Enlarged perivascular spaces are associated with more severe microstructural damage in white matter lesions among patients with relapsing-remitting multiple sclerosis

Enlarged perivascular spaces are associated with more severe microstructural damage in white matter lesions among patients with relapsing-remitting multiple sclerosis | 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 Research Article Enlarged perivascular spaces are associated with more severe microstructural damage in white matter lesions among patients with relapsing-remitting multiple sclerosis Kai Zhang, Qiyuan Zhu, Xiaojuan Dong, Zhuowei Shi, Zichun Yan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9307837/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Objectives: Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS) characterized by lesions in white matter (WM) and gray matter. Growing evidence has linked MRI-visible perivascular spaces (PVS) to MS pathogenesis. However, whether PVS are associated with increased microstructural damage in white matter lesions (WML) remains unclear. In this study, we aimed to explore the associations between PVS and the microstructural damage of WML in relapsing-remitting multiple sclerosis (RRMS) patients with or without disease-modifying therapies (DMT) and their correlations with clinical biomarkers of disability and cognitive function. Methods Sixty-eight RRMS patients and forty-seven age- and sex-matched healthy controls (HCs) were recruited. WML were categorized into four groups based on two factors: whether the lesions were penetrated by perivascular spaces (P_WML vs. NP_WML) through visual assessment, and whether patients received DMT (MS + vs. MS−). The diffusion metrics, including fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), mean diffusivity (MD) and mean kurtosis (MK), were used to assess the microstructural damage in WML. The correlations between diffusion metrics of WML and cognitive performance and clinical disability were performed. Results In the MS- group, the MK index in P_WML was significantly lower than NP_WML (P < 0.001). Compared to the MS- group, both P_WML and NP_WML in the MS+group showed significantly lower MD index (P = 0.005) and higher MK index (P < 0.001). Additionally, the MD index of P_WML in the MS- group was significantly higher than that of NP_WML in the MS+ group (P < 0.001), and the MK index of P_WML in the MS- group was significantly lower than that of NP_WML in the MS+ group (P < 0.001). In the MS+ group, MK index in P_WML were negatively significantly associated with Expanded Disability Status Scale in RRMS (r = -0.500, p = 0.049). Conclusion The presence of PVS were associated with more severe microstructural abnormalities of WML and these abnormalities were related to greater clinical disability of RRMS. Furthermore, diffusion abnormalities in WML were less pronounced in patients receiving DMT. Multiple sclerosis Perivascular Space White matter lesion Diffusion kurtosis imaging Magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS), characterized by the focal demyelinating lesions. White matter lesions (WML) are the most common pathological features and exhibit substantial heterogeneity in myelin, axon and iron content 1 , 2 . Research has revealed that WML contribute to structural brain alterations, functional connectivity disruption and tissue integrity impairment, which are associated with inflammation and neurodegeneration in MS 3 . The perivascular spaces (PVS) are fluid-filled spaces surrounding small perforating vessels which serve as key components of the glymphatic system and play an important role in interstitial fluid (ISF) and solute clearance in the brain 4 . PVS visible on MRI in healthy individuals are typically small. However, aging, neurodegeneration and neuroinflammation may lead to pathological enlargement of PVS, referred to as enlarged PVS. Convergent lines of evidence support that MS patients have a higher PVS burden compared to healthy controls (HCs) 5 , 6 . Additionally, a study described the correlation between PVS load and the burden of WML in MS patients 7 . Given the role of PVS in glymphatic function and perivascular inflammatory processes, PVS may be related with the microenvironment of adjacent white matter tissue 8 . However, existing studies have primarily focused on lesion burden at the macrostructural level, and whether PVS are associated with the microstructural damage of WML remains unclear. Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging, characterizes non-Gaussian water diffusion and provides more sensitivity to tissue microstructural complexity, and has been used to quantify microstructural damage in MS lesions 9 , 10 . The reduction in kurtosis fractional anisotropy (KFA) and fractional anisotropy (FA) was considered to reflect the axonal damage and neuron loss within MS lesions, which may result in impaired fiber integrity 11 . The decreased mean kurtosis (MK) values were correlated with myelin destruction or axon loss and increased mean diffusivity (MD) may reflect enhanced water diffusivity associated with blood-brain barrier disruption and microstructural damage 12 . Therefore, this study focuses on investigating the association between PVS and microstructural damage in WML. By exploring the spatial positional relationship between PVS and WML, the WML were further categorized into PVS penetrating white matter lesions (P_WML) and non-PVS penetrating white matter lesions (NP_WML) in relapsing-remitting multiple sclerosis (RRMS). This study aimed to : (i) examine whether PVS are associated with microstructural damage of WML, (ii) assess whether the microstructural integrity of P_WML and NP_WML differs in relation to disease-modifying therapies (DMT) drug, and (iii) investigate the relationship between microstructural integrity of P_WML/NP_WML and cognitive status and clinical biomarkers of disability. Materials and methods Participants A total of sixty-eight patients with RRMS and forty-seven age- and sex-matched HCs were included in the study. This study was approved by the institutional review board of the First Affiliated Hospital of Chongqing Medical University between 2023 and 2024, and all patients and HCs provided written informed consent. This research was conducted in accordance with the Declaration of Helsinki. Patients with MS were enrolled according to the following inclusion criteria: 1) a confirmed diagnosis of RRMS according to the 2017 revised McDonald’ s diagnostic criteria 13 , 2) age 18–60 years, 3) absence of active relapse at the time of scanning and 4) absence of intravenous corticosteroid use for at least 2 months before imaging. The exclusion criteria included: 1) patients with neurological conditions other than MS, 2) contraindications for MRI scans, 3) image artifacts or 4) incomplete clinical information, and 5) a history of intravenous corticosteroid treatment within 2 months before the imaging examinations (Fig. 1 ). According to whether patients received DMT, patients with RRMS were further divided into The patients who received DMT (MS+) group and patients who did not receive DMT (MS-) group in subsequent analysis. The MS+ group received one of the following DMT drugs: teriflunomide (n = 10), siponimod (n = 5), rituximab (n = 3), mycophenolate mofetil (n = 3), fingolimod (n = 1), and ofatumumab (n = 1). Clinical assessment The clinical and neuropsychological evaluation were recorded the demographic information of each participant and the clinical data of the patient group. The Symbol Digit Modalities Test (SDMT) 14 , Montreal Cognitive Assessment (MoCA) 15 , Mini-Mental State Examination (MMSE) 16 and Digit Span Test (DST) scores 17 were used to assess cognitive performance, and the Expanded Disability Status Scale (EDSS) 18 scores was used to assess the disability. MR imaging acquisition All subjects underwent 3.0T MRI scanner (MAGNETOM Skyra, Siemens, Erlangen, Germany) using a 32-channel head coil. In brief, the 3.0T scan protocols included: (i) sagittal 3-dimensional (3D) T1w magnetization-prepared rapid gradient echo [repetition time (TR) = 2,300 ms, echo-time (TE) = 2.26 ms, inversion time (TI) = 900 ms, 192 slices, field of view (FOV) = 256 mm, voxel size = 1.0 × 1.0 × 1.0 mm 3 ], (ii) axial T2-weighted (T2w), proton-density fast spin echo [TR = 3,600 ms, TE = 9.4 ms/94 ms, TI = 900 ms, 35 slices, FOV = 220 mm, voxel size = 0.3 × 0.3 × 0.3 mm 3 ], (iii) sagittal 3D fluid attenuated inversion recovery (FLAIR) (TR = 5,000 ms, TE = 388 ms, TI = 1,800 ms, 192 slices, FOV = 256 mm, voxel size = 0.5 × 0.5 × 1 mm 3 ), (iv) diffusion kurtosis imaging (DKI) sequence (TE = 97 ms, TR = 5,000 ms, 25 slices, FOV = 220 mm, voxel size = 1.7 × 1.7 × 4.0 mm 3 , Partial-Fourier = 6/8, integrated parallel acquisition techniques acceleration factor = 2 (GRAPPA), and three b values (0, 1,000, and 2,000 s/ mm 2 ) with diffusion encoding in 30 directions. MRI data processing and analysis The data processing pipeline was presented (Fig. 2 ). Quantification of WML The FLAIR and T2w images were registered to pre-contrast T1w MPRAGE images using Advanced Normalization Tools (ANTs). The registered images were then used by two trained radiologists (ZK and DXJ) to delineate each supratentorial lesion and generate the corresponding mask for each one, which were systematically corrected by a third neuroradiologist (LYM, with more than 20 years of MRI experience) using ITK-SNAP software (version 4.0.2; http://www.itksnap.org ) (Fig. S1 in the supplementary). The Dice coefficient was calculated to assess the repeatability about segmentation of WML. Finally,the number of lesions and each lesion volume were then calculated. The infratentorial and cortical lesions were excluded due to the difficulty in identifying PVS in these regions and the focus on subcortical PVS and WML. Furthermore, the lesions delineated in native space were registered to the Montreal Neurological Institute (MNI) space following the T1w images. Classification of WML Based on the T1w MPRAGE, FLAIR images and T2w images, the WML were independently divided into P_WML and NP_WML by two experienced radiologists (CXY and DXJ) blinded to clinical information (Fig. 3 ), with P_WML defined as lesions in direct contact with a visible PVS. The Cohen's kappa coefficient was calculated to assess the repeatability about classification of P_WML and NP_WML. In