Cerebral Small Vessel Disease Score Associated with Brain Hypoperfusion Predicts Cognitive Decline: A Longitudinal Study

Cerebral Small Vessel Disease Score Associated with Brain Hypoperfusion Predicts Cognitive Decline: A Longitudinal 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 Cerebral Small Vessel Disease Score Associated with Brain Hypoperfusion Predicts Cognitive Decline: A Longitudinal Study Xiaoqian Zhang, Sirui Liu, Bo Hou, Xiaoyuan Fan, Hui You, Mingli Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6746909/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Background This study aims to investigate the association between cerebral small vessel disease (CSVD) score and cerebral blood flow (CBF) at baseline in cognitively intact older adults, and explore whether total CSVD burden serves as an imaging marker can predict longitudinal cognitive impairment. Methods MR images acquired from 509 participants with normal cognition were included in the analysis to assess the association between total CSVD burden score and CBF. Imaging protocols included structural scans, pseudo-continuous arterial spin labeling (pCASL) for CBF quantification, and 3D T1-weighted sequences. CSVD burden scores were rated using a validated 5-point scale by assessing white matter hyperintensity, lacune, perivascular space, microbleed. Participants underwent structured telephone cognitive assessments at a mean follow-up of 7.6 ± 0.1 years post-baseline. The differences in CBF between CSVD burden groups were compared using univariate linear models, and logistic regression analysis was conducted to estimate the risk of longitudinal cognitive impairment. The predictive model were evaluated by the receiver operating characteristic (ROC) curve analysis. Results Severe CSVD scores (score > 2) were significantly associated with decreased CBF in widespread cortical regions ( P adj < 0.05). The participants with higher CSVD score were more susceptible to longitudinal cognitive decline (OR = 2.995, 95% CI = [1.540, 5.825], P = 0.001, adjusted for age and sex). The CSVD score model offered good predictive ability for cognitive impairment (AUC = 0.808, P 2 (specificity = 88.9%). Conclusion Severe total CSVD score, which is associated with cortical hypoperfusion, serves as an imaging marker of predicting longitudinal cognitive decline. This offers a clinically accessible tool for risk stratification and individualized health monitoring in aging populations. Cerebral small vessel disease Cerebral blood flow Arterial spin labeling Cognitive impairment Longitudinal study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cerebral small vessel disease (CSVD) is a common age-related small vascular disease of brain which is often insidious in its onset and partially identified only by imaging examination [ 1 ]. Common neuropathological features of CSVD derived from MRI comprise recent small subcortical infarcts, white matter hyperintensity (WMH), lacunes, perivascular spaces (PVSs), cerebral microbleeds (CMBs) and brain atrophy. Considering the temporal coherence and combined effect of these features, an overall measurement called “total CSVD burden score”, which contains WMH, PVS, CMB and lacunes, may better assess the combined effect of neurological changes in CSVD [ 1 , 2 ]. However, imaging markers of “total CSVD burden”, which are also observed in cognitively normal elderly population, have not yet been elucidated in terms of their significance. Various neuroimaging studies have described that CSVD patients underwent global changes in brain structure and metabolism. The total CSVD burden score is associated with local clustering and nodal efficiency of brain network[ 3 ], blood-brain barrier (BBB) permeability[ 4 ] and decreased global and regional cerebral blood flow (CBF)[ 5 ]. Chronic brain hypoperfusion may lead to age-related cognitive deterioration and neurological degeneration [ 6 ]. Regional and tissue-specific atrophy of the grey matter (GM) has been proven to be related to clinical symptoms in several studies [ 7 , 8 ].Several recent studies have indicated that total CSVD burden scores are negatively correlated with cognitive function [ 2 , 9 – 11 ].However, most of these studies have been limited by cross-sectional designs [ 2 , 9 , 11 , 12 ] or based on patient enrollment [ 9 – 11 , 13 ]. Therefore, it remains uncertain whether total CSVD scores along with their baseline brain alterations are associated with cognitive decline in the general normal aging population. Few investigations have linked CSVD burden to quantitative perfusion metrics like arterial spin labeling (ASL)-CBF, a potential gap hindering pathophysiological insight. Subjective cognitive decline (SCD) refers to self-perceived cognitive decline with normal objective cognitive function [ 14 ]. SCD has been considered as both a preclinical stage of Alzheimer's disease continuum and an independent predictor of dementia conversion[ 15 ], and its prevalence is increasing given that the global population is aging rapidly. We also assume SCD as an outcome event of long term cognitive impairment, to retrospectively analyze CSVD characteristics at baseline and explore their potential association. Therefore, our main purpose was to investigate the correlation between total burden score of CSVD and GM CBF alterations in a large sample with normal cognition. A further purpose was to initially test whether the total CSVD score as an effective imaging marker can predict for longitudinal cognitive function decline independently. This prospective cohort study follows Marti-Bonmati criteria (Level 2b). Methods and Materials Participants This study was approved by the Medical Ethics Committee of the Peking Union Medical College Hospital (PUMCH, JS-2653 and K2102). Written informed consent documents were provided by all attendees. This study complied with the Declaration of Helsinki. These attendees were enrolled from two cohort studies (Cohort A and B). Cohort A is a community-based study primarily focused on middle-aged and elderly people from Beijing, China. Cohort B is a joint large-sample human body donation project between PUMCH and the Chinese Academy of Medical Sciences [16] and is still ongoing. Part of our current study data were used for basal ganglia perivascular space research [17]. Our design was observational and ambidirectional cohort study. Figure 1 shows the enrollment flow diagram and exclusion criteria of the study participants. 1054 participants who underwent a head MRI (n=301 for cohort A and n=753 for cohort B) were included in the baseline cross-sectional analysis. The exclusion criteria were as follows: (a) younger than 50 years; (b) whose total Mini-Mental State Exam (MMSE) score <27 was considered to have cognitive impairment [18] or an unavailable MMSE score; (c) self-reported cognitive or memory loss; (d) with stenosis (≥50%) or infarction of major intracranial or extra-cranial artery; (e) diagnosed with genetic/ metabolic related CSVD and other leuko-encephalopathies (e.g. demyelinating disease); (f) a history of intracranial trauma or surgery; (g) with a history of stroke, brain tumor, neuropsychiatric disorder of drug abuse or dependence in the past or present. In total, 327 participants were excluded. The remaining 727 (n=252 for cohort A and n=475 for cohort B) participants were included in imaging analysis. Participants with insufficient image quality for CSVD score assessment (n=40), unavailable pseudocontinuous arterial spin labeling (pCASL) data for post-processing (n=178), and/or incomplete clinical data (n=163) were likewise excluded. A total of 509 participants were filtered and used to investigate the correlation between the total CSVD burden and GM CBF, while 346 participants were screened out to investigate the relationship between CSVD scores and vascular risk factors. Follow-up data were censored if the patients from cohort A (n=146) were lost to follow-up (n=62), unwillingness to cooperate (n=5) or pass away (n=3). A total of 78 people thus completed the final telephone follow-up analysis. MRI protocols All MR examinations of the two cohorts were conducted on a 3.0 Tesla MRI scanner (GE Discovery MR750 for cohort A, GE Healtchare, Milwaukee, WI, USA; GE MR750W for cohort B, GE Healthcare Systems, Chicago IL,USA). Brain MRI was equipped with an 8-channel phased array head coil. Participants from both cohorts underwent standard MRI protocols including structural and perfusion sequences. The MRI protocols for cohort A included such sequences: a sagittal 3D T1 weighted imaging (WI) sequence[inversion time(TI)=400ms, time of repetition(TR)=6.9ms,echo time(TE)=2.6 ms, field of view(FOV) =256×256 mm 2 ,slice thickness=1.0 mm; matrix size=256×256],an axial T2WI sequence (TR=7912ms;TE=92 ms; FOV=256×256 mm 2 ;and slice thickness=4 mm),a fluid-attenuated inversion recovery(FLAIR) sequence (TR=12000ms,TE=120ms,FOV=240×240mm 2 ,and slice thickness=4 mm),a susceptibility WI(SWI) sequence(TR=47ms,TE=28ms, FOV=240×240mm 2 , and slice thickness=2 mm),a diffusion WI(DWI) sequence(TR=4400 ms,TE=67ms, FOV=240×240mm 2 , and slice thickness=4 mm),and an axial pCASL sequence (TR=4,886 ms,TE=10.5 ms; FOV=240×240 mm 2 ,slice thickness=4 mm, post-labeling delay time=2,025 ms, labeling duration=1,450 ms). The MRI protocols for cohort B were as follows: a sagittal 3D T1 sequence (TI=450ms, TR=7.4ms,TE=3.2ms, FOV=256×256 mm 2 ; slice thickness=1.1 mm; matrix size=256×256), an axial T2 sequence(TR=3324ms,TE=81ms,slice thickness=4mm, and FOV=256×256 mm 2 ), a FLAIR sequence (TR=8000ms,TE=147ms, FOV=240×240mm 2 ;and slice thickness=4 mm), a SWI sequence(TR=51ms,TE=26ms, FOV=240×240mm 2 , and slice thickness=1 mm), a DWI sequence(TR=4400ms,TE=67ms, FOV=220×220mm 2 , and slice thickness=4 mm) and an axial pCASL sequence (TR=4,874ms,TE=10.7ms,FOV=240×240 mm 2 ; slice thickness=4 mm; time of post-labeling delay=2,025 ms; labeling duration=1,450 ms). Both cohort studies used similar MRI sequences and parameters for quantification, which could greatly decrease the potential confounding differences between different MRI scanners[19]. GM CBF data acquisition The CBF maps were calculated from 3D-ASL images using Function Tool (AW 4.5 Workstation, GE Healthcare) [20, 21]. Imaging data were preprocessed using SPM12 as implemented in Matlab environment (MathWorks, Natick, MA, USA) [17]. First, 3D T1WI structural images were collected for CBF anatomical co-registration. Subsequently, the co-registered 3D T1 images were segmented into GM, WM and cerebrospinal fluid probability maps, followed by normalization to Montreal Neurologic Institute (MNI) space. Next, spatial smoothing was applied with an isotropic Gaussian smoothing kernel with full width at half maximum (FWHM) of 6 mm. The resultant smoothed probabilistic GM map was thresholded at 0.5 to create a binary GM mask. Finally, GM CBF values of 68 brain regions and global CBF were also extracted according to the Hammers atlas. Of the 68 brain regions retrieved from Hammers' atlas, all infratentorial structures and supratentorial structures without GM (e.g. all ventricles, brain stem, and corpus callosum) were excluded, and the rest of 58 brain regions were employed for subsequent statistical analyses. Scoring of CSVD total burden All four structural MRI markers of CSVD were determined in accordance with consensus guidelines and scored based on STRIVE-2 criteria [1]. The detailed evaluation of the total CSVD burden score is listed in Figure 2 and Table S1 .First, the deep WMH (DWMH) and periventricular WMH (PWMH) were graded separately according to the Fazekas scale [22]. According to the stratification scale, participants with PWMH = 3 or DWMH ⩾ 2 or both were considered to possess a WMH-1 score; otherwise they were regarded as WMH-0. The score of PVS (< 3 mm in diameter) was counted as PVS-1 if there was more than 11 visible perivascular spaces or category ≥ 2 in unilateral basal ganglia, and the more severe side was taken into account when asymmetric bilaterally [23]. One point was awarded for each of the two following markers: presence of one or more lacunes