Cognitive reserve mitigates cognitive impairment in cerebral small vessel disease by protecting white matter fibers: an automated fiber quantification study

Cognitive reserve mitigates cognitive impairment in cerebral small vessel disease by protecting white matter fibers: an automated fiber quantification study | 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 Cognitive reserve mitigates cognitive impairment in cerebral small vessel disease by protecting white matter fibers: an automated fiber quantification study Mingyu Li, Yachen Shi, Lin Ma, Haixia Mao, Min Xu, Qianqian Gao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4592100/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Aug, 2025 Read the published version in Brain Imaging and Behavior → Version 1 posted 9 You are reading this latest preprint version Abstract This study investigates how cognitive reserve (CR), developed through education and other cognitive activities, can slow cognitive dysfunction in patients with cerebral small vessel disease (CSVD) by examining cerebral white matter fiber connectivity. We prospectively enrolled 125 patients with CSVD from the Department of Neurology between 2021 and 2023, including 69 patients with no cognitive impairment (nonCI) and 56 patients with mild cognitive impairment (MCI). Patients were divided into low cognitive reserve (LCR) and high cognitive reserve (HCR) subgroups based on the median of years of education (≤9 vs. >9 years). All participants underwent 3.0T MRI scans and neuropsychological assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values of fiber bundles detected by automated fiber quantification (AFQ) were compared among groups by two-way analysis of variance, considering disease state and CR as factors. Correlation analyses examined the relationships between significant fiber segments and cognitive function. We found that the MCI group exhibited decreased FA and increased MD in specific segments of some fiber tracts, such as the corpus callosum splenium, bilateral thalamic radial tracts, and bilateral inferior frontal occipital tracts compared to the nonCI group. The LCR group had decreased FA in the left corticospinal tract and increased MD in the right corticospinal tract compared to the HCR group. Significant interactions of FA values were observed in the left arcuate fasciculus, particularly in segments related to information processing speed and memory. The MCI group exhibited poorer white matter fiber integrity than the nonCI group. These findings suggest that CR’s protective effects on cognitive dysfunction in patients with CSVD may be partially mediated by the left arcuate fasciculus. cerebral small vessel disease cognitive reserve education automated fiber quantification white matter Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cerebral small vessel disease (CSVD) involves pathological, clinical, and imaging syndromes of small brain vessels caused by a variety of etiologic factors (Pantoni 2010 ). It is a complex group of heterogeneous cerebrovascular syndromes (Clancy et al. 2021 ), and a common cause of vascular cognitive impairment. potentially leading to dementia (Hachinski et al. 2006 ). The discrepancy between imaging severity and cognitive performance has sparked interest in factors influencing cognitive impairment progression in patients with CSVD. Previous studies (Stern 2012 ; Stern et al. 2020 ) suggest that cognitive reserve (CR), developed through education and cognitive activities, can alter brain structure and improve function, potentially mitigating cognitive deficits in neurodegenerative diseases. CR reflects an individual's ability to utilize neural resources to maintain cognitive function despite aging or brain pathology. Higher CR levels correlate with better cognitive function (Stern 2012 ; Dhana et al. 2024 ). Despite its significance, the neural mechanisms underlying CR remain unclear. Education has been shown to reduce the risk of dementia (Wilson et al. 2019 ; Lövdén et al. 2020 ), with years of formal education serving as a proxy for CR, supporting its role in moderating dementia-related risk. The "disjunction hypothesis" posits that CSVD disrupts white matter fiber connections, impairing communication between brain regions (Ye and Bai 2018 ). Damage to cerebral white matter—such as degeneration, ischemia, microhemorrhages, and atrophy—affects brain connectivity, leading to cognitive dysfunction (Pasi et al. 2016 ). Notably, white matter is plastic and maintains fiber integrity through processes like fiber bundle rearrangement and myelin production (Zatorre et al. 2012 ). CR may enhance resilience by influencing the brain's structural network, allowing patients to maintain cognitive performance despite brain injury severity (Stern et al. 2020 ). Therefore, we hypothesized that differences in white matter fiber tract integrity in patients with CSVD are related to CR, affecting cognitive performance. Magnetic resonance-based diffusion tensor imaging (DTI) is a noninvasive technique for observing white matter fiber tracts in vivo (Swift et al. 2021 ; Aronica et al. 2022 ). DTI measures water molecule diffusion in white matter, with parameters like fractional anisotropy (FA) and mean diffusivity (MD) assessing fiber bundle integrity (Egle et al. 2022 ). FA detects anisotropy in water diffusion along axons (Alexander et al. 2011 ), while MD measures average water diffusion in all directions. Loss of microstructural integrity typically decreases FA and/or increases MD. Further, studies (Papma et al. 2014 ; Tuladhar et al. 2015 ) have linked white matter microstructural integrity loss in patients with CSVD to cognitive deficits, particularly in the splenium and genu of the corpus callosum. However, traditional methods like voxel-based morphometry (VBM) (Ashburner and Friston 2000 ) and tract-based spatial statistics (TBSS) (Van der Holst et al. 2015 ; Van der Holst et al. 2018 ) have limitations, such as alignment errors and lack of precise localization. The automatic fiber quantification (AFQ) method overcomes these issues, providing accurate quantitative analysis of specific fiber bundles (Yeatman et al. 2012 ; Hu et al. 2023 )., by reconstructing fiber bundles using whole-brain deterministic fiber bundle imaging methods, segmenting along the number of fiber trajectory customizations, and estimating the point-by-point diffusion parameters of each specific fiber bundle at an anatomically equivalent location, thus providing more accurate information for quantitative analysis studies. Importantly, damage to the microstructure of segmental white matter fibers detected by AFQ may predict CSVD-associated cognitive decline (Huang et al. 2020 ). Therefore, in this study, we used AFQ to explore CR-related white matter connectivity changes in patients with CSVD with or without cognitive impairment. Our aims were (1) identify subtle differences in white matter fibers between patients with CSVD with and without cognitive impairment, (2) investigate CR’s role—using education as a proxy—in modulating these fibers, and (3) explore potential mechanisms by which CR slows cognitive impairment progression in patients with CSVD. Methods 1.1 Participants This prospective study included patients with CSVD admitted to the Department of Neurology of the Affiliated Wuxi People's Hospital of Nanjing Medical University, (Wuxi, Jiangsu, China) between 2021–2023. The general inclusion criteria for all participants were as follows: (1) diagnosis of type I CSVD between ages 50–80 years; (2) at least six years of education; (3) good general health with sufficient visual and auditory acuity for neuropsychological assessment; and (4) no contraindications to MRI. Exclusion criteria: (1) psychiatric disorders (e.g., depressive disorder, schizophrenia) or family history of psychiatric illness; (2) evidence of cerebrovascular disease with larger intracranial vascular lesions; (3) alcohol or drug dependence; (4) traumatic brain injury or other neurological disorders (e.g., Parkinson's disease); and (5) major medical problems (e.g., tumors, severe liver or kidney impairment). CSVD was diagnosed according to the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) criteria (Wardlaw et al. 2013 ; Duering et al. 2023 ) based on MRI evidence. All participants underwent a standardized clinical interview, including demographic, physical, and mental health assessments. Brain imaging included T1-weighted, T2-weighted, fluid-attenuated inversion recovery, magnetization transfer-weighted imaging, diffusion-weighted imaging, and magnetic resonance angiography. A total of 125 participants were included in this study: 69 patients with CSVD without cognitive impairment (CSVD-nonCI) and 56 patients with CSVD with mild cognitive impairment (CSVD-MCI). All participants or their legal guardians provided written informed consent after the details of the study were explained. Ethical approval was granted by the Ethics Committee of the Affiliated Wuxi People's Hospital of Nanjing Medical University (approval number: KY22080). 