Cognitive and oculomotor impairments mediate gait disorder in cerebral small vessel disease

Cognitive and oculomotor impairments mediate gait disorder in cerebral small vessel disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive and oculomotor impairments mediate gait disorder in cerebral small vessel disease Chenxin Shen, Xiaoying Zhao, Shiyao Wang, Yanwei Miao, Zhanhua Liang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8910331/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cerebral small vessel disease (CSVD) is associated with a range of gait disorders that are not entirely accounted for by conventional neuroimaging burden. This study aimed to elucidate the neuroimaging markers related to CSVD, alongside cognitive and oculomotor impairments, to explore potential mediating pathways affecting gait performance. A total of 65 CSVD patients and 35 age-matched healthy controls underwent multi-modal assessments, including quantitative gait analysis, comprehensive neuropsychological tests, and objective eye-tracking measurements. Participants were stratified based on gait status while ensuring a comparable total CSVD load. Correlation analyses revealed significant relationships between neuroimaging markers and measures of gait, cognition, and oculomotor function, while mediation analyses indicated that executive dysfunction, as measured by the Trail Making Test–B, fully mediated the effect of total CSVD load on gait performance. Moreover, processing speed (Trail Making Test–A) and specific oculomotor measures, such as saccade latency and fixation stability, played critical mediating roles. These findings suggest that executive dysfunction and oculomotor deficits are pivotal pathways linking CSVD to gait disorder. Consequently, this study supports a dual-pathway model of network inefficiency and underscores the need for early risk stratification and targeted interventions to improve mobility in CSVD. cerebral small vessel disease gait disorder executive function eye movements mediation analysis perivascular spaces Figures Figure 1 Figure 2 Introduction Cerebral small vessel disease (CSVD) is a common cerebrovascular disorder affecting the elderly population. It is the leading cause of vascular cognitive impairment and a major contributor of gait disorder in older adults [ 1 ]. Gait disorder in CSVD increases the risk of falls, functional dependence and mortality rates [ 2 ]. Thus they significantly reduce patients’ quality of life [ 3 ]. Despite these risks, a notable clinical observation is that the gait performance is very different among CSVD patients [ 4 ]. Some patients with a high burden of white matter hyperintensities or lacunes are ambulatory while others with similar radiological findings experience significant mobility decline [ 5 , 6 ]. This is due to limitations of the traditional model linking neuroimaging lesion load directly with motor deficits, suggesting more complex intermediary physiological mechanisms are involved. Human gait is a complex, goal-directed task rather than a simple automated motor task [ 7 ]. It requires seamless integration of motor, sensory and cognitive systems [ 8 ]. Higher order cognitive functions such as executive functions, attention, and visuospatial skills are now recognized as fundamental for gait control and stability [ 9 ]. While previous studies showed associations between individual CSVD markers (e.g., white matter hyperintensities) and gait parameters, results in relation to these parameters have not been consistent [ 10 , 11 ]. However, even the total CSVD load score does not fully explain the variance in gait performance. This suggests that the specific pathways through which diffuse brain injury leads to motor decline are not fully understood. Previous studies often failed to match patients by overall disease severity when comparing those with and without gait disorder. This may confuse interpretation of motor impairment [ 12 , 13 ]. Eye movement analysis has recently emerged as a powerful, non-invasive window into brain function. Oculomotor control shares many neural substrates with networks critical for cognition and gait, like prefrontal cortex, basal ganglia, brainstem and cerebellum [ 14 ]. Abnormalities in saccades, smooth pursuit and fixation are well known in neurodegenerative diseases like Parkinson's disease, in which they are associated with cognitive decline and freezing of gait [ 15 ]. We propose eye movement can act as sensitive and objective markers for the integrity of neural circuits affected by CSVD. Accordingly, we sought to investigate the neuropsychological mechanisms mediating gait disorder in patients with CSVD. A multi-modal analytical strategy was adopted, consisting of wearable sensor-based quantitative gait analysis, standardized neuropsychological evaluation, and eye movement tracking. Our main hypothesis is that the impact of CSVD neuroimaging burden on gait is indirect, partly and significantly mediated by impairments in specific cognitive domains and oculomotor function. To test this hypothesis, we first identified CSVD patients with and without gait disorder, stringently matched for total lesion burden to thus isolate functional contributors from overall structural damage. Subsequently, we performed mediation analysis to examine whether CSVD neuroimaging markers influence gait impairment indirectly through cognitive and oculomotor deficits. Materials and methods Participants This study enrolled 65 patients with CSVD and 35 age- and sex-matched healthy controls (HCs). All participants were recruited between November 2022 and August 2023 from the Department of Neurology at The First Affiliated Hospital of Dalian Medical University. The study protocol was approved by the hospital's Ethics Committee (Approval No. PJ-KS-2023-04(X), and all participants provided written informed consent. CSVD patients were diagnosed according to the 2022 Chinese guidelines for CSVD, which require the presence of clinical symptoms (e.g., cognitive impairment, gait disorder) along with relevant vascular risk factors and supportive neuroimaging evidence. The neuroimaging evidence includes recent small subcortical infarcts, lacunes, white matter hyperintensities, cerebral microbleeds, or enlarged perivascular spaces. The inclusion criteria for the CSVD group included the following: age between 50 and 80 years; normal or corrected-to-normal visual acuity; a modified Rankin Scale score of 3 or less; and the ability to complete all assessments, including MRI, gait, and eye movement tests. Exclusion criteria encompassed patients in the acute phase of cerebral infarction, stroke from large artery or cardioembolic origins, other neurological diseases such as Parkinson's or Alzheimer's disease, any comorbidity that could significantly affect walking ability, visual or auditory impairments that would interfere with testing, and any contraindication to MRI. Within the CSVD cohort, for the purpose of subgroup analysis, patients were subsequently divided into two groups based on their performance on the Timed Up and Go Test: those with gait disorder (CSVD-GD, defined as TUG ≥ 11.5 s) and those without gait disorder (CSVD-NGD, TUG < 11.5 s). Data collection Demographic and clinical assessment A comprehensive collection of demographic and clinical information was obtained from all participants using a structured questionnaire and a review of medical records. The data included age, gender, body mass index (BMI), years of formal education, and a history of vascular risk factors such as smoking, drinking, hypertension, diabetes, atrial fibrillation, and previous stroke. Neuroimaging acquisition and analysis Following the clinical assessment, neuroimaging data were acquired and analyzed. All participants underwent a standard multi-modal magnetic resonance imaging (MRI) on a 3.0T scanner, including T1-weighted, T2-weighted, FLAIR, SWI, and MRA sequences. The CSVD burden was quantified by two trained neurologists blinded to the clinical and behavioral data. Valid visual rating scales were used to characterize CSVD markers: the Fazekas scale for white matter hyperintensities (WMH), the Age-Related White Matter Changes (ARWMC) score for white matter lesions; the Potter scale for enlarged perivascular spaces (PVS) burden, the Global Cortical Atrophy (GCA) scale for cortical volume loss. Additionally, cerebral microbleeds (CMBs) and lacunar infarcts were counted according to established criteria. A composite total CSVD load score (0–4) was calculated for each patient using these measures. Gait assessment Gait function was assessed using clinical performance tests and quantitative instruments. The Tinetti Balance and Gait Scale was administered to provide a clinical score of balance and gait quality. Functional mobility was measured using the Timed Up and Go test (TUG), which measures the time taken to stand up from a chair, walk 3 meters, turn around, walk back, and sit down. The result was calculated based on the last two TUG trials. For more detailed kinematic analysis, we used the Ruiping V2.0 wearable sensor system. Ten internal measurement unit (IMU) sensors were worn on the chest, waist, wrists, thighs, shanks and feet. Sensors recorded data during the 5-m TUG test, from which spatiotemporal gait parameters were calculated including step length, stride length, gait speed, cadence, stance phase percentage, swing phase percentage, stride length variability, turning duration, and five times sit-to-stand duration. Neuropsychological assessment All participants were given a neuropsychological test battery to assess global cognition and specific cognitive domains. Global cognition was screened using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Executive functions such as processing speed and task switching