or CMBs. The total CSVD burden score was calculated by aggregating the scores of the four mentioned markers for each patient [1]. Thus, the score range of total CSVD burden was from 0 to 4. All participants were assigned to mild (CSVD score ≤ 2) or severe (score＞2) burden groups. Imaging analysis was independently performed by two neuroradiologists (X.Z. and S.L., with 5 and 6 years of experience, respectively) blinded to all clinical data. Discrepancies in CSVD scoring quantification were first resolved through consensus discussion. For persistent disagreements, a senior neuroradiologist (H.Y., over 15 years’ experience) provided final adjudication. Assessment of long-term cognitive impairment The structured survey evacuated their cognitive impairment performance using the Subjective Cognitive Decline-Questionaire 9 (SCD-Q9) [24], telephone MMSE (t-MMSE) and their clinical history. Trained interviewers, blinded to baseline imaging and clinical data, administered standardized cognitive tests via structured telephone interviews. Total t-MMSE scores range from 0-26[25]. Studies have confirmed that the t-MMSE and MMSE scales are strongly correlated and can be used as an alternative tool for screening overall cognitive function [25, 26]. The questionnaire for SCD used are listed in Table S2 .For SCD, there were following necessary requirements[27]: a) the answer needed to be “yes” to the first question “Do you have problem in memory?”; b) a total score >5 presented on the SCD-9 questionnaire; c) objective cognitive examination score within normal range. For participants screening positive for potential mild cognitive impairment (MCI) or poor performance on questionnaire (t-MMSE≤21[28] or SCD-Q9 score >5), secondary clinical evaluations were conducted and specialist neurological review by board-certified neurologists blinded to baseline imaging data. Clinically diagnosed dementia, MCI or SCD was defined as the primary outcome of long-term cognitive impairment. Statistical analysis SPSS statistical software (IBM SPSS Statistics, v 25.0; SPSS Inc., Chicago, IL, USA) and MedClac Version 20.0.8 were used for statistical analysis. Weighted κ value was calculated for the inter-observer of CSVD total burden score. Categorical variables are presented as frequencies and percentages following comparison with chi-square test. Continuous variables were compared with the independent-samples t- test if they were homogeneous of variances; otherwise, Mann–Whitney U test was used, and the results were presented as median (Q25, Q75). To compare the difference in GM CBF in the two groups, gender, age, scanner manufacturer and regional GM volume were entered as covariates into the univariate models model matrix. The adjustment for multiple testing for CBF was conducted by the Benjamini & Hochberg false discovery rate (FDR) correction method[29] and P adj values< 0.05 were considered significant. Logistic regression for binary categorical variables was performed to assess the risk factor of longitudinal cognitive impairment. The results are presented as odds ratios (ORs) along with 95% confidence intervals (CIs).The specificity and sensitivity were obtained from receiver-operating characteristic (ROC) analysis. Results Characteristics of patients Baseline demographic characteristics and imaging features are listed in Table 1 . A total of 509 participants (114 with severe CSVD burden, and 395 with mild CSVD burden) were admitted to the GM CBF analysis: 208 (40.9%) males and 301 (59.1%) females. Among them, 143(28.1%) individuals scored 0 points, 156(30.6%) scored 1 point, 96(18.9%) scored 2 points, 75(14.7%) scored 3 points, and 39(7.7%) scored 4 points. Those subjects in the higher CSVD burden group were highly likely to have more WMHs (90.4% vs 16.5%, P <0.001), more PVSs (96.5% vs 40.3%, P <0.001), lacunes (86.8% vs 13.7%, P <0.001), and CMBs (60.5% vs 17.7%, P <0.001). A total of 346 participants (85 with severe CSVD burden, 261 with mild CSVD burden) were enrolled in the cardiovascular factors analysis: 141 (40.8%) males and 205 (59.2%) females. Table 2 shows the comparison of cardiovascular risk factors for the two groups of participants stratified by the severity of CSVD burden. Severe CSVD burden scores were significantly associated with male ( P = 0.004) and age ( P <0.001).Those subjects in higher CSVD burden group were highly possible to have hypertension ( P = 0.011), higher SBP ( P <0.001), higher DBP ( P = 0.001), and higher total cholesterol (P=0.021). The inter-observer agreements on the presence of the CSVD scores were excellent and good with κ values of 0.867(95% CI 0.836-0.899). Association between total CSVD burden score and GM CBF A total of 509 attendees (mean age: 69.58 ± 7.87, males: 40.9%) were entered into the CBF statistical analyses in Figure 3 and Table 3 . Fifty-eight brain regions were used for perfusion analyses. Table 3 details regional CBF alterations. While the severe CSVD group demonstrated widespread cortical hypo-perfusion except left hippocampus compared with the mild burden group ( P adj <0.05, adjusted for gender, age, scanner, and regional GM volume), neither the basal ganglia (including caudate, putamen, and globus pallidus) nor the thalamus showed significant perfusion differences between groups (all P adj > 0.05 with identical covariate adjustments). Association between total CSVD burden and longitudinal cognitive decline Follow-up by telephone questionnaires was completed in 78 patients (52.7%). Among them, 15 participants experienced long-term cognitive decline: 3 dementia (3.8%), 1 MCI (1.3%), and 11 SCD (14.1%).The mean follow-up age was 74.30±6.96 years, and the average follow-up duration was 7.56±0.06 years. The odds of longitudinal cognitive decline were increased as CSVD total burden score increased (OR= 2.995, 95% CI = [1.540, 5.825], P =0.001, adjusted for age and sex). In line with the ROC analysis, the CSVD total burden model offered good predictive ability for patients with long-term cognitive impairment (AUC=0.808, 95% CI 0.703–0.888, P 2, where the sensitivity was 60% (95% CI 32.3%–83.7%) and the specificity was 88.9% (95% CI 78.4%–95.4%). Discussion In cognitively intact older adults, we found a significant association between total burden score of CSVD and widespread cortical hypoperfusion in GM CBF at baseline. Moreover, the total CSVD score was a more convenient and effective imaging marker linked with long-term cognitive decline. This study extended prior evidence [ 30 , 31 ] by demonstrating that higher CSVD burden may serve as an early indicator of neurodegeneration prior to overt cognitive impairment. Widespread decreased cortical perfusion could be observed in the severe CSVD burden group in comparison to the mild CSVD group. Therefore, it is reasonable to speculate that there is a whole brain level alteration due to severe CSVD burden. The observed cortical hypo-perfusion in severe CSVD aligns with existing evidence linking small vessel pathology to impaired cerebral hemodynamics[ 5 ]. While previous studies primarily focused on patient populations [ 13 ], our findings in cognitively intact elders highlight the insidious progression of CSVD-related perfusion deficits prior to clinical symptom onset. Some results are contradictory in deep nuclei [ 5 , 32 ]. The preserved CBF in subcortical structures (e.g., basal ganglia) despite severe CSVD burden may reflect differential vascular vulnerability between cortical and deep perforating arterioles. Disruption of the BBB and endothelial-pericyte axis and impaired neurovascular coupling may synergistically drive CBF reduction[ 33 ]. Extravasation of intravascular materials causes hyaline degeneration of the small vessel wall and toxic neuronal damage, which contributes to nerve fiber and myelin disruption, astrocyte proliferation, microglial activation and neuroinflammation[ 34 ]. This cascade of events can partially account for lacunes, WMH, and CMB. Loss of small arterial wall tight junctions and elevated permeability obstruct cerebrospinal fluid reflux, which allows for the observation of enlarged PVSs. In addition, GM of the cerebral cortex is more sensitive to ischemic injury than the WM [ 35 ], and is rich in neurons, making it is particularly important to study the total CSVD burden to GM CBF in the elderly. Hypo-perfusion of the brain is linked to neuronal degeneration and impairment of perception [ 36 ]. Although not in a cognitive impairment condition, having a severe CSVD burden puts the patient at risk owing to the underlying pathological vascular disorder. To the best of our knowledge, few previous studies have paid attention to the effect of CSVD scores on long-range cognitive decline [ 2 , 30 ] especially SCD, in cognitively normal older adults. SCD, as a premorbid state of dementia, also bothers the emotion and quality of life of elderly individuals. The fact underscores the importance of identifying individuals with severe CSVD burden early, as they may benefit from targeted lifestyle modifications or pharmacological interventions to mitigate future cognitive decline. CSVD is associated with multiple cognitive declines which have been initially recognized in previous cross-sectional studies [ 2 ], but prior studies have mainly concentrated on the role of individual imaging markers in CSVD. The emergence of a total CSVD burden remedies this deficiency, which is an innovative aspect of the present study. In addition, this study demonstrates that elevated CSVD burden reflects altered intra-cerebral perfusion status at baseline, and the fact that independent use of CSVD scores can be an alternative to the use of multiple markers at baseline to predict long-range cognitive decline, which confers significant implications for individual health management and disease prognosis. The total CSVD burden score is accessible, reliable and stable, as long as standardized definitions are adopted. In contrast, the presence of higher CSVD burden scores may be of warning significance when regional CBF post-processing is cumbersome and clinically relevant sequences are not performed. Our findings advocate a three-tiered screening protocol: (1) Older adults with vascular risk factors: annual CSVD burden scoring; (2) CSVD score ≥ 3: quantitative ASL-CBF mapping; (3) Hypoperfusion subgroups: implement advanced prevention like Cilostazol [ 37 ]. Our study also has several limitations. First, the sample size involved in the telephone follow-up was small probably due to the long follow-up period and the high rate of missed visits. The 47.3% attrition rate over 7.6 years may introduce bias; however, attrition analysis showed no baseline differences between groups. Second, cognitive assessments via telephone, while cost-effective for long-term follow-up, lack the sensitivity of in-person neuropsychological batteries for detecting early subclinical decline. Finally, the visual CSVD scoring system is an observer-dependent mission as well as a semi-quantitative evaluation tool without stratifying the anatomical location and size of lacunes and CMB. Future multi-center studies with automated lesion quantification and detailed cognitive profiling are warranted. Conclusion The current study indicated that in cognitively intact elderly individuals, severe CSVD total burden was associated with widespread cortical hypoperfusion. Our study also uncovered the predictive value of CSVD total burden scores in long-term cognitive decline in elders, which had important implications for managing individual health and stratifying dementia risk in the aging population. Abbreviations Abbreviation Full Name ASL Arterial Spin Labeling AUC Area Under the Curve BBB Blood-Brain Barrier CBF Cerebral Blood Flow CMB Cerebral Microbleed CSVD Cerebral Small Vessel Disease DWI Diffusion-Weighted Imaging FDR False Discovery Rate FLAIR Fluid-Attenuated Inversion Recovery FWHM Full Width at Half Maximum GM Grey Matter MCI Mild Cognitive Impairment MMSE Mini-Mental State Examination MNI Montreal Neurological Institute pCASL Pseudo-Continuous Arterial Spin Labeling PVS Perivascular Space ROC Receiver Operating