1.2 Neuropsychological assessment Participants completed a comprehensive series of neuropsychological assessments: (1) Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) for global cognition; (2) Auditory Verbal Learning Test-Immediate Recall (AVLT-IR) and Auditory Verbal Learning Test-20-minute delayed recall (AVLT-20min DR) for memory; (3) Trail Making Tests A (TMT-A) and Stroop Color and Words Test A and B (Stroop-A and Stroop-B) for information processing speed; (4) Trail Making Tests B (TMT-B), Stroop Color and Word Test C (Stroop-C), and Digit Span Test (DST) for executive function; and (5) Clock Drawing Test (CDT) for visuospatial function. Raw scores were converted to standardized Z-scores and expressed as means and standard deviations. MoCA score < 26 indicated cognitive impairment (Nasreddine et al. 2005 ; Pendlebury et al. 2010 ). Participants were categorized into low cognitive reserve (LCR) and high cognitive reserve (HCR) subgroups based on median years of formal education (≤ 9 vs. >9 years) (Wardlaw et al. 2013 ). 1.3 MRI data acquisition MRI was performed using a 3.0-T MR scanner (Prisma, Siemens Healthcare, Germany) with a 64-channel standard head coil. Subjects’ heads were immobilized using a foam pad and Plexiglas head cradle. High-resolution T1-weighted 3D anatomical images were first acquired using a sagittal magnetization-prepared rapid gradient echo (TR/TE = 3000/2.56ms; flip angle = 7°; FOV = 256×256mm 2 ; matrix = 320×320 mm 2 ; 208 slices; slice thickness = 0.8 mm) for subsequent co-registration and normalization. Each T1-weighted 3D scan lasted 515 s. DWI data were acquired using a single-shot spin-echo echo-planar image (SE-EPI) sequence with 100 noncollinear directions (b = 1500 s/mm 2 , b = 3000 s/mm 2 ) and a reference image without diffusion weighting (b = 0). Array spatial sensitivity encoding was used to reduce susceptibility and eddy-current artifacts. Sections were placed approximately parallel to the anterior commissure-posterior commissure line. Parameters for DTI data acquisition were as follows: TR/TE = 6800/75ms; matrix = 132×128mm 2 ; FOV = 198×192mm 2 ; and section thickness = 1.5 mm. All the images were assessed by expert neuroradiologists. 1.4 Magnetic resonance image preprocessing T1WI and DWI images were preprocessed using FSL tools ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL ). Standardized steps included data format conversion, motion and eddy current correction, bias-field correction, and skull stripping. Vistasoft software ( https://github.com/vistalab/vistasoft/wiki/ACPC-alignment ) aligned T1 images with the AC–PC plane. Voxel-wise FA maps were then calculated using the DWIFIT program (command in the FSL software). Individual FA/MD images in the native space were registered to the Montreal Neurological Institute (MNI) template. Whole-brain voxel-wise analysis was performed using SPM8 software ( http://www.fifil.ion.ucl.ac.uk/spm ). 1.5 AFQ The AFQ package ( https://github.com/yeatmanlab/AFQ ) was used to identify cerebral white matter fiber tracts along their trajectory (Yeatman et al. 2012 ). AFQ divides the entire white matter fiber network into 20 main fiber tracts, including the bilateral anterior thalamic radiation tract (ATR), corticospinal tract (CST), cingulum cingulate (CC), cingulum hippocampus, inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), uncinate, arcuate, and splenium and genu of the corpus callosum. Whole-brain fiber bundle imaging was performed using a deterministic fiber tracking algorithm (FA > 0.2, turn angle < 30°). White matter fiber tract segmentation was based on path points, followed by fiber thinning cleaning using statistical outlier elimination, and the quantization of fiber segmentation was performed. Diffusion measurement values along 100 isometric nodes of each fiber bundle were recorded, and each fiber bundle was represented as a vector containing 100 values. Automated calculations were performed in MATLAB R2016b (MathWorks Inc., Natick, Massachusetts, USA). Two fiber tracts not successfully tracked in all subjects (cingulum hippocampus) were excluded, resulting in 18 fiber tracts for analysis. 1.6 Statistical analysis Data were analyzed using SPSS Version 26, with significance set at p < 0.05. A two-factor ANOVA (disease status; CR) compared demographic and clinical characteristics, including age, years of education, CSVD total load score, and neuropsychological test scores. Mean FA and MD values of the 18 fiber bundles were analyzed using two-way ANOVA to explore interaction and between-group effects of disease status (CSVD-nonCI, CSVD-MCI) and CR (LCR, HCR) at the level of the fiber bundle, with age and sex as covariates. Fiber bundles with significant interactions were further analyzed to identify specific segments with interactions (only differential segments containing three or more neighboring nodes were reported). In addition, the Kolmogorov-Smirnov test was used to assess whether the data conformed to a normal distribution. Correlations between index means and neuropsychological scores were examined for fiber segments with significant interactions. Pearson correlation analyses were used for normally distributed data, and Spearman's correlation analyses were used for skewed distributions. Results 2.1 General demographics and clinical characteristics Demographic information and neuropsychological scores are shown in Table 1. There was no statistically significant difference in sex composition between the nonCI and MCI groups (p > 0.05). The MCI group was significantly older than the nonCI group (p 0.05). The MCI group had a significantly higher CSVD total load score compared to the nonCI group (p 0.05). In cognitive performance, the HCR group scored higher in memory and information processing speed than the LCR group. The MCI group scored lower in cognitive domains such as MMSE, MoCA, memory, information processing speed, executive functioning, and visuospatial ability, compared to the nonCI group. 2.2 Main effects of disease state (nonCI, MCI) After controlling for sex and age, the FA values of the bilateral ATR, right CST, right CC, corpus callosum genu and splenium, bilateral IFOF, bilateral ILF, left SLF and left arcuate fasciculus were significantly different between the nonCI and MCI groups (Fig. 1). In the point-by-point comparison of MD values, significant differences were found in the bilateral ATR, bilateral CST, bilateral CC, corpus callosum splenium, bilateral IFOF, bilateral ILF, bilateral SLF and bilateral arcuate fasciculus between the nonCI and MCI groups (Fig. 2). 2.3 Main effects of CR (LCR, HCR) After controlling for sex and age, the FA values of the left CST, right CC, corpus callosum splenium, bilateral IFOF, bilateral ILF, and left uncinate fasciculus were significantly different between the LCR and HCR groups (Supplementary materials Fig. 1). In MD values, significant differences were observed in the right CST, corpus callosum splenium, right ILF, bilateral SLF and bilateral arcuate fasciculus between the LCR and HCR groups (Supplementary materials Fig. 2). 2.4 Interaction effects of disease state (nonCI, MCI) and CR (LCR, HCR) After controlling for sex and age, a two-way ANOVA revealed a significant interaction between disease state and CR, particularly in the left arcuate fasciculus (Fig. 3 A, B). Fiber tracts with a significant interaction between disease state and CR were selected as regions of interest and finely divided, revealing that among the 100 segments subdivided, significant interactions were mainly concentrated in segments 28–36 and segments 94–100 of the left arcuate fasciculus. 