case of conflict, a third experienced radiologist (LYM) would make a final decision. DKI processing The DKI images were pre-processed including correction of head motion, eddy current-induced distortion, susceptibility-induced distortions, and the “BET” tool used for skull-stripping for b0 by the FMRIB Software Library (FSL, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ). The diffusion kurtosis estimator software (DKE version 2.6, http://www.nitrc.org/projects/dke ) was used to process the DKI data using the constrained linear least-squares quadratic programming algorithm and applied standard parameters (spatial smoothing and robust median filtering) to acquire the diffusion parametric maps including FA, KFA, MD and MK 19 . Then, parametric maps were registered to the skull-stripped-T1w images using b0 maps, respectively. Next, FA ms , KFA ms , MD ms , and MK ms values of ROIs were obtained by averaging the corresponding diffusion parameter maps (including FA, KFA, MK, and MD maps) across the ROI in the MS patients. Meanwhile, we collected diffusion parameter maps from HCs. HCs’ diffusion parameter maps were registered to the MNI space using ANTs, and all HCs’ maps were then averaged to obtain the mean KFA (mKFA), mean FA (mFA), mean MK (mMK), and mean MD (mMD). Then we extracted FA hc , KFA hc , MD hc , and MK hc of registered ROIs by averaging with the corresponding diffusion parameter maps of mFA, mKFA, mMK, and mMD values. Ultimately, we calculated FA index to avoid the influence of intrinsic microstructural damage differences in different brain regions in the following formula, similar to the method employed by Denecke et al 20 . $$\:{\text{}\text{FA}}_{\text{index}}\text{=}{\text{FA}}_{\text{ms}}\text{/}{\text{FA}}_{\text{hc}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ For each lesion, FA ms stands for FA values of WML in MS and FA hc denotes FA values of lesions in corresponding regions of HCs. For patient-wise analysis, we calculated the total FA index , KFA index , MD index and MK index values for each patient, derived from the segmented lesions in the following formula. In the formula above, for each patient, “i” represents the total number of the lesions, F1, and V1 represent the FA index value and the volume of the first WML, respectively. The same calculation method was applied to obtain KFA index , MD index , and MK index . Statistical analysis Statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, United States) and MATLAB 2022. For all continuous data, the Kolmogorov-Smirnov test was used to assess the normality of the data distribution. The Mann-Whitney U test was applied to compare the demographic variables between groups, while the Fisher exact test was used to assess gender distribution. The Kruskal-Wallis H test was conducted to evaluate group differences in MRI metrics, followed by Dunn's test for the post-hoc analysis where appropriate. Before statistical analysis, Z-values were calculated for all the neuropsychological scales to standardize the measures and facilitate comparison. Spearman’s partial correlation analysis was performed in MATLAB to assess: (i) the relationship between diffusion metrics and neuropsychological scores within MS- and MS+ groups, and (ii) the correlation between WML volume and diffusion metrics across the four groups. Age and sex were included as covariates. All p-values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (BH-FDR) method, with p < 0.05 considered statistically significant after correction. All subsequently p-values are FDR-p values. Results Demographic and clinical data The demographics and clinical characteristics of all participants were presented (Table 1 ). Of the 68 patients with MS, 45 (66%) were MS−, while 23 (34%) were MS+. There was no significant difference in age, sex, or years of education among the HCs group, MS- group and MS+ group, and no significant difference in disease duration between the MS- and MS+ groups. Compared to HCs, the MS- group had significantly lower scores on the DST (p = 0.039), SDMT (p = 0.002), MMSE (p = 0.003), and MoCA (p < 0.001). In contrast, no significant difference was found between the HCs and MS+ group. Compared to the MS+ group, the MS- group showed lower scores on the SDMT (p = 0.027) and MoCA (p = 0.002). No significant difference was found in the number of WML, P_WML/Total WML, and WML volume between the MS + and MS- groups. Table 1 Demographic and clinical characteristics of participants. Age (years) HCs (n = 47) MS+ (n = 23) MS- (n = 45) p 1 value p 2 value p 3 value 39.55 ± 9.66 35.78 ± 7.86 39.20 ± 10.76 0.366 0.620 0.620 Sex (male/female) 15/32 4/19 8/37 0.388 0.388 1.000 Education (years) 13.45 ± 4.07 14.65 ± 2.96 13.16 ± 3.55 0.862 0.402 0.402 Disease duration (years) - 8.16 ± 5.12 8.82 ± 5.87 - - 0.912 DST scores 14.77 ± 2.42 14.26 ± 2.43 13.91 ± 3.96 0.348 0.039 0.348 EDSS scores - 1.83 ± 2.04 1.77 ± 1.87 - - 0.973 SDMT scores 50.77 ± 11.59 49.48 ± 12.38 40.08 ± 15.16 0.595 0.002 0.027 MMSE scores 29.11 ± 1.03 28.87 ± 1.10 27.36 ± 3.17 0.336 0.003 0.070 MoCA scores 27.47 ± 2.43 27.09 ± 2.66 24.33 ± 3.81 0.520 < 0.001 0.002 No. of WML - 31.00 (13.00, 44.00) 29.00 (18.00, 46.00) - - 0.800 WML volume (mm 3 ) - 3466.00 (1745.00, 5446.00) 4182.00 (2028.00, 5152.00) - - 0.530 P_WML/Total WML - 0.76 ± 0.12 0.75 ± 0.16 - - 0.800 Comparisons performed by Mann-Whitney U-test and Fisher’s exact (sex). BH-FDR correction (Benjamini-Hochberg procedure) was applied to account for the overall number of pairwise comparisons. Bold values denote statistical significance (p < 0.05). The data were shown as the mean values ± standard deviation. p1 value for MS+ group versus HCs group, p2 value for MS- group versus HCs group, and p3 value for MS+ group versus MS- group. MS+: patients with relapsing-remitting multiple sclerosis receiving disease-modifying therapies; MS-: patients with relapsing-remitting multiple sclerosis not receiving disease-modifying therapies; DST, Digit Span Test; EDSS: Kurtzke expanded disability status scale; SDMT: symbol digit modalities test; MMSE: Mini-Mental State Examination; MoCA, the Montreal Cognitive Assessment; P_WML/Total WML represents the fraction of P_WML within the total WML; No. of WML represents the number of white matter lesions. Difference in diffusion metrics among WML The Dice coefficient for segmentation of WML was 0.752. The Cohen’s kappa coefficient for classifying P_WML and NP_WML was 0.794. (Fig. S2 in the supplementary). For the lesion-wise analysis, 1386 WML of 45 MS- were included in the analysis, among which 1048 WML were P_WML, while 338 WML were NP_WML. Similarly, 746 WML from 23 MS+ patients were analyzed, including 599 P_WML and 147 NP_WML. The distribution of diffusion parameter values in four groups of lesions was exhibited (Fig. 4 and Table S1 in the supplementary). In the MS- group, MK index in P_WML was lower than NP_WML (p < 0.001). In the MS+ group, the lower MK index (p = 0.021) and higher MD index (p = 0.031) was found in P_WML than NP_WML. In the MS+ group, both P_WML and NP_WML had lower MD index than MS- group (p = 0.005). Also, the MK index in both P_WML and NP_WML were significantly lower in the MS- group (p < 0.001). Besides, the MD index of P_WML in the MS- group was significantly higher than NP_WML (p < 0.001) in the MS+ group and the MK index of P_WML in the MS- group was significantly lower than NP_WML in the MS+ group (p < 0.001). No significant difference was found between MS-NP_WML and MS + P_WML in the MD index and MK index . No significant difference was observed among the four lesions in terms of FA index (p = 0.640), KFA index (p = 0.459). Correlations between MRI metrics and neuropsychological scores In the MS- group, EDSS scores were correlated with KFA index of P_WML (r = -0.429, p = 0.011). MMSE were associated with KFA index of NP_WML(r = 0.377, p = 0.033) (Fig. 5 , Table S2 in supplementary). In the MS+ group, EDSS scores were correlated with MK index of P_WML (r = -0.500, p = 0.049). In two groups, no significant association between MRI metrics and MoCA, DST, SDMT scores was found. Correlations among MRI metrics In lesion-wise analysis, P_WML volume was significantly correlated with FA index (r = -0.172, p < 0.001 for MS-; r = -0.121, p = 0.004 for MS+), KFA index (r = -0.161, p < 0.001 for MS-; r = -0.081, p = 0.048 for MS+), MD index (r = 0.255, p < 0.001 for MS-; r = 0.174, p < 0.001 for MS+) and MK index (r = -0.309, p < 0.001 for MS-; r = -0.221, p < 0.001 for MS+) (Fig. 6 ). In addition, NP_WML volume in the MS- group was correlated with MD index (r = 0.120, p = 0.046) and MK index (r = -0.126, p = 0.046). No significant correlation was found in MS + NP_WML volume. In patient-wise analysis, in the MS+ group, WML volume was associated with FA index (r = -0.549, p = 0.026), KFA index (r = -0.498, p = 0.049), and MK index (r = -0.498, p = 0.049) of NP_WML (Table S3 in supplementary). Additionally, in MS+ group, P_WML volume was correlated with FA index (r = -0.534, p = 0.031), KFA index (r = -0.505, p = 0.046), MD index (r = 0.509, p = 0.044), and MK index (r = -0.506, p = 0.046) of NP_WML. Discussion In this study, we explored the potential pathological mechanisms linking PVS and WML microstructural damage and the correlations with clinical scores in MS patients with or without DMT. The WML were stratified into four groups based on whether PVS penetrated WML and DMT status. DTI/DKI parameters were used to assess microstructural alterations in different lesions and their correlation with neuropsychological scores. Our analysis indicated P_WML had significantly lower MK index and higher MD index than NP_WML in MS patients, and all lesions were less severe in patients receiving DMT. Further analysis suggested that the DTI/DKI parameters of P_WML were associated with disability in MS patients. Together, these results suggest that the presence of PVS was associated with more severe microstructural abnormalities and greater disability. Previous studies have mentioned a close relationship between PVS and WML volume. Ineichen et al. found no difference in the degree of overlap with WML between enlarged and non-enlarged PVS 21 , but they did not focus on whether PVS might influence WML. In contrast, Yasuhiko et al. pointed out that the PVS fluid may affect the diffusion values of normal-appearing white matter (NAWM) 22 , reflecting the potential