Characteristic SBP Systolic Blood Pressure SCD Subjective Cognitive Decline SWI Susceptibility-Weighted Imaging t-MMSE Telephone Mini-Mental State Examination WMH White Matter Hyperintensity Declarations Ethics approval and consent to participate This study involving human participants was reviewed and approved by the Medical Ethics Committee of Peking Union Medical College Hospital (JS-2653 and K2102). This study complied with the Declaration of Helsinki. All participants provided written informed consent for this study. The study involved human participants but did not use animal data. Consent for publication All data presented in this manuscript are anonymized and do not compromise participant privacy. Consent for publication of aggregated data was included in the original informed consent forms. Availability of data and material The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests (financial or non-financial). Funding This study was supported by the National Natural Science Foundation of China (grants 82371946 and 82071899), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant 2021-I2M-1-025), and the National High Level Hospital Clinical Research Funding (grant 2022-PUMCH-B-067). Authors’ contributions XZ , SL and FF: conception and design of the study. XZ, SL, BH, YZ, and CM: acquisition of data. XZ, SL, XF, ML and ZZ: preprocessing and interpretation of data. XZ, SL and HY: evaluation of MR images. XZ: the statistical analyses. XZ, ZZ, and FF: drafting of the article. ZZ and FF jointly supervised the work and contributed equally to correspondence. All authors contributed to the article and approved the submitted version. Acknowledgments We thank Yuelun Zhang (Clinical Epidemiology Unit, PUMCH) for statistical support. Permission to acknowledge was obtained from all individuals. References Duering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS et al : Neuroimaging standards for research into small vessel disease-advances since 2013 . Lancet Neurol 2023, 22 (7):602-618. Staals J, Booth T, Morris Z, Bastin ME, Gow AJ, Corley J, Redmond P, Starr JM, Deary IJ, Wardlaw JM: Total MRI load of cerebral small vessel disease and cognitive ability in older people . 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Jiang Y, Wang Y, Yuan Z, Xu K, Zhang K, Zhu Z, Li P, Suo C, Tian W, Fan M et al : Total Cerebral Small Vessel Disease Burden Is Related to Worse Performance on the Mini-Mental State Examination and Incident Dementia: A Prospective 5-Year Follow-Up . Journal of Alzheimer's disease : JAD 2019, 69 (1):253-262. Liu C, Shi L, Zhu W, Yang S, Sun P, Qin Y, Tang X, Zhang S, Yao Y, Wang Z et al : Fiber Connectivity Density in Cerebral Small-Vessel Disease Patients With Mild Cognitive Impairment and Cerebral Small-Vessel Disease Patients With Normal Cognition . Frontiers in neuroscience 2020, 14 :83. Zhong GL, Zhang RT, Jiaerken Y, Yu XF, Zhou Y, Liu C, Lin LT, Tong LS, Lou M: Better Correlation of Cognitive Function to White Matter Integrity than to Blood Supply in Subjects with Leukoaraiosis . Frontiers in Aging Neuroscience 2017, 9 . Li Y, Li M, Zuo L, Shi Q, Qin W, Yang L, Jiang T, Hu W: Compromised Blood-Brain Barrier Integrity Is Associated With Total Magnetic Resonance Imaging Burden of Cerebral Small Vessel Disease . Front Neurol 2018, 9 :221. Rosenberg GA, Wallin A, Wardlaw JM, Markus HS, Montaner J, Wolfson L, Iadecola C, Zlokovic BV, Joutel A, Dichgans M et al : Consensus statement for diagnosis of subcortical small vessel disease . J Cereb Blood Flow Metab 2016, 36 (1):6-25. Bristow MS, Simon JE, Brown RA, Eliasziw M, Hill MD, Coutts SB, Frayne R, Demchuk AM, Mitchell JR: MR perfusion and diffusion in acute ischemic stroke: human gray and white matter have different thresholds for infarction . J Cerebr Blood F Met 2005, 25 (10):1280-1287. Gregg NM, Kim AE, Gurol ME, Lopez OL, Aizenstein HJ, Price JC, Mathis CA, James JA, Snitz BE, Cohen AD et al : Incidental Cerebral Microbleeds and Cerebral Blood Flow in Elderly Individuals . JAMA Neurol 2015, 72 (9):1021-1028. Doubal FN, Blair G, Wardlaw JM: Response by Doubal et al to Letter Regarding Article, "Cilostazol for Secondary Prevention of Stroke and Cognitive Decline: Systematic Review and Meta-Analysis" . Stroke 2020, 51 (12):e377. Tables Table 1. Demographic characteristics and imaging markers of the overall MRI sample classified according to the severity of CSVD total burden. Mild CSVD burden Severe CSVD burden Statistical test P value Number 395 114 Mean age at MRI(SD) 68.20(7.31) 74.36(7.88) t <0.001*** Male (%) 144(36.5) 64(56.1) χ 2 <0.001*** CSVD score 1(0,1) 3(3,4) Mann-Whitney U <0.001*** Moderate-to-severe WMH (%) 65(16.5) 103(90.4) χ 2 10, %) 159(40.3) 110(96.5) χ 2 <0.001*** Presence of LI (%) 54(13.7) 99(86.8) χ 2 <0.001*** Presence of CMB (%) 70(17.7) 69(60.5) χ 2 <0.001*** CSVD, cerebral small vascular disease; SD, standard deviation; WMH, white matter hyperintensity; PVS, Perivascular space. * P <0.05;** P <0.01;*** P <0.001 Table 2. Comparison of cardiovascular risk factors in participants in mild or severe CSVD burden group. Mild CSVD burden Severe CSVD burden Statistical test P value Number 261 85 Mean age(SD) 69.39(7.11) 75.32(8.02) t <0.001*** Male (%) 95(36.4) 46(54.1) χ2 0.004** MMSE score 29(28,30) 29(28,29) Mann-Whitney U 0.098 Mean SBP mm Hg (SD) 135.80(18.32) 144.91(19.16) t <0.001*** Mean DBP mm Hg (SD) 71.39(9.81) 75.49(11.42) t 0.001** TChol mg/dL(SD) 199.80(41.91) 187.43(44.79) t 0.021* HDL-C mg/dL (SD) 53.25(13.31) 49.19(13.44) t 0.015* Smoker (%) 47(18.00) 21(24.70) χ2 0.177 Diabetes (%) 46(17.60) 16(18.80) χ2 0.802 Hypertension (%) 112(42.90) 50(58.80) χ2 0.011* CSVD, cerebral small vessel disease; SD, standard deviation; MMSE, Mini-Mental State Examination; SBP, systolic blood pressure; DBP, diastolic blood pressure; TChol, total cholesterol; HDL-C, high-density lipoprotein cholesterol.* P <0.05;** P <0.01;*** P <0.001. Table 3 . Regional CBF comparison between CSVD burden groups. CBF (mL/100g/min) Region Mild CSVD burden group Severe CSVD burden group P adj Hippocampus L 45.37±7.59 44.48±7.58 0.082 Hippocampus R 45.37±7.54 43.63±7.65 0.009* Amygdala L 39.30±6.26 38.01±6.30 0.015* Amygdala R 39.06±6.63 37.20±6.34 0.008* AntMedTeLo L 31.64±5.56 27.85±5.11 <0.001* AntMedTeLo R 31.95±5.60 28.41±5.34 <0.001* AntLatTeLo L 32.32±6.83 28.72±6.29 0.005* AntLatTeLo R 33.54±6.80 29.56±6.35 0.002* Parahippocampal gyrus L 38.35±6.19 36.32±6.28 0.023* Parahippocampal gyrus R 38.59±6.25 36.22±6.52 0.006* Superior temporal gyrus L 49.60±7.79 45.26±7.81 <0.001* Superior temporal gyrus R 50.43±7.80 45.93±7.68 <0.001* InfMidTemGy L 41.56±6.89 37.43±6.57 <0.001* InfMidTemGy R 43.07±7.11 38.82±6.99 <0.001* Fusiform gyrus L 34.22±5.92 31.33±5.70 0.002* Fusiform gyrus R 34.93±5.89 32.35±5.76 0.006* Insula L 43.63±6.26 40.92±6.11 0.002* Insula R 43.68±6.47 40.83±6.02 <0.001* Lateral occipital lobe L 36.48±8.43 31.15±8.46 <0.001* Lateral occipital lobe R 37.20±8.80 31.09±8.06 <0.001* Anterior cingulate gyrus L 50.22±8.75 46.75±8.34 0.012* Anterior cingulate gyrus R 50.22±8.65 46.31±7.90 0.016* Posterior cingulate gyrus L 64.55±10.85 59.51±10.78 <0.001* Posterior cingulate gyrus R 65.73±10.66 60.15±10.84 <0.001* Middle frontal gyrus L 41.16±7.33 36.21±5.80 <0.001* Middle frontal gyrus R 42.21±7.13 36.83±5.82 <0.001* PosTeLo L 44.67±7.45 40.21±7.18 <0.001* PosTeLo R 44.35±7.59 39.72±6.88 <0.001* Inferior lateral parietal lobe L 40.94±7.29 35.44±6.73 <0.001* Inferior lateral parietal lobe R 42.15±8.02 36.48±6.60 <0.001* Caudate L 30.64±7.49 29.28±6.69 0.889 Caudate R 31.40±7.65 28.97±6.40 0.225 Accumbens L 40.99±8.77 41.06±7.53 0.759 Accumbens R 40.54±8.84 39.41±7.77 0.249 Putamen L 42.22±7.08 43.25±6.89 0.916 Putamen R 41.55±7.46 42.47±7.08 0.800 Thalamus L 47.40±10.39 43.71±8.75 0.085 Thalamus R 46.18±10.80 42.86±8.45 0.183 Pallidum L 41.74±8.94 43.03±8.49 0.877 Pallidum R 40.57±8.36 41.32±8.35 0.904 Precentral gyrus L 41.84±6.91 37.73±6.47 <0.001* Precentral gyrus R 42.49±6.83 38.21±5.94 <0.001* Rectal gyrus L 44.54±8.37 40.61±7.60 0.021* Rectal gyrus R 44.25±8.46 39.84±7.23 0.004* Orbital frontal gyrus L 36.28±7.36 32.77±5.55 0.002* Orbital frontal gyrus R 36.85±6.92 33.12±5.82 <0.001* Inferior frontal gyrus L 41.52±8.32 37.52±7.46 0.021* Inferior frontal gyrus R 44.36±7.91 39.84±7.16 <0.001* Superior frontal gyrus L 37.09±7.00 33.67±5.71 0.002* Superior frontal gyrus R 36.80±7.16 33.32±5.88 0.005* Postcentral gyrus L 40.97±6.87 36.31±6.49 <0.001* Postcentral gyrus R 41.08±6.95 36.35±6.12 <0.001* Superior parietal gyrus L 41.88±8.78 35.07±8.43 <0.001* Superior parietal gyrus R 42.44±8.86 35.20±8.20 <0.001* Lingual gyrus L 47.07±10.27 42.11±10.65 0.005* Lingual gyrus R 48.97±10.41 43.56±10.65 0.003* Cuneus L 40.97±10.45 35.40±10.83 0.002* Cuneus R 42.88±10.62 36.46±10.84 0.002* Note: Other than P values, data are means ± standard deviation AntMedTeLo = Anterior middle temporal lobe, AntLatTeLo = Anterior lateral temporal lobe, InfMidTemGy = Inferior middle temporal gyrus, PosTeLo = Posterior temporal lobe. CBF, cerebral blood flow; CSVD, Cerebral small vessel disease; L, left; R, right; *Significant at P adj ＜ 0.05; this difference was significant Table 4 . Criterion values and ROC performance parameters of CSVD scores. Criterion of CSVD score Sensitivity 95%CI Specificity 95%CI ≥0 1.000 0.782-1.000 0 0-0.057 ＞0 0.933 0.681-0.998 0.429 0.305-0.560 ＞1 0.667 0.384-0.882 0.730 0.603-0.834 ＞2 0.600 0.323-0.837 0.889 0.784-0.954 ＞3 0.333 0.118-0.616 1.000 0.943-1.000 ＞4 0 0-0.218 1.000 0.943-1.000 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 05 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor invited by journal 04 Jun, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 26 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6746909","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467690208,"identity":"3f895a6c-66b1-48ae-b307-d51a8025e3e2","order_by":0,"name":"Xiaoqian Zhang","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqian","middleName":"","lastName":"Zhang","suffix":""},{"id":467690209,"identity":"f1ac8bee-3758-4015-a32a-068f577f0915","order_by":1,"name":"Sirui Liu","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Sirui","middleName":"","lastName":"Liu","suffix":""},{"id":467690210,"identity":"72be9e75-637c-4933-a953-e154e9fb93d2","order_by":2,"name":"Bo Hou","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Hou","suffix":""},{"id":467690211,"identity":"6980c729-6832-4f8d-8966-89d468b4778a","order_by":3,"name":"Xiaoyuan Fan","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyuan","middleName":"","lastName":"Fan","suffix":""},{"id":467690212,"identity":"a3f4bab8-c1e3-4c80-a449-7b5f6c1df6ca","order_by":4,"name":"Hui You","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"You","suffix":""},{"id":467690213,"identity":"57effdc3-d51b-485d-81a1-21fa3f7f438f","order_by":5,"name":"Mingli Li","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mingli","middleName":"","lastName":"Li","suffix":""},{"id":467690214,"identity":"45fea608-b9ef-4f46-b00f-766c2c46dfdb","order_by":6,"name":"Yicheng Zhu","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yicheng","middleName":"","lastName":"Zhu","suffix":""},{"id":467690215,"identity":"0b6a2e40-4826-461b-9b82-460f210dd91f","order_by":7,"name":"Chao Ma","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Ma","suffix":""},{"id":467690216,"identity":"69b98109-96ee-43dd-ab40-221c07f9683a","order_by":8,"name":"Zhentao Zuo","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhentao","middleName":"","lastName":"Zuo","suffix":""},{"id":467690217,"identity":"5a21df3d-1f12-4b92-8f8d-b1a31367e8b0","order_by":9,"name":"Feng Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBAC9uYDQLICxOQhUgvPsQQgeYZkLYxtJGlhYz728Ou8O4lr288eYPi5gygtbOnGstueJW47k5fA2HuGCC328j1m0pLbDiduu8FjwAxxIUFb+L9JS84hTQsPm+THBtK0sJlJMxx7ZrztTI7BwV7itDA/k/xRc0d22/Ezhg9+EqMFBJh5GA6AGQeI1ACMyR8kKB4Fo2AUjIIRCACIrjb1knhKhAAAAABJRU5ErkJggg==","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-05-26 04:23:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6746909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6746909/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-025-01967-9","type":"published","date":"2025-11-13T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84304249,"identity":"d4be4323-8b1f-41ed-bbb3-9be1439f478f","added_by":"auto","created_at":"2025-06-10 11:22:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":277374,"visible":true,"origin":"","legend":"\u003cp\u003eEnrollment flow diagram and exclusion criteria of study participants.