2.5 Correlation analysis FA values for left arcuate fascicular segments 28–36 were negatively correlated with Stroop A scores (rs=-0.190, p=0.039) and positively correlated with DST-backward scores (rs= 0.186, p=0.043). The details are shown in Fig. 4. Discussion This study used the AFQ method to investigate white matter microstructure differences in patients with CSVD with varying disease states and the role of CR in CSVD progression. The main findings were: (1) The MCI group exhibited more extensive FA reduction and MD increase across several fiber tracts, including the corpus callosum splenium, bilateral ATR and bilateral IFOF, compared to the nonCI group. (2) The left arcuate fasciculus showed a significant interaction between disease state and CR, suggesting CR may protect white matter connectivity, particularly in the left arcuate fasciculus. (3) Specific fiber segments of the left arcuate fasciculus correlated with cognitive domains such as information processing speed and executive function, indicating that CR helps maintain cognitive performance by preserving its integrity. Consistent with previous findings (Patel and Markus 2011 ; Croall et al. 2017 ; Zeestraten et al. 2017 ), there was a more extensive decrease in FA values and an increase in MD values in most white matter fibers in the MCI group compared to the nonCI group. The present study revealed multiple fiber tracts significantly impaired in the MCI group compared to the nonCI group in patients with CSVD, with 66.67% (12/18) of the fiber tracts having decreased FA values and 83.33% (15/18) having increased MD values. This suggests that the integrity of cerebral white matter fibers in the MCI group was worse than in the nonCI group, and these differences affect cognitive function performance. Extensive disruption of white matter fiber bundles, such as white matter hyperintensity load, is commonly associated with increased stroke, dementia, and mortality (Debette et al. 2019 ). Notably, previous studies (Huang et al. 2020 ) found that damage to white matter fibers in patients with CSVD does not occur along the entire trajectory of the fiber bundle but rather along some vulnerable segments. Notably, the AFQ method analyzes fiber bundles point-by-point, detecting more damaged segments than fiber bundle-level analysis (Yeatman et al. 2012 ). The DTI-based AFQ method allows precise localization of damage by dividing the fiber bundles into subsections. We observed damaged fibers in three systems: association white matter fibers (CC, IFOF, ILF, SLF and arcuate fasciculus), commissural white matter fibers (corpus callosum splenium and genu), and projection white matter fibers (CST and ATR). The damaged segments corresponded with high-signal areas on T2-weighted images of patients with CSVD, likely due to chronic white matter ischemia from small artery atherosclerosis, lipid hyalinosis, and blood-brain barrier impairment, causing macromolecule leakage and astrocyte activation (Prins and Scheltens 2015 ; Sarbu et al. 2016 ). A previous study found that CR (using education and work experience as proxies) plays an important buffering role in CSVD’s clinical manifestations, explaining the variability between imaging and symptoms (Jokinen et al. 2016 ). This suggests that there are factors that alter an individual's risk of cognitive dysfunction and dementia, affecting cognitive performance. The CR hypothesis has been suggested as a possible explanation for these risk-modifying factors (Pinter et al. 2015 ), though its mechanism remains unclear. In fact, while highly educated individuals bear more disease burden, they show better cognitive functioning, indicating compensatory brain mechanisms (Jung et al. 2018 ). Our study showed a significant interaction between disease state (nonCI, MCI) and CR (using years of formal education as proxy) in the left arcuate fasciculus, suggesting that CR preserves white matter integrity against CSVD brain injury. Structural network connectivity may provide a neural basis for CR (Teipel et al. 2009 ), indicating neurocompensatory mechanisms mediated by structural networks. For example, higher brain white matter connectivity (measured by node degree) is associated with better cognitive performance and modulates cognitive dysfunction associated with white matter hyperintensity (Dejong et al. 2023 ). Our study used years of formal education as a proxy for CR, which is a complex, multifactorial variable that affects cognitive function in different ways (Mungas et al. 2018 ), to explore structural connectivity mechanisms affecting CR. Differences in white matter connectivity patterns between individuals with HCR and LCR under cerebral pathological load highlight CR’s role in brain structural connectivity. The arcuate fasciculus, located at the posterior end of the lateral sulcus, connects major language centers (Wernicke’s and Broca's areas) and is crucial for language function (Giampiccolo & Duffau, 2022 ). Repeated CSVD exposure damages the arcuate fasciculus, affecting frontal-temporal connectivity and cognitive functions. Therefore, our findings suggest that education may affect cognitive domains by maintaining the fiber integrity of the left arcuate fasciculus. Deficits in cerebral white matter connectivity may underlie CSVD’s clinical symptoms (Dey et al. 2016 ). However, the exact mechanism by which CR compensates for brain damage in patients with CSVD remains unknown. Previous studies have found no one-to-one correspondence between fiber bundles and cognitive domains, and fiber bundle selectivity for cognitive domains relates to projection diversity (Forkel et al. 2022 ). We subdivided the left arcuate fasciculus using the AFQ method to identify specific CR-affected segments and found that FA values of segments 28–36 were negatively correlated with Stroop A scale scores and positively correlated with DST-backward scores, indicating that high CR preserves the integrity of the left arcuate fasciculus to maintain information processing speed and executive function. However, this study has several limitations. First, we did not track the bilateral cingulum hippocampus due to fiber-tracking threshold settings, possibly losing important information. Second, while this preliminary study reveals the possible CR mechanisms from the perspective of white matter connectivity; multimodal magnetic resonance analyses are still required for comprehensive assessment. Finally, as a cross-sectional study, it did infer directionality or causality. Future large-sample and longitudinal studies are needed to confirm these findings, especially changes in cerebral white matter fiber bundles over time with CSVD development. Conclusion Our study provides evidence that CR may protect cognitive function in patients with CSVD by modulating the fiber connections of the left arcuate fasciculus. Declarations Acknowledgments We thank all participants in the present study for their cooperation. Funding This study was funded by Medical Expert Team Program of Wuxi Taihu Talent Plan 2021 (2021 THRC-DJ-FXM-2023) and Promotion Projects of Technological Achievements and Suitable Technology (T202335). Conflicts of interest/competing interests The authors declare no conflict of interest. Ethics approval Ethical approval was granted by the Ethics Committee of the Affiliated Wuxi People's Hospital of Nanjing Medical University (approval number: KY22080). Consent to participate The authors all agreed to participate. Consent for publication Consent for publication was obtained from all participants. Availability of data and material The datasets generated during the current study are available. Code availability Not applicable. Author Contributions Mingyu Li (MY Li, First Author): conception and study design, statistical analysis, interpretation of results, drafting the manuscript work, agreement to be accountable for the integrity and accuracy of all aspects of the work Yachen Shi (YC Shi, First Author): conception and study design, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work Lin Ma (L Ma): data collection, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work Haixia Mao (HX Mao): data acquisition, agreement to be accountable for the integrity and accuracy of all aspects of the work Min Xu (M Xu): data collection, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work Qianqian Gao(QQ Gao): data acquisition, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work Jiayi Yang (JY Yang): data collection, agreement to be accountable for the integrity and accuracy of all aspects of the work Feng Wang (F Wang, Corresponding Author): conception and study design, revising it critically for important intellectual content and approval of final version to be published, agreement to be accountable for the integrity and accuracy of all aspects of the work Xiangming Fang (XM Fang, Corresponding Author): conception and study design, revising it critically for important intellectual content and approval of final version to be published, agreement to be accountable for the integrity and accuracy of all aspects of the work Xiaoyun Hu (XY Hu, Corresponding Author): conception and study design, revising it critically for important intellectual content and approval of final version to be published, agreement to be accountable for the integrity and accuracy of all aspects of the work Data Availability Available from the corresponding author upon request. 