were assessed using the Trail Making Test (TMT) parts A and B. Visuospatial abilities were assessed using the Clock Drawing Test (CDT) and Judgment of Line Orientation (JLO). Language function was tested using the Boston Naming Test (BNT) and auditory verbal memory was assessed using the Digit Span Test (DST). Mood was assessed using the Hamilton Anxiety Rating Scale (HAMA) and the Hamilton Depression Rating Scale (HAMD). Autonomic functions related to bladder control were screened using the Overactive Bladder Syndrome Score (OABSS) and functional independence was measured using the Activities of Daily Living (ADL) scale. Eye movement assessment We quantitatively assessed eye movement using the EyeKnow intelligent eye movement analysis system (Beijing CAS-Ruiyi Information Technology Co., Ltd). After 5-point calibration, participants completed four standardized eye movement tasks. The overlap saccade task asked participants to move quickly to a target. The anti-saccade task asked them to stop looking toward the target and instead look to the mirror-opposite location. The lateral gaze task asked participants to determine if they could maintain stable fixation on the central point. The smooth pursuit task asked participants to track a target moving sinusoidally along horizontal and vertical axis. Parameters for the overlap saccade task included saccadic accuracy, latency, average reaction time, and average velocity. The anti-saccade task parameters consisted of accuracy, average reaction time, error correction rate, and error correction reaction time for incorrect initial saccades. Fixation stability during the lateral gaze task was assessed by the number of deviations exceeding 2°, total deviation amplitude exceeding 4°, total deviation time, and fixation accuracy. Smooth pursuit quality was evaluated based on the number of deviations exceeding 4° and the total deviation amplitude. Statistical Analysis All statistical analyses were performed using JASP software (version 0.18.3). Continuous data are presented as mean ± standard deviation for normally distributed variables, and median (interquartile range) otherwise. Categorical data are summarized as frequency (percentages). Normal distribution was assessed using the Shapiro-Wilk test, and group comparisons (CSVD vs. HC) were performed using the independent sample t-test or Mann-Whitney U test, as appropriate. Homogeneity of variances was assessed with Levene's test. Comparisons across the three groups (HC, CSVD-NGD, CSVD-GD) were analyzed using one-way ANOVA with Bonferroni post-hoc test, or the Kruskal-Wallis H test with Dunn's post-hoc test and Bonferroni correction. Categorical variables were compared using the Chi-square test or Fisher's exact test. Correlations between variables were evaluated using Pearson’s correlation coefficient for normal data and Spearman’s rank correlation coefficient for non-normal data. To test the mediating effects of cognitive and oculomotor functions, simple mediation analyses were performed using JASP. We used a standard mediation analysis module, based on regression path analysis and bootstrapping. We specified the neuroimaging variable (e.g. total CSVD load) as the independent variable, the cognitive or oculomotor measure (e.g. Trail Making Test-B) as the mediator, and gait performance as the dependent variable. We adjusted for age, education, body mass index, and vascular risk factors. The significance of the indirect effect was tested using bias-corrected bootstrapping with 5000 resamples. Mediation was considered statistically significant if the 95% confidence interval of the indirect effect excluded zero. For significant models the proportion mediated was calculated and reported. Two-tailed p-value < 0.05 defined statistical significance for all tests, unless otherwise adjusted for multiple comparisons. Results Participant characteristics and neuroimaging burden The final analysis included 65 CSVD patients and 35 well-matched healthy controls (HCs). Both groups were comparable in age, gender, BMI and education (all P > 0.05), ensuring that functional comparisons would not be influenced by these basic demographics. As expected, the CSVD group had a significantly higher prevalence of vascular risk factors, such as hypertension and a history of stroke (both P < 0.001, Table 1 ). Neuroimaging assessments showed that the CSVD group had a significantly higher burden of small vessel pathology. Specifically, the CSVD group had significantly higher scores on the Fazekas scale for white matter hyperintensities (periventricular and deep), the Potter scale for enlarged perivascular spaces (PVS), and the cerebral microbleeds anatomical score (all P < 0.001). Thus, the total CSVD load score was significantly higher in the patient group than in the HC group ( P 0.05, Table 1 ). Table 1 Demographic, clinical, and neuroimaging characteristics. Measures CSVD (n = 65) HC (n = 35) P- value Demographics Age, years 67.46 ± 6.43 64.94 ± 5.94 0.058 Female, n (%) 33 (50.8) 23 (65.7) 0.151 BMI, kg/m² 24.72 ± 2.65 24.84 ± 2.52 0.832 Education, years 9.89 ± 3.58 10.12 ± 2.59 0.793 Vascular Risk Factors Hypertension, n (%) 46 (70.8) 11 (31.4) < 0.001 *** Previous stroke, n (%) 26 (40.0) 1 (2.9) < 0.001 *** Diabetes, n (%) 18 (27.7) 9 (25.7) 0.832 Atrial fibrillation, n (%) 2 (3.1) 1 (2.9) 1.000 Neuroimaging Burden Total CSVD load score 3 (3, 4) 2 (1, 3) < 0.001 *** Fazekas-PV WMHs 3 (2, 3) 1 (1, 2) < 0.001 *** Fazekas-DWM WMHs 2 (1, 3) 0 (0, 1) < 0.001 *** PVS-Potter score 3 (2, 4) 0 (0, 1) < 0.001 *** CMB anatomical score 2 (0, 4) 0 (0, 0) < 0.001 *** Global cortical atrophy 1 (0, 1) 0 (0, 1) 0.159 Data are presented as mean ± SD, n (%), or median (IQR). BMI, body mass index; PV, periventricular; DWM, deep white matter; WMHs, white matter hyperintensities; CMB, cerebral microbleed; PVS, perivascular space. *** P < 0.001. CSVD patients exhibit a wide range of gait, cognitive, and oculomotor impairments. The overall assessment showed significant functional impairments in all domains in the CSVD group compared to healthy controls. In Table 2 , CSVD patients showed marked mobility impairments with longer TUG duration, lower Tinetti scores, decreased gait speed, shorter step length, shorter stride length, and increased stride length variability. Furthermore, cognitive performance showed significant decline: the CSVD group performed poorly on global cognitive screening tests (MMSE, MoCA) and in all tested domains, including executive function, visuospatial ability, language, and memory. Oculomotor function was also significantly impaired, as evidenced by prolonged saccadic latency, reduced anti-saccade accuracy, and poorer performance in fixation stability and smooth pursuit tasks. In addition to these impairments, patients also exhibited higher levels of anxiety and depression, more severe autonomic symptoms, and a significant decrease in daily living activities (all P < 0.05). For convenience, Table 2 summarizes the representative measures; however, the CSVD group performed much worse on all other gait parameters, cognitive tests, and oculomotor tasks not presented here. Table 2 Functional impairments in CSVD patients compared to healthy controls. Domain Key measures CSVD (n = 65) HC (n = 35) P -value Gait TUG (s) 11.26 (10.18, 15.52) 8.68 (7.75, 9.79) < 0.001 *** Tinetti score 26 (22, 28) 28 (28, 28) < 0.001 *** Gait speed (m/s) 0.77 ± 0.21 1.03 ± 0.18 < 0.001 *** Step length (cm) 45.07 ± 11.41 57.97 ± 7.82 < 0.001 *** Stride length variability (%) 6.42 (4.57, 9.63) 4.25 (2.92, 6.40) 0.001 ** Cognition MMSE 24 (21, 27) 28 (28, 30) < 0.001 *** MoCA 20 (16, 23) 26 (25, 27) < 0.001 *** Trail making test A (s) 75 (50, 133) 50 (38, 67) < 0.001 *** Trail making test B (s) 191 (152, 287) 125 (100, 155) < 0.001 *** Clock drawing test 9 (6, 10) 10 (10, 10) < 0.001 *** Eye Movement Overlap saccade latency (ms) 370.93 ± 80.69 311.45 ± 49.72 < 0.001 *** Anti-saccade correct rate (%) 18.34 (0.00, 46.59) 59.38 (44.27, 85.71) < 0.001 *** Lateral gaze accuracy (%) 83.53 (73.74, 89.99) 91.69 (89.57, 92.72) < 0.001 *** Other HAMA 10 (7, 16) 6 (4, 9) 0.001 ** HAMD 9 (5, 13) 5 (3, 8) < 0.001 *** ADL 20 (20, 24) 20 (20, 20) < 0.001 *** Data are presented as mean ± SD for normally distributed data or median (IQR) for non-normally distributed data. TUG, Timed Up and Go Test; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; ADL, Activities of Daily Living. ** P < 0.01, *** P < 0.001. Cognitive and oculomotor deficits differentiate CSVD patients with gait disorder We compared CSVD patients with gait disorder (CSVD-GD, n = 30) to those without (CSVD-NGD, n = 35) to identify the main factors contributing to gait disorder independent of overall neuroimaging burden. The two subgroups were well matched for age, gender, vascular risk factors, and the total CSVD load score ( P > 0.05). The CMB anatomical score was the only neuroimaging measure that differed significantly between the groups, with higher values observed in the CSVD-GD group ( P < 0.05). Despite similar overall small vessel disease burden, the CSVD-GD group showed significantly more severe impairments in certain functional domains (Table 3 ): global cognition (MMSE, MoCA), executive function (Trail Making Test - A/B), and visuospatial ability (Clock Drawing Test, Judgment of Line Orientation). There were no significant differences between the subgroups in language (Boston Naming Test), memory (Digit Span Test), or mood scores. Regarding eye movement, widespread oculomotor deficits were observed in the CSVD-GD group. They had poorer fixation stability (increased lateral gaze deviations), slower saccadic initiation (longer overlap saccade latency), impaired inhibitory control (reduced anti-saccade accuracy rate), and decreased smooth pursuit accuracy. In conclusion, the