-4c51-8967-68e2dc6ed558","added_by":"auto","created_at":"2025-01-27 16:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4291144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4461906/v1/02ec3a26-cf81-4f8f-a903-027f821185ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reorganization of Gray Matter Networks in Patients with Moyamoya Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMoyamoya disease (MMD) is a rare cerebrovascular disorder involving the progressive narrowing of the distal internal carotid arteries and proximal intracranial arteries. Collateral vessels arise from the base of the skull, which appears as a puff of cigarette smoke on digital subtraction angiography (DSA) \u003csup\u003e1\u003c/sup\u003e. Reduced cerebral blood flow in the major vessels can lead to transient ischemic attack, infarction, cerebral hemorrhage, and cognitive impairment \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMMD patients without apparent structural brain damage still have subtle changes in brain microstructure caused by chronic hypoperfusion. Neuroimaging studies have revealed remarkable changes in brain structure and networks in MMD. For example, MMD patients showed decreased gray matter volume and damaged white matter fiber integrity compared to healthy controls. Additionally, damage to several brain regions was associated with cognitive impairment \u003csup\u003e3,4\u003c/sup\u003e. Lei et al.\u003csup\u003e5\u003c/sup\u003e found that the network topology in MMD was significantly reorganized based on resting-state functional MRI. Brain functional network connectivity changes result in the brain requiring more energy to accomplish specific tasks \u003csup\u003e6\u003c/sup\u003e. Additionally, patients with MMD who exhibit different symptoms also show variations in collateral compensatory blood vessels, cerebral perfusion, and brain structure indicating that the brain network may be affected \u003csup\u003e7,8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe morphological data of the human brain contains a variety of information, and networks constructed based on statistical correlations of morphological indices between cerebral regions reflect developmental coordination or synchronized maturation between brain areas and characterize the brain's structural organization \u003csup\u003e9\u003c/sup\u003e. A previous study found a reorganization of brain structural covariance networks in ischemic MMD compared to healthy controls \u003csup\u003e10\u003c/sup\u003e. However, this method can only be analyzed at the group level and loses individual brain morphology information. Fortunately, new methods have been proposed to construct single-subject morphometric networks, which have proven meaningful and reliable \u003csup\u003e11,12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study analyzed the individual gray matter volume network of MMD patients and proposed the following hypotheses: 1) The network of patients differs from that of healthy controls. 2) Types of symptoms influence the topological properties of individual networks. 3) Individual networks have a particular potential for predicting patients and brain structural lesions.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and Group Matching\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the age and sex ratio of patients and HCs were well-matched. The gray matter and white matter volumes of the patients were lower than those of the HCs. The difference was still significant after correction for total intracranial volume.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical characteristics of MMD patients and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC (99)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMMD (136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et/χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(40.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(37.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(8.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(54.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(48.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(40.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(42.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(30.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(39.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(12.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(13.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(25.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(18.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e633.02\u0026thinsp;\u0026plusmn;\u0026thinsp;58.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603.07\u0026thinsp;\u0026plusmn;\u0026thinsp;61.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e515.43\u0026thinsp;\u0026plusmn;\u0026thinsp;55.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488.35\u0026thinsp;\u0026plusmn;\u0026thinsp;60.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMV/TIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMV/TIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1433.22\u0026thinsp;\u0026plusmn;\u0026thinsp;135.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1403.87\u0026thinsp;\u0026plusmn;\u0026thinsp;11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eGMV, gray matter volume; WMV, white matter volume; TIV, total intracranial volume.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eChanges in Brain Network Properties Between HC and MMD\u003c/h2\u003e \u003cp\u003eIn the defined threshold range, both groups exhibited a small-world topology as indicated by all γ\u0026thinsp;\u0026gt;\u0026thinsp;1 with λ\u0026thinsp;\u0026asymp;\u0026thinsp;1 or σ\u0026thinsp;\u0026gt;\u0026thinsp;1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients showed a significant decrease in aEloc (p\u0026thinsp;=\u0026thinsp;0.013) and aCp (p\u0026thinsp;=\u0026thinsp;0.006) compared to HCs. No significant difference was observed in other global measures (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of global network properties between HC and MMD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC (99)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMMD (136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaEg (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaEloc (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaCp (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaLp (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaγ (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaλ (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaσ (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eaEg\u0026thinsp;=\u0026thinsp;AUC of global efficiency; aEloc\u0026thinsp;=\u0026thinsp;AUC of local efficiency; aCp\u0026thinsp;=\u0026thinsp;AUC of clustering coefficient; aLp\u0026thinsp;=\u0026thinsp;AUC of characteristic path length; aγ\u0026thinsp;=\u0026thinsp;AUC of normalized clustering coefficient; aλ\u0026thinsp;=\u0026thinsp;AUC of normalized characteristic path length; aσ\u0026thinsp;=\u0026thinsp;AUC of small-worldness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays brain regions that showed significant between-group differences in at least one nodal metric in patients compared to HCs (FDR corrected, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): nodal DC of bilateral olfactory cortex (OLF) and nodal BC of right median cingulate and paracingulate gyri (DCG.R), fusiform gyrus (FFG. R) and left pallidum (PAL.L) were significantly lower in HCs, whereas MMD patients showed significantly decreased nodal DC in left orbital part of inferior frontal gyrus (ORBinf.L), left medial orbital part of superior frontal gyrus (ORBsupmed.L) and right calcarine fissure and surrounding cortex (CAL.R).