\u003c/p\u003e\n\u003cp\u003eMMSE, Mini-Mental State Exam; CSVD, cerebral small vessel disease; pCASL, pseudo-continuous arterial spin labeling; CBF, cerebral blood flow.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6746909/v1/97d1674f525e57fbf8acaf32.jpeg"},{"id":84306793,"identity":"880d70db-d4f7-4b0a-bb7e-14f952d61737","added_by":"auto","created_at":"2025-06-10 11:30:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183543,"visible":true,"origin":"","legend":"\u003cp\u003eReference images for cerebral small vessel disease (CSVD) scoring.\u003c/p\u003e\n\u003cp\u003eThe CSVD total burden score is obtained by adding up scores of the above four imaging markers. The region shown in the rectangular box represents the localized image of the final row of images.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6746909/v1/b2543aace60cc6801497944b.jpeg"},{"id":84306792,"identity":"63a854cb-a1ac-443b-8aac-248021b3a4f1","added_by":"auto","created_at":"2025-06-10 11:30:54","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143923,"visible":true,"origin":"","legend":"\u003cp\u003eGrey matter cerebral blood flow (CBF) differences across CSVD burden groups.\u003c/p\u003e\n\u003cp\u003eThe severe CSVD group demonstrated widespread cortical hypo-perfusion except left hippocampus compared with the mild burden group (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e \u0026lt;0.05, adjusted for gender, age, scanner, and regional GM volume). The F-score baris shown on the right.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6746909/v1/4a8f8c773ee8fa56ba094fd9.jpeg"},{"id":84304253,"identity":"4b8db700-504d-4a6d-ab16-b6af5081bd4f","added_by":"auto","created_at":"2025-06-10 11:22:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75003,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of total CSVD burden stratification.\u003c/p\u003e\n\u003cp\u003eThe CSVD total burden model offered good predictive ability for patients with long-term cognitive impairment (AUC=0.808, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The optimal cut-off value of this predictive model was \u0026gt;2.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6746909/v1/46aefb515b3be76bda22bead.jpeg"},{"id":96105135,"identity":"18df04f0-e68d-4d20-ab8d-46ee5b013c2a","added_by":"auto","created_at":"2025-11-17 16:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3434637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6746909/v1/68165e98-a1ae-4d81-9d9c-89f2c2d5130e.pdf"},{"id":84304248,"identity":"6a9a26a0-b322-462d-9566-92d371a36abe","added_by":"auto","created_at":"2025-06-10 11:22:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16003,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6746909/v1/2832145473ee016a54392ef6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cerebral Small Vessel Disease Score Associated with Brain Hypoperfusion Predicts Cognitive Decline: A Longitudinal Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral small vessel disease (CSVD) is a common age-related small vascular disease of brain which is often insidious in its onset and partially identified only by imaging examination [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Common neuropathological features of CSVD derived from MRI comprise recent small subcortical infarcts, white matter hyperintensity (WMH), lacunes, perivascular spaces (PVSs), cerebral microbleeds (CMBs) and brain atrophy. Considering the temporal coherence and combined effect of these features, an overall measurement called \u0026ldquo;total CSVD burden score\u0026rdquo;, which contains WMH, PVS, CMB and lacunes, may better assess the combined effect of neurological changes in CSVD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, imaging markers of \u0026ldquo;total CSVD burden\u0026rdquo;, which are also observed in cognitively normal elderly population, have not yet been elucidated in terms of their significance.\u003c/p\u003e \u003cp\u003eVarious neuroimaging studies have described that CSVD patients underwent global changes in brain structure and metabolism. The total CSVD burden score is associated with local clustering and nodal efficiency of brain network[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], blood-brain barrier (BBB) permeability[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and decreased global and regional cerebral blood flow (CBF)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Chronic brain hypoperfusion may lead to age-related cognitive deterioration and neurological degeneration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Regional and tissue-specific atrophy of the grey matter (GM) has been proven to be related to clinical symptoms in several studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Several recent studies have indicated that total CSVD burden scores are negatively correlated with cognitive function [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].However, most of these studies have been limited by cross-sectional designs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] or based on patient enrollment [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it remains uncertain whether total CSVD scores along with their baseline brain alterations are associated with cognitive decline in the general normal aging population. Few investigations have linked CSVD burden to quantitative perfusion metrics like arterial spin labeling (ASL)-CBF, a potential gap hindering pathophysiological insight.\u003c/p\u003e \u003cp\u003eSubjective cognitive decline (SCD) refers to self-perceived cognitive decline with normal objective cognitive function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SCD has been considered as both a preclinical stage of Alzheimer's disease continuum and an independent predictor of dementia conversion[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and its prevalence is increasing given that the global population is aging rapidly. We also assume SCD as an outcome event of long term cognitive impairment, to retrospectively analyze CSVD characteristics at baseline and explore their potential association.\u003c/p\u003e \u003cp\u003eTherefore, our main purpose was to investigate the correlation between total burden score of CSVD and GM CBF alterations in a large sample with normal cognition. A further purpose was to initially test whether the total CSVD score as an effective imaging marker can predict for longitudinal cognitive function decline independently. This prospective cohort study follows Marti-Bonmati criteria (Level 2b).\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of the Peking Union Medical College Hospital (PUMCH, JS-2653 and K2102). Written informed consent documents were provided by all attendees. This study complied with the Declaration of Helsinki. These attendees were enrolled from two cohort studies (Cohort A and B). Cohort A is a community-based study primarily focused on middle-aged and elderly people from Beijing, China. Cohort B is a joint large-sample human body donation project between PUMCH and the Chinese Academy of Medical Sciences\u0026nbsp;[16]\u0026nbsp;and is still ongoing.\u0026nbsp;Part of our current study data were used for basal ganglia perivascular space research\u0026nbsp;[17]. Our design was observational and ambidirectional cohort study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u0026nbsp;\u003c/strong\u003eshows the enrollment flow diagram and exclusion criteria of the study participants. 1054 participants who underwent a head MRI (n=301 for cohort A and n=753 for cohort B) were included in the baseline cross-sectional analysis. The exclusion criteria were as follows: (a) younger than 50 years; (b) whose total Mini-Mental State Exam (MMSE) score \u0026lt;27 was considered to have cognitive impairment [18] or an unavailable MMSE score; (c) self-reported cognitive or memory loss; (d) with stenosis (\u0026ge;50%) or infarction of major intracranial or extra-cranial artery; (e) diagnosed with genetic/ metabolic related CSVD and other leuko-encephalopathies (e.g. demyelinating disease); (f) a history of intracranial trauma or surgery; (g) with a history of stroke, brain tumor, neuropsychiatric disorder of drug abuse or dependence in the past or present. In total, 327 participants were excluded. The remaining 727 (n=252 for cohort A and n=475 for cohort B) participants were included in imaging analysis. Participants with insufficient image quality for CSVD score assessment (n=40), unavailable pseudocontinuous arterial spin labeling (pCASL) data for post-processing (n=178), and/or incomplete clinical data (n=163) were likewise excluded. A total of 509 participants were filtered and used to investigate the correlation between the total CSVD burden and GM CBF, while 346 participants were screened out to investigate the relationship between CSVD scores and vascular risk factors. Follow-up data were censored if the patients from cohort A (n=146) were lost to follow-up (n=62), unwillingness to cooperate (n=5) or pass away (n=3). A total of 78 people thus completed the final telephone follow-up analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI protocols \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll MR examinations of the two cohorts were conducted on a 3.0 Tesla MRI scanner (GE Discovery MR750 for cohort A, GE Healtchare, Milwaukee, WI, USA; GE MR750W for cohort B, GE Healthcare Systems, Chicago IL,USA). Brain MRI was equipped with an 8-channel phased array head coil. Participants from both cohorts underwent standard MRI protocols including structural and perfusion sequences. The MRI protocols for cohort A included such sequences: a sagittal 3D T1 weighted imaging (WI) sequence[inversion time(TI)=400ms, time of repetition(TR)=6.9ms,echo time(TE)=2.6 ms, field of view(FOV) =256\u0026times;256 mm\u003csup\u003e2\u003c/sup\u003e,slice thickness=1.0 mm; matrix size=256\u0026times;256],an axial T2WI sequence (TR=7912ms;TE=92 ms; FOV=256\u0026times;256 mm\u003csup\u003e2\u003c/sup\u003e;and slice thickness=4 mm),a fluid-attenuated inversion recovery(FLAIR) sequence (TR=12000ms,TE=120ms,FOV=240\u0026times;240mm\u003csup\u003e2\u003c/sup\u003e,and slice thickness=4 mm),a susceptibility WI(SWI) sequence(TR=47ms,TE=28ms, FOV=240\u0026times;240mm\u003csup\u003e2\u003c/sup\u003e, and slice thickness=2 mm),a diffusion WI(DWI) sequence(TR=4400 ms,TE=67ms, FOV=240\u0026times;240mm\u003csup\u003e2\u003c/sup\u003e, and slice thickness=4 mm),and an axial pCASL sequence (TR=4,886 ms,TE=10.5 ms; FOV=240\u0026times;240 mm\u003csup\u003e2\u003c/sup\u003e,slice thickness=4 mm, post-labeling delay time=2,025 ms, labeling duration=1,450 ms). The MRI protocols for cohort B were as follows: a sagittal 3D T1 sequence (TI=450ms, TR=7.4ms,TE=3.2ms, FOV=256\u0026times;256 mm\u003csup\u003e2\u003c/sup\u003e; slice thickness=1.1 mm; matrix size=256\u0026times;256), an axial T2 sequence(TR=3324ms,TE=81ms,slice thickness=4mm, and FOV=256\u0026times;256 mm\u003csup\u003e2\u003c/sup\u003e), a FLAIR sequence (TR=8000ms,TE=147ms, FOV=240\u0026times;240mm\u003csup\u003e2\u003c/sup\u003e;and slice thickness=4 mm), a SWI sequence(TR=51ms,TE=26ms, FOV=240\u0026times;240mm\u003csup\u003e2\u003c/sup\u003e, and slice thickness=1 mm), a DWI sequence(TR=4400ms,TE=67ms, FOV=220\u0026times;220mm\u003csup\u003e2\u003c/sup\u003e, and slice thickness=4 mm) and an axial pCASL sequence (TR=4,874ms,TE=10.7ms,FOV=240\u0026times;240 mm\u003csup\u003e2\u003c/sup\u003e; slice thickness=4 mm; time of post-labeling delay=2,025 ms; labeling duration=1,450 ms). Both cohort studies used similar MRI sequences and parameters for quantification, which could