CSVD-GD group performed worse on all other oculomotor parameters not listed in Table 3 . Table 3 Comparison of CSVD subgroups with and without gait disorder. Domain Measure CSVD-GD (n = 30) CSVD-NGD (n = 35) P -value Neuroimaging Total CSVD load score 3.5 (3, 4) 3 (3, 4) 0.056 CMB anatomical score 3 (0, 5.5) 1 (0, 3) 0.038 * Gait TUG (s) 15.55 (13.36, 17.80) 10.21 (9.03, 10.78) < 0.001 *** Gait speed (m/s) 0.64 ± 0.18 0.90 ± 0.15 < 0.001 *** Cognition MMSE 22 (19, 24) 26 (24, 28) < 0.001 *** MoCA 17 (12, 18) 23 (20, 25) < 0.001 *** Trail making test A (s) 102 (67, 191) 63 (48, 79) 0.003 ** Trail making test B (s) 249 (191, 377) 164 (144, 200) < 0.001 *** Clock drawing test 7 (4, 9) 9 (8, 10) 0.004 ** Judgment of line orientation 16 (11, 19) 21 (17, 23) 2°) 29.50 (15.50, 62.75) 21.00 (10.50, 30.00) 0.005 ** Overlap saccade latency (ms) 395.14 ± 77.96 349.58 ± 78.02 0.023 * Anti-saccade correct rate (%) 10.10 (0.00, 27.70) 33.33 (7.42, 60.00) 0.004 ** Horizontal smooth pursuit deviation count (> 4°) 51.00 (41.75, 72.50) 35.00 (20.00, 52.25) 0.002 ** Data are presented as mean ± SD or median (IQR). CSVD-GD, CSVD with gait disorder; CSVD-NGD, CSVD without gait disorder; CMB, Cerebral Microbleed. The two subgroups were matched for age, sex, and vascular risk factors (all P > 0.05). * P < 0.05, ** P < 0.01, *** P < 0.001. Correlation networks link neuroimaging burden to functional deficits We conducted a correlation analysis of neuroimaging markers, cognitive and oculomotor functions and gait performance. Figure 1 summarizes the results, showing that the total CSVD load score was most significantly associated with poor gait performance (longer TUG time, lower Tinetti score), poorer global cognition (lower MMSE and MoCA scores), slower processing speed and executive function (longer Trail Making Test-A/B times), and slower oculomotor speed (long overlap saccade latency). Among the specific MRI markers, the PVS-Potter score had the strongest associations with gait and cognition. The PVS-Potter score was significantly associated with longer TUG time, higher gait variability and poorer executive function (Trail Making Test-B). The gait parameters (except cadence) showed strong correlations with key cognitive domains (executive and visuospatial function) and multiple oculomotor metrics, suggesting a close relationship between motor performance and brain function. Mediation analyses uncover the psychological mechanisms of gait disorder We performed mediation analyses, adjusting for covariates, to test whether cognitive and oculomotor deficits mediate the relationship between neuroimaging markers of CSVD burden and gait disorder. Trail Making Test-B (TMT-B) was found to be a complete mediator in the pathway from total CSVD load to TUG time. The indirect effect was significant (effect = 0.81, 95% CI [0.21 to 1.76]), while the direct effect was not statistically significant (effect = 0.91, P > 0.05, Fig. 2 A). Trail Making Test-A (TMT-A), assessing processing speed and attention, was a major partial mediator between total CSVD load and TUG. It accounted for 32.6% of the total effect (indirect effect = 0.56, 95% CI [0.12 to 1.27], Fig. 2 B). Overlap saccade latency, reflecting oculomotor speed, also served as a major partial mediator between total CSVD load and TUG. It explained 19.9% of the total effect (indirect effect = 0.33, 95% CI [0.02 to 0.92], Fig. 2 C). The PVS–Potter score, measuring PVS severity, and TMT-B were again complete mediators (Fig. 2 D). The lateral gaze deviation count, which measures fixation stability, was also a major partial mediator, accounting for 22.0% of the effect on TUG (indirect effect = 0.24, 95% CI [0.01 to 0.61], Fig. 2 E). Discussion This study was designed to address a persistent clinical paradox in CSVD: patients with similar neuroimaging burden often exhibit heterogeneous gait disorder. We used a multimodal approach that included quantitative gait analysis, neuropsychological assessment, and specific objective eye movement measures. By crucially selecting a cohort with similar total CSVD load, we identified underlying neuropsychological mechanisms. Our results showed that CSVD patients with gait disorder exhibit significantly greater deficits in executive function, visuospatial abilities and oculomotor control compared to those without gait disorder, despite having similar structural burden. Enlarged PVS, measured by the Potter Score, had the strongest association with gait and cognitive impairments among all CSVD neuroimaging markers. Mediation analyses showed that executive dysfunction (measured by TMT-B) fully mediated the relationship between total CSVD load and gait disorder, and processing speed (measured by TMT-A) and oculomotor deficits (saccade latency and fixation stability) served as significant partial mediators. These results not only validate earlier reports of associations, but also show that executive function mediates a central role in gait disorders. This is consistent with the assumption that gait is strongly dependent on cognitive function, as Cai et al. have shown [ 16 ]. Moreover, our observation of significant oculomotor deficits in CSVD supports earlier smaller-scale studies showing that oculomotor measurements are sensitive indicators of neural circuit dysfunction in this disease [ 17 ]. Notably, our results differ from some studies of genetically defined CSVD (e.g. CADASIL) with weaker links between cognition and gait [ 18 ]. This may reflect basic differences between study populations such as age and disease. CADASIL patients are usually younger and less affected by multisystemic age-related declines than sporadic CSVD patients. In contrast, our cohort of older adults with sporadic hypertensive CSVD likely has age-related problems, including sarcopenia, visual or proprioceptive sensory deficits and subclinical balance disorders. Under these circumstances, the automaticity of gait is already compromised; walking requires more attentional resources and relies heavily on efficient cognitive control systems. Specifically, impairment of certain cognitive functions by CSVD increases the likelihood of developing gait disorder. The strong mediation effect of cognition on gait observed in our study may be due to the combination of pathological and age-related effects on the less resilient sensorimotor system in older individuals. Furthermore, there is a strong association between PVS burden and impairments in gait and executive function. This finding is strongly supported by evidence showing that PVS play an important role in CSVD and its related functional impairments [ 19 – 21 ]. Moreover, the association between PVS burden and multidimensional functional decline is stronger than its association with white matter hyperintensity severity, which further supports the growing evidence [ 22 – 24 ]. This evidence suggests that PVS may not only be a passive marker of vascular injury but also a symptom of impaired glymphatic clearance, a dysfunctional waste removal mechanism that may contribute to CSVD symptoms beyond ischemic injury. Based on our mediation results and correlation networks, we propose a dual-path model for gait disorder related to CSVD. Diffuse injury in CSVD damages frontal-subcortical circuits essential for gait control, leading to executive and visuospatial impairments that directly disrupt gait planning and real-time adaptation. These same frontal and subcortical regions are also core nodes of the oculomotor network. In CSVD patients, dysfunction of the neural network that integrates cognition and sensorimotor coordination in walking is the primary pathway leading to gait disorder [ 2 , 25 , 26 ]. At the same time, the strong link between PVS burden and both oculomotor and gait deficits suggests a second pathway mediated by impaired glymphatic clearance. We propose that enlarged PVS may result from glymphatic dysfunction [ 27 , 28 ], which leads to neuroinflammation [ 29 , 30 ] and reduces efficiency of the neural networks supporting sensorimotor integration. Oculomotor control is one of the main functions of these networks [ 31 , 32 ]. If compromised, it directly disrupts real-time visuomotor coordination required for walking, representing a major pathway to gait disorder parallel to the primary cognitive pahway. This moel defines CSVD-related gait disorder not as motor sequela but as behavior-related network failure, which results in a disruption of communication between networks. We explain clinical heterogeneity clearly, since patients may be present along a spectrum in which cognitive integration and sensorimotor integration are the most affected pathways. We show that oculomotor dysfunction is not just a partial mediator but also that inefficiencies in shared sensorimotor and visuomotor circuits directly lead to mobility deficits. The results of this matched-cohort multimodal approach could help improve understanding gait disorder related to CSVD. These results may directly impact clinical outcomes. First, oculomotor assessment (e.g. saccade latency, fixation stability) is a promising objective and noninvasive method for early detection of patients at high risk of gait disorder. Second, the model predicts possible intervention targets. Therapies that improve executive function, processing speed and oculomotor stability (e.g. cognitive training and visual feedback rehabilitation) might reduce gait disorder. Third, PVS burdens in the basal ganglia, may be a useful neuroimaging marker to stratify risk of gait disorder in CSVD patients. Several limitations of this study should be acknowledged. First, the cross-sectional design cannot establish causality, therefore longitudinal studies are needed to verify the proposed pathways and their timing. Second, our sample was recruited from one clinical