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics in Different Types of MMD\u003c/h2\u003e \u003cp\u003eMMD patients were divided into three groups according to their symptoms. The demographic and clinical characteristics of 136 patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No significant difference was found among the three groups in gray matter volume, white matter volume, the ratio of white matter hyperintensity volume to total intracranial volume, white matter hyperintensity, and number of lacunes after Bonferroni correction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical characteristics of MMD patients with different symptoms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschemic (62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemorrhagic (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsymptomatic (62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(38.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(33.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(37.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(8.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(43.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(53.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(46.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(41.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(28.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(48.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(33.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(14.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(12.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(24.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(16.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRA stage left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(8.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(74.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(66.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(59.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(16.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(30.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(1.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRA stage right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(6.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(67.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(58.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(62.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(22.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(29.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(1.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMV/TIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMV/TIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMH (Lesion Volume)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMH (Number of Lesions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMH/TIV (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times {e}^{-2}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599.17\u0026thinsp;\u0026plusmn;\u0026thinsp;60.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e606.53\u0026thinsp;\u0026plusmn;\u0026thinsp;56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e607.15\u0026thinsp;\u0026plusmn;\u0026thinsp;63.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e484.65\u0026thinsp;\u0026plusmn;\u0026thinsp;57.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e474.63\u0026thinsp;\u0026plusmn;\u0026thinsp;58.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e494.7\u0026thinsp;\u0026plusmn;\u0026thinsp;64.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1403.68\u0026thinsp;\u0026plusmn;\u0026thinsp;133.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1413.07\u0026thinsp;\u0026plusmn;\u0026thinsp;111.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1402.28\u0026thinsp;\u0026plusmn;\u0026thinsp;145.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLacune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGMV, gray matter volume; WMV, white matter volume; TIV, total intracranial volume; WMH, white matter hyperintensity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAlterations in Brain Network Properties in Different Types of MMD\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the global network properties among the three types of MMD patients. The aEg of ischemic MMD patients was significantly lower than that of hemorrhagic and asymptomatic patients (p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.003, respectively, Bonferroni corrected). The aLp showed a significant increase in ischemic patients compared to the other two types (p\u0026thinsp;=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;0.003, respectively, Bonferroni corrected). The aλ of ischemic patients was significantly higher than that of hemorrhagic patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of global network properties in MMD patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschemic (62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemorrhagic (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsymptomatic (62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaEg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.247\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.252\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.250\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaEloc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.363\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.365\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.365\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaCp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.312\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.313\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.313\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaLp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.050\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.033\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaγ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.958\u0026thinsp;\u0026plusmn;\u0026thinsp;0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.913\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.943\u0026thinsp;\u0026plusmn;\u0026thinsp;0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaλ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.582\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.576\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.580\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaσ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.806\u0026thinsp;\u0026plusmn;\u0026thinsp;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.778\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.797\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eaEg\u0026thinsp;=\u0026thinsp;AUC of global efficiency; aEloc\u0026thinsp;=\u0026thinsp;AUC of local efficiency; aCp\u0026thinsp;=\u0026thinsp;AUC of clustering coefficient; aLp\u0026thinsp;=\u0026thinsp;AUC of characteristic path length; aγ\u0026thinsp;=\u0026thinsp;AUC of normalized clustering coefficient; aλ\u0026thinsp;=\u0026thinsp;AUC of normalized characteristic path length; aσ\u0026thinsp;=\u0026thinsp;AUC of small-worldness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the regional network measures, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, BC of the right hippocampus (HIP.R) was significantly increased in hemorrhagic patients compared to ischemic patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Bonferroni corrected). Moreover, hemorrhagic patients showed a significant decrease in DC of the right triangular part of the inferior frontal gyrus (IFGtriang.R) compared with asymptomatic patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Bonferroni corrected).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNetwork-Based Statistic (NBS) Prediction\u003c/h2\u003e \u003cp\u003eNBS prediction was used to identify MMD patients from HCs based on the gray matter network AUC\u0026thinsp;=\u0026thinsp;0.631 (95% CI: 0.623, 0.639, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The subnetworks included frontal, temporal, and occipital lobes. NBS-predict regression analysis was also used to predict white matter hyperintensity volume with Pearson\u0026rsquo;s correlation coefficients of 0.318 (95% CI: 0.286, 0.349, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the individual brain network was constructed based on gray matter volume. In comparison with HCs, MMD patients had significant brain atrophy, and the individual brain network of patients had significant changes in both global and local properties. Meanwhile, patients with diverse symptom patterns also have differences in the topological properties of brain networks. The NBS-predict method was also used to analyze the subnetworks in individual structural networks related to both MMD and white matter hyperintensity. These results suggest that individual gray matter volume networks responded to the pathophysiological processes of MMD.\u003c/p\u003e \u003cp\u003eBoth healthy controls and patients had brain networks with small-world properties, indicating well-established networks. Information can be processed in local regions and the whole network with low wiring costs, which means that networks with small-world topography show high efficiency for functional segregation and integration.\u003c/p\u003e \u003cp\u003eHowever, the global properties, including Cp and Eloc, show differences between patients and HCs. Cp reflects functional integration, while Eloc reflects the efficiency of local communication. Eloc and Cp decreased in patients, indicating that the ability to combine specialized information from distributed brain regions rapidly was impaired in MMD patients, and the network was less error-tolerant than HCs. This evidence points to a transition towards a \"weaker small-world\" pattern in the individual brain networks of MMD patients, which is supported by other research \u003csup\u003e5,10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilar changes were observed in patients with different symptoms. We observed decreased Eloc and increased aLp in ischemic MMD patients. The Lp represents the average shortest path length between all pairs of nodes in the network. The shorter the Lp, the less energy is consumed when integrating local information. It can be explained by the viewpoint that asymptomatic patients were considered at an early stage of MMD \u003csup\u003e8\u003c/sup\u003e and hemorrhagic MMD patients without apparent brain structural damage had relatively good collateral circulation and higher bran perfusion \u003csup\u003e13,14\u003c/sup\u003e. Thus, compared to ischemic MMD, patients of the other two types may have relatively good brain functional compensation.\u003c/p\u003e \u003cp\u003eIn addition to changes in global network characteristics, alterations in regional topological measures of several nodes were also observed. Regional DC values of ORBinf.L, ORBsupmed.L, and CAL.R significantly decreased in MMD patients after FDR correlation. The left ORBinf is strongly associated with semantic retrieval and produces sustained activity of semantic representations, thus playing a core role in the semantic attentional system \u003csup\u003e15\u003c/sup\u003e. Hu et al. found lower white matter fibers and functional connectivity between the left supplementary motor area and the left ORBinf in MMD. Additionally, the functional connectivity was correlated with cognitive function \u003csup\u003e16\u003c/sup\u003e. The ORBsupmed.L is a vital node in the default mode network which is involved in cognitive control, learning and memory \u003csup\u003e17\u003c/sup\u003e. The CAL cortex contains most primary visual cortex and associated with visual processing and spatial memory \u003csup\u003e18\u003c/sup\u003e. In mild cognitive impairment patients, both the structural and metabolic status of CAL.R were changed compared to HCs \u003csup\u003e19\u003c/sup\u003e. The altered topological properties of these nodes may account for the severe memory and attention deficits in MMD \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompared to healthy controls, patients with MMD showed increased DC of bilateral OLF and BC of DCG.R, FFG.R, and PAL.L. The OLF has close connections with the insula, hypothalamus, and hippocampus, which are essential for learning and memory \u003csup\u003e21\u003c/sup\u003e. The aberration of the surface area of the OLF has been reported as a biomarker for cognitive dysfunction in patients with Parkinson's disease\u003csup\u003e22\u003c/sup\u003e. The DCG