impact of PVS on adjacent WM. To the best of our knowledge, there is a lack of studies that have explicitly investigated the association between PVS and the microstructural alterations and WML in MS patients. Our analysis showed that P_WML exhibited a lower MK index than NP_WML in both MS + and MS− groups, which may indicate more pronounced microstructural damage in P_WML, possibly related to underlying inflammatory processes and increased tissue complexity 23 . These findings suggest that P_WML may undergo a more complex pathological process that is closely associated with neuroinflammation. Along with cerebrospinal fluid, leukocytes entering PVS are thought to release soluble toxic substances 24 – 26 , which may diffuse into the surrounding parenchyma and be associated with local inflammation and blood-brain barrier disruption. Notably, tissue-resident memory T cells have been observed to actively infiltrate WML through PVS in MS 27 , 28 . These processes are associated with edema, degeneration, and necrosis of neural tissue, such as swollen and hypertrophic astrocytes and their processes, infiltrating hematogenous cells and macrophages, and occasional apoptotic cells 29 . Additionally, dysfunction in the drainage of fluid within PVS has been suggested to be associated with the accumulation of inflammatory factors within brain parenchyma 30 . Therefore, the presence of PVS within WML may be associated with complex pathological changes, such as local microvascular damage, blood-brain barrier impairment, or immune cell infiltration. These factors may be related to local edema or damage to glial cells, thereby affecting the diffusion patterns of water molecules, which is indirectly reflected in significant changes in MD and MK. However, our findings did not establish whether PVS play a determining role in WML development or whether WML formation leads to secondary PVS enlargement, or both. Therefore, these findings should be interpreted with caution. Our study found that, compared with MS-, WML in MS+ group showed significantly higher MK index and lower MD index , regardless of PVS involvement, which suggested myelin integrity and axonal density reduction of WML in MS- may be more severe. Notably, the differences in diffusion metrics between the MS + and MS− group were more pronounced in P_WML than in NP_WML. These findings indicate that DMT treatment status is associated with better microstructural integrity in both lesion types, and that this association is stronger in P_WML. One possible explanation is that P_WML may have a higher baseline inflammatory burden. Additionally, a significant difference in MD index between P_WML and NP_WML was observed only in the MS+ group, which may reflect changes, such as gliosis, cellular reorganization, or tissue compaction, induced by the interplay between treatment and higher inflammation load. In contrast, the difference of MK index between P_WML and NP_WML observed in both MS + and MS- groups, whereas MD showed significant difference only in the MS+ group, which suggested that MK is a more sensitive parameter than MD for detecting subtle microstructural alterations, which is consistent with previous studies 31 , 32 . However, no significant difference in FA index and KFA index was observed between P_WML and NP_WML in MS + and MS- group. One possible explanation is that the presence of PVS in WML may have minimal impact on the orderly arrangement of fiber bundles (such as the orientation of WM fibers), or the fiber structure had not been severely damaged and can still maintain certain directional diffusivity 33 . In lesion-wise analysis, our study suggests that P_WML volume is strongly associated with diffusion values, suggesting that lesion burden may be associated with microstructural injury. This association appears to weaken in MS+, indicating that the association between lesion burden and microstructural damage differs by treatment status, consistent with a previous study 34 . In patient-wise analysis, our results reveal that WML volume and P_WML volume were correlated with diffusion metrics of NP_WML, which may imply a potential link between P_WML burden and microstructural alterations in NP_WML. This pattern suggests that the burden of P_WML is selectively associated with microstructural alterations in NP_WML. However, the underlying mechanisms remain unclear and warrant further investigation. In MS-, KFA index of NP_WML was significantly correlated with MMSE scores, suggesting a potential association between microstructural alterations and global cognitive performance 35 , 36 . However, this association was not observed in MS+, which may be attributed by differences in disease severity, the effects of DMT, and variations in cognitive status between the groups. Consistent with prior research, we did not observe the association between any diffusion value of lesions and SDMT or DST scores 37 suggesting that microstructural damage within lesions may not be associated with information processing speed or attention and working memory in patients with MS. In the correlation analysis, distinct correlation patterns emerged between EDSS scores and DKI parameters in both MS + and MS- groups. Although no statistically significant difference was observed in EDSS scores between the two groups, possibly due to the multifactorial nature of EDSS, which is influenced by spinal cord lesions, brainstem involvement, and disease duration, similar EDSS scores may not reflect equivalent levels of underlying pathology or microstructural damage. In the MS+ groups, EDSS scores were negatively correlated with MK index of P_WML, suggesting that more pronounced disability might be associated with reduced microstructural complexity across different lesion groups. In contrast, EDSS scores were primarily associated with decreased KFA index of P_WML in the MS-, indicating that directional microstructural destruction may be more closely related to disability in the untreated patients. Taken together, these findings indicate that diffusion metrics from both groups correlate with clinical disability, although the underlying microstructural damage patterns differ between groups, which supports the prior study 38 . Our results provide novel evidence for the association between PVS and WML in MS, highlighting the importance of considering PVS characteristics when assessing lesion severity, cognitive function and disability level. Therefore, the role of PVS in CNS pathology warrants further investigation. Notably, cortical lesions were excluded from our analysis due to their small size and limited visibility on conventional MRI. Currently, a recent advanced three-dimensional turbo spin-echo sequence at 7 Tesla made the first successful identification of cortical PVS 39 , providing a promising approach to investigate their relationship with cortical lesions. Future studies utilizing ultra-high-field MRI should prioritize the detection and pathological characterization of cortical PVS, thereby deepening our understanding of PVS-driven mechanisms in MS and other neurodegenerative or demyelinating disorders. To the best of our knowledge, this is the first study to explore the correlation between PVS and the microstructural damage of WML, while taking DMT treatment status into account in MS patients. Previous studies have suggested that PVS were associated with WML; however, the underlying mechanism remains unclear. This study may provide evidence that the presence of PVS is associated with more severe WML damage, providing new insights into possible pathological mechanisms. Our study had some limitations that should be considered. First, the cohort of MS patients mainly consisted of RRMS patients. Although we further explored the association between DMT, PVS and WML microstructure, the sample size was relatively small. Second, our study was cross-sectional and cannot infer the direct impact of PVS and DMT on microstructure on WML. Third, while the exact duration of DMT exposure was not documented, patients in the MS+ group were all on stable DMT regimens at the time of scanning. Fourth, the diffusion acquisition is the relatively low through-plane resolution, which may introduce partial volume effects and reduce sensitivity to microstructural differences between lesion types. Fifth, normalized diffusion indices were used to account for regional variability; however, this approach is not standard and has not been formally validated, and may introduce potential bias. Although this method was intended to reduce the confounding influence of intrinsic microstructural differences across regions, the findings should be interpreted with caution. Finally, while contralateral normal-appearing regions are commonly used to reduce background variability, this approach may be less applicable in multiple sclerosis due to the widespread distribution of lesions. Therefore, we adopted a normalization strategy using HCs as references. However, this method has not been formally validated and may introduce potential bias. Future longitudinal studies with larger cohorts are needed to better characterize the evolution of WML over time and validate our findings. Also, studies using animal models or in vitro experiments are needed to clarify the molecular and cellular mechanisms between PVS and MS lesions. These findings may deepen our mechanistic understanding of PVS in MS pathogenesis, potentially revealing novel therapeutic targets. Conclusion Overall, our study suggested that the presence of PVS within WML is associated with the greater microstructural damage of the lesions and less severe abnormalities in treated patients. These findings suggest that future research on WML should comprehensively account for the association with PVS, thereby advancing our understanding of disease progression, treatment responses, and the underlying pathophysiological mechanisms involved. Declarations Funding This study has received funding by Joint Project of Chongqing Health Commission and Science and Technology Bureau (No. 2023ZDXM006), the National Natural Science Foundation of China (No. 82302150) and the Master's Research Innovation Project of the First Clinical College, Chongqing Medical University (No. CYYY-SSCX202529). Author Contribution K.Z.