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Neurology , 92 (10), e1041–e1050-e50. https://doi.org/10.1212/WNL.0000000000007036 Ye, Q., & Bai, F. (2018). Contribution of diffusion, perfusion and functional MRI to the disconnection hypothesis in subcortical vascular cognitive impairment. Stroke & Vascular Neurology , 3 (3), 131–139. https://doi.org/10.1136/svn-2017-000080 Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract profiles of white matter properties: automating fiber-tract quantification. PLOS ONE , 7 (11), e49790. https://doi.org/10.1371/journal.pone.0049790 Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nature Neuroscience , 15 (4), 528–536. https://doi.org/10.1038/nn.3045 Zeestraten, E. A., Lawrence, A. J., Lambert, C., et al. (2017). Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease. Neurology , 89 (18), 1869–1876. https://doi.org/10.1212/WNL.0000000000004594 Table Additional Declarations Competing interest reported. The authors declare no conflict of interest. Supplementary Files SupplementarymaterialsFig1d1.tif Additional information Supplementary materials Fig. 1 Pointwise Comparison of Fractional Anisotropy (FA) Profiles Between Patient Subgroups Plots of the FA profiles of 18 identified fiber tracts from patient subgroups (purple for CSVD-LCR and blue for CSVD-HCR). The solid lines represent the means, and shaded areas indicate the standard deviations (SDs). Red bars under the FA profile indicate regions of significant differences between subgroups. The x-axis represents the location between the beginning and termination waypoints of interest. L, left; R, right; CSVD-LCR, cerebral small vessel disease with low cognitive reserve; CSVD-HCR, cerebral small vessel disease with high cognitive reserve. SupplementarymaterialsFig2d1.tif Fig. 2 Pointwise Comparison of Mean Diffusivity (MD) Profiles Between Patient Subgroups Plots of the MD profiles of 18 identified fiber tracts from patient subgroups (purple for CSVD-LCR and blue for CSVD-HCR). The solid lines represent the means, and shaded areas indicate the standard deviations (SDs). Red bars under the MD profile indicate regions of significant differences between subgroups. The x-axis represents the location between the beginning and termination waypoints of interest. L, left; R, right; CSVD-LCR, cerebral small vessel disease with low cognitive reserve; CSVD-HCR, cerebral small vessel disease with high cognitive reserve. Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2025 Read the published version in Brain Imaging and Behavior → Version 1 posted Editorial decision: Revision requested 10 Oct, 2024 Reviews received at journal 05 Oct, 2024 Reviewers agreed at journal 17 Sep, 2024 Reviews received at journal 12 Aug, 2024 Reviewers agreed at journal 25 Jul, 2024 Reviewers invited by journal 25 Jul, 2024 Editor assigned by journal 25 Jul, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4592100","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315718921,"identity":"3b3758c2-8bb0-4214-b4cc-4120a3af1b56","order_by":0,"name":"Mingyu Li","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Li","suffix":""},{"id":315718924,"identity":"bf39b953-a81a-4e9a-9be6-5d3ab5f3ff07","order_by":1,"name":"Yachen Shi","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Yachen","middleName":"","lastName":"Shi","suffix":""},{"id":315718925,"identity":"20c463f5-45a1-4e78-9528-7c7ba0824bf8","order_by":2,"name":"Lin Ma","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Ma","suffix":""},{"id":315718926,"identity":"29d0936b-5861-406c-9752-956ef67dcd9e","order_by":3,"name":"Haixia Mao","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Haixia","middleName":"","lastName":"Mao","suffix":""},{"id":315718928,"identity":"3792b8b1-b868-4465-8496-b63b052114ee","order_by":4,"name":"Min Xu","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Xu","suffix":""},{"id":315718930,"identity":"297dba79-6b26-4cd7-8b9b-9801a83e592d","order_by":5,"name":"Qianqian Gao","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Gao","suffix":""},{"id":315718932,"identity":"7b2a6d2d-6272-4b2d-8df7-54e8c7e755a3","order_by":6,"name":"Jiayi Yang","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Yang","suffix":""},{"id":315718934,"identity":"6d7bc2fb-0406-4b98-864c-a4ad9939a2a9","order_by":7,"name":"Feng Wang","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wang","suffix":""},{"id":315718936,"identity":"ea720f50-029e-4b04-8a81-ced1ca7675ed","order_by":8,"name":"Xiangming Fang","email":"","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":false,"prefix":"","firstName":"Xiangming","middleName":"","lastName":"Fang","suffix":""},{"id":315718937,"identity":"ad91c2fd-b15b-46ff-86fe-04c4752e943b","order_by":9,"name":"Xiaoyun Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDCCAwwMzAwVchD2A+K1nDGGsBOI1sLYBtHCQJQWvttnzKQL5xnIGVw7/BBoi52cbgMBLZLn0tKkZ24zMJacnWYA1JJsbHaAgBaDM8zHpHm3/Unsl04AaTmQuI2wFsY2ad45Bolt0ukfiNUCsqXBAGhLDpG2SJ5hS7bmOQbyS07BgQQDIvzCd4bH8DZPDTDEbqdv/vChwk6OoBYgYJFAcidh5SDA/IE4daNgFIyCUTBiAQCJy0HYIVkGjQAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-06-17 06:21:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4592100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4592100/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11682-025-01032-7","type":"published","date":"2025-08-23T16:29:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60355533,"identity":"b1e489e1-0a6a-488c-aeab-d6ad740ecc07","added_by":"auto","created_at":"2024-07-16 00:07:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":377958,"visible":true,"origin":"","legend":"\u003cp\u003ePointwise Comparison of Fractional Anisotropy (FA) Profiles Between Patient Subgroups Plots of the FA profiles of 18 identified fiber tracts from patient subgroups (orange for CSVD-nonCI and green for CSVD-MCI). The solid lines represent the means, and the shaded areas indicate the standard deviations (SDs). Red bars under the FA profile indicate regions of significant differences between subgroups. The x-axis represents the location between the beginning and termination waypoints of interest. L, left; R, right; CSVD-nonCI, cerebral small vessel disease without cognitive impairment; CSVD-MCI, cerebral small vessel disease with mild cognitive impairment.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/e24296f85b14bba710722cc3.jpg"},{"id":60354201,"identity":"48f4efa3-c6fb-4863-aad9-9d9c99df235c","added_by":"auto","created_at":"2024-07-15 23:51:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":371832,"visible":true,"origin":"","legend":"\u003cp\u003ePointwise Comparison of Mean Diffusivity (MD) Profiles Between Patient Subgroups\u003c/p\u003e\n\u003cp\u003ePlots of the MD profiles of 18 identified fiber tracts from patient subgroups (orange for CSVD-non-CI and green for CSVD-MCI). The solid lines represent the means, and shaded areas indicate the standard deviations (SDs). Red bars under the MD profile indicate regions of significant differences between subgroups. The x-axis represents the location between the beginning and termination waypoints of interest. L, left; R, right; CSVD-nonCI, cerebral small vessel disease without cognitive impairment; CSVD-MCI, cerebral small vessel disease with mild cognitive impairment.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/87a1d3771af7dccf8c829513.jpg"},{"id":60355063,"identity":"2e4c2e12-cd4e-4039-8ac6-6071c5ba8523","added_by":"auto","created_at":"2024-07-15 23:59:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172532,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed interaction effects of disease state (CSVD-nonCI、CSVD-MC) and CR (LCR, HCR) in white matter tract level analysis. (A) The color scale represents the core of the left arcuate tract; (B) detailed interaction effects. Abbreviations: LCR, low cognitive reserve; HCR, high cognitive reserve; CSVD-nonCI, cerebral small vessel disease without cognitive impairment; CSVD-MCI, cerebral small vessel disease with mild cognitive impairment.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/37a13f4b4a7965e9c6d05aaa.jpg"},{"id":60354200,"identity":"354fa9b1-812d-42bb-a698-7439808704fd","added_by":"auto","created_at":"2024-07-15 23:51:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169926,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of individual cognitive performance and segment of left arcuate tract after adjustment for age and sex. (A) The mean FA value of the anteroinferior component of the left arcuate tract (seg 28–36) was correlated negatively with Stroop A (rs=-0.190, p=0.039); and (B) the mean FA value of the anteroinferior component of the left arcuate tract (seg 28–36) was correlated positively with DST-backward (rs=0.186, p=0.043).