Impaired White Matter Network Topology Mediates the Association Between White Matter Hyperintensities of Presumed Vascular Origin and Cognitive in the Elderly

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The study enrolled 54 cognitively unimpaired individuals, stratified into two groups: 26 with m/sWMH and 28 with mild WMH (mWMH). We used graph theory analysis to investigate the global and nodal topological disruptions between two groups and relate WM networks topological alterations to cognitive function. The results provided that, compared to mWMH group, m/sWMH group exhibit disruptions of global properties and nodal properties, including decreased clustering coefficient (Cp) and shortest path length (Lp), decreased nodal clustering coefficient (Ncp), increased nodal efficiency (Ne), both increased and decreased nodal degree centrality (Dc) and betweenness centrality (Bc) in regions such as body of corpus callosum, left sagittal stratum (SS) (including the inferior longitudinal fasciculus and inferior fronto-occipital fasciculus, ILF and IFOF), the anterior limb of the internal capsule, cingulate gyrus, and inferior longitudinal fasciculus. The pearson’s correlation analysis revealed that the nodal properties (Ncp and Dc) in SS (including ILF and IFOF) were significantly correlation with scores of cognitions in m/sWMH group. Increased WMH burden leads to the disintegration of WM network topology, and this disruption is closely associated with cognitive dysfunction (global cognitive function, attention and processing speed). The SS may be a specific target for cognitive impairment in patients with m/sWMH. white matter hyperintensities cognition topological organization resting-state functional magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction White Matter Hyperintensities (WMH) are imaging biomarkers of cerebral small vessel disease (CSVD), and WMH can lead to the decline of cognitive function and may eventually evolve into dementia (Habes et al., 2016, Hong et al., 2024). Different degrees of WMH have different effects on cognitive impairment. Neuroimaging studies have confirmed that expanded WMH burden serves as a critical neuroimaging biomarker for elevated risks of AD conversion (Yang et al., 2024, Jochems et al., 2022), and reached Fazekas scale 3 might be a significant indicator pointing towards future cognitive decline (Zeng et al., 2020). The elderly patients with m/sWMH, despite normal overall cognitive function, have alterations in working memory and episodic memory (Zeng et al., 2020). The WMH group had lower MMSE/MoCA versus non-WMH group, alongside total scores. Specific deficits included attention, recall, and language on MMSE, and executive function, naming, abstraction, and orientation on MoCA (Chen et al., 2024). WMH burden was associated with faster decline in perceptual speed in both the whole group as well as in non-demented participants (Arfanakis et al., 2020). Therefore, early detection of potential biological markers for cerebral white matter lesions in cognitively intact elderly patients is clinically imperative. Understanding how these markers correlate with cognitive function further constitutes an urgent research priority. The resting-state functional magnetic resonance imaging (rs-fMRI) have revealed that WMH may indirectly induce abnormalities in the WM-FN by disrupting the structural integrity of white matter fiber pathways (Keřkovský et al., 2019, Huang et al.,2023). Our previous study had reported that cognitively normal adults with moderate to severe WMH burden exhibited altered spontaneous brain activity, and altered ALFF in the right prefrontal cortex was associated with worse immediate recall and recognition. This might indicate that the mediating effects of brain activity elucidate the underlying mechanisms linking WMH to cognitive impairment (Xing et al., 2022). Existing research has investigated the relationship between WMH burden and functional connectivity (FC) alterations, consistently demonstrating that individuals with higher WMH volumes exhibit accelerated cognitive decline, likely mediated by disrupted large-scale network integration (Kumar et al., 2020). The confluent WMH significantly disrupt intrinsic connectivity within the DMN, and are strongly associated with episodic memory decline. However, nonconfluent WMH predominantly affect the executive control network (ECN), leading to impaired performance on executive function tasks (Kumar et al., 2020). Modern research based on graph theory has provided a comprehensive understanding of the complexity of brain functional network connectivity, offering a valuable tool for investigating topological properties of brain functional networks. Graph theory reconstructs and evaluates brain connectivity networks by representing cortical and subcortical regions as nodes and white matter tract interconnections as edges in mathematical graphs, thus enabling the analysis of brain network topology and elucidating interrelationships between distinct brain regions. Current research exploring WMH biomarkers using graph theory techniques remains in its nascent stage, with relatively limited studies available. The aim of this study was to determine the differences in topological organization variance between patients with m/sWMH and patients with mWMH, and examine their associations with cognitive function. Materials and Methods 2.1 Participants The study involved 54 patients with WMH and normal cognitive function from the Department of Neurology at Xuanwu Hospital. Subjects were enrolled based on the following inclusion criteria: ( 1 ) age ≥ 55, and ≤ 75 years; ( 2 ) right-handed; ( 3 ) normal cognitive function: clinical dementia rating (CDR) = 0 (Morris, 1993); ( 4 ) white matter lesions on MRI without cortical or watershed infarcts, hemorrhages or hydrocephalus; and ( 5 ) could cooperate during neuropsychological examination. The exclusion criteria included: ( 1 ) WMH caused by non-vascular factors, including encephalitis, poisoning, multiple sclerosis and infection; ( 2 ) transient ischemic attack within 3 months or a high-intensity lacunar infarction on DWI images; ( 3 ) any physical impairment or use of medication that might affect cognitive functions; and ( 4 ) serious organ diseases or uncontrolled mental diseases, e.g. gastrointestinal, hepatic, infectious, or cardiovascular diseases, cancer, or drug addiction. The WMH severity of participants were categorized by an experienced neurologist and an experienced radiologist by the Fazekas rating scale from 0 to 3 using the following rubric: 0 - no periventricular or deep white matter WMH; 1 - pencil-thin lining or capped periventricular WMH and/or punctate foci in deep white matter; 2 - smooth periventricular WMH halo and/or beginning of deep WMH confluence; and 3 - irregular periventricular signal extending to deep white matter and/or large deep white matter confluence (Fazekas et al., 1987). All participants were divided into two groups: those with Fazekas 1 were assigned to the mWMH group (n = 28), and those with Fazekas 2–3 were assigned to the m/sWMH group (n = 26). The study protocol was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (approval no. 2024-144-002), and written informed consent was obtained from all participants prior to their involvement in the study. 2.2 MRI data acquisition MRI data were acquired using the 3.0-T Siemens Skyra scanner with a 24-channel head coil. High-resolution 3D T1-weighted anatomical images were acquired using the following parameters: repetition time (TR) = 1690 ms, echo time (TE) = 2.56 ms, slice thickness = 1mm, 176 slices, flip angle = 12°, field of view (FOV) = 256×256 mm 2 , matrix size = 256×256. Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired using the following parameters: TR = 2000 ms, TE = 30 ms, slice thickness = 3 mm, 35 slices, gap = 1 mm, flip angle = 83°, FOV = 224 × 224 mm 2 , matrix size = 64 × 64, duration = 6 min. Coronal 3D Flair images were obtained using the following parameters: TR = 5000 ms, TE = 392 ms, FOV = 240 × 240mm 2 , inversion time = 1800 ms, flip angle = 120°, slice thickness = 1 mm. 2.3 fMRI data processing The rs-fMRI preprocessing was performed using the Data Processing and Analysis of Brain Imaging (DPABI 2.3) software, which is based on statistical parametric mapping (SPM12). The preprocessing of rs-fMRI data was included the following steps: (a) Format conversion. (b) Removal of the first 10 time points to account for signal equilibration. (c) Slice timing correction to eliminate within-volume time differences during acquisition. (d) Head motion correction. (e) Alignment of T1 and fMRI images. (f) Segmentation. (g) Spatial normalization. (h) Regressing out cerebrospinal fluid signals and Friston 24 head motion parameters to minimize their impact and enhance accuracy. (i) White matter mask generation and extraction of white matter-specific fMRI signals. (j) Smoothing. 2.4 White matter networks construction Based on the JHU white matter atlas, the brain's white matter was divided into 50 regions of interest (ROIs). The mean time series for each ROI was extracted from the preprocessed fMRI images. Pearson correlation coefficients were computed for all ROI pairs, then transformed into Fisher's z-values to obtain a 50 × 50 connectivity matrix for each subject. 2.5 White matter networks topology analysis The GRETNA 2.0 toolkit ( http://www.nitrc.org/projects/gretna/ ) was used to calculate topological properties of white matter networks, the processing steps are as follows: First, define a sparsity threshold range from 5% to 50% with a step size of 5% for network sparsification. Second, define the network as a weighted network. This study analyzed the topological properties of white matter networks, calculating 7 global topological properties, including clustering coefficient, global efficiency, hierarchy, local efficiency, shortest length, small-world and synchronization. Additionally, 4 nodal topological properties were measured, including nodal clustering coefficient (Ncp), nodal efficiency (Ne), betweenness centrality (Bc) and nodal degree centrality (Dc). Furthermore, we calculated the area under the curve (AUC) for each global topological property over the range of sparsity (0.08–0.5) to provide a summarized scalar independent of single threshold selection. 2.6 Cognitive assessment All participants underwent neuropsychological assessments. The global cognitive function was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). We employed standardized tests to evaluate specific cognitive subdomains, including the Auditory Verbal Learning Test (WHO-UCLA AVLT) for memory assessment (covering immediate recall, long-delay recall, and long-delay recognition, with higher scores indicating better memory function), Digit Span Test (DST) for attention evaluation, the Trail Making Test-Parts B (TMT-B) for executive function evaluation, Trail Making Test-Parts A (TMT-A) for processing speed assessment, the Boston Naming Test (BNT) for language function assessment, and the Geriatric Depression Scale (GDS) for measuring emotion domain. 2.7 Statistical analysis All statistical analyses were performed by SPSS 26.0. The normality of continuous variables was evaluated using the Kolmogorov-Smirnov test. For comparing demographic characteristics and neuropsychological test scores between the mWMH and m/sWMH groups, we employed independent samples t-tests for normally distributed continuous data and chi-square tests (or Fisher's exact tests when applicable) for categorical variables. In cases where data showed non-normal distributions, the Mann-Whitney U test was used as a non-parametric alternative. Significance levels were set to P < 0.05. To assess whether there were significant differences in the topological properties of the WM networks between mWMH and m/s WMH groups, two-sample t-test was performed to compare the AUC value of each network metric between two groups (global and nodal properties). Global topological properties were considered significant at P < 0.05, while nodal topological properties underwent correction by Network-Based Statistics (NBS), and the criterion for statistical significance was set to P < 0.05. Once significant intergroup differences were identified in any nodal topological metrics, we further assessed the correlations between these metrics and clinical parameters in the m/sWMH group. A pearson correlation analysis was used to test the association, which was used a statistical significance level of P < 0.05. Results 3.1 Demographic and clinical characteristics The demographic and cognitive characteristics of mWMH group (28 participants with mild WMH) and m/sWMH group (26 moderate to severe WMH) are presented in Table 1 . There were no significant differences in gender, age, years of education, presence of diabetes, and hyperlipidemia between mWMH and m/sWMH groups. However, there was a significant difference in the rate of hypertension (P = 0.007), with a higher incidence in m/sWMH group compared to the mWMH group. Regarding cognitive function, participants with m/sWMHs performed significantly worse on the delay free recall (P = 0.017), TMT-A (P = 0.013) and BNT (P = 0.004). Table 1 Demographicdata and neuropsychological data for mWMH group and m/sWMH group mWMH m/sWMH group P -value Demographic Age 63.39 ± 6.38 67.31 ± 8.03 0.051 Gender(female/male) 19/9 18/8 0.914 Education (year) 11.82 ± 2.96 11.50 ± 2.49 0.669 Medical history (Y/N) Hypertension 8/20 17/9 0.007* Diabetes 3/25 6/20 0.223 Hyperlipidemia 9/19 10/16 0.627 Neuropsychological assessment MMSE 28.93 ± 1.09 28.64 ± 1.08 0.337 MoCA 27.71 ± 1.67 25.44 ± 2.80 0.050 WHO-UCLA AVLT Immediate recall 28.89 ± 7.45 27.23 ± 4.45 0.332 Delayed free recall 11.17 ± 2.61 9.62 ± 1.96 0.017* Recognition 12.79 ± 3.10 11.54 ± 2.02 0.099 BNT 26.15 ± 2.33 24.12 ± 2.64 0.004* Digit span forward 8.57 ± 0.92 7.50 ± 1.30 0.268 Digit span backward 5.04 ± 1.29 4.77 ± 1.31 0.454 TMT-A 42.49 ± 14.33 56.78 ± 25.08 0.013* TMT- B 90.36 ± 51.12 106.08 ± 53.95 0.287 GDS 5.00 ± 5.03 7.46 ± 6.96 0.140 All the participants were matched for sex and education. Data are presented as the means ± SD. Statistically significant P-value(P < 0.05)was shown in red. Abbreviations: WMH white matter hyperintensities, MMSE mini-mental state examination, MoCA Montreal Cognitive Assessment, WHOUCLAV AVLT WHO-UCLA auditory verbal learning test, BNT Boston naming test, TMT Trail Making Test, GDS Geriatric Depression Scale. * represent P < 0.05. 3.2 Global topological of white matter networks Compared to the mWMH group, patients in the m/sWMH group showed a significantly (P 0.05) in global efficiency (P = 0.059, Fig. 1 B), hierarchy (P = 0.157, Fig. 1 C), local efficiency (P = 0.269, Fig. 1 D), small-world index (P = 0.633, Fig. 1 F), and synchronization (P = 0.804, Fig. 1 G) between two groups. 3.3 Nodal topological of white matter networks Compared to mWMH group, the m/sWMH group exhibited decreased Ncp in the right anterior limb of the internal capsule, bilateral posterior thalamic radiation, bilateral SS (including ILF and IFOF), left cingulum, and right tapetum (Fig. 2 , P<0.05, NBS corrected). Compared with mWMH group, the m/sWMH group displayed increased Ne in body of corpus callosum, left cingulum, and right uncinate fasciculus (Fig. 3 , P<0.05, NBS corrected). When directly comparing the Dc between two groups, those with m/sWMH patients showed an increased Dc of right corticospinal tract, left medial lemniscus, left posterior thalamic radiation (including the optic radiation), sagittal stratum (SS) (including the inferior longitudinal fasciculus and inferior fronto-occipital fasciculus, ILF and IFOF), left cingulum, and left tapetum, and decreased Dc of right superior fronto-occipital fasciculus compared to mWMH group (Fig. 4, P<0.05, NBS corrected). Furthermore, m/sWMH group showed significantly increased Bc in the right anterior limb of the internal capsule, left cingulum, and right uncinate fasciculus, and decreased in the left internal capsule, left SS (including ILF and IFOF ) compared to mWMH group (Fig. 5 , P<0.05, NBS corrected). 