greatly decrease the potential confounding differences between different MRI scanners[19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGM CBF data acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CBF maps were calculated from 3D-ASL images using Function Tool (AW 4.5 Workstation, GE Healthcare) [20, 21]. Imaging data were preprocessed using SPM12 as implemented in Matlab environment (MathWorks, Natick, MA, USA) [17]. First, 3D T1WI structural images were collected for CBF anatomical co-registration. Subsequently, the co-registered 3D T1 images were segmented into GM, WM and cerebrospinal fluid probability maps, followed by normalization to Montreal Neurologic Institute (MNI) space. Next, spatial smoothing was applied with an isotropic Gaussian smoothing kernel with full width at half maximum (FWHM) of 6 mm. The resultant smoothed probabilistic GM map was thresholded at 0.5 to create a binary GM mask. Finally, GM CBF values of 68 brain regions and global CBF were also extracted according to the Hammers atlas. Of the 68 brain regions retrieved from Hammers\u0026apos; atlas, all infratentorial structures and supratentorial structures without GM (e.g. all ventricles, brain stem, and corpus callosum) were excluded, and the rest of 58 brain regions were employed for subsequent statistical analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScoring of CSVD total burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll four structural MRI markers of CSVD were determined in accordance with consensus guidelines and scored based on STRIVE-2 criteria [1]. The detailed evaluation of the total CSVD burden score is listed in \u003cstrong\u003eFigure 2\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Table S1\u003c/strong\u003e.First, the deep WMH (DWMH) and periventricular WMH (PWMH) were graded separately according to the Fazekas scale [22]. According to the stratification scale, participants with PWMH = 3 or DWMH\u0026nbsp;⩾\u0026nbsp;2 or both were considered to possess a WMH-1 score; otherwise they were regarded as WMH-0.\u0026nbsp;The score of PVS (\u0026lt;\u0026thinsp;3 mm in diameter) was counted as PVS-1 if there was more than 11 visible perivascular spaces or category \u0026ge; 2 in unilateral basal ganglia, and the more severe side was taken into account when asymmetric bilaterally\u0026nbsp;[23]. One point was awarded for each of the two following markers: presence of one or more lacunes or CMBs. The total CSVD burden score was calculated by aggregating the scores of the four mentioned markers for each patient\u0026nbsp;[1]. Thus, the score range of total CSVD burden was from 0 to 4.\u0026nbsp;All participants were assigned to mild (CSVD score \u0026le; 2) or severe (score＞2) burden groups.\u003c/p\u003e\n\u003cp\u003eImaging analysis was independently performed by two neuroradiologists (X.Z. and S.L., with 5 and 6 years of experience, respectively) blinded to all clinical data. Discrepancies in CSVD scoring quantification were first resolved through consensus discussion. For persistent disagreements, a senior neuroradiologist (H.Y., over 15 years\u0026rsquo; experience) provided final adjudication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of long-term cognitive impairment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structured survey evacuated their cognitive impairment performance using the Subjective Cognitive Decline-Questionaire 9 (SCD-Q9) [24], telephone MMSE (t-MMSE) and their clinical history. Trained interviewers, blinded to baseline imaging and clinical data, administered standardized cognitive tests via structured telephone interviews.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal t-MMSE scores range from 0-26[25]. Studies have confirmed that the t-MMSE and MMSE scales are strongly correlated and can be used as an alternative tool for screening overall cognitive function [25, 26]. The questionnaire for SCD used are listed in \u003cstrong\u003eTable S2\u003c/strong\u003e.For SCD, there were following necessary requirements[27]: a) the answer needed to be \u0026ldquo;yes\u0026rdquo; to the first question \u0026ldquo;Do you have problem in memory?\u0026rdquo;; b) a total score \u0026gt;5 presented on the SCD-9 questionnaire; c) objective cognitive examination score within normal range. For participants screening positive for potential mild cognitive impairment (MCI) or poor performance on questionnaire (t-MMSE\u0026le;21[28] or SCD-Q9 score \u0026gt;5), secondary clinical evaluations were conducted and specialist neurological review by board-certified neurologists blinded to baseline imaging data. Clinically diagnosed dementia, MCI or SCD was defined as the primary outcome of long-term cognitive impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS statistical software (IBM SPSS Statistics, v 25.0; SPSS Inc., Chicago, IL, USA) and MedClac Version 20.0.8 were used for statistical analysis. Weighted \u0026kappa; value was calculated for the inter-observer of CSVD total burden score. Categorical variables are presented as frequencies and percentages following comparison with chi-square test. Continuous variables were compared with the independent-samples t- test if they were homogeneous of variances; otherwise, Mann\u0026ndash;Whitney U test was used, and the results were presented as median (Q25, Q75).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo compare the difference in GM CBF in the two groups, gender, age, scanner manufacturer and regional GM volume were entered as covariates into the univariate models model matrix. The adjustment for multiple testing for CBF was conducted by the Benjamini \u0026amp; Hochberg false discovery rate (FDR) correction method[29] and \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e values\u0026lt; 0.05 were considered significant. Logistic regression for binary categorical variables was performed to assess the risk factor of longitudinal cognitive impairment. The results are presented as odds ratios (ORs) along with 95% confidence intervals (CIs).The specificity and sensitivity were obtained from receiver-operating characteristic (ROC) analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline demographic characteristics and imaging features are listed in \u003cstrong\u003eTable 1\u003c/strong\u003e. A total of 509 participants (114 with severe CSVD burden, and 395 with mild CSVD burden) were admitted to\u0026nbsp;the\u0026nbsp;GM CBF analysis: 208 (40.9%) males and 301 (59.1%) females. Among them,\u0026nbsp;143(28.1%) individuals scored 0 points, 156(30.6%) scored 1 point, 96(18.9%) scored 2 points, 75(14.7%) scored 3 points, and 39(7.7%) scored 4 points. Those subjects in the higher CSVD burden group were highly likely to have more WMHs (90.4% vs 16.5%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), more PVSs (96.5% vs 40.3%, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001), lacunes (86.8% vs 13.7%, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001), and CMBs (60.5% vs 17.7%, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eA total of 346 participants (85 with severe CSVD burden, 261 with mild CSVD burden) were enrolled\u0026nbsp;in\u0026nbsp;the\u0026nbsp;cardiovascular factors analysis: 141 (40.8%) males and 205 (59.2%) females.\u003cstrong\u003e\u0026nbsp;Table 2\u0026nbsp;\u003c/strong\u003eshows the comparison of cardiovascular risk factors for the two groups of participants stratified by the severity of CSVD burden. Severe CSVD burden scores were significantly associated with male (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.004) and age (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001).Those subjects in higher CSVD burden group were highly possible to have hypertension (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.011), higher SBP (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.001), higher DBP (\u003cem\u003eP\u003c/em\u003e = 0.001), and higher total cholesterol (P=0.021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The inter-observer agreements on the presence of the CSVD scores were excellent and good with \u0026kappa; values of 0.867(95% CI 0.836-0.899).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between total CSVD burden score and GM CBF\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 509 attendees (mean age: 69.58\u0026thinsp;\u0026plusmn;\u0026thinsp;7.87, males: 40.9%) were entered into the CBF statistical analyses in \u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eTable 3\u003c/strong\u003e. Fifty-eight brain regions were used for perfusion analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003edetails regional CBF alterations. While the severe CSVD group demonstrated widespread cortical hypo-perfusion except left hippocampus compared with the mild burden group (\u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e \u0026lt;0.05, adjusted for gender, age, scanner, and regional GM volume), neither the basal ganglia (including caudate, putamen, and globus pallidus) nor the thalamus showed significant perfusion differences between groups (all \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05 with identical covariate adjustments).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between total CSVD burden and longitudinal cognitive decline\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollow-up by telephone questionnaires was completed in 78 patients (52.7%). Among them, 15 participants experienced long-term cognitive decline: 3 dementia (3.8%), 1 MCI (1.3%), and 11 SCD (14.1%).The mean follow-up age was 74.30\u0026plusmn;6.96 years, and the average follow-up duration was 7.56\u0026plusmn;0.06 years. The odds of longitudinal cognitive decline were increased as CSVD total burden score increased (OR= 2.995, 95% CI = [1.540, 5.825], \u003cem\u003eP\u0026nbsp;\u003c/em\u003e=0.001, adjusted for age and sex).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn line with the ROC analysis, the CSVD total burden model offered good predictive ability for patients with long-term cognitive impairment (AUC=0.808, 95% CI 0.703\u0026ndash;0.888, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) in \u003cstrong\u003eFigure 4\u003c/strong\u003e. According to the ROC analysis in \u003cstrong\u003eTable 4\u003c/strong\u003e, the optimal cut-off value of this predictive model was \u0026gt;2, where the sensitivity was 60% (95% CI 32.3%\u0026ndash;83.7%) and the specificity was 88.9% (95% CI 78.4%\u0026ndash;95.4%).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn cognitively intact older adults, we found a significant association between total burden score of CSVD and widespread cortical hypoperfusion in GM CBF at baseline. Moreover, the total CSVD score was a more convenient and effective imaging marker linked with long-term cognitive decline. This study extended prior evidence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] by demonstrating that higher CSVD burden may serve as an early indicator of neurodegeneration prior to overt cognitive impairment.