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. Q.Y. Z.: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing - review and editing. X.J.D.: Software, Writing - review and editing. Z.W.S.: Visualization, Writing - review and editing. Z.C.Y: Visualization, Validation, Writing - review and editing. F.Y.Y.: Methodology. B.Y.: Writing - review and editing. Y.X.L: Methodology. X.Y.C.: Conceptualization, Methodology, Writing – review and editing. Y.M.L: Writing – review and editing, Supervision, Funding acquisition.. Acknowledgement All authors would like to thank all the subjects who participated in this study. References Rahmanzadeh R, Lu PJ, Barakovic M, Weigel M, Maggi P, Nguyen TD et al (2021) Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging. Brain 144(6):1684–1696 Goldschmidt T, Antel J, König FB, Brück W, Kuhlmann T (2009) Remyelination capacity of the MS brain decreases with disease chronicity. 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Brain Commun 2(2):fcaa077 Preziosa P, Storelli L, Meani A, Moiola L, Rodegher M, Filippi M et al (2021) Effects of Fingolimod and Natalizumab on Brain T1-/T2-Weighted and Magnetization Transfer Ratios: a 2-Year Study. Neurotherapeutics 18(2):878–888 Sandry J, Simonet DV, Brandstadter R, Krieger S, Katz Sand I, Graney RA et al (2021) The Symbol Digit Modalities Test (SDMT) is sensitive but non-specific in MS: Lexical access speed, memory, and information processing speed independently contribute to SDMT performance. Mult Scler Relat Disord 51:102950 Dassanayake TL, Hewawasam C, Baminiwatta A, Ariyasinghe DI (2021) Regression-based, demographically adjusted norms for Victoria Stroop Test, Digit Span, and Verbal Fluency for Sri Lankan adults. Clin Neuropsychol 35(sup1):S32–S49 Hu H, Ye L, Ding S, Zhu Q, Yan Z, Chen X et al (2022) The heterogeneity of tissue destruction between iron rim lesions and non-iron rim lesions in multiple sclerosis: A diffusion MRI study. Mult Scler Relat Disord 66:104070 Ocampo-Pineda M, Cagol A, Benkert P, Barakovic M, Lu PJ, Muller J et al (2025) White Matter Tract Degeneration in Multiple Sclerosis Patients With Progression Independent of Relapse Activity. Neurol Neuroimmunol Neuroinflamm 12(3):e200388 Demir Z, Saib G, Talagala L, Taylor P, Kwan JY, Koretsky A (2025) Exploring cortical perivascular space as a biomarker in neurodegenerative diseases through optimized CSF-only MRI at 7T. Alzheimer's &. Dementia 20:S8 Additional Declarations No competing interests reported. Supplementary Files SupplementalFileForPeerReviewOnly.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9307837","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634378366,"identity":"5cfaa06a-8db4-40aa-94eb-30f4f3eea97d","order_by":0,"name":"Kai Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhang","suffix":""},{"id":634378367,"identity":"275ea4fc-8494-4ffc-8f41-f2e3b5490f9b","order_by":1,"name":"Qiyuan Zhu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiyuan","middleName":"","lastName":"Zhu","suffix":""},{"id":634378369,"identity":"73fc8fa0-732a-4aeb-8239-9849bcfaa496","order_by":2,"name":"Xiaojuan Dong","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Dong","suffix":""},{"id":634378370,"identity":"65a9af1f-0512-4fa6-b55f-db2f1f82990c","order_by":3,"name":"Zhuowei Shi","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuowei","middleName":"","lastName":"Shi","suffix":""},{"id":634378371,"identity":"fa3432c7-e603-4e2c-b70f-a32b2ae12b79","order_by":4,"name":"Zichun Yan","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zichun","middleName":"","lastName":"Yan","suffix":""},{"id":634378372,"identity":"7f858617-d076-4eb0-b9b1-bb9da3a38470","order_by":5,"name":"Feiyue Yin","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feiyue","middleName":"","lastName":"Yin","suffix":""},{"id":634378376,"identity":"c54f739c-3697-4216-bfc4-97c7bf0d9a5e","order_by":6,"name":"Bin Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yang","suffix":""},{"id":634378377,"identity":"ed9668e7-e57b-4a68-b945-0a4ae75f0926","order_by":7,"name":"Yixian Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yixian","middleName":"","lastName":"Li","suffix":""},{"id":634378379,"identity":"7a236425-b869-43c9-984c-63aaedd44303","order_by":8,"name":"Xiaoya Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoya","middleName":"","lastName":"Chen","suffix":""},{"id":634378380,"identity":"5c0045b1-1cf0-470a-898d-46a38d71d16d","order_by":9,"name":"Yongmei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACPiBmBjHsDzMfOPDhBxFa2GBaGI63JR6c2UOSljNnjA9zsBGjRSI7+XNBzR27xhk5Hw4z8DDI84sdIKQld4PxjGPPkpuBjMMFFgyGM2cnENaSzMN2OBnEODyDhyHB4DYRWg7z/DuczCOR8+AwDxtxWjY287YdtpPgOcNApBaet5uZefsOJxiwtxkAA1mCsF/42XM3f+b5dtjegJn58YcPP2zk+aUJaGEQgChIbIBwJQgoB1tzAEzZE6F0FIyCUTAKRioAAGZDRewv+CddAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongmei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-04-03 02:09:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9307837/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9307837/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108838505,"identity":"6fd62edb-46c0-463b-9c57-648e4deb5632","added_by":"auto","created_at":"2026-05-09 00:36:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62368,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart showing the selection of MS patients and HCs.\u003c/p\u003e\n\u003cp\u003eRRMS, relapsing-remitting multiple sclerosis. HCs, healthy controls.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/46f9e7313dc30d4a81dafb83.png"},{"id":108838503,"identity":"6071504a-5e0e-4213-b235-2148f0351abe","added_by":"auto","created_at":"2026-05-09 00:36:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148235,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of post-processing pipeline.\u003c/p\u003e\n\u003cp\u003eFA, fractional anisotropy; mFA, mean fractional anisotropy; Ants, Advanced Normalization Tools; WML, white matter lesions; MNI, Montreal Neurological Institute.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/44d9d36676b137ac38997599.png"},{"id":108977463,"identity":"ec095f65-8764-4e5f-a03f-3a05d8c4018b","added_by":"auto","created_at":"2026-05-11 11:31:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":604703,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of P_WML on pre-contrast T1w (A and D), T2w (B) and FLAIR (C) and NP_WML on pre-contrast T1w (E and H), T2w (F) and FLAIR (G). P_WML, perivascular space penetrating white matter lesions; NP_WML, non-perivascular space penetrating white matter lesions.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/636a75c11cd72acb04c1ccc0.png"},{"id":109220755,"identity":"cd5ba994-cdb7-4840-bd9f-edc08f13991c","added_by":"auto","created_at":"2026-05-13 20:35:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":349498,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution difference of the DTI/DKI parameters in four groups of lesions.\u003c/p\u003e\n\u003cp\u003eMS-: relapsing-remitting multiple sclerosis not receiving disease-modifying therapies; MS+: relapsing-remitting multiple sclerosis receiving disease-modifying therapies; P_WML, perivascular space penetrating white matter lesions; NP_WML, non-perivascular space penetrating white matter lesions; FA, fractional anisotropy; KFA, kurtosis fractional anisotropy; MD, mean diffusivity; MK, mean kurtosis. *** shows significant at the 0.001 level (two-tailed), ** shows significant at the 0.01 level (two-tailed) and * shows significant at the 0.05 level (two-tailed)。\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/4d24d78de8385d4b87b3ef27.png"},{"id":108977455,"identity":"3d4977d5-9a5e-4132-be99-94b5fc71751f","added_by":"auto","created_at":"2026-05-11 11:31:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":144307,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlations among the imaging metrics, cognitive assessment, and EDSS scores.\u003c/p\u003e\n\u003cp\u003eMS+: patients with relapsing-remitting multiple sclerosis receiving disease-modifying therapies; MS-: patients with relapsing-remitting multiple sclerosis not receiving disease-modifying therapies; P_WML, perivascular space penetrating white matter lesions; NP_WML, non-perivascular space penetrating white matter lesions; FA, fractional anisotropy; KFA, kurtosis fractional anisotropy; MD, mean diffusivity; MK, mean kurtosis; No. of WML represents the number of white matter lesions; DST, Digit Span Test; SDMT, Symbol Digit Modalities Test; MoCA, the Montreal Cognitive Assessment; MMSE, Mini-Mental State Examination; EDSS, Expanded Disability Status Scale. * denote statistical significance (p\u0026lt;0.05). * denote statistical significance (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/c8a7d00e00ffc679b32a3b26.png"},{"id":108976780,"identity":"d2a30d4e-e8a6-493e-9fda-de758c0c69cc","added_by":"auto","created_at":"2026-05-11 11:28:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":77832,"visible":true,"origin":"","legend":"\u003cp\u003eThe heatmap depicts the correlation between WML volume in four groups and DKI parameters in lesion-wise analysis. MS+: patients with relapsing-remitting multiple sclerosis receiving disease-modifying therapies; MS-: patients with relapsing-remitting multiple sclerosis not receiving disease-modifying therapies; MS+P_WML: patients with RRMS sclerosis receiving disease-modifying therapies; MS-P_WML, perivascular space penetrating white matter lesions in MS-; MS+NP_WML, non-perivascular space penetrating white matter lesions in MS+; MS-NP_WML, non-perivascular space penetrating white matter lesions in MS-; FA, fractional anisotropy; KFA, kurtosis fractional anisotropy; MD, mean diffusivity; MK, mean kurtosis. *** shows significant at the 0.001 level (two-tailed), ** shows significant at the 0.01 level (two-tailed) and * shows significant at the 0.05 level (two-tailed).