3.4 The correlation analysis of WM network topological in abnormal regions and cognition in m/sWMH group By performing pearson correlation analysis, we found that the global topological showed no significantly correlation with cognition function in m/sWMH group. For nodal properties, we investigated only the nodes with significant group differences. The correlations between nodal properties (Ncp, Ne, Dc, Bc) and cognition assessment were analyzed in patients with m/sWMH. The results demonstrated that the left SS ( including ILF and IFOF) of Ncp was significantly positive correlation with MoCA (r = 0.444, P = 0.030, Fig. 6 A), TMT-A (r = -0.4629, P = 0.021, Fig. 6 B) and digit span backward (r = 0.651, P<0.0001, Fig. 6 C). Meanwhile, the left SS (including ILF and IFOF) of Dc was significantly positive correlation with digit span backward (r = 0.528, P = 0.007, Fig. 6 D). Discussion In the present study, we investigated topological properties of resting state functional networks in cognitively healthy older adults with WMH using graph theoretical analysis, and the relationship among global topological properties, nodal topological properties and cognitive impairment in m/sWMH group. We founded that patients with m/sWMH showed disturbed white matter networks (significantly decreased Cp and Lp of gobal topological properties), compared to patients with mWMH. At the regional level, compared with the mWMH group, the m/sWMH group displayed a decreased Ncp, increased Ne, and heterogeneous changes in Dc and Bc, with both increases and decreases observed. Moreover, SS (including ILF and IFOF) was significantly associated with global cognitive function, subdomains of cognitive in processing speed and attention. These findings provide novel insights into the neural mechanisms underlying WMH and their impact on cognitive function. In our study, m/sWMH group showed white matter functional networks with significantly decreased Cp and Lp compared to mWMH group, and in the m/sWMH group, no significant correlations were observed between global topological properties and cognitive function. This may indicate changes characterized by randomization and reduced efficiency in the white matter networks of patients with m/sWMH. Consistent with previous studies, this study reveals a decreased clustering coefficient, indicating that patients with greater WMH burden exhibit reduced structural WM network connectivity compared to those with less burden (Huang et al., 2023, Wang et al., 2022, Xin et al., 2022). While the decreased shortest path length may suggest compensatory mechanisms of white matter networks in patients with m/sWMH. White matter damage, by disrupting efficient long-range connections (Chen et al., 2020, Araque et al., 2018, Sui et al., 2025), may prompt the network to compensate through increased redundancy or strengthened short-range connections. This compensatory rewiring can yield Lp at the cost of decreased local clustering, reflecting suboptimal network reorganization. The alterations observed in these network properties have been associated with cognitive decline (Chen et al., 2019, Liu et al., 2019, Schulz et al., 2021). Meanwhile, compared to the mWMH group, the m/sWMH group exhibited significantly decreased Ncp, increased Ne, mostly increased Dc and Bc in multiple white matter bundles, including the anterior limb of the internal capsule, posterior thalamic radiation, SS (including ILF and IFOF), cingulum, tapetum, coropus callosum and uncinate fasciculus. White matter bundles, critical white matter network interacting with many brain areas, are key targets in neurodegenerative diseases (Peng et al., 2024, Ricchi et al., 2025). Disrupting nodal of white matter directly weakens the network’s capacity for efficient, stable information flow. Given the Ncp reflect the connection density of reaction node local network. The Ne reaction node serves as a network accelerator, determining the efficiency of global information propagation. The Dc reaction node acts as a structural architect, regulating neighborhood-level node interactions and link formations. The Bc reaction node functions as an information gatekeeper, with its strategic value quantified by its recurrence across all optimal transmission pathways (Bullmore et al., 2009). We found decreased Ncp, increased Ne, mostly increased Dc and Bc in multiple brain regions in the m/sWMH group, which may reflect compensatory mechanisms aimed at maintaining neural equilibrium. Severe WMH may disrupt local white matter tracts, reducing connectivity in originally tightly interconnected regional networks and leading to decreased clustering coefficients, which was consistent with previous study (Xin et al., 2022). Concurrently, to preserve overall information transfer, the neural network may undergo compensatory adaptations, such as enhancing connections in certain hub nodes (increased Dc). These modals might become more involved in relaying information across distinct brain regions (increased Bc), thereby improving Ne. The experts speculated that WMH are pathologically attributed to chronic cerebral hypoperfusion resulting from age-related degeneration of penetrating arterioles, lipohyalinosis, and blood-brain barrier dysfunction (Wardlaw et al., 2019, Wong et al.,2019, Zhang et al., 2018). The association between WMH and hypoperfusion has been confirmed by multiple imaging and pathological studies, with both contributing to cognitive impairment by disrupting white matter microstructure and brain network dynamics (Li et al., 2024, Arslan et al., 2020). Furthermore, the decreased nodal properties of Ncp and Dc in SS (including ILF and IFOF) were significantly correlation with MoCA, TMT-A and digit span backward, indicating potential early imaging biomarkers for m/sWMH. The SS is formed by ILF IFOF, optic radiation, and other associational fibers and SS plays a vital role in connecting cortical and subcortical regions. The ILF facilitates visual-memory integration, while the IFOF connects frontal, occipital, and temporal regions to support executive functions, language processing, and visual attention modulation (Al-Juboori AA et al., 2025, Di Carlo DT et al, 2019). When the SS is damaged-whether by disease or surgery-patients often experience severe deficits in language, vision, and cognitive abilities (Paus T et al, 1999, Robles DJ et al, 2022). Given their existing white matter degeneration, older adults face a substantially higher risk of accelerated cognitive decline when SS damage compounds these effects (Robles DJ et al, 2022). Recent studies demonstrate that microstructural damage in cerebral white matter can significantly impair patients' cognitive function. Importantly, neurodegenerative processes extend beyond radiologically detectable white matter hyperintensity lesions to affect remote brain regions through white matter fiber networks (Yuan et al., 2017; Lu et al., 2021, Xin et al., 2022). Our study shows key differences between mild (mWMH) and moderate/severe (m/sWMH) white matter hyperintensity patients. The more severe white matter damage in m/sWMH patients - seen in reduced clustering coefficients and degree centrality in ILF/IFOF pathways - appears before cognitive symptoms, potentially serving as early warning signs. These structural changes disrupt brain network communication, particularly in fronto-occipito-temporal connections, leading to: ( 1 ) memory and language problems, and ( 2 ) slower thinking and task-switching speeds that characterize m/sWMH patients. Additionally, this study found a significantly higher prevalence of hypertension in the m/sWMH group (P = 0.007), consistent with previous findings indicating that for every 10 mmHg increase above normal systolic pressure, WMH burden increases by approximately 1.126 (Wartolowska et al., 2021). The underlying pathology likely involves multiple interacting mechanisms: chronic hypertension induces structural remodeling of cerebral small arteries (e.g., hyaline degeneration) and impairs vascular autoregulation, leading to hypoperfusion or high shear stress damage in white matter, promoting myelin degeneration (Zhao et al., 2019). Furthermore, blood pressure fluctuations activate endothelial inflammation, upregulating matrix metalloproteinase-9 (MMP-9) expression, which disrupts tight junctions of the blood-brain barrier, allowing toxic substances such as fibrinogen to infiltrate and damage oligodendrocytes, thereby accelerating white matter rarefaction (Zhao et al., 2025). Hypertension may also amplify the effects of other