connects various regions in the fronto-parietal network and is also a critical part of the limbic system, which is essential for cognitive integration and emotion processing \u003csup\u003e23\u003c/sup\u003e. The FFG is another important node in the visual ventral stream, which participates in various visual cognitive functions, including face perception, word recognition, and semantic processing \u003csup\u003e24\u003c/sup\u003e. As mentioned above, the importance of CAL was found to be reduced in MMD patients. The study results suggest that MMD patients experience functional remodeling of the visual cortex. However, further investigation is needed to determine the relationship between disease pathophysiology and visual cognition in MMD. As a component of the basal ganglia, the PAL is responsible for controlling muscle contractions and motor processes. The PAL has been reported to receive inputs from regions in the limbic system, such as the prefrontal cortex, hippocampus, and amygdala, which regulate motivation. Moreover, the cortex\u0026ndash;striatum\u0026ndash;pallidum\u0026ndash;thalamus\u0026ndash;cortex loop is associated with the reward system and may be involved in reinforcement learning \u003csup\u003e25\u003c/sup\u003e. The increased importance of the above nodes in the grey matter brain networks of MMD may imply compensatory alterations for the maintenance of the corresponding functions, the exact mechanisms of which need to be further explored together with multimodal brain networks and cognitive assessments.\u003c/p\u003e \u003cp\u003eChanges in local network properties were observed among MMD patients with different symptomatic phenotypes. Specifically, the BC of HIP.R was decreased in ischemic patients compared to hemorrhagic patients, while the DC of IFGtriang.R was decreased in hemorrhagic patients compared to asymptomatic patients. The HIP encodes, stores, and retrieves memories \u003csup\u003e26\u003c/sup\u003e, while the IFGtriang belongs to the execution control network. Sun et al. found that the IFGtriang is a key node related to acroparesthesia in MMD patients \u003csup\u003e27\u003c/sup\u003e. Hu et al. revealed that hemorrhagic MMD patients have an increase in cortical thickness in several brain regions compared with ischemic patients and HCs. The increased cortical thickness is associated with higher cerebral blood flow and integrity of white matter fiber \u003csup\u003e7\u003c/sup\u003e. Given that asymptomatic MMD may be in the early stages with relatively stable hemodynamic status \u003csup\u003e8\u003c/sup\u003e, and hemorrhagic MMD patients tend to have well compensated cerebral perfusion \u003csup\u003e13,14\u003c/sup\u003e, it is reasonable to infer that the cerebral perfusion is the main reason for the difference in local network properties among MMD patients.\u003c/p\u003e \u003cp\u003eThe individual brain network of patients with ischemic MMD tended to deteriorate when compared to asymptomatic patients and hemorrhagic patients without significant cerebral structural damage. This phenomenon was hypothesized to be closely linked to variations in cerebral hemodynamic compensation among patients. The load on the collateral vessels in hemorrhagic MMD is often excessive, as frequently observed in patients with abnormally dilated choroidal anastomotic arteries\u003csup\u003e28,29\u003c/sup\u003e. The combination of low overall cerebral blood flow and relatively high local blood flow constitutes a distinct pathophysiological condition in hemorrhagic MMD, leading to unique alterations in the topological properties of the cerebral network in these patients that differ from those observed in ischemic or asymptomatic patients. Future research should include studies of cerebral perfusion, together with a comprehensive investigation of angiographic features and genetic variation, to elucidate the pathophysiological mechanisms underlying different symptomatic types of MMD and their impact on brain networks.\u003c/p\u003e \u003cp\u003eNBS-predict is an algorithm developed based on network-based statistics \u003csup\u003e30\u003c/sup\u003e. NBS applies traditional clustering statistics to graph theory analysis to identify connected structures or components in a network, and the statistical validity of this method was enhanced. By combining graph theory with machine learning algorithms, NBS-predict effectively mitigates the curse of dimensionality and makes results more generalizable.\u003c/p\u003e \u003cp\u003eThe NBS-predict method was utilized to differentiate between MMD patients and HCs. The subnetwork discovered involved the limbic, vision, sensory, and motor modules and the default network, which had a moderate differentiating effect. Additionally, the correlation analysis with white matter hyperintensity volume indicated that most of the work nodes were affected, which is consistent with the diffuse nature of the lesion. Therefore, individual brain networks were found to be associated with MMD-related lesions.\u003c/p\u003e \u003cp\u003eThe individual brain network constructed based on cortical morphology similarity is consistent with networks constructed using other modalities, such as functional and white matter fiber networks. Intercortical morphology similarity contains anatomical, functional, and genetically relevant information. The similarity in cytoarchitectural categorization may be the anatomical basis for morphology similarity connectivity. The classification of morphologically similar brain regions will likely be the same \u003csup\u003e11\u003c/sup\u003e. Additionally, regions with similar cortical thickness may have more white matter fiber connections and exhibit greater functional coherence and correlation with cognitive performance \u003csup\u003e31,32\u003c/sup\u003e. Thus, individual brain networks may contain more biological information.\u003c/p\u003e \u003cp\u003eAbnormalities in individual networks have been shown to be associated with cognitive impairment diseases \u003csup\u003e11\u003c/sup\u003e. Furthermore, individual network abnormalities have been reported in cerebrovascular diseases such as cerebral small-vessel disease and carotid stenosis \u003csup\u003e33,34\u003c/sup\u003e. This study investigated the individual brain networks of patients with MMD, a cerebrovascular disease commonly associated with cognitive impairment. The study provides new information on the reorganization of brain structure and networks in MMD patients.