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/3317a66cc4f0a84b4668a663.png"},{"id":109249700,"identity":"01591413-b810-41a9-9427-706c40631538","added_by":"auto","created_at":"2026-05-14 08:59:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1805341,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/56de66ee-9ad9-423d-aa43-cd3716323a22.pdf"},{"id":108976633,"identity":"141e6c8b-0b94-404c-9560-1fc6cbd12603","added_by":"auto","created_at":"2026-05-11 11:26:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":590877,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFileForPeerReviewOnly.docx","url":"https://assets-eu.researchsquare.com/files/rs-9307837/v1/b6d67c3bd97161bcde1b88f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enlarged perivascular spaces are associated with more severe microstructural damage in white matter lesions among patients with relapsing-remitting multiple sclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS), characterized by the focal demyelinating lesions. White matter lesions (WML) are the most common pathological features and exhibit substantial heterogeneity in myelin, axon and iron content\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Research has revealed that WML contribute to structural brain alterations, functional connectivity disruption and tissue integrity impairment, which are associated with inflammation and neurodegeneration in MS\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe perivascular spaces (PVS) are fluid-filled spaces surrounding small perforating vessels which serve as key components of the glymphatic system and play an important role in interstitial fluid (ISF) and solute clearance in the brain\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. PVS visible on MRI in healthy individuals are typically small. However, aging, neurodegeneration and neuroinflammation may lead to pathological enlargement of PVS, referred to as enlarged PVS.\u003c/p\u003e \u003cp\u003eConvergent lines of evidence support that MS patients have a higher PVS burden compared to healthy controls (HCs)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Additionally, a study described the correlation between PVS load and the burden of WML in MS patients\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Given the role of PVS in glymphatic function and perivascular inflammatory processes, PVS may be related with the microenvironment of adjacent white matter tissue\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. However, existing studies have primarily focused on lesion burden at the macrostructural level, and whether PVS are associated with the microstructural damage of WML remains unclear. Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging, characterizes non-Gaussian water diffusion and provides more sensitivity to tissue microstructural complexity, and has been used to quantify microstructural damage in MS lesions\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The reduction in kurtosis fractional anisotropy (KFA) and fractional anisotropy (FA) was considered to reflect the axonal damage and neuron loss within MS lesions, which may result in impaired fiber integrity\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The decreased mean kurtosis (MK) values were correlated with myelin destruction or axon loss and increased mean diffusivity (MD) may reflect enhanced water diffusivity associated with blood-brain barrier disruption and microstructural damage\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, this study focuses on investigating the association between PVS and microstructural damage in WML. By exploring the spatial positional relationship between PVS and WML, the WML were further categorized into PVS penetrating white matter lesions (P_WML) and non-PVS penetrating white matter lesions (NP_WML) in relapsing-remitting multiple sclerosis (RRMS). This study aimed to : (i) examine whether PVS are associated with microstructural damage of WML, (ii) assess whether the microstructural integrity of P_WML and NP_WML differs in relation to disease-modifying therapies (DMT) drug, and (iii) investigate the relationship between microstructural integrity of P_WML/NP_WML and cognitive status and clinical biomarkers of disability.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of sixty-eight patients with RRMS and forty-seven age- and sex-matched HCs were included in the study. This study was approved by the institutional review board of the First Affiliated Hospital of Chongqing Medical University between 2023 and 2024, and all patients and HCs provided written informed consent. This research was conducted in accordance with the Declaration of Helsinki. Patients with MS were enrolled according to the following inclusion criteria: 1) a confirmed diagnosis of RRMS according to the 2017 revised McDonald\u0026rsquo; s diagnostic criteria\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, 2) age 18\u0026ndash;60 years, 3) absence of active relapse at the time of scanning and 4) absence of intravenous corticosteroid use for at least 2 months before imaging. The exclusion criteria included: 1) patients with neurological conditions other than MS, 2) contraindications for MRI scans, 3) image artifacts or 4) incomplete clinical information, and 5) a history of intravenous corticosteroid treatment within 2 months before the imaging examinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to whether patients received DMT, patients with RRMS were further divided into The patients who received DMT (MS+) group and patients who did not receive DMT (MS-) group in subsequent analysis. The MS+ group received one of the following DMT drugs: teriflunomide (n\u0026thinsp;=\u0026thinsp;10), siponimod (n\u0026thinsp;=\u0026thinsp;5), rituximab (n\u0026thinsp;=\u0026thinsp;3), mycophenolate mofetil (n\u0026thinsp;=\u0026thinsp;3), fingolimod (n\u0026thinsp;=\u0026thinsp;1), and ofatumumab (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical assessment\u003c/h3\u003e\n\u003cp\u003eThe clinical and neuropsychological evaluation were recorded the demographic information of each participant and the clinical data of the patient group. The Symbol Digit Modalities Test (SDMT)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, Montreal Cognitive Assessment (MoCA)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, Mini-Mental State Examination (MMSE)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e and Digit Span Test (DST) scores\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e were used to assess cognitive performance, and the Expanded Disability Status Scale (EDSS)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e scores was used to assess the disability.\u003c/p\u003e\n\u003ch3\u003eMR imaging acquisition\u003c/h3\u003e\n\u003cp\u003eAll subjects underwent 3.0T MRI scanner (MAGNETOM Skyra, Siemens, Erlangen, Germany) using a 32-channel head coil. In brief, the 3.0T scan protocols included: (i) sagittal 3-dimensional (3D) T1w magnetization-prepared rapid gradient echo [repetition time (TR)\u0026thinsp;=\u0026thinsp;2,300 ms, echo-time (TE)\u0026thinsp;=\u0026thinsp;2.26 ms, inversion time (TI)\u0026thinsp;=\u0026thinsp;900 ms, 192 slices, field of view (FOV)\u0026thinsp;=\u0026thinsp;256 mm, voxel size\u0026thinsp;=\u0026thinsp;1.0 \u0026times; 1.0 \u0026times; 1.0 mm\u003csup\u003e3\u003c/sup\u003e], (ii) axial T2-weighted (T2w), proton-density fast spin echo [TR\u0026thinsp;=\u0026thinsp;3,600 ms, TE\u0026thinsp;=\u0026thinsp;9.4 ms/94 ms, TI\u0026thinsp;=\u0026thinsp;900 ms, 35 slices, FOV\u0026thinsp;=\u0026thinsp;220 mm, voxel size\u0026thinsp;=\u0026thinsp;0.3 \u0026times; 0.3 \u0026times; 0.3 mm\u003csup\u003e3\u003c/sup\u003e], (iii) sagittal 3D fluid attenuated inversion recovery (FLAIR) (TR\u0026thinsp;=\u0026thinsp;5,000 ms, TE\u0026thinsp;=\u0026thinsp;388 ms, TI\u0026thinsp;=\u0026thinsp;1,800 ms, 192 slices, FOV\u0026thinsp;=\u0026thinsp;256 mm, voxel size\u0026thinsp;=\u0026thinsp;0.5 \u0026times; 0.5 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e), (iv) diffusion kurtosis imaging (DKI) sequence (TE\u0026thinsp;=\u0026thinsp;97 ms, TR\u0026thinsp;=\u0026thinsp;5,000 ms, 25 slices, FOV\u0026thinsp;=\u0026thinsp;220 mm, voxel size\u0026thinsp;=\u0026thinsp;1.7 \u0026times; 1.7 \u0026times; 4.0 mm\u003csup\u003e3\u003c/sup\u003e, Partial-Fourier\u0026thinsp;=\u0026thinsp;6/8, integrated parallel acquisition techniques acceleration factor\u0026thinsp;=\u0026thinsp;2 (GRAPPA), and three b values (0, 1,000, and 2,000 s/ mm\u003csup\u003e2\u003c/sup\u003e) with diffusion encoding in 30 directions.\u003c/p\u003e\n\u003ch3\u003eMRI data processing and analysis\u003c/h3\u003e\n\u003cp\u003eThe data processing pipeline was presented (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eQuantification of WML\u003c/h3\u003e\n\u003cp\u003eThe FLAIR and T2w images were registered to pre-contrast T1w MPRAGE images using Advanced Normalization Tools (ANTs). The registered images were then used by two trained radiologists (ZK and DXJ) to delineate each supratentorial lesion and generate the corresponding mask for each one, which were systematically corrected by a third neuroradiologist (LYM, with more than 20 years of MRI experience) using ITK-SNAP software (version 4.0.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.itksnap.org\u003c/span\u003e\u003cspan address=\"http://www.itksnap.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the supplementary). The Dice coefficient was calculated to assess the repeatability about segmentation of WML. Finally,the number of lesions and each lesion volume were then calculated. The infratentorial and cortical lesions were excluded due to the difficulty in identifying PVS in these regions and the focus on subcortical PVS and WML. Furthermore, the lesions delineated in native space were registered to the Montreal Neurological Institute (MNI) space following the T1w images.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClassification of WML\u003c/h2\u003e \u003cp\u003eBased on the T1w MPRAGE, FLAIR images and T2w images, the WML were independently divided into P_WML and NP_WML by two experienced radiologists (CXY and DXJ) blinded to clinical information (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with P_WML defined as lesions in direct contact with a visible PVS. The Cohen's kappa coefficient was calculated to assess the repeatability about classification of P_WML and NP_WML. In case of conflict, a third experienced radiologist (LYM) would make a final decision.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDKI processing\u003c/h3\u003e\n\u003cp\u003eThe DKI images were pre-processed including correction of head motion, eddy current-induced distortion, susceptibility-induced distortions, and the \u0026ldquo;BET\u0026rdquo; tool used for skull-stripping for b0 by the FMRIB Software Library (FSL, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\u003c/span\u003e\u003cspan address=\"http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The diffusion kurtosis estimator software (DKE version 2.