\u003c/p\u003e \u003cp\u003eWidespread decreased cortical perfusion could be observed in the severe CSVD burden group in comparison to the mild CSVD group. Therefore, it is reasonable to speculate that there is a whole brain level alteration due to severe CSVD burden. The observed cortical hypo-perfusion in severe CSVD aligns with existing evidence linking small vessel pathology to impaired cerebral hemodynamics[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While previous studies primarily focused on patient populations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], our findings in cognitively intact elders highlight the insidious progression of CSVD-related perfusion deficits prior to clinical symptom onset. Some results are contradictory in deep nuclei [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The preserved CBF in subcortical structures (e.g., basal ganglia) despite severe CSVD burden may reflect differential vascular vulnerability between cortical and deep perforating arterioles. Disruption of the BBB and endothelial-pericyte axis and impaired neurovascular coupling may synergistically drive CBF reduction[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Extravasation of intravascular materials causes hyaline degeneration of the small vessel wall and toxic neuronal damage, which contributes to nerve fiber and myelin disruption, astrocyte proliferation, microglial activation and neuroinflammation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This cascade of events can partially account for lacunes, WMH, and CMB. Loss of small arterial wall tight junctions and elevated permeability obstruct cerebrospinal fluid reflux, which allows for the observation of enlarged PVSs. In addition, GM of the cerebral cortex is more sensitive to ischemic injury than the WM [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and is rich in neurons, making it is particularly important to study the total CSVD burden to GM CBF in the elderly. Hypo-perfusion of the brain is linked to neuronal degeneration and impairment of perception [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although not in a cognitive impairment condition, having a severe CSVD burden puts the patient at risk owing to the underlying pathological vascular disorder.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, few previous studies have paid attention to the effect of CSVD scores on long-range cognitive decline [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] especially SCD, in cognitively normal older adults. SCD, as a premorbid state of dementia, also bothers the emotion and quality of life of elderly individuals. The fact underscores the importance of identifying individuals with severe CSVD burden early, as they may benefit from targeted lifestyle modifications or pharmacological interventions to mitigate future cognitive decline. CSVD is associated with multiple cognitive declines which have been initially recognized in previous cross-sectional studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but prior studies have mainly concentrated on the role of individual imaging markers in CSVD. The emergence of a total CSVD burden remedies this deficiency, which is an innovative aspect of the present study. In addition, this study demonstrates that elevated CSVD burden reflects altered intra-cerebral perfusion status at baseline, and the fact that independent use of CSVD scores can be an alternative to the use of multiple markers at baseline to predict long-range cognitive decline, which confers significant implications for individual health management and disease prognosis. The total CSVD burden score is accessible, reliable and stable, as long as standardized definitions are adopted. In contrast, the presence of higher CSVD burden scores may be of warning significance when regional CBF post-processing is cumbersome and clinically relevant sequences are not performed. Our findings advocate a three-tiered screening protocol: (1) Older adults with vascular risk factors: annual CSVD burden scoring; (2) CSVD score\u0026thinsp;\u0026ge;\u0026thinsp;3: quantitative ASL-CBF mapping; (3) Hypoperfusion subgroups: implement advanced prevention like Cilostazol [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study also has several limitations. First, the sample size involved in the telephone follow-up was small probably due to the long follow-up period and the high rate of missed visits. The 47.3% attrition rate over 7.6 years may introduce bias; however, attrition analysis showed no baseline differences between groups. Second, cognitive assessments via telephone, while cost-effective for long-term follow-up, lack the sensitivity of in-person neuropsychological batteries for detecting early subclinical decline. Finally, the visual CSVD scoring system is an observer-dependent mission as well as a semi-quantitative evaluation tool without stratifying the anatomical location and size of lacunes and CMB. Future multi-center studies with automated lesion quantification and detailed cognitive profiling are warranted.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study indicated that in cognitively intact elderly individuals, severe CSVD total burden was associated with widespread cortical hypoperfusion. Our study also uncovered the predictive value of CSVD total burden scores in long-term cognitive decline in elders, which had important implications for managing individual health and stratifying dementia risk in the aging population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eASL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArterial Spin Labeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlood-Brain Barrier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCerebral Blood Flow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCerebral Microbleed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCSVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCerebral Small Vessel Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiffusion-Weighted Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFalse Discovery Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFLAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFluid-Attenuated Inversion Recovery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFWHM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull Width at Half Maximum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGrey Matter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMild Cognitive Impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMini-Mental State Examination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMontreal Neurological Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003epCASL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePseudo-Continuous Arterial Spin Labeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePerivascular Space\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSubjective Cognitive Decline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSusceptibility-Weighted Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003et-MMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTelephone Mini-Mental State Examination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Matter Hyperintensity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants was reviewed and approved by the Medical Ethics Committee of Peking Union Medical College Hospital (JS-2653 and K2102). This study complied with the Declaration of Helsinki. All participants provided written informed consent for this study. The study involved human participants but did not use animal data.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data presented in this manuscript are anonymized and do not compromise participant privacy. Consent for publication of aggregated data was included in the original informed consent forms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have \u003cstrong\u003eno competing interests\u003c/strong\u003e (financial or non-financial).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (grants 82371946 and 82071899), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant 2021-I2M-1-025), and the National High Level Hospital Clinical Research Funding (grant 2022-PUMCH-B-067).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXZ , SL and FF: conception and design of the study. XZ, SL, BH, YZ, and CM: acquisition of data. XZ, SL, XF, ML and ZZ: preprocessing and interpretation of data. XZ, SL and HY: evaluation of MR images. XZ: the statistical analyses. XZ, ZZ, and FF: drafting of the article. ZZ and FF jointly supervised the work and contributed equally to correspondence. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Yuelun Zhang (Clinical Epidemiology Unit, PUMCH) for statistical support. Permission to acknowledge was obtained from all individuals.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDuering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNeuroimaging standards for research into small vessel disease-advances since 2013\u003c/strong\u003e. \u003cem\u003eLancet Neurol \u003c/em\u003e2023, \u003cstrong\u003e22\u003c/strong\u003e(7):602-618.\u003c/li\u003e\n\u003cli\u003eStaals J, Booth T, Morris Z, Bastin ME, Gow AJ, Corley J, Redmond P, Starr JM, Deary IJ, Wardlaw JM: \u003cstrong\u003eTotal MRI load of cerebral small vessel disease and cognitive ability in older people\u003c/strong\u003e. \u003cem\u003eNeurobiology of aging \u003c/em\u003e2015, \u003cstrong\u003e36\u003c/strong\u003e(10):2806-2811.\u003c/li\u003e\n\u003cli\u003eXu X, Lau KK, Wong YK, Mak HKF, Hui ES: \u003cstrong\u003eThe effect of the total small vessel disease burden on the structural brain network\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(1):7442.\u003c/li\u003e\n\u003cli\u003eLi Y, Li M, Yang L, Qin W, Yang S, Yuan J, Jiang T, Hu W: \u003cstrong\u003eThe relationship between blood-brain barrier permeability and enlarged perivascular spaces: a cross-sectional study\u003c/strong\u003e. \u003cem\u003eClinical interventions in aging \u003c/em\u003e2019, \u003cstrong\u003e14\u003c/strong\u003e:871-878.\u003c/li\u003e\n\u003cli\u003eYu C, Lu W, Qiu J, Wang F, Li J, Wang L: \u003cstrong\u003eAlterations of the Whole Cerebral Blood Flow in Patients With Different Total Cerebral Small Vessel Disease Burden\u003c/strong\u003e. \u003cem\u003eFront Aging Neurosci \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e:175.\u003c/li\u003e\n\u003cli\u003eStaffaroni AM, Cobigo Y, Elahi FM, Casaletto KB, Walters SM, Wolf A, Lindbergh CA, Rosen HJ, Kramer JH: \u003cstrong\u003eA longitudinal characterization of perfusion in the aging brain and associations with cognition and neural structure\u003c/strong\u003e. \u003cem\u003eHum Brain Mapp \u003c/em\u003e2019, \u003cstrong\u003e40\u003c/strong\u003e(12):3522-3533.\u003c/li\u003e\n\u003cli\u003eLast N, Tufts E, Auger LE: \u003cstrong\u003eThe Effects of Meditation on Grey Matter Atrophy and Neurodegeneration: A Systematic Review\u003c/strong\u003e. \u003cem\u003eJournal of Alzheimer\u0026apos;s disease : JAD \u003c/em\u003e2017, \u003cstrong\u003e56\u003c/strong\u003e(1):275-286.\u003c/li\u003e\n\u003cli\u003eBenedict RHB, Amato MP, DeLuca J, Geurts JJG: \u003cstrong\u003eCognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues\u003c/strong\u003e. \u003cem\u003eLancet Neurol \u003c/em\u003e2020, \u003cstrong\u003e19\u003c/strong\u003e(10):860-871.\u003c/li\u003e\n\u003cli\u003eHuijts M, Duits A, van Oostenbrugge RJ, Kroon AA, de Leeuw PW, Staals J: \u003cstrong\u003eAccumulation of MRI Markers of Cerebral Small Vessel Disease is Associated with Decreased Cognitive Function. 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Disease Burden Is Related to Worse Performance on the Mini-Mental State Examination and Incident Dementia: A Prospective 5-Year Follow-Up\u003c/strong\u003e. \u003cem\u003eJournal of Alzheimer\u0026apos;s disease : JAD \u003c/em\u003e2019, \u003cstrong\u003e69\u003c/strong\u003e(1):253-262.\u003c/li\u003e\n\u003cli\u003eLiu C, Shi L, Zhu W, Yang S, Sun P, Qin Y, Tang X, Zhang S, Yao Y, Wang Z\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFiber Connectivity Density in Cerebral Small-Vessel Disease Patients With Mild Cognitive Impairment and Cerebral Small-Vessel Disease Patients With Normal Cognition\u003c/strong\u003e. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e2020, \u003cstrong\u003e14\u003c/strong\u003e:83.\u003c/li\u003e\n\u003cli\u003eZhong GL, Zhang RT, Jiaerken Y, Yu XF, Zhou Y, Liu C, Lin LT, Tong LS, Lou M: \u003cstrong\u003eBetter Correlation of Cognitive Function to White Matter Integrity than to Blood Supply in Subjects with Leukoaraiosis\u003c/strong\u003e. \u003cem\u003eFrontiers in Aging Neuroscience \u003c/em\u003e2017, \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLi Y, Li M, Zuo L, Shi Q, Qin W, Yang L, Jiang T, Hu W: \u003cstrong\u003eCompromised Blood-Brain Barrier Integrity Is Associated With Total Magnetic Resonance Imaging Burden of Cerebral Small Vessel Disease\u003c/strong\u003e. \u003cem\u003eFront Neurol \u003c/em\u003e2018, \u003cstrong\u003e9\u003c/strong\u003e:221.