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/b218a3bf9f51fe0496a26d44.jpg"},{"id":89847204,"identity":"d73f890b-c370-4041-94a8-bf9e7464edc6","added_by":"auto","created_at":"2025-08-25 16:42:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1863766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/9efb8df2-40e1-482f-a477-457778cefe1d.pdf"},{"id":60354196,"identity":"9b8f8dfc-6ed2-4618-b2fd-18bdcffce360","added_by":"auto","created_at":"2024-07-15 23:51:25","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":828774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePointwise Comparison of Fractional Anisotropy (FA) Profiles Between Patient Subgroups\u003c/p\u003e\n\u003cp\u003ePlots of the FA profiles of 18 identified fiber tracts from patient subgroups (purple for CSVD-LCR and blue for CSVD-HCR). The solid lines represent the means, and shaded areas indicate the standard deviations (SDs). Red bars under the FA profile indicate regions of significant differences between subgroups. The x-axis represents the location between the beginning and termination waypoints of interest. L, left; R, right; CSVD-LCR, cerebral small vessel disease with low cognitive reserve; CSVD-HCR, cerebral small vessel disease with high cognitive reserve.\u003c/p\u003e","description":"","filename":"SupplementarymaterialsFig1d1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/71acc83b261e2dfd8cf25302.tif"},{"id":60355065,"identity":"0df4d3df-db40-4b59-b44b-b5ac8117fabf","added_by":"auto","created_at":"2024-07-15 23:59:25","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":688619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePointwise Comparison of Mean Diffusivity (MD) Profiles Between Patient Subgroups\u003c/p\u003e\n\u003cp\u003ePlots of the MD profiles of 18 identified fiber tracts from patient subgroups (purple for CSVD-LCR and blue for CSVD-HCR). The solid lines represent the means, and shaded areas indicate the standard deviations (SDs). Red bars under the MD profile indicate regions of significant differences between subgroups. The x-axis represents the location between the beginning and termination waypoints of interest. L, left; R, right; CSVD-LCR, cerebral small vessel disease with low cognitive reserve; CSVD-HCR, cerebral small vessel disease with high cognitive reserve.\u003c/p\u003e","description":"","filename":"SupplementarymaterialsFig2d1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4592100/v1/baad155dfde31eef92a6359b.tif"}],"financialInterests":"Competing interest reported. The authors declare no conflict of interest.","formattedTitle":"Cognitive reserve mitigates cognitive impairment in cerebral small vessel disease by protecting white matter fibers: an automated fiber quantification study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral small vessel disease (CSVD) involves pathological, clinical, and imaging syndromes of small brain vessels caused by a variety of etiologic factors (Pantoni \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is a complex group of heterogeneous cerebrovascular syndromes (Clancy et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and a common cause of vascular cognitive impairment. potentially leading to dementia (Hachinski et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The discrepancy between imaging severity and cognitive performance has sparked interest in factors influencing cognitive impairment progression in patients with CSVD.\u003c/p\u003e \u003cp\u003ePrevious studies (Stern \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stern et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggest that cognitive reserve (CR), developed through education and cognitive activities, can alter brain structure and improve function, potentially mitigating cognitive deficits in neurodegenerative diseases. CR reflects an individual's ability to utilize neural resources to maintain cognitive function despite aging or brain pathology. Higher CR levels correlate with better cognitive function (Stern \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dhana et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite its significance, the neural mechanisms underlying CR remain unclear. Education has been shown to reduce the risk of dementia (Wilson et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; L\u0026ouml;vd\u0026eacute;n et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with years of formal education serving as a proxy for CR, supporting its role in moderating dementia-related risk.\u003c/p\u003e \u003cp\u003eThe \"disjunction hypothesis\" posits that CSVD disrupts white matter fiber connections, impairing communication between brain regions (Ye and Bai \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Damage to cerebral white matter\u0026mdash;such as degeneration, ischemia, microhemorrhages, and atrophy\u0026mdash;affects brain connectivity, leading to cognitive dysfunction (Pasi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, white matter is plastic and maintains fiber integrity through processes like fiber bundle rearrangement and myelin production (Zatorre et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). CR may enhance resilience by influencing the brain's structural network, allowing patients to maintain cognitive performance despite brain injury severity (Stern et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, we hypothesized that differences in white matter fiber tract integrity in patients with CSVD are related to CR, affecting cognitive performance.\u003c/p\u003e \u003cp\u003eMagnetic resonance-based diffusion tensor imaging (DTI) is a noninvasive technique for observing white matter fiber tracts in vivo (Swift et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aronica et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). DTI measures water molecule diffusion in white matter, with parameters like fractional anisotropy (FA) and mean diffusivity (MD) assessing fiber bundle integrity (Egle et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). FA detects anisotropy in water diffusion along axons (Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while MD measures average water diffusion in all directions. Loss of microstructural integrity typically decreases FA and/or increases MD. Further, studies (Papma et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tuladhar et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) have linked white matter microstructural integrity loss in patients with CSVD to cognitive deficits, particularly in the splenium and genu of the corpus callosum. However, traditional methods like voxel-based morphometry (VBM) (Ashburner and Friston \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and tract-based spatial statistics (TBSS) (Van der Holst et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Van der Holst et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) have limitations, such as alignment errors and lack of precise localization. The automatic fiber quantification (AFQ) method overcomes these issues, providing accurate quantitative analysis of specific fiber bundles (Yeatman et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)., by reconstructing fiber bundles using whole-brain deterministic fiber bundle imaging methods, segmenting along the number of fiber trajectory customizations, and estimating the point-by-point diffusion parameters of each specific fiber bundle at an anatomically equivalent location, thus providing more accurate information for quantitative analysis studies. Importantly, damage to the microstructure of segmental white matter fibers detected by AFQ may predict CSVD-associated cognitive decline (Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, in this study, we used AFQ to explore CR-related white matter connectivity changes in patients with CSVD with or without cognitive impairment. Our aims were (1) identify subtle differences in white matter fibers between patients with CSVD with and without cognitive impairment, (2) investigate CR\u0026rsquo;s role\u0026mdash;using education as a proxy\u0026mdash;in modulating these fibers, and (3) explore potential mechanisms by which CR slows cognitive impairment progression in patients with CSVD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Participants\u003c/h2\u003e \u003cp\u003eThis prospective study included patients with CSVD admitted to the Department of Neurology of the Affiliated Wuxi People's Hospital of Nanjing Medical University, (Wuxi, Jiangsu, China) between 2021\u0026ndash;2023. The general inclusion criteria for all participants were as follows: (1) diagnosis of type I CSVD between ages 50\u0026ndash;80 years; (2) at least six years of education; (3) good general health with sufficient visual and auditory acuity for neuropsychological assessment; and (4) no contraindications to MRI. Exclusion criteria: (1) psychiatric disorders (e.g., depressive disorder, schizophrenia) or family history of psychiatric illness; (2) evidence of cerebrovascular disease with larger intracranial vascular lesions; (3) alcohol or drug dependence; (4) traumatic brain injury or other neurological disorders (e.g., Parkinson's disease); and (5) major medical problems (e.g., tumors, severe liver or kidney impairment). CSVD was diagnosed according to the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) criteria (Wardlaw et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Duering et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) based on MRI evidence.