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/dke\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/dke\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to process the DKI data using the constrained linear least-squares quadratic programming algorithm and applied standard parameters (spatial smoothing and robust median filtering) to acquire the diffusion parametric maps including FA, KFA, MD and MK\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Then, parametric maps were registered to the skull-stripped-T1w images using b0 maps, respectively.\u003c/p\u003e \u003cp\u003eNext, FA\u003csub\u003ems\u003c/sub\u003e, KFA\u003csub\u003ems\u003c/sub\u003e, MD\u003csub\u003ems\u003c/sub\u003e, and MK\u003csub\u003ems\u003c/sub\u003e values of ROIs were obtained by averaging the corresponding diffusion parameter maps (including FA, KFA, MK, and MD maps) across the ROI in the MS patients. Meanwhile, we collected diffusion parameter maps from HCs. HCs\u0026rsquo; diffusion parameter maps were registered to the MNI space using ANTs, and all HCs\u0026rsquo; maps were then averaged to obtain the mean KFA (mKFA), mean FA (mFA), mean MK (mMK), and mean MD (mMD).\u003c/p\u003e \u003cp\u003eThen we extracted FA\u003csub\u003ehc\u003c/sub\u003e, KFA\u003csub\u003ehc\u003c/sub\u003e, MD\u003csub\u003ehc\u003c/sub\u003e, and MK\u003csub\u003ehc\u003c/sub\u003e of registered ROIs by averaging with the corresponding diffusion parameter maps of mFA, mKFA, mMK, and mMD values. Ultimately, we calculated FA\u003csub\u003eindex\u003c/sub\u003e to avoid the influence of intrinsic microstructural damage differences in different brain regions in the following formula, similar to the method employed by Denecke et al\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{}\\text{FA}}_{\\text{index}}\\text{=}{\\text{FA}}_{\\text{ms}}\\text{/}{\\text{FA}}_{\\text{hc}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor each lesion, FA\u003csub\u003ems\u003c/sub\u003e stands for FA values of WML in MS and FA\u003csub\u003ehc\u003c/sub\u003e denotes FA values of lesions in corresponding regions of HCs.\u003c/p\u003e \u003cp\u003eFor patient-wise analysis, we calculated the total FA\u003csub\u003eindex\u003c/sub\u003e, KFA\u003csub\u003eindex\u003c/sub\u003e, MD\u003csub\u003eindex\u003c/sub\u003e and MK\u003csub\u003eindex\u003c/sub\u003e values for each patient, derived from the segmented lesions in the following formula.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"430\" height=\"40\"\u003e\u003c/p\u003e \u003cp\u003eIn the formula above, for each patient, \u0026ldquo;i\u0026rdquo; represents the total number of the lesions, F1, and V1 represent the FA\u003csub\u003eindex\u003c/sub\u003e value and the volume of the first WML, respectively. The same calculation method was applied to obtain KFA\u003csub\u003eindex\u003c/sub\u003e, MD\u003csub\u003eindex\u003c/sub\u003e, and MK\u003csub\u003eindex\u003c/sub\u003e.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, United States) and MATLAB 2022. For all continuous data, the Kolmogorov-Smirnov test was used to assess the normality of the data distribution. The Mann-Whitney U test was applied to compare the demographic variables between groups, while the Fisher exact test was used to assess gender distribution. The Kruskal-Wallis H test was conducted to evaluate group differences in MRI metrics, followed by Dunn's test for the post-hoc analysis where appropriate. Before statistical analysis, Z-values were calculated for all the neuropsychological scales to standardize the measures and facilitate comparison. Spearman\u0026rsquo;s partial correlation analysis was performed in MATLAB to assess: (i) the relationship between diffusion metrics and neuropsychological scores within MS- and MS+ groups, and (ii) the correlation between WML volume and diffusion metrics across the four groups. Age and sex were included as covariates. All p-values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (BH-FDR) method, with p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05\u003c/em\u003e considered statistically significant after correction. All subsequently p-values are FDR-p values.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical data\u003c/h2\u003e \u003cp\u003eThe demographics and clinical characteristics of all participants were presented (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the 68 patients with MS, 45 (66%) were MS\u0026minus;, while 23 (34%) were MS+. There was no significant difference in age, sex, or years of education among the HCs group, MS- group and MS+ group, and no significant difference in disease duration between the MS- and MS+ groups. Compared to HCs, the MS- group had significantly lower scores on the DST (p\u0026thinsp;=\u0026thinsp;0.039), SDMT (p\u0026thinsp;=\u0026thinsp;0.002), MMSE (p\u0026thinsp;=\u0026thinsp;0.003), and MoCA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, no significant difference was found between the HCs and MS+ group. Compared to the MS+ group, the MS- group showed lower scores on the SDMT (p\u0026thinsp;=\u0026thinsp;0.027) and MoCA (p\u0026thinsp;=\u0026thinsp;0.002). No significant difference was found in the number of WML, P_WML/Total WML, and WML volume between the MS\u0026thinsp;+\u0026thinsp;and MS- groups.\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\u003eDemographic and clinical characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS+\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS-\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003csub\u003e1\u003c/sub\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003csub\u003e2\u003c/sub\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003csub\u003e3\u003c/sub\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.78\u0026thinsp;\u0026plusmn;\u0026thinsp;7.86\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.20\u0026thinsp;\u0026plusmn;\u0026thinsp;10.76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8/37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDSS scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDMT scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.08\u0026thinsp;\u0026plusmn;\u0026thinsp;15.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of WML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.00 (13.00, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.00 (18.00, 46.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWML volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3466.00 (1745.00, 5446.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4182.00 (2028.00, 5152.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_WML/Total WML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eComparisons performed by Mann-Whitney U-test and Fisher\u0026rsquo;s exact (sex). BH-FDR correction (Benjamini-Hochberg procedure) was applied to account for the overall number of pairwise comparisons. Bold values denote statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The data were shown as the mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. p1 value for MS+ group versus HCs group, p2 value for MS- group versus HCs group, and p3 value for MS+ group versus MS- group. MS+: patients with relapsing-remitting multiple sclerosis receiving disease-modifying therapies; MS-: patients with relapsing-remitting multiple sclerosis not receiving disease-modifying therapies; DST, Digit Span Test; EDSS: Kurtzke expanded disability status scale; SDMT: symbol digit modalities test; MMSE: Mini-Mental State Examination; MoCA, the Montreal Cognitive Assessment; P_WML/Total WML represents the fraction of P_WML within the total WML; No. of WML represents the number of white matter lesions.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifference in diffusion metrics among WML\u003c/h2\u003e \u003cp\u003eThe Dice coefficient for segmentation of WML was 0.752. The Cohen\u0026rsquo;s kappa coefficient for classifying P_WML and NP_WML was 0.794. (Fig. S2 in the supplementary). For the lesion-wise analysis, 1386 WML of 45 MS- were included in the analysis, among which 1048 WML were P_WML, while 338 WML were NP_WML. Similarly, 746 WML from 23 MS+ patients were analyzed, including 599 P_WML and 147 NP_WML.\u003c/p\u003e \u003cp\u003eThe distribution of diffusion parameter values in four groups of lesions was exhibited (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the supplementary).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the MS- group, MK\u003csub\u003eindex\u003c/sub\u003e in P_WML was lower than NP_WML (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the MS+ group, the lower MK\u003csub\u003eindex\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.021) and higher MD\u003csub\u003eindex\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.031) was found in P_WML than NP_WML.\u003c/p\u003e \u003cp\u003eIn the MS+ group, both P_WML and NP_WML had lower MD\u003csub\u003eindex\u003c/sub\u003e than MS- group (p\u0026thinsp;=\u0026thinsp;0.005). Also, the MK\u003csub\u003eindex\u003c/sub\u003e in both P_WML and NP_WML were significantly lower in the MS- group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eBesides, the MD\u003csub\u003eindex\u003c/sub\u003e of P_WML in the MS- group was significantly higher than NP_WML (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the MS+ group and the MK\u003csub\u003eindex\u003c/sub\u003e of P_WML in the MS- group was significantly lower than NP_WML in the MS+ group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was found between MS-NP_WML and MS\u0026thinsp;+\u0026thinsp;P_WML in the MD\u003csub\u003eindex\u003c/sub\u003e and MK\u003csub\u003eindex\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eNo significant difference was observed among the four lesions in terms of FA\u003csub\u003eindex\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.640), KFA\u003csub\u003eindex\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.459).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between MRI metrics and neuropsychological scores\u003c/h2\u003e \u003cp\u003eIn the MS- group, EDSS scores were correlated with KFA\u003csub\u003eindex\u003c/sub\u003e of P_WML (r = -0.429, p\u0026thinsp;=\u0026thinsp;0.011). MMSE were associated with KFA\u003csub\u003eindex\u003c/sub\u003e of NP_WML(r\u0026thinsp;=\u0026thinsp;0.377, p\u0026thinsp;=\u0026thinsp;0.033) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S2 in supplementary).