risk factors such as diabetes and hyperhomocysteinemia (Tanaka et al., 2024). Based on this evidence, strict monitoring of dynamic blood pressure (particularly nocturnal and morning surge hypertension) is recommended for WMH patients. Additionally, a comprehensive management strategy should be implemented for individuals with multiple vascular risk factors to slow the progression of white matter damage. Despite the valuable insights this study provides into the relationship between WMH and cognitive function, several limitations should be noted. First, the relatively small sample size may limit the generalizability and statistical power of the findings. Second, as this study utilized cross-sectional data, it cannot capture the temporal dynamics of network topological changes. Future longitudinal studies are needed to elucidate causal relationships between brain network alterations and cognitive decline. Moreover, this study focused primarily on topological properties of brain networks without considering other factors such as cerebral blood flow and microstructural damage, which may also influence cognitive function. Future research should incorporate a multimodal approach to provide a more comprehensive understanding of the interplay between these factors. Declarations Acknowledgments We would like to thank all the participants and their families for their support. Author contributions Author contributions included conception and study design (P.W., Y.X., J.L.,), data collection or acquisition (Y.X.), statistical analysis (P.W.and Z.L.), interpretation of results (P.W., J.W., Y.X., and J.L.), drafting the manuscript work or revising it critically for important intellectual content (P.W., Z.L., J.W., Y.X., and J.L.) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors). Funding: This study was supported by the National Key Research and Development Program of China (2023YFC3603200), the National Natural Science Foundation of China (82371199) and Huizhi Ascent Project of Xuanwu Hospital (HZ2021ZCLJ005). Ethics approval and consent to participate All procedures performed in studies involving human participants were approved by the ethical standards of the committee of Xuanwu Hospital, Capital Medical University, and performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study. Consent for publication The work described has not been published before. It is not under consideration for publication elsewhere. Its publication has been approved by all co-authors, if any. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Feb, 2026 Reviews received at journal 01 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor assigned by journal 23 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 09 Sep, 2025 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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1","display":"","copyAsset":false,"role":"figure","size":633769,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in global topological properties of the white matter networks between the mWMH and m/sWMH groups. Data points marked with a star indicate significant (P \u0026lt; 0.05) intergroup differences in the global white matter network metric under a corresponding sparsity threshold. mWMH mild white matter hyperintensities, m/sWMH moderate-to-severe white matter hyperintensities.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/23870c54bc00f6c73bb21df0.png"},{"id":96454656,"identity":"a233d02e-94e0-4f3d-827e-bea7ba7a67f1","added_by":"auto","created_at":"2025-11-21 10:03:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212073,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions identified as abnormal clustering coefficient in m/sWMH group. Yellow colors show the regions of decreased clustering coefficient in patients with m/sWMH relative to mWMH (P \u0026lt; 0.05, NBS corrected). R right, L left.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/c1660da994a050e31b3026c5.png"},{"id":96399925,"identity":"470f84fa-5d9e-4e76-9371-51b2fc5d1572","added_by":"auto","created_at":"2025-11-20 16:02:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245242,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions identified as abnormal efficiency in m/sWMH group. Blue colors show the regions of increased efficiency in patients with m/sWMH relative to mWMH (P \u0026lt; 0.05, NBS corrected). R right, L left.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/5aec79d25edb0fe0eec68c10.png"},{"id":96453594,"identity":"a06461f6-3e20-40d3-821e-d445aa2e66c3","added_by":"auto","created_at":"2025-11-21 10:00:55","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168728,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions identified as abnormal degree centrality in m/sWMH group. Blue colors show the regions of increased degree centrality in patients with m/sWMH relative to mWMH, whereas, yellow colors show the regions of decreased degree centrality in patients with m/sWMH relative to mWMH (P \u0026lt; 0.05, NBS corrected). R right, L left.\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/7b11c8f67e843f7289f52ab4.jpeg"},{"id":96454585,"identity":"0083ce75-0077-4389-b1ac-f9661fbd5ac9","added_by":"auto","created_at":"2025-11-21 10:02:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":220282,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions identified as abnormal betweeness centrality in m/sWMH group. Blue colors show the regions of increased betweeness centrality in patients with m/sWMH relative to mWMH, whereas, yellow colors show the regions of decreased betweeness centrality in patients with m/sWMH relative to mWMH (P \u0026lt; 0.05, NBS corrected). R right, L left.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/6f3d2ec0a1e586be6b46bb33.png"},{"id":96399927,"identity":"f2391508-a644-413b-b55b-947c285af6ba","added_by":"auto","created_at":"2025-11-20 16:02:43","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":175346,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between nodal topological properties and clinical parameters in m/sWMH group. Ncp nodal clustering coefficient, Dc nodal degree centrality, TMT-A Trail Making Test A. The nodal topological properties underwent correction via Network-Based Statistics (NBS), with a significance threshold of corrected P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/292772e36c5ee7e003985503.jpeg"},{"id":96456876,"identity":"385b73f1-1640-44fa-a980-4fa4527e585f","added_by":"auto","created_at":"2025-11-21 10:08:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7569436/v1/139a357c-ca9d-44ad-b081-1f9728af390b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impaired White Matter Network Topology Mediates the Association Between White Matter Hyperintensities of Presumed Vascular Origin and Cognitive in the Elderly","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhite Matter Hyperintensities (WMH) are imaging biomarkers of cerebral small vessel disease (CSVD), and WMH can lead to the decline of cognitive function and may eventually evolve into dementia (Habes et al., 2016, Hong et al., 2024). Different degrees of WMH have different effects on cognitive impairment. Neuroimaging studies have confirmed that expanded WMH burden serves as a critical neuroimaging biomarker for elevated risks of AD conversion (Yang et al., 2024, Jochems et al., 2022), and reached Fazekas scale 3 might be a significant indicator pointing towards future cognitive decline (Zeng et al., 2020). The elderly patients with m/sWMH, despite normal overall cognitive function, have alterations in working memory and episodic memory (Zeng et al., 2020). The WMH group had lower MMSE/MoCA versus non-WMH group, alongside total scores. Specific deficits included attention, recall, and language on MMSE, and executive function, naming, abstraction, and orientation on MoCA (Chen et al., 2024). WMH burden was associated with faster decline in perceptual speed in both the whole group as well as in non-demented participants (Arfanakis et al., 2020). Therefore, early detection of potential biological markers for cerebral white matter lesions in cognitively intact elderly patients is clinically imperative. Understanding how these markers correlate with cognitive function