center and the sample size was relatively small. Future multi-center studies with larger sample sizes will help generalize our results. Third, neuroimaging assessments relied on semi-quantitative visual rating scales; automated fully quantitative imaging would allow more precise lesion localization and network analyses. Addressing these limitations in future work will be crucial to validate the proposed mechanistic model and to translate our results into clinical applications. In summary, this study extend the investigation of CSVD-related gait disorder beyond descriptive associations, to delineate the underlying specific neuropsychological mechanisms. We demonstrate that executive dysfunction is closely associated with CSVD burden, and oculomotor deficits serve as a key contributor to gait disorder in this population. These results support the dual pathways model of gait disorder in CSVD driven by cognitive and sensorimotor-integrative network failures. Oculomotor assessment enables early prediction of gait disorder risk, PVS burden correlates with gait disorder, and cognitive-oculomotor training represents a promising interventional strategy. Collectively, these results identify actionable therapeutic targets for future interventions aimed at preserving mobility in patients with CSVD. Declarations Acknowledgements We thank all the patients and healthy control participants. We also thank the technical team responsible for gait analysis and eye movement tracking. Author Contributions C. Shen and X. Zhao conceptualized the study, designed the procedure, recruited patients, assessed patients, and collected data. S. Wang performed neuropsychological tests, acquired and processed eye movement and gait data, and performed statistical analyses. Y. Miao assisted with neuroimaging acquisition and analysis. Z. Liang offered guidance on gait and eye movements analysis. B. Zhang and L. Liu was responsible for the entire project, provided critical insights throughout the study, and obtained funding. All authors participated in data interpretation, manuscript drafting, and review, and approved the final version for submission. Source of Funding This work was supported by the Xingliao Yingcai Program - Medical Masters (XLYC2401902), the Dalian Medical Key Specialty “Summit Plan” Research Project (2022), and Liaoning Provincial Science and Technology Joint Plan (2024-MSLH-082). Ethical Approval This study was carried out following the principles of the 1975 Declaration of Helsinki and was approved by the Medical Ethical Committee of The First Affiliated Hospital of Dalian Medical University (Approval No. PJ-KS-2023-04(X)). Informed Consent Written informed consent was obtained from all subjects. Conflicts of Interest The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References Wardlaw JM, Smith CDichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 2019;18:684–96. 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Gait Disorders and Magnetic Resonance Imaging Characteristics in Older Adults with Cerebral Small Vessel Disease. J Integr Neurosci. 2022;21:129. Jayakody O, Breslin M, Beare R, Siejka TP, Gujjari S, Srikanth VK, et al. The association between simple reaction time variability and gait variability: The Tasmanian Study of Cognition and Gait. Gait Posture. 2021;89:206–10. Alizadeh AVan Opstal AJ. Dynamic control of eye-head gaze shifts by a spiking neural network model of the superior colliculus. Front Comput Neurosci. 2022;16:1040646. Wu L, Wang Q, Zhao L, Jiang CY, Xu Q, Wu SC, et al. Clinical and Oculomotor Correlates With Freezing of Gait in a Chinese Cohort of Parkinson's Disease Patients. Front Aging Neurosci. 2020;12:237. Cai M, Jacob MA, Norris DG, Duering M, de Leeuw FETuladhar AM. Cognition mediates the relation between structural network efficiency and gait in small vessel disease. Neuroimage Clin. 2021;30:102667. Pinkhardt EH, Issa H, Gorges M, Jurgens R, Lule D, Heimrath J, et al. Do eye movement impairments in patients with small vessel cerebrovascular disease depend on lesion load or on cognitive deficits? A video-oculographic and MRI study. J Neurol. 2014;261:791–803. Finsterwalder S, Wuehr M, Gesierich B, Dietze A, Konieczny MJ, Schmidt R, et al. Minor gait impairment despite white matter damage in pure small vessel disease. Ann Clin Transl Neurol. 2019;6:2026–36. Lin F, Yang B, Chen Y, Zhao W, Li BJia W. Enlarged perivascular spaces are linked to freezing of gait in Parkinson's disease. Front Neurol. 2022;13:985294. Elias-Mas A, Wang JY, Rodriguez-Revenga L, Kim K, Tassone F, Hessl D, et al. Enlarged perivascular spaces and their association with motor, cognition, MRI markers and cerebrovascular risk factors in male fragile X premutation carriers. J Neurol Sci. 2024;461:123056. Yang S, Jiang J, Wang L, Zhao M, Li W, Duan Y, et al. Impact of enlarged perivascular spaces in the basal ganglia on gait in cerebral small vessel disease. Aging Clin Exp Res. 2025;37:138. Yu L, Hu X, Li HZhao Y. Perivascular Spaces, Glymphatic System and MR. Front Neurol. 2022;13:844938. Gouveia-Freitas KBastos-Leite AJ. Perivascular spaces and brain waste clearance systems: relevance for neurodegenerative and cerebrovascular pathology. Neuroradiology. 2021;63:1581–97. Ai L, Li Z, Huang H, Huang C, Chen S, Zhou X, et al. Glymphatic system impairment in cerebral small vessel disease: associations with perivascular space volume and cognition. Front Aging Neurosci. 2025;17:1680094. Wilson J, Allcock L, Mc Ardle R, Taylor JPRochester L. The neural correlates of discrete gait characteristics in ageing: A structured review. Neurosci Biobehav Rev. 2019;100:344–69. Kim SL, Lee MJLee MS. Cognitive dysfunction associated with falls in progressive supranuclear palsy. Gait Posture. 2014;40:605–9. Wardlaw JM, Benveniste H, Nedergaard M, Zlokovic BV, Mestre H, Lee H, et al. Perivascular spaces in the brain: anatomy, physiology and pathology. Nat Rev Neurol. 2020;16:137–53. Chong S, Wang S, Gao T, Yuan K, Han Y, Shi L, et al. Glymphatic function decline as a mediator of core memory-related brain structures atrophy in aging. J Transl Int Med. 2025;13:65–77. Ineichen BV, Okar SV, Proulx ST, Engelhardt B. Lassmann HReich DS. Perivascular spaces and their role in neuroinflammation. Neuron. 2022;110:3566–81. Fu X, Cai H, Quan S, Ren Z, Xu YJia L. Immune cells in Alzheimer's disease: insights into pathogenesis and potential therapeutic targets. Med Rev (2021).2025;5:179–202. Vassar RLRose J. Motor systems and postural instability. Handb Clin Neurol. 2014;125:237–51. Deravet N, Blohm G, de Xivry JOLefevre P. Weighted integration of short-term memory and sensory signals in the oculomotor system. J Vis. 2018;18:16. Additional Declarations No competing interests reported. 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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-8910331","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601530779,"identity":"0fcf385e-a0e5-4891-acd6-5eed4bedefbc","order_by":0,"name":"Chenxin Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenxin","middleName":"","lastName":"Shen","suffix":""},{"id":601530780,"identity":"4723a3ca-c7aa-4396-9b55-be80709578a0","order_by":1,"name":"Xiaoying Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Zhao","suffix":""},{"id":601530781,"identity":"72f17ead-512f-4989-8440-0b667ca193dd","order_by":2,"name":"Shiyao Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shiyao","middleName":"","lastName":"Wang","suffix":""},{"id":601530782,"identity":"533f90e3-230e-4f23-b5eb-b80a0bca1cdf","order_by":3,"name":"Yanwei Miao","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanwei","middleName":"","lastName":"Miao","suffix":""},{"id":601530783,"identity":"43bfeb33-92cd-4fa5-a413-3090a660ccab","order_by":4,"name":"Zhanhua Liang","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhanhua","middleName":"","lastName":"Liang","suffix":""},{"id":601530784,"identity":"6122da9b-6704-4e70-91ee-bcd3baae7d92","order_by":5,"name":"Lin Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""},{"id":601530785,"identity":"5b8b23aa-cf1f-4e5c-bd88-14bda7e36dde","order_by":6,"name":"Bingwei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACCSBmbJAAsw/wMDDIsbG3HyBNizEfz5kEYrRAOUAtifMkHAzw6pCf3fzs4dcdFnnyEckHD7ypsEtvk2BIYPhRsQ2nFsY5x8yNZc9IFBveSEs4OOdMcm6bdOMBxp4zt3FqYZZIMJOWbJNI3Dg7x+Awb9uB3DaZAwnMjG24tbBJpH+Dasn/ANKSziaRYIBXC49EjpnkR6CW+dI5DCAtCQS1SEjklEkznpFI3CD/zADkF8M2YCAfxOcX+Rnp2yR/7qhLnN9z+PEHYIjJy7e3H3zwowK3FnAQAKODweAAksgB7AoRgPEHyLoGQspGwSgYBaNgxAIAunxbI57QHI8AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Bingwei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-18 14:38:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8910331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8910331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104302619,"identity":"716001fc-8a19-4bb1-a4a6-cf7baecf921f","added_by":"auto","created_at":"2026-03-10 09:20:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":342515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of correlation analysis.\u003c/strong\u003e (A) Correlation analysis between gait parameters and neuroimaging markers, cognitive scores and oculomotor metrics. (B) Correlation analysis between neuroimaging markers and cognitive and oculomotor metrics. The heatmap shows Pearson correlation coefficients, with significance levels as follows: *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (two-tailed test of zero correlation).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8910331/v1/9d68ce8e622e2352b4e7529b.png"},{"id":104302618,"identity":"046b702a-580f-4c03-afb2-492352a5d4ed","added_by":"auto","created_at":"2026-03-10 09:20:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":263628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation analysis of cognitive and oculomotor pathways linking CSVD burden to gait disorder.