\u003c/li\u003e\n\u003cli\u003eRosenberg GA, Wallin A, Wardlaw JM, Markus HS, Montaner J, Wolfson L, Iadecola C, Zlokovic BV, Joutel A, Dichgans M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eConsensus statement for diagnosis of subcortical small vessel disease\u003c/strong\u003e. \u003cem\u003eJ Cereb Blood Flow Metab \u003c/em\u003e2016, \u003cstrong\u003e36\u003c/strong\u003e(1):6-25.\u003c/li\u003e\n\u003cli\u003eBristow MS, Simon JE, Brown RA, Eliasziw M, Hill MD, Coutts SB, Frayne R, Demchuk AM, Mitchell JR: \u003cstrong\u003eMR perfusion and diffusion in acute ischemic stroke: human gray and white matter have different thresholds for infarction\u003c/strong\u003e. \u003cem\u003eJ Cerebr Blood F Met \u003c/em\u003e2005, \u003cstrong\u003e25\u003c/strong\u003e(10):1280-1287.\u003c/li\u003e\n\u003cli\u003eGregg NM, Kim AE, Gurol ME, Lopez OL, Aizenstein HJ, Price JC, Mathis CA, James JA, Snitz BE, Cohen AD\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIncidental Cerebral Microbleeds and Cerebral Blood Flow in Elderly Individuals\u003c/strong\u003e. \u003cem\u003eJAMA Neurol \u003c/em\u003e2015, \u003cstrong\u003e72\u003c/strong\u003e(9):1021-1028.\u003c/li\u003e\n\u003cli\u003eDoubal FN, Blair G, Wardlaw JM: \u003cstrong\u003eResponse by Doubal et al to Letter Regarding Article, \u0026quot;Cilostazol for Secondary Prevention of Stroke and Cognitive Decline: Systematic Review and Meta-Analysis\u0026quot;\u003c/strong\u003e. \u003cem\u003eStroke \u003c/em\u003e2020, \u003cstrong\u003e51\u003c/strong\u003e(12):e377.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic characteristics and imaging markers of the overall MRI sample classified according to the severity of CSVD total burden.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMild CSVD burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere CSVD burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eStatistical test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMean age at MRI(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e68.20(7.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e74.36(7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e144(36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e64(56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCSVD score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1(0,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3(3,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eMann-Whitney U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eModerate-to-severe WMH (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e65(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e103(90.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePVS (\u0026gt;10, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e159(40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e110(96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePresence of LI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e54(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e99(86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePresence of CMB (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e70(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e69(60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCSVD, cerebral small vascular disease; SD, standard deviation; WMH, white matter hyperintensity; PVS, Perivascular space.\u0026nbsp;*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05;**\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01;***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eComparison of cardiovascular risk factors in participants in mild or severe\u0026nbsp;CSVD burden group.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMild CSVD burden\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSevere CSVD burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eStatistical test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMean age(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e69.39(7.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e75.32(8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e95(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e46(54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMMSE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e29(28,30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e29(28,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMann-Whitney U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMean SBP mm Hg (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e135.80(18.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e144.91(19.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMean DBP mm Hg (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e71.39(9.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e75.49(11.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eTChol mg/dL(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e199.80(41.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e187.43(44.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHDL-C mg/dL (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e53.25(13.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e49.19(13.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSmoker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e47(18.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e21(24.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e46(17.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e16(18.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e112(42.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e50(58.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCSVD, cerebral small vessel disease; SD, standard deviation; MMSE, Mini-Mental State Examination; SBP, systolic blood pressure; DBP, diastolic blood pressure; TChol, total cholesterol; HDL-C, high-density lipoprotein cholesterol.*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05;**\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01;***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eRegional CBF comparison between CSVD burden groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBF (mL/100g/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMild CSVD \u0026nbsp;burden group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere CSVD burden group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHippocampus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.37\u0026plusmn;7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.48\u0026plusmn;7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHippocampus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.37\u0026plusmn;7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.63\u0026plusmn;7.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmygdala L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.30\u0026plusmn;6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.01\u0026plusmn;6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmygdala R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.06\u0026plusmn;6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.20\u0026plusmn;6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntMedTeLo L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.64\u0026plusmn;5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27.85\u0026plusmn;5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntMedTeLo R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.95\u0026plusmn;5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.41\u0026plusmn;5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntLatTeLo L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.32\u0026plusmn;6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.72\u0026plusmn;6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntLatTeLo R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.54\u0026plusmn;6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.56\u0026plusmn;6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParahippocampal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.35\u0026plusmn;6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.32\u0026plusmn;6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParahippocampal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.59\u0026plusmn;6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.22\u0026plusmn;6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior temporal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49.60\u0026plusmn;7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.26\u0026plusmn;7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior temporal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e50.43\u0026plusmn;7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.93\u0026plusmn;7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfMidTemGy\u0026nbsp;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.56\u0026plusmn;6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.43\u0026plusmn;6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInfMidTemGy\u0026nbsp;R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.07\u0026plusmn;7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.82\u0026plusmn;6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFusiform gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34.22\u0026plusmn;5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.33\u0026plusmn;5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFusiform gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34.93\u0026plusmn;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.35\u0026plusmn;5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInsula L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.63\u0026plusmn;6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.92\u0026plusmn;6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInsula R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.68\u0026plusmn;6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.83\u0026plusmn;6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLateral occipital lobe L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.48\u0026plusmn;8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.15\u0026plusmn;8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLateral occipital lobe R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.20\u0026plusmn;8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.09\u0026plusmn;8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnterior cingulate gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e50.22\u0026plusmn;8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.75\u0026plusmn;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnterior cingulate gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e50.22\u0026plusmn;8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.31\u0026plusmn;7.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePosterior cingulate gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.55\u0026plusmn;10.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59.51\u0026plusmn;10.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePosterior cingulate gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65.73\u0026plusmn;10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e60.15\u0026plusmn;10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle frontal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.16\u0026plusmn;7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.21\u0026plusmn;5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle frontal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.21\u0026plusmn;7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.83\u0026plusmn;5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePosTeLo L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.67\u0026plusmn;7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.21\u0026plusmn;7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePosTeLo R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.35\u0026plusmn;7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.72\u0026plusmn;6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInferior lateral parietal lobe L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.94\u0026plusmn;7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.44\u0026plusmn;6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInferior lateral parietal lobe R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.15\u0026plusmn;8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.48\u0026plusmn;6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaudate L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.64\u0026plusmn;7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.28\u0026plusmn;6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaudate R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.40\u0026plusmn;7.