further constitutes an urgent research priority. The resting-state functional magnetic resonance imaging (rs-fMRI) have revealed that WMH may indirectly induce abnormalities in the WM-FN by disrupting the structural integrity of white matter fiber pathways (Keřkovsk\u0026yacute; et al., 2019, Huang et al.,2023). Our previous study had reported that cognitively normal adults with moderate to severe WMH burden exhibited altered spontaneous brain activity, and altered ALFF in the right prefrontal cortex was associated with worse immediate recall and recognition. This might indicate that the mediating effects of brain activity elucidate the underlying mechanisms linking WMH to cognitive impairment (Xing et al., 2022). Existing research has investigated the relationship between WMH burden and functional connectivity (FC) alterations, consistently demonstrating that individuals with higher WMH volumes exhibit accelerated cognitive decline, likely mediated by disrupted large-scale network integration (Kumar et al., 2020). The confluent WMH significantly disrupt intrinsic connectivity within the DMN, and are strongly associated with episodic memory decline. However, nonconfluent WMH predominantly affect the executive control network (ECN), leading to impaired performance on executive function tasks (Kumar et al., 2020). Modern research based on graph theory has provided a comprehensive understanding of the complexity of brain functional network connectivity, offering a valuable tool for investigating topological properties of brain functional networks. Graph theory reconstructs and evaluates brain connectivity networks by representing cortical and subcortical regions as nodes and white matter tract interconnections as edges in mathematical graphs, thus enabling the analysis of brain network topology and elucidating interrelationships between distinct brain regions. Current research exploring WMH biomarkers using graph theory techniques remains in its nascent stage, with relatively limited studies available. The aim of this study was to determine the differences in topological organization variance between patients with m/sWMH and patients with mWMH, and examine their associations with cognitive function.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThe study involved 54 patients with WMH and normal cognitive function from the Department of Neurology at Xuanwu Hospital. Subjects were enrolled based on the following inclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age\u0026thinsp;\u0026ge;\u0026thinsp;55, and \u0026le;\u0026thinsp;75 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) right-handed; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) normal cognitive function: clinical dementia rating (CDR)\u0026thinsp;=\u0026thinsp;0 (Morris, 1993); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) white matter lesions on MRI without cortical or watershed infarcts, hemorrhages or hydrocephalus; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) could cooperate during neuropsychological examination. The exclusion criteria included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) WMH caused by non-vascular factors, including encephalitis, poisoning, multiple sclerosis and infection; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) transient ischemic attack within 3 months or a high-intensity lacunar infarction on DWI images; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) any physical impairment or use of medication that might affect cognitive functions; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) serious organ diseases or uncontrolled mental diseases, e.g. gastrointestinal, hepatic, infectious, or cardiovascular diseases, cancer, or drug addiction.\u003c/p\u003e\u003cp\u003eThe WMH severity of participants were categorized by an experienced neurologist and an experienced radiologist by the Fazekas rating scale from 0 to 3 using the following rubric: 0 - no periventricular or deep white matter WMH; 1 - pencil-thin lining or capped periventricular WMH and/or punctate foci in deep white matter; 2 - smooth periventricular WMH halo and/or beginning of deep WMH confluence; and 3 - irregular periventricular signal extending to deep white matter and/or large deep white matter confluence (Fazekas et al., 1987). All participants were divided into two groups: those with Fazekas 1 were assigned to the mWMH group (n\u0026thinsp;=\u0026thinsp;28), and those with Fazekas 2\u0026ndash;3 were assigned to the m/sWMH group (n\u0026thinsp;=\u0026thinsp;26). The study protocol was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (approval no. 2024-144-002), and written informed consent was obtained from all participants prior to their involvement in the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 MRI data acquisition\u003c/h2\u003e\u003cp\u003eMRI data were acquired using the 3.0-T Siemens Skyra scanner with a 24-channel head coil. High-resolution 3D T1-weighted anatomical images were acquired using the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;1690 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;2.56 ms, slice thickness\u0026thinsp;=\u0026thinsp;1mm, 176 slices, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, field of view (FOV)\u0026thinsp;=\u0026thinsp;256\u0026times;256 mm\u003csup\u003e2\u003c/sup\u003e, matrix size\u0026thinsp;=\u0026thinsp;256\u0026times;256. Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired using the following parameters: TR\u0026thinsp;=\u0026thinsp;2000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, slice thickness\u0026thinsp;=\u0026thinsp;3 mm, 35 slices, gap\u0026thinsp;=\u0026thinsp;1 mm, flip angle\u0026thinsp;=\u0026thinsp;83\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;224 \u0026times; 224 mm\u003csup\u003e2\u003c/sup\u003e, matrix size\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, duration\u0026thinsp;=\u0026thinsp;6 min. Coronal 3D Flair images were obtained using the following parameters: TR\u0026thinsp;=\u0026thinsp;5000 ms, TE\u0026thinsp;=\u0026thinsp;392 ms, FOV\u0026thinsp;=\u0026thinsp;240 \u0026times; 240mm\u003csup\u003e2\u003c/sup\u003e, inversion time\u0026thinsp;=\u0026thinsp;1800 ms, flip angle\u0026thinsp;=\u0026thinsp;120\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;1 mm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 fMRI data processing\u003c/h2\u003e\u003cp\u003eThe rs-fMRI preprocessing was performed using the Data Processing and Analysis of Brain Imaging (DPABI 2.3) software, which is based on statistical parametric mapping (SPM12). The preprocessing of rs-fMRI data was included the following steps: (a) Format conversion. (b) Removal of the first 10 time points to account for signal equilibration. (c) Slice timing correction to eliminate within-volume time differences during acquisition. (d) Head motion correction. (e) Alignment of T1 and fMRI images. (f) Segmentation. (g) Spatial normalization. (h) Regressing out cerebrospinal fluid signals and Friston 24 head motion parameters to minimize their impact and enhance accuracy. (i) White matter mask generation and extraction of white matter-specific fMRI signals. (j) Smoothing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 White matter networks construction\u003c/h2\u003e\u003cp\u003eBased on the JHU white matter atlas, the brain's white matter was divided into 50 regions of interest (ROIs). The mean time series for each ROI was extracted from the preprocessed fMRI images. Pearson correlation coefficients were computed for all ROI pairs, then transformed into Fisher's z-values to obtain a 50 \u0026times; 50 connectivity matrix for each subject.