\u003c/p\u003e \u003cp\u003eAll participants underwent a standardized clinical interview, including demographic, physical, and mental health assessments. Brain imaging included T1-weighted, T2-weighted, fluid-attenuated inversion recovery, magnetization transfer-weighted imaging, diffusion-weighted imaging, and magnetic resonance angiography.\u003c/p\u003e \u003cp\u003eA total of 125 participants were included in this study: 69 patients with CSVD without cognitive impairment (CSVD-nonCI) and 56 patients with CSVD with mild cognitive impairment (CSVD-MCI).\u003c/p\u003e \u003cp\u003e All participants or their legal guardians provided written informed consent after the details of the study were explained. Ethical approval was granted by the Ethics Committee of the Affiliated Wuxi People's Hospital of Nanjing Medical University (approval number: KY22080).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Neuropsychological assessment\u003c/h2\u003e \u003cp\u003eParticipants completed a comprehensive series of neuropsychological assessments: (1) Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) for global cognition; (2) Auditory Verbal Learning Test-Immediate Recall (AVLT-IR) and Auditory Verbal Learning Test-20-minute delayed recall (AVLT-20min DR) for memory; (3) Trail Making Tests A (TMT-A) and Stroop Color and Words Test A and B (Stroop-A and Stroop-B) for information processing speed; (4) Trail Making Tests B (TMT-B), Stroop Color and Word Test C (Stroop-C), and Digit Span Test (DST) for executive function; and (5) Clock Drawing Test (CDT) for visuospatial function. Raw scores were converted to standardized Z-scores and expressed as means and standard deviations.\u003c/p\u003e \u003cp\u003eMoCA score\u0026thinsp;\u0026lt;\u0026thinsp;26 indicated cognitive impairment (Nasreddine et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pendlebury et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Participants were categorized into low cognitive reserve (LCR) and high cognitive reserve (HCR) subgroups based on median years of formal education (\u0026le;\u0026thinsp;9 vs. \u0026gt;9 years) (Wardlaw et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.3 MRI data acquisition\u003c/h3\u003e\n\u003cp\u003eMRI was performed using a 3.0-T MR scanner (Prisma, Siemens Healthcare, Germany) with a 64-channel standard head coil. Subjects\u0026rsquo; heads were immobilized using a foam pad and Plexiglas head cradle. High-resolution T1-weighted 3D anatomical images were first acquired using a sagittal magnetization-prepared rapid gradient echo (TR/TE\u0026thinsp;=\u0026thinsp;3000/2.56ms; flip angle\u0026thinsp;=\u0026thinsp;7\u0026deg;; FOV\u0026thinsp;=\u0026thinsp;256\u0026times;256mm\u003csup\u003e2\u003c/sup\u003e; matrix\u0026thinsp;=\u0026thinsp;320\u0026times;320 mm\u003csup\u003e2\u003c/sup\u003e; 208 slices; slice thickness\u0026thinsp;=\u0026thinsp;0.8 mm) for subsequent co-registration and normalization. Each T1-weighted 3D scan lasted 515 s. DWI data were acquired using a single-shot spin-echo echo-planar image (SE-EPI) sequence with 100 noncollinear directions (b\u0026thinsp;=\u0026thinsp;1500 s/mm\u003csup\u003e2\u003c/sup\u003e, b\u0026thinsp;=\u0026thinsp;3000 s/mm\u003csup\u003e2\u003c/sup\u003e) and a reference image without diffusion weighting (b\u0026thinsp;=\u0026thinsp;0). Array spatial sensitivity encoding was used to reduce susceptibility and eddy-current artifacts. Sections were placed approximately parallel to the anterior commissure-posterior commissure line. Parameters for DTI data acquisition were as follows: TR/TE\u0026thinsp;=\u0026thinsp;6800/75ms; matrix\u0026thinsp;=\u0026thinsp;132\u0026times;128mm\u003csup\u003e2\u003c/sup\u003e; FOV\u0026thinsp;=\u0026thinsp;198\u0026times;192mm\u003csup\u003e2\u003c/sup\u003e; and section thickness\u0026thinsp;=\u0026thinsp;1.5 mm. All the images were assessed by expert neuroradiologists.\u003c/p\u003e\n\u003ch3\u003e1.4 Magnetic resonance image preprocessing\u003c/h3\u003e\n\u003cp\u003eT1WI and DWI images were preprocessed using FSL tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Standardized steps included data format conversion, motion and eddy current correction, bias-field correction, and skull stripping. Vistasoft software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/vistalab/vistasoft/wiki/ACPC-alignment\u003c/span\u003e\u003cspan address=\"https://github.com/vistalab/vistasoft/wiki/ACPC-alignment\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) aligned T1 images with the AC\u0026ndash;PC plane. Voxel-wise FA maps were then calculated using the DWIFIT program (command in the FSL software). Individual FA/MD images in the native space were registered to the Montreal Neurological Institute (MNI) template. Whole-brain voxel-wise analysis was performed using SPM8 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fifil.ion.ucl.ac.uk/spm\u003c/span\u003e\u003cspan address=\"http://www.fifil.ion.ucl.ac.uk/spm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5 AFQ\u003c/h2\u003e \u003cp\u003eThe AFQ package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/yeatmanlab/AFQ\u003c/span\u003e\u003cspan address=\"https://github.com/yeatmanlab/AFQ\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify cerebral white matter fiber tracts along their trajectory (Yeatman et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). AFQ divides the entire white matter fiber network into 20 main fiber tracts, including the bilateral anterior thalamic radiation tract (ATR), corticospinal tract (CST), cingulum cingulate (CC), cingulum hippocampus, inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), uncinate, arcuate, and splenium and genu of the corpus callosum.\u003c/p\u003e \u003cp\u003eWhole-brain fiber bundle imaging was performed using a deterministic fiber tracking algorithm (FA\u0026thinsp;\u0026gt;\u0026thinsp;0.2, turn angle\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026deg;). White matter fiber tract segmentation was based on path points, followed by fiber thinning cleaning using statistical outlier elimination, and the quantization of fiber segmentation was performed. Diffusion measurement values along 100 isometric nodes of each fiber bundle were recorded, and each fiber bundle was represented as a vector containing 100 values. Automated calculations were performed in MATLAB R2016b (MathWorks Inc., Natick, Massachusetts, USA). Two fiber tracts not successfully tracked in all subjects (cingulum hippocampus) were excluded, resulting in 18 fiber tracts for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e1.6 Statistical analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS Version 26, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A two-factor ANOVA (disease status; CR) compared demographic and clinical characteristics, including age, years of education, CSVD total load score, and neuropsychological test scores.