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28.97\u0026plusmn;6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccumbens L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.99\u0026plusmn;8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.06\u0026plusmn;7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccumbens R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.54\u0026plusmn;8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.41\u0026plusmn;7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePutamen L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.22\u0026plusmn;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.25\u0026plusmn;6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePutamen R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.55\u0026plusmn;7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.47\u0026plusmn;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eThalamus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47.40\u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.71\u0026plusmn;8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eThalamus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.18\u0026plusmn;10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.86\u0026plusmn;8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePallidum L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.74\u0026plusmn;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.03\u0026plusmn;8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePallidum R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.57\u0026plusmn;8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.32\u0026plusmn;8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrecentral gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.84\u0026plusmn;6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.73\u0026plusmn;6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrecentral gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.49\u0026plusmn;6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.21\u0026plusmn;5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRectal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.54\u0026plusmn;8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.61\u0026plusmn;7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRectal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.25\u0026plusmn;8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.84\u0026plusmn;7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOrbital frontal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.28\u0026plusmn;7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32.77\u0026plusmn;5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOrbital frontal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.85\u0026plusmn;6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.12\u0026plusmn;5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInferior frontal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.52\u0026plusmn;8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.52\u0026plusmn;7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInferior frontal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44.36\u0026plusmn;7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.84\u0026plusmn;7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior frontal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37.09\u0026plusmn;7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.67\u0026plusmn;5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior frontal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.80\u0026plusmn;7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.32\u0026plusmn;5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePostcentral gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.97\u0026plusmn;6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.31\u0026plusmn;6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePostcentral gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.08\u0026plusmn;6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.35\u0026plusmn;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior parietal gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41.88\u0026plusmn;8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.07\u0026plusmn;8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSuperior parietal gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.44\u0026plusmn;8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.20\u0026plusmn;8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLingual gyrus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47.07\u0026plusmn;10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.11\u0026plusmn;10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLingual gyrus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.97\u0026plusmn;10.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43.56\u0026plusmn;10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCuneus L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.97\u0026plusmn;10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.40\u0026plusmn;10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCuneus R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.88\u0026plusmn;10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.46\u0026plusmn;10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Other than P values, data are means \u0026plusmn; standard deviation\u003c/p\u003e\n\u003cp\u003eAntMedTeLo = Anterior middle temporal lobe, AntLatTeLo = Anterior lateral temporal lobe, InfMidTemGy = Inferior middle temporal gyrus, PosTeLo = Posterior temporal lobe. CBF, cerebral blood flow; CSVD, Cerebral small vessel disease; L, left; R, right;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*Significant at \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e＜ 0.05; this difference was significant \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Criterion values and ROC performance parameters of CSVD scores.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;Criterion of CSVD score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026ge;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.782-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e＞0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.681-0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.305-0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e＞1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.384-0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.603-0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e＞2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.323-0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.784-0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e＞3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.118-0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.943-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e＞4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0-0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.943-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cerebral small vessel disease, Cerebral blood flow, Arterial spin labeling, Cognitive impairment, Longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-6746909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6746909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the association between cerebral small vessel disease (CSVD) score and cerebral blood flow (CBF) at baseline in cognitively intact older adults, and explore whether total CSVD burden serves as an imaging marker can predict longitudinal cognitive impairment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMR images acquired from 509 participants with normal cognition were included in the analysis to assess the association between total CSVD burden score and CBF. Imaging protocols included structural scans, pseudo-continuous arterial spin labeling (pCASL) for CBF quantification, and 3D T1-weighted sequences. CSVD burden scores were rated using a validated 5-point scale by assessing white matter hyperintensity, lacune, perivascular space, microbleed. Participants underwent structured telephone cognitive assessments at a mean follow-up of 7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 years post-baseline. The differences in CBF between CSVD burden groups were compared using univariate linear models, and logistic regression analysis was conducted to estimate the risk of longitudinal cognitive impairment. The predictive model were evaluated by the receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSevere CSVD scores (score\u0026thinsp;\u0026gt;\u0026thinsp;2) were significantly associated with decreased CBF in widespread cortical regions (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026lt; 0.05). The participants with higher CSVD score were more susceptible to longitudinal cognitive decline (OR\u0026thinsp;=\u0026thinsp;2.995, 95% CI = [1.540, 5.825], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, adjusted for age and sex). The CSVD score model offered good predictive ability for cognitive impairment (AUC\u0026thinsp;=\u0026thinsp;0.808, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with an optimal cut-off value of grade\u0026gt;2 (specificity\u0026thinsp;=\u0026thinsp;88.9%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSevere total CSVD score, which is associated with cortical hypoperfusion, serves as an imaging marker of predicting longitudinal cognitive decline. This offers a clinically accessible tool for risk stratification and individualized health monitoring in aging populations.\u003c/p\u003e","manuscriptTitle":"Cerebral Small Vessel Disease Score Associated with Brain Hypoperfusion Predicts Cognitive Decline: A Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 11:22:50","doi":"10.21203/rs.3.rs-6746909/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-05T14:57:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T08:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171313353795892441767884321325300148656","date":"2025-06-26T21:04:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T13:49:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166142508395347855964433571805423096470","date":"2025-06-06T17:38:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T12:00:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-04T07:29:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T10:59:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T10:55:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-05-26T04:19:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4faeb67-4e51-4859-a345-f415de091e32","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:03:27+00:00","versionOfRecord":{"articleIdentity":"rs-6746909","link":"https://doi.org/10.1186/s12880-025-01967-9","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2025-11-13 15:58:27","publishedOnDateReadable":"November 13th, 2025"},"versionCreatedAt":"2025-06-10 11:22:50","video":"","vorDoi":"10.1186/s12880-025-01967-9","vorDoiUrl":"https://doi.org/10.1186/s12880-025-01967-9","workflowStages":[]},"version":"v1","identity":"rs-6746909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6746909","identity":"rs-6746909","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}