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 White matter networks topology analysis\u003c/h2\u003e\u003cp\u003eThe GRETNA 2.0 toolkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/gretna/\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/gretna/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to calculate topological properties of white matter networks, the processing steps are as follows: First, define a sparsity threshold range from 5% to 50% with a step size of 5% for network sparsification. Second, define the network as a weighted network. This study analyzed the topological properties of white matter networks, calculating 7 global topological properties, including clustering coefficient, global efficiency, hierarchy, local efficiency, shortest length, small-world and synchronization. Additionally, 4 nodal topological properties were measured, including nodal clustering coefficient (Ncp), nodal efficiency (Ne), betweenness centrality (Bc) and nodal degree centrality (Dc). Furthermore, we calculated the area under the curve (AUC) for each global topological property over the range of sparsity (0.08\u0026ndash;0.5) to provide a summarized scalar independent of single threshold selection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Cognitive assessment\u003c/h2\u003e\u003cp\u003eAll participants underwent neuropsychological assessments. The global cognitive function was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). We employed standardized tests to evaluate specific cognitive subdomains, including the Auditory Verbal Learning Test (WHO-UCLA AVLT) for memory assessment (covering immediate recall, long-delay recall, and long-delay recognition, with higher scores indicating better memory function), Digit Span Test (DST) for attention evaluation, the Trail Making Test-Parts B (TMT-B) for executive function evaluation, Trail Making Test-Parts A (TMT-A) for processing speed assessment, the Boston Naming Test (BNT) for language function assessment, and the Geriatric Depression Scale (GDS) for measuring emotion domain.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed by SPSS 26.0. The normality of continuous variables was evaluated using the Kolmogorov-Smirnov test. For comparing demographic characteristics and neuropsychological test scores between the mWMH and m/sWMH groups, we employed independent samples t-tests for normally distributed continuous data and chi-square tests (or Fisher's exact tests when applicable) for categorical variables. In cases where data showed non-normal distributions, the Mann-Whitney U test was used as a non-parametric alternative. Significance levels were set to P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eTo assess whether there were significant differences in the topological properties of the WM networks between mWMH and m/s WMH groups, two-sample t-test was performed to compare the AUC value of each network metric between two groups (global and nodal properties). Global topological properties were considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while nodal topological properties underwent correction by Network-Based Statistics (NBS), and the criterion for statistical significance was set to P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Once significant intergroup differences were identified in any nodal topological metrics, we further assessed the correlations between these metrics and clinical parameters in the m/sWMH group. A pearson correlation analysis was used to test the association, which was used a statistical significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic and clinical characteristics\u003c/h2\u003e\u003cp\u003eThe demographic and cognitive characteristics of mWMH group (28 participants with mild WMH) and m/sWMH group (26 moderate to severe WMH) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences in gender, age, years of education, presence of diabetes, and hyperlipidemia between mWMH and m/sWMH groups. However, there was a significant difference in the rate of hypertension (P\u0026thinsp;=\u0026thinsp;0.007), with a higher incidence in m/sWMH group compared to the mWMH group. Regarding cognitive function, participants with m/sWMHs performed significantly worse on the delay free recall (P\u0026thinsp;=\u0026thinsp;0.017), TMT-A (P\u0026thinsp;=\u0026thinsp;0.013) and BNT (P\u0026thinsp;=\u0026thinsp;0.004).\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\u003eDemographicdata and neuropsychological data for mWMH group and m/sWMH group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emWMH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003em/sWMH group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003cem\u003e-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.39\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.31\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(female/male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.669\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMedical history (Y/N)\u003c/p\u003e\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\u003e8/20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3/25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6/20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9/19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10/16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNeuropsychological assessment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO-UCLA AVLT\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmediate recall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelayed free recall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecognition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigit span forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigit span backward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTMT-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.49\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.78\u0026thinsp;\u0026plusmn;\u0026thinsp;25.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTMT- B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.36\u0026thinsp;\u0026plusmn;\u0026thinsp;51.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.08\u0026thinsp;\u0026plusmn;\u0026thinsp;53.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAll the participants were matched for sex and education. Data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Statistically significant P-value(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)was shown in red. Abbreviations: \u003cem\u003eWMH\u003c/em\u003e white matter hyperintensities, \u003cem\u003eMMSE\u003c/em\u003e mini-mental state examination, \u003cem\u003eMoCA\u003c/em\u003e Montreal Cognitive Assessment, \u003cem\u003eWHOUCLAV AVLT\u003c/em\u003e WHO-UCLA auditory verbal learning test, BNT Boston naming test, TMT Trail Making Test, GDS Geriatric Depression Scale. * represent P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Global topological of white matter networks\u003c/h2\u003e\u003cp\u003eCompared to the mWMH group, patients in the m/sWMH group showed a significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decreased Cp (P\u0026thinsp;=\u0026thinsp;0.021, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and Lp (P\u0026thinsp;=\u0026thinsp;0.027, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) over a wide range of sparsity thresholds. However, there were no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in global efficiency (P\u0026thinsp;=\u0026thinsp;0.059, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), hierarchy (P\u0026thinsp;=\u0026thinsp;0.157, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), local efficiency (P\u0026thinsp;=\u0026thinsp;0.269, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), small-world index (P\u0026thinsp;=\u0026thinsp;0.633, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), and synchronization (P\u0026thinsp;=\u0026thinsp;0.804, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) between two groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nodal topological of white matter networks\u003c/h2\u003e\u003cp\u003eCompared to mWMH group, the m/sWMH group exhibited decreased Ncp in the right anterior limb of the internal capsule, bilateral posterior thalamic radiation, bilateral SS (including ILF and IFOF), left cingulum, and right tapetum (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, P\u0026lt;0.05, NBS corrected). Compared with mWMH group, the m/sWMH group displayed increased Ne in body of corpus callosum, left cingulum, and right uncinate fasciculus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, P\u0026lt;0.05, NBS corrected). When directly comparing the Dc between two groups, those with m/sWMH patients showed an increased Dc of right corticospinal tract, left medial lemniscus, left posterior thalamic radiation (including the optic radiation), sagittal stratum (SS) (including the inferior longitudinal fasciculus and inferior fronto-occipital fasciculus, ILF and IFOF), left cingulum, and left tapetum, and decreased Dc of right superior fronto-occipital fasciculus compared to mWMH group (Fig.