\u003c/strong\u003e (A) and (B) Cognitive functions (TMT-B and TMT-A) mediate the relationship between total CSVD load and TUG time. (C) Oculomotor latency (overlap saccade latency) mediates the relationship between total CSVD load and TUG time. (D) and (E) Oculomotor control (lateral gaze deviation count) mediates the relationships between PVS burden and TUG time. Path coefficients (β) with 95% confidence intervals are shown. *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. CSVD, cerebral small vessel disease; TUG, Timed Up and Go; TMT, Trail Making Test; PVS, perivascular spaces.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8910331/v1/7385443f64cfbadeb096c8e0.png"},{"id":106402504,"identity":"3a768855-8ddb-4ce1-8a9f-6054b4e9de8e","added_by":"auto","created_at":"2026-04-08 09:12:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1833007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8910331/v1/092dba33-86b4-4555-bcf6-dc79c60b6c2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive and oculomotor impairments mediate gait disorder in cerebral small vessel disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral small vessel disease (CSVD) is a common cerebrovascular disorder affecting the elderly population. It is the leading cause of vascular cognitive impairment and a major contributor of gait disorder in older adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Gait disorder in CSVD increases the risk of falls, functional dependence and mortality rates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus they significantly reduce patients\u0026rsquo; quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite these risks, a notable clinical observation is that the gait performance is very different among CSVD patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Some patients with a high burden of white matter hyperintensities or lacunes are ambulatory while others with similar radiological findings experience significant mobility decline [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This is due to limitations of the traditional model linking neuroimaging lesion load directly with motor deficits, suggesting more complex intermediary physiological mechanisms are involved.\u003c/p\u003e \u003cp\u003eHuman gait is a complex, goal-directed task rather than a simple automated motor task [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It requires seamless integration of motor, sensory and cognitive systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Higher order cognitive functions such as executive functions, attention, and visuospatial skills are now recognized as fundamental for gait control and stability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While previous studies showed associations between individual CSVD markers (e.g., white matter hyperintensities) and gait parameters, results in relation to these parameters have not been consistent [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, even the total CSVD load score does not fully explain the variance in gait performance. This suggests that the specific pathways through which diffuse brain injury leads to motor decline are not fully understood. Previous studies often failed to match patients by overall disease severity when comparing those with and without gait disorder. This may confuse interpretation of motor impairment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEye movement analysis has recently emerged as a powerful, non-invasive window into brain function. Oculomotor control shares many neural substrates with networks critical for cognition and gait, like prefrontal cortex, basal ganglia, brainstem and cerebellum [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Abnormalities in saccades, smooth pursuit and fixation are well known in neurodegenerative diseases like Parkinson's disease, in which they are associated with cognitive decline and freezing of gait [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We propose eye movement can act as sensitive and objective markers for the integrity of neural circuits affected by CSVD.\u003c/p\u003e \u003cp\u003eAccordingly, we sought to investigate the neuropsychological mechanisms mediating gait disorder in patients with CSVD. A multi-modal analytical strategy was adopted, consisting of wearable sensor-based quantitative gait analysis, standardized neuropsychological evaluation, and eye movement tracking. Our main hypothesis is that the impact of CSVD neuroimaging burden on gait is indirect, partly and significantly mediated by impairments in specific cognitive domains and oculomotor function. To test this hypothesis, we first identified CSVD patients with and without gait disorder, stringently matched for total lesion burden to thus isolate functional contributors from overall structural damage. Subsequently, we performed mediation analysis to examine whether CSVD neuroimaging markers influence gait impairment indirectly through cognitive and oculomotor deficits.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study enrolled 65 patients with CSVD and 35 age- and sex-matched healthy controls (HCs). All participants were recruited between November 2022 and August 2023 from the Department of Neurology at The First Affiliated Hospital of Dalian Medical University. The study protocol was approved by the hospital's Ethics Committee (Approval No. PJ-KS-2023-04(X), and all participants provided written informed consent. CSVD patients were diagnosed according to the 2022 Chinese guidelines for CSVD, which require the presence of clinical symptoms (e.g., cognitive impairment, gait disorder) along with relevant vascular risk factors and supportive neuroimaging evidence. The neuroimaging evidence includes recent small subcortical infarcts, lacunes, white matter hyperintensities, cerebral microbleeds, or enlarged perivascular spaces.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the CSVD group included the following: age between 50 and 80 years; normal or corrected-to-normal visual acuity; a modified Rankin Scale score of 3 or less; and the ability to complete all assessments, including MRI, gait, and eye movement tests. Exclusion criteria encompassed patients in the acute phase of cerebral infarction, stroke from large artery or cardioembolic origins, other neurological diseases such as Parkinson's or Alzheimer's disease, any comorbidity that could significantly affect walking ability, visual or auditory impairments that would interfere with testing, and any contraindication to MRI.\u003c/p\u003e \u003cp\u003eWithin the CSVD cohort, for the purpose of subgroup analysis, patients were subsequently divided into two groups based on their performance on the Timed Up and Go Test: those with gait disorder (CSVD-GD, defined as TUG\u0026thinsp;\u0026ge;\u0026thinsp;11.5 s) and those without gait disorder (CSVD-NGD, TUG\u0026thinsp;\u0026lt;\u0026thinsp;11.5 s).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical assessment\u003c/p\u003e \u003cp\u003eA comprehensive collection of demographic and clinical information was obtained from all participants using a structured questionnaire and a review of medical records. The data included age, gender, body mass index (BMI), years of formal education, and a history of vascular risk factors such as smoking, drinking, hypertension, diabetes, atrial fibrillation, and previous stroke.\u003c/p\u003e \u003cp\u003eNeuroimaging acquisition and analysis\u003c/p\u003e \u003cp\u003eFollowing the clinical assessment, neuroimaging data were acquired and analyzed. All participants underwent a standard multi-modal magnetic resonance imaging (MRI) on a 3.0T scanner, including T1-weighted, T2-weighted, FLAIR, SWI, and MRA sequences. The CSVD burden was quantified by two trained neurologists blinded to the clinical and behavioral data. Valid visual rating scales were used to characterize CSVD markers: the Fazekas scale for white matter hyperintensities (WMH), the Age-Related White Matter Changes (ARWMC) score for white matter lesions; the Potter scale for enlarged perivascular spaces (PVS) burden, the Global Cortical Atrophy (GCA) scale for cortical volume loss. Additionally, cerebral microbleeds (CMBs) and lacunar infarcts were counted according to established criteria. A composite total CSVD load score (0\u0026ndash;4) was calculated for each patient using these measures.\u003c/p\u003e \u003cp\u003eGait assessment\u003c/p\u003e \u003cp\u003eGait function was assessed using clinical performance tests and quantitative instruments. The Tinetti Balance and Gait Scale was administered to provide a clinical score of balance and gait quality. Functional mobility was measured using the Timed Up and Go test (TUG), which measures the time taken to stand up from a chair, walk 3 meters, turn around, walk back, and sit down. The result was calculated based on the last two TUG trials. For more detailed kinematic analysis, we used the Ruiping V2.0 wearable sensor system. Ten internal measurement unit (IMU) sensors were worn on the chest, waist, wrists, thighs, shanks and feet. Sensors recorded data during the 5-m TUG test, from which spatiotemporal gait parameters were calculated including step length, stride length, gait speed, cadence, stance phase percentage, swing phase percentage, stride length variability, turning duration, and five times sit-to-stand duration.