\u003c/p\u003e \u003cp\u003eMean FA and MD values of the 18 fiber bundles were analyzed using two-way ANOVA to explore interaction and between-group effects of disease status (CSVD-nonCI, CSVD-MCI) and CR (LCR, HCR) at the level of the fiber bundle, with age and sex as covariates. Fiber bundles with significant interactions were further analyzed to identify specific segments with interactions (only differential segments containing three or more neighboring nodes were reported). In addition, the Kolmogorov-Smirnov test was used to assess whether the data conformed to a normal distribution. Correlations between index means and neuropsychological scores were examined for fiber segments with significant interactions. Pearson correlation analyses were used for normally distributed data, and Spearman's correlation analyses were used for skewed distributions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1\u003c/strong\u003e \u003cstrong\u003eGeneral demographics and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic information and neuropsychological scores are shown in Table 1. There was no statistically significant difference in sex composition between the nonCI and MCI groups (p \u0026gt; 0.05). The MCI group was significantly older than the nonCI group (p \u0026lt; 0.001), but no significant age difference was observed between the LCR and HCR groups (p \u0026gt; 0.05). The MCI group had a significantly higher \u0026nbsp; \u0026nbsp;\u0026nbsp;CSVD total load score compared to the nonCI group (p \u0026lt; 0.001), whereas there was no significant difference in CSVD total load scores between the LCR and HCR groups (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn cognitive performance, the HCR group scored higher in memory and information processing speed than the LCR group. The MCI group scored lower in cognitive domains such as MMSE, MoCA, memory, information processing speed, executive functioning, and visuospatial ability, compared to the nonCI group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u003c/strong\u003e \u003cstrong\u003eMain effects of disease state (nonCI, MCI)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter controlling for sex and age, the FA values of the bilateral ATR, right CST, right CC, corpus callosum genu and splenium, bilateral IFOF, bilateral ILF, left SLF and left arcuate fasciculus were significantly different between the nonCI and MCI groups (Fig. 1). In the point-by-point comparison of MD values, significant differences were found in the bilateral ATR, bilateral CST, bilateral CC, corpus callosum splenium, bilateral IFOF, bilateral ILF, bilateral SLF \u0026nbsp; and bilateral arcuate fasciculus between the nonCI and MCI groups (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u003c/strong\u003e \u003cstrong\u003eMain effects of CR (LCR, HCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter controlling for sex and age, the FA values of the left CST, right CC, corpus callosum splenium, bilateral IFOF, bilateral ILF, and left uncinate fasciculus were significantly different between the LCR and HCR groups (Supplementary materials Fig. 1). In MD values, significant differences were observed in the right CST, corpus callosum splenium, right ILF, bilateral SLF and bilateral arcuate fasciculus between the LCR and HCR groups (Supplementary materials Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u003c/strong\u003e \u003cstrong\u003eInteraction effects of disease state (nonCI, MCI) and CR (LCR, HCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter controlling for sex and age, a two-way ANOVA revealed a significant interaction between disease state and CR, particularly in the left arcuate fasciculus (Fig. 3 A, B). Fiber tracts with a significant interaction between disease state and CR were selected as regions of interest and finely divided, revealing that among the 100 segments subdivided, significant interactions were mainly concentrated in segments 28\u0026ndash;36 and segments 94\u0026ndash;100 of the left arcuate fasciculus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e \u003cstrong\u003eCorrelation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFA values for left arcuate fascicular segments 28\u0026ndash;36 were negatively correlated with Stroop A scores (rs=-0.190, p=0.039) and positively correlated with DST-backward scores (rs= 0.186, p=0.043). The details are shown in Fig. 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used the AFQ method to investigate white matter microstructure differences in patients with CSVD with varying disease states and the role of CR in CSVD progression. The main findings were: (1) The MCI group exhibited more extensive FA reduction and MD increase across several fiber tracts, including the corpus callosum splenium, bilateral ATR and bilateral IFOF, compared to the nonCI group. (2) The left arcuate fasciculus showed a significant interaction between disease state and CR, suggesting CR may protect white matter connectivity, particularly in the left arcuate fasciculus. (3) Specific fiber segments of the left arcuate fasciculus correlated with cognitive domains such as information processing speed and executive function, indicating that CR helps maintain cognitive performance by preserving its integrity.\u003c/p\u003e \u003cp\u003eConsistent with previous findings (Patel and Markus \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Croall et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zeestraten et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), there was a more extensive decrease in FA values and an increase in MD values in most white matter fibers in the MCI group compared to the nonCI group. The present study revealed multiple fiber tracts significantly impaired in the MCI group compared to the nonCI group in patients with CSVD, with 66.67% (12/18) of the fiber tracts having decreased FA values and 83.33% (15/18) having increased MD values. This suggests that the integrity of cerebral white matter fibers in the MCI group was worse than in the nonCI group, and these differences affect cognitive function performance. Extensive disruption of white matter fiber bundles, such as white matter hyperintensity load, is commonly associated with increased stroke, dementia, and mortality (Debette et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notably, previous studies (Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that damage to white matter fibers in patients with CSVD does not occur along the entire trajectory of the fiber bundle but rather along some vulnerable segments.\u003c/p\u003e \u003cp\u003eNotably, the AFQ method analyzes fiber bundles point-by-point, detecting more damaged segments than fiber bundle-level analysis (Yeatman et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The DTI-based AFQ method allows precise localization of damage by dividing the fiber bundles into subsections. We observed damaged fibers in three systems: association white matter fibers (CC, IFOF, ILF, SLF and arcuate fasciculus), commissural white matter fibers (corpus callosum splenium and genu), and projection white matter fibers (CST and ATR). The damaged segments corresponded with high-signal areas on T2-weighted images of patients with CSVD, likely due to chronic white matter ischemia from small artery atherosclerosis, lipid hyalinosis, and blood-brain barrier impairment, causing macromolecule leakage and astrocyte activation (Prins and Scheltens \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sarbu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA previous study found that CR (using education and work experience as proxies) plays an important buffering role in CSVD\u0026rsquo;s clinical manifestations, explaining the variability between imaging and symptoms (Jokinen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This suggests that there are factors that alter an individual's risk of cognitive dysfunction and dementia, affecting cognitive performance. The CR hypothesis has been suggested as a possible explanation for these risk-modifying factors (Pinter et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), though its mechanism remains unclear. In fact, while highly educated individuals bear more disease burden, they show better cognitive functioning, indicating compensatory brain mechanisms (Jung et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our study showed a significant interaction between disease state (nonCI, MCI) and CR (using years of formal education as proxy) in the left arcuate fasciculus, suggesting that CR preserves white matter integrity against CSVD brain injury.\u003c/p\u003e \u003cp\u003eStructural network connectivity may provide a neural basis for CR (Teipel et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), indicating neurocompensatory mechanisms mediated by structural networks. For example, higher brain white matter connectivity (measured by node degree) is associated with better cognitive performance and modulates cognitive dysfunction associated with white matter hyperintensity (Dejong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study used years of formal education as a proxy for CR, which is a complex, multifactorial variable that affects cognitive function in different ways (Mungas et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), to explore structural connectivity mechanisms affecting CR. Differences in white matter connectivity patterns between individuals with HCR and LCR under cerebral pathological load highlight CR\u0026rsquo;s role in brain structural connectivity. The arcuate fasciculus, located at the posterior end of the lateral sulcus, connects major language centers (Wernicke\u0026rsquo;s and Broca's areas) and is crucial for language function (Giampiccolo \u0026amp; Duffau, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Repeated CSVD exposure damages the arcuate fasciculus, affecting frontal-temporal connectivity and cognitive functions. Therefore, our findings suggest that education may affect cognitive domains by maintaining the fiber integrity of the left arcuate fasciculus.