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. First, the structural brain images of the MMD patients included in this study were acquired by two MRI scans of the same model and used the same sequence settings. Only gray matter volume was analyzed in this study, and the data were normalized for subsequent analyses, thus minimizing the effects caused by differences in MRI equipment. Second, this study could not explore the factors associated with network reorganization in patients with MMD because of a lack of information on cognitive function and genes. Third, because most patients with hemorrhagic MMD have undergone ventricular puncture and hematoma removal surgery, resulting in significant structural brain damage, the number of patients with hemorrhagic MMD included in this study was relatively small. Hemorrhagic MMD patients are unique in terms of cerebral perfusion, and the course of the disease, and the study of the brain networks of such patients still needs more data.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings suggest that there is a reorganization of individual brain networks in both HCs and MMD patients, as well as among different types of MMD patients. In addition, it has been observed that individual brain networks are associated with MMD-related brain structure alternation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThis study was approved by the ethics committee of Beijing Tiantan Hospital, Capital Medical University (KY2023-275-03) and all methods were performed in accordance with relevant guidelines and regulations. All MMD patients and healthy control participants were volunteers and provided informed consent. The information of 140 patients diagnosed with MMD in our hospital from January 2019 to September 2023 was reviewed. The inclusion criteria were as follows: 1) diagnosed with MMD according to the criteria of the Research Committee on Spontaneous Occlusion of the Circle of Willis \u003csup\u003e35\u003c/sup\u003e; 2) over 18 years of age; 3) no evidence of brain lesions on T1-weighted images larger than 15 mm in diameter; 4) no cranial surgery prior to recruitment; 5) no history of other diseases with cognitive impairment or use of drugs that may alter cognitive function; 6) no MRI contraindications. 100 healthy controls (HCs) matched for age, sex, and educational background were recruited using the following criteria: 1) no history of neurological, psychiatric, or cognitive disease; 2) no history of drug use that could alter cognitive function; 3) no MRI contraindications.\u003c/p\u003e\n \u003cp\u003eParticipants with incomplete clinical information and MRI data that did not meet quality control standards were excluded. 136 patients with MMD and 99 HCs were finally selected for the study. The MRA stage of MMD was recorded \u003csup\u003e1\u003c/sup\u003e. Lacune was defined as a round or ovoid subcortical cavity of cerebrospinal fluid signal with a diameter of 3 to 15 mm, and the number of lacunes was recorded \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThe baseline clinical information of all participants was recorded, including details such as age, gender, and education. T1-MPRAGE, fluid-attenuated inversion recovery (FLAIR), and time-of-flight magnetic resonance angiography (TOF-MRA) images were subsequently examined. Gray matter volume, white matter volume, and total brain volume were derived from T1-MPRAGE images, accompanied by the construction of individual gray matter volume networks. FLAIR was employed to evaluate lacunes and white matter hyperintensity lesions, while MRA served as the diagnostic criteria for MMD and its stage assessment. The study compared alterations in individual networks among patients and investigated sub-networks associated with MMD.\u003c/p\u003e\n \u003cp\u003eThe medical history of each patient was collected in detail. Patients with transient limb weakness, hemiparesis, and aphasia that can recover without symptoms, and no cerebral infarction observed on brain MRI, were classified as ischemic type. Hemorrhagic MMD was diagnosed based on clinical presentation, including severe headache and consciousness disturbance, and confirmed by computed tomography (CT) scan, with a time interval between symptom onset and the first CT scan of less than 24 hours. Asymptomatic MMD was defined as patients without a history of cerebrovascular events such as transient ischemic attack, cerebral infarction, intracranial hemorrhage, seizure, or involuntary movement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eMRI Data Acquisition\u003c/h2\u003e\n \u003cp\u003eMRI data were acquired using an Ingenia 3.0 Tesla scanner (Philip Medical Systems, Best, Netherlands) equipped with a 32-channel head coil. A T1-weighted MPRAGE sequence with the following parameters was used for all scans: repetition time (TR) / echo time (TE)\u0026thinsp;=\u0026thinsp;6.84/3.09 ms, flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;, field of view (FOV)\u0026thinsp;=\u0026thinsp;240\u0026times;240 mm\u003csup\u003e2\u003c/sup\u003e, acquisition matrix\u0026thinsp;=\u0026thinsp;240\u0026times;240, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, voxel size 1.0\u0026times;1.0\u0026times;1.0 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eA T2-weighted fluid-attenuated inversion recovery sequence with the following parameters was used for all scans: TR/TE/inversion time (TI)\u0026thinsp;=\u0026thinsp;4800/340/1650 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, voxel size\u0026thinsp;=\u0026thinsp;1.0\u0026times;1.0\u0026times;1.0 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eA TOF-MRA sequence with the following parameters was used for all scans: TR/TE\u0026thinsp;=\u0026thinsp;22/3.5 ms, flip angle\u0026thinsp;=\u0026thinsp;18\u0026deg;, acquisition matrix\u0026thinsp;=\u0026thinsp;384\u0026times;250, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, voxel size 1.0\u0026times;1.0\u0026times;1.0 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eStructural Image Preprocessing\u003c/h2\u003e\n \u003cp\u003eAll T1 images were manually checked for scanning artifacts and then subjected to standard preprocessing using voxel-based morphometry (VBM) based on statistical parametric mapping 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003c/span\u003e). The VBM analysis steps were: 1) The segmentation tool was first used to segment individual structural data to obtain gray matter images. 2) A study-specific template based on Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) was created, which is based on the gray matter images of all the participants \u003csup\u003e37\u003c/sup\u003e. 3) The gray matter images of each participant were warped into the template and then normalized to the standard Montreal Neurological Institute (MNI) standard space. 