\u003c/p\u003e \u003cp\u003eNeuropsychological assessment\u003c/p\u003e \u003cp\u003eAll participants were given a neuropsychological test battery to assess global cognition and specific cognitive domains. Global cognition was screened using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Executive functions such as processing speed and task switching were assessed using the Trail Making Test (TMT) parts A and B. Visuospatial abilities were assessed using the Clock Drawing Test (CDT) and Judgment of Line Orientation (JLO). Language function was tested using the Boston Naming Test (BNT) and auditory verbal memory was assessed using the Digit Span Test (DST). Mood was assessed using the Hamilton Anxiety Rating Scale (HAMA) and the Hamilton Depression Rating Scale (HAMD). Autonomic functions related to bladder control were screened using the Overactive Bladder Syndrome Score (OABSS) and functional independence was measured using the Activities of Daily Living (ADL) scale.\u003c/p\u003e \u003cp\u003eEye movement assessment\u003c/p\u003e \u003cp\u003eWe quantitatively assessed eye movement using the EyeKnow intelligent eye movement analysis system (Beijing CAS-Ruiyi Information Technology Co., Ltd). After 5-point calibration, participants completed four standardized eye movement tasks. The overlap saccade task asked participants to move quickly to a target. The anti-saccade task asked them to stop looking toward the target and instead look to the mirror-opposite location. The lateral gaze task asked participants to determine if they could maintain stable fixation on the central point. The smooth pursuit task asked participants to track a target moving sinusoidally along horizontal and vertical axis. Parameters for the overlap saccade task included saccadic accuracy, latency, average reaction time, and average velocity. The anti-saccade task parameters consisted of accuracy, average reaction time, error correction rate, and error correction reaction time for incorrect initial saccades. Fixation stability during the lateral gaze task was assessed by the number of deviations exceeding 2\u0026deg;, total deviation amplitude exceeding 4\u0026deg;, total deviation time, and fixation accuracy. Smooth pursuit quality was evaluated based on the number of deviations exceeding 4\u0026deg; and the total deviation amplitude.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using JASP software (version 0.18.3). Continuous data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed variables, and median (interquartile range) otherwise. Categorical data are summarized as frequency (percentages). Normal distribution was assessed using the Shapiro-Wilk test, and group comparisons (CSVD vs. HC) were performed using the independent sample t-test or Mann-Whitney U test, as appropriate. Homogeneity of variances was assessed with Levene's test. Comparisons across the three groups (HC, CSVD-NGD, CSVD-GD) were analyzed using one-way ANOVA with Bonferroni post-hoc test, or the Kruskal-Wallis H test with Dunn's post-hoc test and Bonferroni correction. Categorical variables were compared using the Chi-square test or Fisher's exact test. Correlations between variables were evaluated using Pearson\u0026rsquo;s correlation coefficient for normal data and Spearman\u0026rsquo;s rank correlation coefficient for non-normal data.\u003c/p\u003e \u003cp\u003eTo test the mediating effects of cognitive and oculomotor functions, simple mediation analyses were performed using JASP. We used a standard mediation analysis module, based on regression path analysis and bootstrapping. We specified the neuroimaging variable (e.g. total CSVD load) as the independent variable, the cognitive or oculomotor measure (e.g. Trail Making Test-B) as the mediator, and gait performance as the dependent variable. We adjusted for age, education, body mass index, and vascular risk factors. The significance of the indirect effect was tested using bias-corrected bootstrapping with 5000 resamples. Mediation was considered statistically significant if the 95% confidence interval of the indirect effect excluded zero. For significant models the proportion mediated was calculated and reported.\u003c/p\u003e \u003cp\u003eTwo-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defined statistical significance for all tests, unless otherwise adjusted for multiple comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics and neuroimaging burden\u003c/h2\u003e \u003cp\u003eThe final analysis included 65 CSVD patients and 35 well-matched healthy controls (HCs). Both groups were comparable in age, gender, BMI and education (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), ensuring that functional comparisons would not be influenced by these basic demographics. As expected, the CSVD group had a significantly higher prevalence of vascular risk factors, such as hypertension and a history of stroke (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeuroimaging assessments showed that the CSVD group had a significantly higher burden of small vessel pathology. Specifically, the CSVD group had significantly higher scores on the Fazekas scale for white matter hyperintensities (periventricular and deep), the Potter scale for enlarged perivascular spaces (PVS), and the cerebral microbleeds anatomical score (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, the total CSVD load score was significantly higher in the patient group than in the HC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was found in the global cortical atrophy scale (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, clinical, and neuroimaging characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSVD (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.46\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascular Risk Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeuroimaging Burden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal CSVD load score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFazekas-PV WMHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFazekas-DWM WMHs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVS-Potter score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMB anatomical score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal cortical atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n (%), or median (IQR). BMI, body mass index; PV, periventricular; DWM, deep white matter; WMHs, white matter hyperintensities; CMB, cerebral microbleed; PVS, perivascular space. \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCSVD patients exhibit a wide range of gait, cognitive, and oculomotor impairments.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe overall assessment showed significant functional impairments in all domains in the CSVD group compared to healthy controls. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, CSVD patients showed marked mobility impairments with longer TUG duration, lower Tinetti scores, decreased gait speed, shorter step length, shorter stride length, and increased stride length variability. Furthermore, cognitive performance showed significant decline: the CSVD group performed poorly on global cognitive screening tests (MMSE, MoCA) and in all tested domains, including executive function, visuospatial ability, language, and memory. Oculomotor function was also significantly impaired, as evidenced by prolonged saccadic latency, reduced anti-saccade accuracy, and poorer performance in fixation stability and smooth pursuit tasks. In addition to these impairments, patients also exhibited higher levels of anxiety and depression, more severe autonomic symptoms, and a significant decrease in daily living activities (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For convenience, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the representative measures; however, the CSVD group performed much worse on all other gait parameters, cognitive tests, and oculomotor tasks not presented here.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional impairments in CSVD patients compared to healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSVD (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eGait\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTUG (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.26 (10.18, 15.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.68 (7.75, 9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTinetti score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (22, 28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (28, 28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGait speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStep length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.97\u0026thinsp;\u0026plusmn;\u0026thinsp;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStride length variability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.42 (4.57, 9.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25 (2.92, 6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eCognition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (21, 27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (28, 30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (16, 23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (25, 27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail making test A (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (50, 133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (38, 67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail making test B (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (152, 287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (100, 155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClock drawing test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eEye Movement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap saccade latency (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370.93\u0026thinsp;\u0026plusmn;\u0026thinsp;80.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e311.45\u0026thinsp;\u0026plusmn;\u0026thinsp;49.