\u003c/p\u003e \u003cp\u003eDeficits in cerebral white matter connectivity may underlie CSVD\u0026rsquo;s clinical symptoms (Dey et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the exact mechanism by which CR compensates for brain damage in patients with CSVD remains unknown. Previous studies have found no one-to-one correspondence between fiber bundles and cognitive domains, and fiber bundle selectivity for cognitive domains relates to projection diversity (Forkel et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We subdivided the left arcuate fasciculus using the AFQ method to identify specific CR-affected segments and found that FA values of segments 28\u0026ndash;36 were negatively correlated with Stroop A scale scores and positively correlated with DST-backward scores, indicating that high CR preserves the integrity of the left arcuate fasciculus to maintain information processing speed and executive function.\u003c/p\u003e \u003cp\u003eHowever, this study has several limitations. First, we did not track the bilateral cingulum hippocampus due to fiber-tracking threshold settings, possibly losing important information. Second, while this preliminary study reveals the possible CR mechanisms from the perspective of white matter connectivity; multimodal magnetic resonance analyses are still required for comprehensive assessment. Finally, as a cross-sectional study, it did infer directionality or causality. Future large-sample and longitudinal studies are needed to confirm these findings, especially changes in cerebral white matter fiber bundles over time with CSVD development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides evidence that CR may protect cognitive function in patients with CSVD by modulating the fiber connections of the left arcuate fasciculus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants in the present study for their cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Medical Expert Team Program of Wuxi Taihu Talent Plan 2021 (2021 THRC-DJ-FXM-2023) and Promotion Projects of Technological Achievements and Suitable Technology (T202335).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Ethics Committee of the Affiliated Wuxi People\u0026apos;s Hospital of Nanjing Medical University (approval number: KY22080).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors all agreed to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMingyu Li (MY Li, First Author): conception and study design, statistical analysis, interpretation of results, drafting the manuscript work, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eYachen Shi (YC Shi, First Author): conception and study design, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eLin Ma (L Ma): data collection, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eHaixia Mao (HX Mao): data acquisition, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eMin Xu (M Xu): data collection, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eQianqian Gao(QQ Gao): data acquisition, revising it critically for important intellectual content, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eJiayi Yang (JY Yang): data collection, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eFeng Wang (F Wang, Corresponding Author): conception and study design, revising it critically for important intellectual content and approval of final version to be published, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003eXiangming Fang (XM Fang, Corresponding Author): conception and study design, revising it critically for important intellectual content and approval of final version to be published, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Xiaoyun Hu (XY Hu, Corresponding Author): conception and study design, revising it critically for important intellectual content and approval of final version to be published, agreement to be accountable for the integrity and accuracy of all aspects of the work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailable from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexander A. 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Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cerebral small vessel disease, cognitive reserve, education, automated fiber quantification, white matter ","lastPublishedDoi":"10.21203/rs.3.rs-4592100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4592100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates how cognitive reserve (CR), developed through education and other cognitive activities, can slow cognitive dysfunction in patients with cerebral small vessel disease (CSVD) by examining cerebral white matter fiber connectivity. We prospectively enrolled 125 patients with CSVD from the Department of Neurology between 2021 and 2023, including 69 patients with no cognitive impairment (nonCI) and 56 patients with mild cognitive impairment (MCI). Patients were divided into low cognitive reserve (LCR) and high cognitive reserve (HCR) subgroups based on the median of years of education (≤9 vs. \u0026gt;9 years). All participants underwent 3.0T MRI scans and neuropsychological assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values of fiber bundles detected by automated fiber quantification (AFQ) were compared among groups by two-way analysis of variance, considering disease state and CR as factors. Correlation analyses examined the relationships between significant fiber segments and cognitive function. We found that the MCI group exhibited decreased FA and increased MD in specific segments of some fiber tracts, such as the corpus callosum splenium, bilateral thalamic radial tracts, and bilateral inferior frontal occipital tracts compared to the nonCI group. The LCR group had decreased FA in the left corticospinal tract and increased MD in the right corticospinal tract compared to the HCR group. Significant interactions of FA values were observed in the left arcuate fasciculus, particularly in segments related to information processing speed and memory. The MCI group exhibited poorer white matter fiber integrity than the nonCI group. These findings suggest that CR’s protective effects on cognitive dysfunction in patients with CSVD may be partially mediated by the left arcuate fasciculus.\u003c/p\u003e","manuscriptTitle":"Cognitive reserve mitigates cognitive impairment in cerebral small vessel disease by protecting white matter fibers: an automated fiber quantification study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:51:19","doi":"10.21203/rs.3.rs-4592100/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-10T18:19:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-05T06:32:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51077327440768611400081617726575740780","date":"2024-09-17T15:47:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-12T15:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109786984412550085063334312490167255369","date":"2024-07-25T14:50:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-25T11:12:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-25T11:07:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-18T05:56:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2024-06-17T06:17:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35024d6e-5c9e-4033-9059-12f6aab3fb73","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:36:00+00:00","versionOfRecord":{"articleIdentity":"rs-4592100","link":"https://doi.org/10.1007/s11682-025-01032-7","journal":{"identity":"brain-imaging-and-behavior","isVorOnly":false,"title":"Brain Imaging and Behavior"},"publishedOn":"2025-08-23 16:29:31","publishedOnDateReadable":"August 23rd, 2025"},"versionCreatedAt":"2024-07-15 23:51:19","video":"","vorDoi":"10.1007/s11682-025-01032-7","vorDoiUrl":"https://doi.org/10.1007/s11682-025-01032-7","workflowStages":[]},"version":"v1","identity":"rs-4592100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4592100","identity":"rs-4592100","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}