4) Voxel values in individual gray matter images were then modulated and resampled to a resolution of 2 mm\u003csup\u003e3\u003c/sup\u003e. 5) Finally, all gray matter images were smoothed with a 15 mm full width at half maximum (FWMH) Gaussian kernel \u003csup\u003e38\u003c/sup\u003e. In addition, the volumes of gray matter, white matter, and total intracranial volume were also calculated for further analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of Individual Cortical Thickness Networks\u003c/h2\u003e\n \u003cp\u003eThe graynet toolbox was used to construct individual networks \u003csup\u003e39\u003c/sup\u003e. Network nodes were defined based on the Automated Anatomical Labeling (AAL) 90 atlas. Histogram-weighted networks (HiWeNet) based on gray matter volume were constructed. For each node according to the AAL90 atlas, the voxel-wise gray matter volume was transformed into a histogram and 5% outliers from the distribution were discarded. The histogram was further binned into uniformly spaced N\u0026thinsp;=\u0026thinsp;100 bins and the histogram counts were then normalized for k\u0026thinsp;=\u0026thinsp;1: N. The network edges were calculated as the histogram distance between two nodes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A detailed description of the method can be found in the original articles \u003csup\u003e12\u003c/sup\u003e. The weight of the edge was thus defined as the statistical similarity of the morphological distributions and rescaled to [0, 1] using the min-max scaling. Since the weight of the edge was negatively correlated with the morphological similarity between the two nodes, the network matrix was subtraction normalized, with each edge subtracted by 1 to generate the traditional structural connectivity matrix \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eWhite Matter Hyperintensity\u003c/h2\u003e\n \u003cp\u003eWhite matter hyperintensity burden was automatically segmented by the lesion prediction algorithm as implemented in the Lesion Segmentation Tool (LST) version 3.0.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.statistical-modelling.de/lst.html\u003c/span\u003e\u003c/span\u003e) for SPM \u003csup\u003e40\u003c/sup\u003e. The total number and size (ml) of white matter hyperintensity were generated..\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eNetwork Properties\u003c/h2\u003e\n \u003cp\u003eAll network properties of each brain network were calculated using the GRETNA toolbox in MATLAB \u003csup\u003e41\u003c/sup\u003e. According to a previous study \u003csup\u003e34\u003c/sup\u003e, the sparsity range was set from 10\u0026ndash;60% with an interval of 5%. The global and nodal network metrics were calculated at each sparsity level, and the area under the curve (AUC) for each property across the entire sparsity range was used for statistical analysis.\u003c/p\u003e\n \u003cp\u003eGlobal network properties, including small worldness and network efficiency, were analyzed. Five metrics: clustering coefficient (Cp), characteristic path length (Lp), normalized clustering coefficient (\u0026gamma;), normalized characteristic path length (\u0026lambda;), and small worldness (\u0026sigma;) indicate the degree of small-world organization. Cp is the average clustering coefficient of all nodes and reflects the degree of local interconnectivity. Lp is the average shortest path length between any two nodes in a network. \u0026gamma; and \u0026lambda; refer to normalized Cp and normalized Lp, respectively. \u0026sigma; is calculated as aCp/aLp. A brain network with \u0026gamma;\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u0026lambda;\u0026thinsp;\u0026asymp;\u0026thinsp;1 or \u0026sigma;\u0026thinsp;\u0026gt;\u0026thinsp;1 has a small-world property, reflecting an optimal balance of integration and segregation for a network. Network efficiency includes global efficiency (Eglob) and local efficiency (Eloc). Eglob measures the efficiency of parallel information transfer in the network. Eloc reflects the communication efficiency in the neighborhood of the nodes, which reflects the fault tolerance level of the network.\u003c/p\u003e\n \u003cp\u003eNode degree centrality (DC) and betweenness centrality (BC) were chosen for local topological characteristics. DC reflects the ability to communicate information, and BC captures the importance of a given node in the flow of information. Detailed definitions of the above metrics and the formulas can be found in Rubinov\u0026apos;s review \u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eNetwork-Based Statistic (NBS) Prediction\u003c/h2\u003e\n \u003cp\u003eThe NBS prediction method was used to test the effect of MMD and white matter hyperintensity on the individual gray matter volume networks \u003csup\u003e30\u003c/sup\u003e. Parameters were selected as recommended: 40-fold, 50 repeated cross-validation (CV) procedures, hyperparameters with the grid search algorithm, and auto-optimization for classification and regression algorithms. The initial p value was 0.01, and the edge weight threshold was 0.8 to visualize a subnetwork with relevant edges.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAnalysis of demographic and clinical data was performed using IBM SPSS Statistics 25.0. Continuous variables underwent comparison using two-tailed independent samples t-test and analysis of variance (ANOVA), with Bonferroni\u0026rsquo;s correction applied for post hoc comparisons. Categorical variables were assessed using the Chi-squared test.\u003c/p\u003e\n \u003cp\u003eNetwork metrics were statistically examined through the GRETNA toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/gretna/\u003c/span\u003e\u003c/span\u003e) at a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Age, gender, and education level were adjusted for as confounding variables. The False Discovery Rate (FDR) was employed to adjust for multiple comparisons. Classification algorithms such as logistic regression, support vector classification, decision tree classification, and linear discriminant analysis were employed for identifying MMD-related subnetworks. Regression algorithms including linear regression, support vector regression, and decision tree regression were utilized to identify the subnetwork linked to white matter hyperintensity. This part of the statistical analysis was based on the NBS-Predict toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/nbspredict/\u003c/span\u003e\u003c/span\u003e), with the most effective algorithms automatically selected.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Contract grant number: 82371915).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Z. and P.W. conceived and designed the research. H.Z. performed statistical analysis and drafted the manuscript. W.L., Q.Z., C.Z. and T.L. enrolled patients and acquired brain magnetic resonance imaging. T.Y., X.L., Q.Z., J.Z. and Y.Z. critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data that support the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eResearch Committee on the Pathology and Treatment of Spontaneous Occlusion of the Circle of Willis \u0026amp; Health Labour Sciences Research Grant for Research on Measures for Infractable Diseases. Guidelines for diagnosis and treatment of moyamoya disease (spontaneous occlusion of the circle of Willis). \u003cem\u003eNeurol Med Chir (Tokyo)\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 245\u0026ndash;266 (2012).