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the MS+ group, EDSS scores were correlated with MK\u003csub\u003eindex\u003c/sub\u003e of P_WML (r = -0.500, p\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e \u003cp\u003eIn two groups, no significant association between MRI metrics and MoCA, DST, SDMT scores was found.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations among MRI metrics\u003c/h2\u003e \u003cp\u003eIn lesion-wise analysis, P_WML volume was significantly correlated with FA\u003csub\u003eindex\u003c/sub\u003e (r = -0.172, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for MS-; r = -0.121, p\u0026thinsp;=\u0026thinsp;0.004 for MS+), KFA\u003csub\u003eindex\u003c/sub\u003e (r = -0.161, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for MS-; r = -0.081, p\u0026thinsp;=\u0026thinsp;0.048 for MS+), MD\u003csub\u003eindex\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.255, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for MS-; r\u0026thinsp;=\u0026thinsp;0.174, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for MS+) and MK\u003csub\u003eindex\u003c/sub\u003e (r = -0.309, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for MS-; r = -0.221, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for MS+) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In addition, NP_WML volume in the MS- group was correlated with MD\u003csub\u003eindex\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.120, p\u0026thinsp;=\u0026thinsp;0.046) and MK\u003csub\u003eindex\u003c/sub\u003e (r = -0.126, p\u0026thinsp;=\u0026thinsp;0.046). No significant correlation was found in MS\u0026thinsp;+\u0026thinsp;NP_WML volume.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn patient-wise analysis, in the MS+ group, WML volume was associated with FA\u003csub\u003eindex\u003c/sub\u003e (r = -0.549, p\u0026thinsp;=\u0026thinsp;0.026), KFA\u003csub\u003eindex\u003c/sub\u003e (r = -0.498, p\u0026thinsp;=\u0026thinsp;0.049), and MK\u003csub\u003eindex\u003c/sub\u003e (r = -0.498, p\u0026thinsp;=\u0026thinsp;0.049) of NP_WML (Table S3 in supplementary). Additionally, in MS+ group, P_WML volume was correlated with FA\u003csub\u003eindex\u003c/sub\u003e (r = -0.534, p\u0026thinsp;=\u0026thinsp;0.031), KFA\u003csub\u003eindex\u003c/sub\u003e (r = -0.505, p\u0026thinsp;=\u0026thinsp;0.046), MD\u003csub\u003eindex\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.509, p\u0026thinsp;=\u0026thinsp;0.044), and MK\u003csub\u003eindex\u003c/sub\u003e (r = -0.506, p\u0026thinsp;=\u0026thinsp;0.046) of NP_WML.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the potential pathological mechanisms linking PVS and WML microstructural damage and the correlations with clinical scores in MS patients with or without DMT. The WML were stratified into four groups based on whether PVS penetrated WML and DMT status. DTI/DKI parameters were used to assess microstructural alterations in different lesions and their correlation with neuropsychological scores. Our analysis indicated P_WML had significantly lower MK\u003csub\u003eindex\u003c/sub\u003e and higher MD\u003csub\u003eindex\u003c/sub\u003e than NP_WML in MS patients, and all lesions were less severe in patients receiving DMT. Further analysis suggested that the DTI/DKI parameters of P_WML were associated with disability in MS patients. Together, these results suggest that the presence of PVS was associated with more severe microstructural abnormalities and greater disability.\u003c/p\u003e \u003cp\u003ePrevious studies have mentioned a close relationship between PVS and WML volume. Ineichen et al. found no difference in the degree of overlap with WML between enlarged and non-enlarged PVS\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, but they did not focus on whether PVS might influence WML. In contrast, Yasuhiko et al. pointed out that the PVS fluid may affect the diffusion values of normal-appearing white matter (NAWM)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, reflecting the potential impact of PVS on adjacent WM. To the best of our knowledge, there is a lack of studies that have explicitly investigated the association between PVS and the microstructural alterations and WML in MS patients.\u003c/p\u003e \u003cp\u003eOur analysis showed that P_WML exhibited a lower MK\u003csub\u003eindex\u003c/sub\u003e than NP_WML in both MS\u0026thinsp;+\u0026thinsp;and MS\u0026minus; groups, which may indicate more pronounced microstructural damage in P_WML, possibly related to underlying inflammatory processes and increased tissue complexity\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These findings suggest that P_WML may undergo a more complex pathological process that is closely associated with neuroinflammation. Along with cerebrospinal fluid, leukocytes entering PVS are thought to release soluble toxic substances\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, which may diffuse into the surrounding parenchyma and be associated with local inflammation and blood-brain barrier disruption. Notably, tissue-resident memory T cells have been observed to actively infiltrate WML through PVS in MS\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e27\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These processes are associated with edema, degeneration, and necrosis of neural tissue, such as swollen and hypertrophic astrocytes and their processes, infiltrating hematogenous cells and macrophages, and occasional apoptotic cells\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Additionally, dysfunction in the drainage of fluid within PVS has been suggested to be associated with the accumulation of inflammatory factors within brain parenchyma\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Therefore, the presence of PVS within WML may be associated with complex pathological changes, such as local microvascular damage, blood-brain barrier impairment, or immune cell infiltration. These factors may be related to local edema or damage to glial cells, thereby affecting the diffusion patterns of water molecules, which is indirectly reflected in significant changes in MD and MK. However, our findings did not establish whether PVS play a determining role in WML development or whether WML formation leads to secondary PVS enlargement, or both. Therefore, these findings should be interpreted with caution.\u003c/p\u003e \u003cp\u003eOur study found that, compared with MS-, WML in MS+ group showed significantly higher MK\u003csub\u003eindex\u003c/sub\u003e and lower MD\u003csub\u003eindex\u003c/sub\u003e, regardless of PVS involvement, which suggested myelin integrity and axonal density reduction of WML in MS- may be more severe. Notably, the differences in diffusion metrics between the MS\u0026thinsp;+\u0026thinsp;and MS\u0026minus; group were more pronounced in P_WML than in NP_WML. These findings indicate that DMT treatment status is associated with better microstructural integrity in both lesion types, and that this association is stronger in P_WML. One possible explanation is that P_WML may have a higher baseline inflammatory burden. Additionally, a significant difference in MD\u003csub\u003eindex\u003c/sub\u003e between P_WML and NP_WML was observed only in the MS+ group, which may reflect changes, such as gliosis, cellular reorganization, or tissue compaction, induced by the interplay between treatment and higher inflammation load.\u003c/p\u003e \u003cp\u003eIn contrast, the difference of MK\u003csub\u003eindex\u003c/sub\u003e between P_WML and NP_WML observed in both MS\u0026thinsp;+\u0026thinsp;and MS- groups, whereas MD showed significant difference only in the MS+ group, which suggested that MK is a more sensitive parameter than MD for detecting subtle microstructural alterations, which is consistent with previous studies\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. However, no significant difference in FA\u003csub\u003eindex\u003c/sub\u003e and KFA\u003csub\u003eindex\u003c/sub\u003e was observed between P_WML and NP_WML in MS\u0026thinsp;+\u0026thinsp;and MS- group. One possible explanation is that the presence of PVS in WML may have minimal impact on the orderly arrangement of fiber bundles (such as the orientation of WM fibers), or the fiber structure had not been severely damaged and can still maintain certain directional diffusivity\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn lesion-wise analysis, our study suggests that P_WML volume is strongly associated with diffusion values, suggesting that lesion burden may be associated with microstructural injury. This association appears to weaken in MS+, indicating that the association between lesion burden and microstructural damage differs by treatment status, consistent with a previous study\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn patient-wise analysis, our results reveal that WML volume and P_WML volume were correlated with diffusion metrics of NP_WML, which may imply a potential link between P_WML burden and microstructural alterations in NP_WML. This pattern suggests that the burden of P_WML is selectively associated with microstructural alterations in NP_WML. However, the underlying mechanisms remain unclear and warrant further investigation.\u003c/p\u003e \u003cp\u003eIn MS-, KFA\u003csub\u003eindex\u003c/sub\u003e of NP_WML was significantly correlated with MMSE scores, suggesting a potential association between microstructural alterations and global cognitive performance\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. However, this association was not observed in MS+, which may be attributed by differences in disease severity, the effects of DMT, and variations in cognitive status between the groups. Consistent with prior research, we did not observe the association between any diffusion value of lesions and SDMT or DST scores\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e suggesting that microstructural damage within lesions may not be associated with information processing speed or attention and working memory in patients with MS.