\u0026nbsp;4, P\u0026lt;0.05, NBS corrected). Furthermore, m/sWMH group showed significantly increased Bc in the right anterior limb of the internal capsule, left cingulum, and right uncinate fasciculus, and decreased in the left internal capsule, left SS (including ILF and IFOF ) compared to mWMH group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, P\u0026lt;0.05, NBS corrected).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 The correlation analysis of WM network topological in abnormal regions and cognition in m/sWMH group\u003c/h2\u003e\u003cp\u003eBy performing pearson correlation analysis, we found that the global topological showed no significantly correlation with cognition function in m/sWMH group. For nodal properties, we investigated only the nodes with significant group differences. The correlations between nodal properties (Ncp, Ne, Dc, Bc) and cognition assessment were analyzed in patients with m/sWMH. The results demonstrated that the left SS ( including ILF and IFOF) of Ncp was significantly positive correlation with MoCA (r\u0026thinsp;=\u0026thinsp;0.444, P\u0026thinsp;=\u0026thinsp;0.030, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), TMT-A (r = -0.4629, P\u0026thinsp;=\u0026thinsp;0.021, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) and digit span backward (r\u0026thinsp;=\u0026thinsp;0.651, P\u0026lt;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Meanwhile, the left SS (including ILF and IFOF) of Dc was significantly positive correlation with digit span backward (r\u0026thinsp;=\u0026thinsp;0.528, P\u0026thinsp;=\u0026thinsp;0.007, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we investigated topological properties of resting state functional networks in cognitively healthy older adults with WMH using graph theoretical analysis, and the relationship among global topological properties, nodal topological properties and cognitive impairment in m/sWMH group. We founded that patients with m/sWMH showed disturbed white matter networks (significantly decreased Cp and Lp of gobal topological properties), compared to patients with mWMH. At the regional level, compared with the mWMH group, the m/sWMH group displayed a decreased Ncp, increased Ne, and heterogeneous changes in Dc and Bc, with both increases and decreases observed. Moreover, SS (including ILF and IFOF) was significantly associated with global cognitive function, subdomains of cognitive in processing speed and attention. These findings provide novel insights into the neural mechanisms underlying WMH and their impact on cognitive function.\u003c/p\u003e\u003cp\u003eIn our study, m/sWMH group showed white matter functional networks with significantly decreased Cp and Lp compared to mWMH group, and in the m/sWMH group, no significant correlations were observed between global topological properties and cognitive function. This may indicate changes characterized by randomization and reduced efficiency in the white matter networks of patients with m/sWMH. Consistent with previous studies, this study reveals a decreased clustering coefficient, indicating that patients with greater WMH burden exhibit reduced structural WM network connectivity compared to those with less burden (Huang et al., 2023, Wang et al., 2022, Xin et al., 2022). While the decreased shortest path length may suggest compensatory mechanisms of white matter networks in patients with m/sWMH. White matter damage, by disrupting efficient long-range connections (Chen et al., 2020, Araque et al., 2018, Sui et al., 2025), may prompt the network to compensate through increased redundancy or strengthened short-range connections. This compensatory rewiring can yield Lp at the cost of decreased local clustering, reflecting suboptimal network reorganization. The alterations observed in these network properties have been associated with cognitive decline (Chen et al., 2019, Liu et al., 2019, Schulz et al., 2021).\u003c/p\u003e\u003cp\u003eMeanwhile, compared to the mWMH group, the m/sWMH group exhibited significantly decreased Ncp, increased Ne, mostly increased Dc and Bc in multiple white matter bundles, including the anterior limb of the internal capsule, posterior thalamic radiation, SS (including ILF and IFOF), cingulum, tapetum, coropus callosum and uncinate fasciculus. White matter bundles, critical white matter network interacting with many brain areas, are key targets in neurodegenerative diseases (Peng et al., 2024, Ricchi et al., 2025). Disrupting nodal of white matter directly weakens the network\u0026rsquo;s capacity for efficient, stable information flow. Given the Ncp reflect the connection density of reaction node local network. The Ne reaction node serves as a network accelerator, determining the efficiency of global information propagation. The Dc reaction node acts as a structural architect, regulating neighborhood-level node interactions and link formations. The Bc reaction node functions as an information gatekeeper, with its strategic value quantified by its recurrence across all optimal transmission pathways (Bullmore et al., 2009). We found decreased Ncp, increased Ne, mostly increased Dc and Bc in multiple brain regions in the m/sWMH group, which may reflect compensatory mechanisms aimed at maintaining neural equilibrium. Severe WMH may disrupt local white matter tracts, reducing connectivity in originally tightly interconnected regional networks and leading to decreased clustering coefficients, which was consistent with previous study (Xin et al., 2022). Concurrently, to preserve overall information transfer, the neural network may undergo compensatory adaptations, such as enhancing connections in certain hub nodes (increased Dc). These modals might become more involved in relaying information across distinct brain regions (increased Bc), thereby improving Ne. The experts speculated that WMH are pathologically attributed to chronic cerebral hypoperfusion resulting from age-related degeneration of penetrating arterioles, lipohyalinosis, and blood-brain barrier dysfunction (Wardlaw et al., 2019, Wong et al.,2019, Zhang et al., 2018). The association between WMH and hypoperfusion has been confirmed by multiple imaging and pathological studies, with both contributing to cognitive impairment by disrupting white matter microstructure and brain network dynamics (Li et al., 2024, Arslan et al., 2020).\u003c/p\u003e\u003cp\u003eFurthermore, the decreased nodal properties of Ncp and Dc in SS (including ILF and IFOF) were significantly correlation with MoCA, TMT-A and digit span backward, indicating potential early imaging biomarkers for m/sWMH. The SS is formed by ILF IFOF, optic radiation, and other associational fibers and SS plays a vital role in connecting cortical and subcortical regions. The ILF facilitates visual-memory integration, while the IFOF connects frontal, occipital, and temporal regions to support executive functions, language processing, and visual attention modulation (Al-Juboori AA et al., 2025, Di Carlo DT et al, 2019). When the SS is damaged-whether by disease or surgery-patients often experience severe deficits in language, vision, and cognitive abilities (Paus T et al, 1999, Robles DJ et al, 2022). Given their existing white matter degeneration, older adults face a substantially higher risk of accelerated cognitive decline when SS damage compounds these effects (Robles DJ et al, 2022). Recent studies demonstrate that microstructural damage in cerebral white matter can significantly impair patients' cognitive function. Importantly, neurodegenerative processes extend beyond radiologically detectable white matter hyperintensity lesions to affect remote brain regions through white matter fiber networks (Yuan et al., 2017; Lu et al., 2021, Xin et al., 2022). Our study shows key differences between mild (mWMH) and moderate/severe (m/sWMH) white matter hyperintensity patients. The more severe white matter damage in m/sWMH patients - seen in reduced clustering coefficients and degree centrality in ILF/IFOF pathways - appears before cognitive symptoms, potentially serving as early warning signs. These structural changes disrupt brain network communication, particularly in fronto-occipito-temporal connections, leading to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) memory and language problems, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) slower thinking and task-switching speeds that characterize m/sWMH patients.