\u003c/li\u003e\n\u003cli\u003eKarzmark, P., Zeifert, P. D., Bell-Stephens, T. E., Steinberg, G. K. \u0026amp; Dorfman, L. J. Neurocognitive impairment in adults with moyamoya disease without stroke. \u003cem\u003eNeurosurgery\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 634\u0026ndash;638 (2012).\u003c/li\u003e\n\u003cli\u003eKazumata, K. \u003cem\u003eet al.\u003c/em\u003e Combined structural and diffusion tensor imaging detection of ischemic injury in moyamoya disease: relation to disease advancement and cerebral hypoperfusion. \u003cem\u003eJ Neurosurg\u003c/em\u003e \u003cstrong\u003e134\u003c/strong\u003e, 1155\u0026ndash;1164 (2020).\u003c/li\u003e\n\u003cli\u003eZuo, Z. \u003cem\u003eet al.\u003c/em\u003e Atrophy in subcortical gray matter in adult patients with moyamoya disease. \u003cem\u003eNeurol Sci\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 1709\u0026ndash;1717 (2023).\u003c/li\u003e\n\u003cli\u003eLei, Y. \u003cem\u003eet al.\u003c/em\u003e Reconfigured functional network dynamics in adult moyamoya disease: a resting-state fMRI study. \u003cem\u003eBrain Imaging Behav\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 715\u0026ndash;727 (2020).\u003c/li\u003e\n\u003cli\u003eZheng, G. \u003cem\u003eet al.\u003c/em\u003e Changes in Brain Functional Network Connectivity in Adult Moyamoya Diseases. \u003cem\u003eCogn Neurodyn\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 861\u0026ndash;872 (2021).\u003c/li\u003e\n\u003cli\u003eHu, J. \u003cem\u003eet al.\u003c/em\u003e Moyamoya Disease With Initial Ischemic or Hemorrhagic Attack Shows Different Brain Structural and Functional Features: A Pilot Study. \u003cem\u003eFront Neurol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 871421 (2022).\u003c/li\u003e\n\u003cli\u003eSun, H. \u003cem\u003eet al.\u003c/em\u003e Angiographic and Hemodynamic Features in Asymptomatic Hemispheres of Patients With Moyamoya Disease. \u003cem\u003eStroke\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 210\u0026ndash;217 (2022).\u003c/li\u003e\n\u003cli\u003eAlexander-Bloch, A., Giedd, J. N. \u0026amp; Bullmore, E. Imaging structural co-variance between human brain regions. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 322\u0026ndash;336 (2013).\u003c/li\u003e\n\u003cli\u003eWang, P. \u003cem\u003eet al.\u003c/em\u003e Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis. \u003cem\u003eFront Aging Neurosci\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 788661 (2022).\u003c/li\u003e\n\u003cli\u003eCai, M. \u003cem\u003eet al.\u003c/em\u003e Individual-level brain morphological similarity networks: Current methodologies and applications. \u003cem\u003eCNS Neurosci Ther\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 3713\u0026ndash;3724 (2023).\u003c/li\u003e\n\u003cli\u003ePr, R. \u0026amp; Sc, S. Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight. \u003cem\u003eBrain structure \u0026amp; function\u003c/em\u003e \u003cstrong\u003e225\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eYin, H. \u003cem\u003eet al.\u003c/em\u003e A Novel Staging System to Evaluate Cerebral Hypoperfusion in Patients With Moyamoya Disease. \u003cem\u003eStroke\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 2837\u0026ndash;2843 (2018).\u003c/li\u003e\n\u003cli\u003eLiu, Z.-W. \u003cem\u003eet al.\u003c/em\u003e Collateral Circulation in Moyamoya Disease: A New Grading System. \u003cem\u003eStroke\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 2708\u0026ndash;2715 (2019).\u003c/li\u003e\n\u003cli\u003eMatchin, W. G. A neuronal retuning hypothesis of sentence-specificity in Broca\u0026rsquo;s area. \u003cem\u003ePsychon Bull Rev\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1682\u0026ndash;1694 (2018).\u003c/li\u003e\n\u003cli\u003eHu, J. \u003cem\u003eet al.\u003c/em\u003e Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease. \u003cem\u003eBMC Neurol\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 179 (2022).\u003c/li\u003e\n\u003cli\u003eBuckner, R. L. \u0026amp; DiNicola, L. M. The brain\u0026rsquo;s default network: updated anatomy, physiology and evolving insights. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 593\u0026ndash;608 (2019).\u003c/li\u003e\n\u003cli\u003eYuan, Z. \u003cem\u003eet al.\u003c/em\u003e Altered functional connectivity of the right caudate nucleus in chronic migraine: a resting-state fMRI study. \u003cem\u003eJ Headache Pain\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 154 (2022).\u003c/li\u003e\n\u003cli\u003eWei, C. \u003cem\u003eet al.\u003c/em\u003e A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment. \u003cem\u003eFront Aging Neurosci\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 774607 (2021).\u003c/li\u003e\n\u003cli\u003eShi, Z., Wen, Y.-J., Huang, Z., Yu, L.-B. \u0026amp; Zhang, D. Different aspects of cognitive function in adult patients with moyamoya disease and its clinical subtypes. \u003cem\u003eStroke Vasc Neurol\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 86\u0026ndash;96 (2020).\u003c/li\u003e\n\u003cli\u003eBothwell, A. R., Resnick, S. M., Ferrucci, L. \u0026amp; Tian, Q. Associations of olfactory function with brain structural and functional outcomes. A systematic review. \u003cem\u003eAgeing Res Rev\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 102095 (2023).\u003c/li\u003e\n\u003cli\u003eLi, Y. \u003cem\u003eet al.\u003c/em\u003e Baseline cerebral structural morphology predict freezing of gait in early drug-na\u0026iuml;ve Parkinson\u0026rsquo;s disease. \u003cem\u003eNPJ Parkinsons Dis\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 176 (2022).\u003c/li\u003e\n\u003cli\u003eHuang, X., Zhang, M., Li, B., Shang, H. \u0026amp; Yang, J. Structural and functional brain abnormalities in idiopathic cervical dystonia: A multimodal meta-analysis. \u003cem\u003eParkinsonism \u0026amp; Related Disorders\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 153\u0026ndash;165 (2022).\u003c/li\u003e\n\u003cli\u003eZhang, W. \u003cem\u003eet al.\u003c/em\u003e Functional organization of the fusiform gyrus revealed with connectivity profiles. \u003cem\u003eHum Brain Mapp\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 3003\u0026ndash;3016 (2016).\u003c/li\u003e\n\u003cli\u003eTachibana, Y. \u0026amp; Hikosaka, O. The primate ventral pallidum encodes expected reward value and regulates motor action. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 826\u0026ndash;837 (2012).\u003c/li\u003e\n\u003cli\u003eLisman, J. \u003cem\u003eet al.\u003c/em\u003e Viewpoints: how the hippocampus contributes to memory, navigation and cognition. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1434\u0026ndash;1447 (2017).\u003c/li\u003e\n\u003cli\u003eSun, R. \u003cem\u003eet al.\u003c/em\u003e White matter structural and network topological changes in moyamoya disease with limb paresthesia: A study based on diffusion kurtosis imaging. \u003cem\u003eFront Neurosci\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1029388 (2022).\u003c/li\u003e\n\u003cli\u003eFujimura, M. \u003cem\u003eet al.\u003c/em\u003e Intrinsic development of choroidal and thalamic collaterals in hemorrhagic-onset moyamoya disease: case-control study of the Japan Adult Moyamoya Trial. \u003cem\u003eJ Neurosurg\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, 1453\u0026ndash;1459 (2019).\u003c/li\u003e\n\u003cli\u003eZhang, Q. \u003cem\u003eet al.\u003c/em\u003e Hemorrhagic patterns and their risk factors in patients with moyamoya disease. \u003cem\u003eEur J Neurol\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 2499\u0026ndash;2507 (2020).\u003c/li\u003e\n\u003cli\u003eSerin, E., Zalesky, A., Matory, A., Walter, H. \u0026amp; Kruschwitz, J. D. NBS-Predict: A prediction-based extension of the network-based statistic. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e244\u003c/strong\u003e, 118625 (2021).\u003c/li\u003e\n\u003cli\u003eSun, L. \u003cem\u003eet al.\u003c/em\u003e Structural insight into the individual variability architecture of the functional brain connectome. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e259\u003c/strong\u003e, 119387 (2022).\u003c/li\u003e\n\u003cli\u003eGong, G., He, Y., Chen, Z. J. \u0026amp; Evans, A. C. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 1239\u0026ndash;1248 (2012).\u003c/li\u003e\n\u003cli\u003eRen, J. \u003cem\u003eet al.\u003c/em\u003e Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks. \u003cem\u003eFront. Aging Neurosci.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1091829 (2023).\u003c/li\u003e\n\u003cli\u003eGao, Y. \u003cem\u003eet al.\u003c/em\u003e Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease. \u003cem\u003eBrain Sci\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1359 (2023).\u003c/li\u003e\n\u003cli\u003eKURODA, S. \u003cem\u003eet al.\u003c/em\u003e Diagnostic Criteria for Moyamoya Disease - 2021 Revised Version. \u003cem\u003eNeurol Med Chir (Tokyo)\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 307\u0026ndash;312 (2022).\u003c/li\u003e\n\u003cli\u003eWardlaw, J. M. \u003cem\u003eet al.\u003c/em\u003e Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. \u003cem\u003eThe Lancet Neurology\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 822\u0026ndash;838 (2013).\u003c/li\u003e\n\u003cli\u003eAshburner, J. A fast diffeomorphic image registration algorithm. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 95\u0026ndash;113 (2007).\u003c/li\u003e\n\u003cli\u003eLi, Y. \u003cem\u003eet al.\u003c/em\u003e Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e235\u003c/strong\u003e, 118018 (2021).\u003c/li\u003e\n\u003cli\u003eReddy Raamana, P. \u0026amp; C. Strother, S. graynet: single-subject morphometric networks for neuroscience connectivity applications. \u003cem\u003eJOSS\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 924 (2018).\u003c/li\u003e\n\u003cli\u003eSchmidt, P. Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging. (Ludwig-Maximilians-Universit\u0026auml;t M\u0026uuml;nchen, 2017).\u003c/li\u003e\n\u003cli\u003eWang, J. \u003cem\u003eet al.\u003c/em\u003e GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, (2015).\u003c/li\u003e\n\u003cli\u003eRubinov, M. \u0026amp; Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1059\u0026ndash;1069 (2010).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Moyamoya Disease, Gray Matter Network, Graph Theory, Gray Matter Volume, Network-Based Statistic","lastPublishedDoi":"10.21203/rs.3.rs-4461906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4461906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePatients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed. Compared to HCs, MMD patients exhibited decreased clustering coefficient (Cp) and local efficiency (Eloc). Ischemic patients showed decreased Eloc and increased characteristic path length (Lp) compared to asymptomatic and hemorrhagic patients. MMD patients had significant regional abnormalities, including decreased degree centrality (DC) in the left medial orbital superior frontal gyrus, left orbital inferior frontal gyrus, and right calcarine fissure and surrounding cortex. Increased DC was found in bilateral olfactory regions, with higher betweenness centrality (BC) in the right median cingulate, paracingulate fusiform gyrus, and left pallidum. Ischemic patients had lower BC in the right hippocampus compared to hemorrhagic patients, while hemorrhagic patients had decreased DC in the right triangular part of the inferior frontal gyrus compared to asymptomatic patients. Subnetworks related to MMD and white matter hyperintensity volume were identified. There is significant reorganization of gray matter networks in patients compared to HCs, and among different types of patients. Gray matter networks can effectively detect MMD-related brain structural changes.\u003c/p\u003e","manuscriptTitle":"Reorganization of Gray Matter Networks in Patients with Moyamoya Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 18:22:04","doi":"10.21203/rs.3.rs-4461906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-21T04:09:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-09T15:02:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149885580705149098906321485809882819184","date":"2024-09-17T06:28:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T19:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109786984412550085063334312490167255369","date":"2024-07-06T12:46:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-13T02:46:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-13T02:41:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-24T14:16:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-24T08:35:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-22T15:16:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24d93ced-fa2b-4e87-99cf-ee62d69b8d0f","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:02:44+00:00","versionOfRecord":{"articleIdentity":"rs-4461906","link":"https://doi.org/10.1038/s41598-025-86553-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-22 15:57:47","publishedOnDateReadable":"January 22nd, 2025"},"versionCreatedAt":"2024-06-11 18:22:04","video":"","vorDoi":"10.1038/s41598-025-86553-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-86553-3","workflowStages":[]},"version":"v1","identity":"rs-4461906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4461906","identity":"rs-4461906","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}