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnti-saccade correct rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.34 (0.00, 46.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.38 (44.27, 85.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLateral gaze accuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.53 (73.74, 89.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.69 (89.57, 92.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7, 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (4, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5, 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (20, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20, 20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normally distributed data or median (IQR) for non-normally distributed data. TUG, Timed Up and Go Test; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; ADL, Activities of Daily Living. \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCognitive and oculomotor deficits differentiate CSVD patients with gait disorder\u003c/h2\u003e \u003cp\u003eWe compared CSVD patients with gait disorder (CSVD-GD, n\u0026thinsp;=\u0026thinsp;30) to those without (CSVD-NGD, n\u0026thinsp;=\u0026thinsp;35) to identify the main factors contributing to gait disorder independent of overall neuroimaging burden. The two subgroups were well matched for age, gender, vascular risk factors, and the total CSVD load score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The CMB anatomical score was the only neuroimaging measure that differed significantly between the groups, with higher values observed in the CSVD-GD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eDespite similar overall small vessel disease burden, the CSVD-GD group showed significantly more severe impairments in certain functional domains (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): global cognition (MMSE, MoCA), executive function (Trail Making Test - A/B), and visuospatial ability (Clock Drawing Test, Judgment of Line Orientation). There were no significant differences between the subgroups in language (Boston Naming Test), memory (Digit Span Test), or mood scores. Regarding eye movement, widespread oculomotor deficits were observed in the CSVD-GD group. They had poorer fixation stability (increased lateral gaze deviations), slower saccadic initiation (longer overlap saccade latency), impaired inhibitory control (reduced anti-saccade accuracy rate), and decreased smooth pursuit accuracy. In conclusion, the CSVD-GD group performed worse on all other oculomotor parameters not listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CSVD subgroups with and without gait disorder.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSVD-GD (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCSVD-NGD (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNeuroimaging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal CSVD load score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (3, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMB anatomical score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0, 5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGait\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTUG (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.55 (13.36, 17.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.21 (9.03, 10.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGait speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eCognition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (19, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (24, 28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (12, 18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (20, 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail making test A (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (67, 191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (48, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail making test B (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (191, 377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (144, 200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClock drawing test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (8, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJudgment of line orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (17, 23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoston naming test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (21, 27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (23, 27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigit span test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (12, 17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (13, 18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEye Movement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLateral gaze deviation count (\u0026gt;\u0026thinsp;2\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.50 (15.50, 62.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.00 (10.50, 30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap saccade latency (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e395.14\u0026thinsp;\u0026plusmn;\u0026thinsp;77.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349.58\u0026thinsp;\u0026plusmn;\u0026thinsp;78.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnti-saccade correct rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.10 (0.00, 27.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.33 (7.42, 60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHorizontal smooth pursuit deviation count (\u0026gt;\u0026thinsp;4\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.00 (41.75, 72.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.00 (20.00, 52.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR). CSVD-GD, CSVD with gait disorder; CSVD-NGD, CSVD without gait disorder; CMB, Cerebral Microbleed. The two subgroups were matched for age, sex, and vascular risk factors (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation networks link neuroimaging burden to functional deficits\u003c/h3\u003e\n\u003cp\u003eWe conducted a correlation analysis of neuroimaging markers, cognitive and oculomotor functions and gait performance. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the results, showing that the total CSVD load score was most significantly associated with poor gait performance (longer TUG time, lower Tinetti score), poorer global cognition (lower MMSE and MoCA scores), slower processing speed and executive function (longer Trail Making Test-A/B times), and slower oculomotor speed (long overlap saccade latency). Among the specific MRI markers, the PVS-Potter score had the strongest associations with gait and cognition. The PVS-Potter score was significantly associated with longer TUG time, higher gait variability and poorer executive function (Trail Making Test-B). The gait parameters (except cadence) showed strong correlations with key cognitive domains (executive and visuospatial function) and multiple oculomotor metrics, suggesting a close relationship between motor performance and brain function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMediation analyses uncover the psychological mechanisms of gait disorder\u003c/h3\u003e\n\u003cp\u003eWe performed mediation analyses, adjusting for covariates, to test whether cognitive and oculomotor deficits mediate the relationship between neuroimaging markers of CSVD burden and gait disorder.\u003c/p\u003e \u003cp\u003eTrail Making Test-B (TMT-B) was found to be a complete mediator in the pathway from total CSVD load to TUG time. The indirect effect was significant (effect\u0026thinsp;=\u0026thinsp;0.81, 95% CI [0.21 to 1.76]), while the direct effect was not statistically significant (effect\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTrail Making Test-A (TMT-A), assessing processing speed and attention, was a major partial mediator between total CSVD load and TUG. It accounted for 32.6% of the total effect (indirect effect\u0026thinsp;=\u0026thinsp;0.56, 95% CI [0.12 to 1.27], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Overlap saccade latency, reflecting oculomotor speed, also served as a major partial mediator between total CSVD load and TUG. It explained 19.9% of the total effect (indirect effect\u0026thinsp;=\u0026thinsp;0.33, 95% CI [0.02 to 0.92], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe PVS\u0026ndash;Potter score, measuring PVS severity, and TMT-B were again complete mediators (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The lateral gaze deviation count, which measures fixation stability, was also a major partial mediator, accounting for 22.0% of the effect on TUG (indirect effect\u0026thinsp;=\u0026thinsp;0.24, 95% CI [0.01 to 0.61], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was designed to address a persistent clinical paradox in CSVD: patients with similar neuroimaging burden often exhibit heterogeneous gait disorder. We used a multimodal approach that included quantitative gait analysis, neuropsychological assessment, and specific objective eye movement measures. By crucially selecting a cohort with similar total CSVD load, we identified underlying neuropsychological mechanisms. Our results showed that CSVD patients with gait disorder exhibit significantly greater deficits in executive function, visuospatial abilities and oculomotor control compared to those without gait disorder, despite having similar structural burden. Enlarged PVS, measured by the Potter Score, had the strongest association with gait and cognitive impairments among all CSVD neuroimaging markers. Mediation analyses showed that executive dysfunction (measured by TMT-B) fully mediated the relationship between total CSVD load and gait disorder, and processing speed (measured by TMT-A) and oculomotor deficits (saccade latency and fixation stability) served as significant partial mediators.