\u003c/p\u003e\u003cp\u003eAdditionally, this study found a significantly higher prevalence of hypertension in the m/sWMH group (P\u0026thinsp;=\u0026thinsp;0.007), consistent with previous findings indicating that for every 10 mmHg increase above normal systolic pressure, WMH burden increases by approximately 1.126 (Wartolowska et al., 2021). The underlying pathology likely involves multiple interacting mechanisms: chronic hypertension induces structural remodeling of cerebral small arteries (e.g., hyaline degeneration) and impairs vascular autoregulation, leading to hypoperfusion or high shear stress damage in white matter, promoting myelin degeneration (Zhao et al., 2019). Furthermore, blood pressure fluctuations activate endothelial inflammation, upregulating matrix metalloproteinase-9 (MMP-9) expression, which disrupts tight junctions of the blood-brain barrier, allowing toxic substances such as fibrinogen to infiltrate and damage oligodendrocytes, thereby accelerating white matter rarefaction (Zhao et al., 2025). Hypertension may also amplify the effects of other risk factors such as diabetes and hyperhomocysteinemia (Tanaka et al., 2024). Based on this evidence, strict monitoring of dynamic blood pressure (particularly nocturnal and morning surge hypertension) is recommended for WMH patients. Additionally, a comprehensive management strategy should be implemented for individuals with multiple vascular risk factors to slow the progression of white matter damage.\u003c/p\u003e\u003cp\u003eDespite the valuable insights this study provides into the relationship between WMH and cognitive function, several limitations should be noted. First, the relatively small sample size may limit the generalizability and statistical power of the findings. Second, as this study utilized cross-sectional data, it cannot capture the temporal dynamics of network topological changes. Future longitudinal studies are needed to elucidate causal relationships between brain network alterations and cognitive decline. Moreover, this study focused primarily on topological properties of brain networks without considering other factors such as cerebral blood flow and microstructural damage, which may also influence cognitive function. Future research should incorporate a multimodal approach to provide a more comprehensive understanding of the interplay between these factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the participants and their families for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor contributions included conception and study design (P.W., Y.X., J.L.,), data collection or acquisition (Y.X.), statistical analysis (P.W.and Z.L.), interpretation of results (P.W., J.W., Y.X., and J.L.), drafting the manuscript work or revising it critically for important intellectual content (P.W., Z.L., J.W., Y.X., and J.L.) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Key Research and Development Program of China (2023YFC3603200), the National Natural Science Foundation of China (82371199) and Huizhi Ascent Project of Xuanwu Hospital (HZ2021ZCLJ005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were approved by the ethical standards of the committee of Xuanwu Hospital, Capital Medical University, and performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work described has not been published before. It is not under consideration for publication elsewhere. Its publication has been approved by all co-authors, if any. Its publication has been approved by the responsible authorities at the institution where the work is carried out.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Juboori AA, Badran SA, Sulaiman II, Shahadha AA, Alsamok AS, Al-Badri SG, Al-Taie RH, Ismail M (2025) Clinical implications of sagittal stratum damage: Laterality, neuroanatomical developmental considerations, and functional outcomes. 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J Int Med Res 47:3681-3689. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"white matter hyperintensities, cognition, topological organization, resting-state functional magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-7569436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7569436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe aimed to investigate the alterations in white matter (WM) networks topology caused by varying white matter hyperintensities (WMH) burden, and assess the relationship between topological changes of WM networks and cognitive function in the moderate-to-severe WMH (m/sWMH) patients. The study enrolled 54 cognitively unimpaired individuals, stratified into two groups: 26 with m/sWMH and 28 with mild WMH (mWMH). We used graph theory analysis to investigate the global and nodal topological disruptions between two groups and relate WM networks topological alterations to cognitive function. The results provided that, compared to mWMH group, m/sWMH group exhibit disruptions of global properties and nodal properties, including decreased clustering coefficient (Cp) and shortest path length (Lp), decreased nodal clustering coefficient (Ncp), increased nodal efficiency (Ne), both increased and decreased nodal degree centrality (Dc) and betweenness centrality (Bc) in regions such as body of corpus callosum, left sagittal stratum (SS) (including the inferior longitudinal fasciculus and inferior fronto-occipital fasciculus, ILF and IFOF), the anterior limb of the internal capsule, cingulate gyrus, and inferior longitudinal fasciculus. The pearson\u0026rsquo;s correlation analysis revealed that the nodal properties (Ncp and Dc) in SS (including ILF and IFOF) were significantly correlation with scores of cognitions in m/sWMH group. Increased WMH burden leads to the disintegration of WM network topology, and this disruption is closely associated with cognitive dysfunction (global cognitive function, attention and processing speed). The SS may be a specific target for cognitive impairment in patients with m/sWMH.\u003c/p\u003e","manuscriptTitle":"Impaired White Matter Network Topology Mediates the Association Between White Matter Hyperintensities of Presumed Vascular Origin and Cognitive in the Elderly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 16:02:38","doi":"10.21203/rs.3.rs-7569436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-08T17:40:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T14:53:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T15:08:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T21:00:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119439257618482353911180300704253106828","date":"2025-11-13T19:20:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28904019865221415234410242983555039175","date":"2025-11-11T16:08:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194903597483680830777733354434443860078","date":"2025-11-11T12:18:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4204640667923246477231650275089571415","date":"2025-11-10T18:29:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T18:19:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T03:15:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T07:03:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2025-09-09T05:06:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e3784c81-5f42-4334-adf2-e12e194cdf53","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T18:54:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 16:02:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7569436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7569436","identity":"rs-7569436","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}