\u003c/p\u003e \u003cp\u003eIn the correlation analysis, distinct correlation patterns emerged between EDSS scores and DKI parameters in both MS\u0026thinsp;+\u0026thinsp;and MS- groups. Although no statistically significant difference was observed in EDSS scores between the two groups, possibly due to the multifactorial nature of EDSS, which is influenced by spinal cord lesions, brainstem involvement, and disease duration, similar EDSS scores may not reflect equivalent levels of underlying pathology or microstructural damage. In the MS+ groups, EDSS scores were negatively correlated with MK\u003csub\u003eindex\u003c/sub\u003e of P_WML, suggesting that more pronounced disability might be associated with reduced microstructural complexity across different lesion groups. In contrast, EDSS scores were primarily associated with decreased KFA\u003csub\u003eindex\u003c/sub\u003e of P_WML in the MS-, indicating that directional microstructural destruction may be more closely related to disability in the untreated patients. Taken together, these findings indicate that diffusion metrics from both groups correlate with clinical disability, although the underlying microstructural damage patterns differ between groups, which supports the prior study\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results provide novel evidence for the association between PVS and WML in MS, highlighting the importance of considering PVS characteristics when assessing lesion severity, cognitive function and disability level. Therefore, the role of PVS in CNS pathology warrants further investigation. Notably, cortical lesions were excluded from our analysis due to their small size and limited visibility on conventional MRI. Currently, a recent advanced three-dimensional turbo spin-echo sequence at 7 Tesla made the first successful identification of cortical PVS\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, providing a promising approach to investigate their relationship with cortical lesions. Future studies utilizing ultra-high-field MRI should prioritize the detection and pathological characterization of cortical PVS, thereby deepening our understanding of PVS-driven mechanisms in MS and other neurodegenerative or demyelinating disorders.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to explore the correlation between PVS and the microstructural damage of WML, while taking DMT treatment status into account in MS patients. Previous studies have suggested that PVS were associated with WML; however, the underlying mechanism remains unclear. This study may provide evidence that the presence of PVS is associated with more severe WML damage, providing new insights into possible pathological mechanisms.\u003c/p\u003e \u003cp\u003eOur study had some limitations that should be considered. First, the cohort of MS patients mainly consisted of RRMS patients. Although we further explored the association between DMT, PVS and WML microstructure, the sample size was relatively small. Second, our study was cross-sectional and cannot infer the direct impact of PVS and DMT on microstructure on WML. Third, while the exact duration of DMT exposure was not documented, patients in the MS+ group were all on stable DMT regimens at the time of scanning. Fourth, the diffusion acquisition is the relatively low through-plane resolution, which may introduce partial volume effects and reduce sensitivity to microstructural differences between lesion types. Fifth, normalized diffusion indices were used to account for regional variability; however, this approach is not standard and has not been formally validated, and may introduce potential bias. Although this method was intended to reduce the confounding influence of intrinsic microstructural differences across regions, the findings should be interpreted with caution. Finally, while contralateral normal-appearing regions are commonly used to reduce background variability, this approach may be less applicable in multiple sclerosis due to the widespread distribution of lesions. Therefore, we adopted a normalization strategy using HCs as references. However, this method has not been formally validated and may introduce potential bias. Future longitudinal studies with larger cohorts are needed to better characterize the evolution of WML over time and validate our findings. Also, studies using animal models or in vitro experiments are needed to clarify the molecular and cellular mechanisms between PVS and MS lesions. These findings may deepen our mechanistic understanding of PVS in MS pathogenesis, potentially revealing novel therapeutic targets.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, our study suggested that the presence of PVS within WML is associated with the greater microstructural damage of the lesions and less severe abnormalities in treated patients. These findings suggest that future research on WML should comprehensively account for the association with PVS, thereby advancing our understanding of disease progression, treatment responses, and the underlying pathophysiological mechanisms involved.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study has received funding by Joint Project of Chongqing Health Commission and Science and Technology Bureau (No. 2023ZDXM006), the National Natural Science Foundation of China (No. 82302150) and the Master's Research Innovation Project of the First Clinical College, Chongqing Medical University (No. CYYY-SSCX202529).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.Z.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. Q.Y. Z.: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing - review and editing. X.J.D.: Software, Writing - review and editing. Z.W.S.: Visualization, Writing - review and editing. Z.C.Y: Visualization, Validation, Writing - review and editing. F.Y.Y.: Methodology. B.Y.: Writing - review and editing. Y.X.L: Methodology. X.Y.C.: Conceptualization, Methodology, Writing \u0026ndash; review and editing. Y.M.L: Writing \u0026ndash; review and editing, Supervision, Funding acquisition..\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAll authors would like to thank all the subjects who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRahmanzadeh R, Lu PJ, Barakovic M, Weigel M, Maggi P, Nguyen TD et al (2021) Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging. 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Neurol Neuroimmunol Neuroinflamm 12(3):e200388\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemir Z, Saib G, Talagala L, Taylor P, Kwan JY, Koretsky A (2025) Exploring cortical perivascular space as a biomarker in neurodegenerative diseases through optimized CSF-only MRI at 7T. Alzheimer's \u0026amp;. Dementia 20:S8\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multiple sclerosis, Perivascular Space, White matter lesion, Diffusion kurtosis imaging, Magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-9307837/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9307837/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground and Objectives:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMultiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS) characterized by lesions in white matter (WM) and gray matter. Growing evidence has linked MRI-visible perivascular spaces (PVS) to MS pathogenesis. However, whether PVS are associated with increased microstructural damage in white matter lesions (WML) remains unclear. In this study, we aimed to explore the associations between PVS and the microstructural damage of WML in relapsing-remitting multiple sclerosis (RRMS) patients with or without disease-modifying therapies (DMT) and their correlations with clinical biomarkers of disability and cognitive function.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSixty-eight RRMS patients and forty-seven age- and sex-matched healthy controls (HCs) were recruited. WML were categorized into four groups based on two factors: whether the lesions were penetrated by perivascular spaces (P_WML vs. NP_WML) through visual assessment, and whether patients received DMT (MS\u0026thinsp;+\u0026thinsp;vs. MS\u0026minus;). The diffusion metrics, including fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), mean diffusivity (MD) and mean kurtosis (MK), were used to assess the microstructural damage in WML. The correlations between diffusion metrics of WML and cognitive performance and clinical disability were performed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the MS- group, the MK\u003csub\u003eindex\u003c/sub\u003e in P_WML was significantly lower than NP_WML (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to the MS- group, both P_WML and NP_WML in the MS+group showed significantly lower MD\u003csub\u003eindex\u003c/sub\u003e (P\u0026thinsp;=\u0026thinsp;0.005) and higher MK\u003csub\u003eindex\u003c/sub\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the MD\u003csub\u003eindex\u003c/sub\u003e of P_WML in the MS- group was significantly higher than that of NP_WML in the MS+ group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the MK\u003csub\u003eindex\u003c/sub\u003e of P_WML in the MS- group was significantly lower than that of NP_WML in the MS+ group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the MS+ group, MK\u003csub\u003eindex\u003c/sub\u003e in P_WML were negatively significantly associated with Expanded Disability Status Scale in RRMS (r = -0.500, p\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe presence of PVS were associated with more severe microstructural abnormalities of WML and these abnormalities were related to greater clinical disability of RRMS. Furthermore, diffusion abnormalities in WML were less pronounced in patients receiving DMT.\u003c/p\u003e","manuscriptTitle":"Enlarged perivascular spaces are associated with more severe microstructural damage in white matter lesions among patients with relapsing-remitting multiple sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:36:51","doi":"10.21203/rs.3.rs-9307837/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd80c674-7615-42bb-a532-7f3ff880d3d7","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"199926554686804227632050785776124621869","date":"2026-05-11T13:58:02+00:00","index":23,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:36:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:36:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9307837","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9307837","identity":"rs-9307837","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}