\u003c/p\u003e \u003cp\u003eThese results not only validate earlier reports of associations, but also show that executive function mediates a central role in gait disorders. This is consistent with the assumption that gait is strongly dependent on cognitive function, as Cai et al. have shown [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, our observation of significant oculomotor deficits in CSVD supports earlier smaller-scale studies showing that oculomotor measurements are sensitive indicators of neural circuit dysfunction in this disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, our results differ from some studies of genetically defined CSVD (e.g. CADASIL) with weaker links between cognition and gait [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This may reflect basic differences between study populations such as age and disease. CADASIL patients are usually younger and less affected by multisystemic age-related declines than sporadic CSVD patients. In contrast, our cohort of older adults with sporadic hypertensive CSVD likely has age-related problems, including sarcopenia, visual or proprioceptive sensory deficits and subclinical balance disorders. Under these circumstances, the automaticity of gait is already compromised; walking requires more attentional resources and relies heavily on efficient cognitive control systems. Specifically, impairment of certain cognitive functions by CSVD increases the likelihood of developing gait disorder. The strong mediation effect of cognition on gait observed in our study may be due to the combination of pathological and age-related effects on the less resilient sensorimotor system in older individuals. Furthermore, there is a strong association between PVS burden and impairments in gait and executive function. This finding is strongly supported by evidence showing that PVS play an important role in CSVD and its related functional impairments [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, the association between PVS burden and multidimensional functional decline is stronger than its association with white matter hyperintensity severity, which further supports the growing evidence [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This evidence suggests that PVS may not only be a passive marker of vascular injury but also a symptom of impaired glymphatic clearance, a dysfunctional waste removal mechanism that may contribute to CSVD symptoms beyond ischemic injury.\u003c/p\u003e \u003cp\u003eBased on our mediation results and correlation networks, we propose a dual-path model for gait disorder related to CSVD. Diffuse injury in CSVD damages frontal-subcortical circuits essential for gait control, leading to executive and visuospatial impairments that directly disrupt gait planning and real-time adaptation. These same frontal and subcortical regions are also core nodes of the oculomotor network. In CSVD patients, dysfunction of the neural network that integrates cognition and sensorimotor coordination in walking is the primary pathway leading to gait disorder [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. At the same time, the strong link between PVS burden and both oculomotor and gait deficits suggests a second pathway mediated by impaired glymphatic clearance. We propose that enlarged PVS may result from glymphatic dysfunction [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which leads to neuroinflammation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and reduces efficiency of the neural networks supporting sensorimotor integration. Oculomotor control is one of the main functions of these networks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. If compromised, it directly disrupts real-time visuomotor coordination required for walking, representing a major pathway to gait disorder parallel to the primary cognitive pahway. This moel defines CSVD-related gait disorder not as motor sequela but as behavior-related network failure, which results in a disruption of communication between networks. We explain clinical heterogeneity clearly, since patients may be present along a spectrum in which cognitive integration and sensorimotor integration are the most affected pathways. We show that oculomotor dysfunction is not just a partial mediator but also that inefficiencies in shared sensorimotor and visuomotor circuits directly lead to mobility deficits.\u003c/p\u003e \u003cp\u003eThe results of this matched-cohort multimodal approach could help improve understanding gait disorder related to CSVD. These results may directly impact clinical outcomes. First, oculomotor assessment (e.g. saccade latency, fixation stability) is a promising objective and noninvasive method for early detection of patients at high risk of gait disorder. Second, the model predicts possible intervention targets. Therapies that improve executive function, processing speed and oculomotor stability (e.g. cognitive training and visual feedback rehabilitation) might reduce gait disorder. Third, PVS burdens in the basal ganglia, may be a useful neuroimaging marker to stratify risk of gait disorder in CSVD patients.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, the cross-sectional design cannot establish causality, therefore longitudinal studies are needed to verify the proposed pathways and their timing. Second, our sample was recruited from one clinical center and the sample size was relatively small. Future multi-center studies with larger sample sizes will help generalize our results. Third, neuroimaging assessments relied on semi-quantitative visual rating scales; automated fully quantitative imaging would allow more precise lesion localization and network analyses. Addressing these limitations in future work will be crucial to validate the proposed mechanistic model and to translate our results into clinical applications.\u003c/p\u003e \u003cp\u003eIn summary, this study extend the investigation of CSVD-related gait disorder beyond descriptive associations, to delineate the underlying specific neuropsychological mechanisms. We demonstrate that executive dysfunction is closely associated with CSVD burden, and oculomotor deficits serve as a key contributor to gait disorder in this population. These results support the dual pathways model of gait disorder in CSVD driven by cognitive and sensorimotor-integrative network failures. Oculomotor assessment enables early prediction of gait disorder risk, PVS burden correlates with gait disorder, and cognitive-oculomotor training represents a promising interventional strategy. Collectively, these results identify actionable therapeutic targets for future interventions aimed at preserving mobility in patients with CSVD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the patients and healthy control participants. We also thank the technical team responsible for gait analysis and eye movement tracking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC. Shen and X. Zhao conceptualized the study, designed the procedure, recruited patients, assessed patients, and collected data. S. Wang performed neuropsychological tests, acquired and processed eye movement and gait data, and performed statistical analyses. Y. Miao assisted with neuroimaging acquisition and analysis. Z. Liang offered guidance on gait and eye movements analysis. B. Zhang and L. Liu was responsible for the entire project, provided critical insights throughout the study, and obtained funding. All authors participated in data interpretation, manuscript drafting, and review, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Xingliao Yingcai Program - Medical Masters (XLYC2401902), the Dalian Medical Key Specialty \u0026ldquo;Summit Plan\u0026rdquo; Research Project (2022), and Liaoning Provincial Science and Technology Joint Plan (2024-MSLH-082).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out following the principles of the 1975 Declaration of Helsinki and was approved by the Medical Ethical Committee of The First Affiliated Hospital of Dalian Medical University (Approval No. PJ-KS-2023-04(X)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWardlaw JM, Smith CDichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 2019;18:684\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Zhang C, Li L, Zhang Y, Zhang W, Yin W, et al. Altered Brain Function in Cerebral Small Vessel Disease Patients With Gait Disorders: A Resting-State Functional MRI Study. Front Aging Neurosci. 2020;12:234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBower K, Thilarajah S, Pua YH, Williams G, Tan D, Mentiplay B, et al. Dynamic balance and instrumented gait variables are independent predictors of falls following stroke. 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Perivascular spaces and their role in neuroinflammation. Neuron. 2022;110:3566\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu X, Cai H, Quan S, Ren Z, Xu YJia L. Immune cells in Alzheimer's disease: insights into pathogenesis and potential therapeutic targets. Med Rev (2021).2025;5:179\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVassar RLRose J. Motor systems and postural instability. Handb Clin Neurol. 2014;125:237\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeravet N, Blohm G, de Xivry JOLefevre P. Weighted integration of short-term memory and sensory signals in the oculomotor system. J Vis. 2018;18:16.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cerebral small vessel disease, gait disorder, executive function, eye movements, mediation analysis, perivascular spaces","lastPublishedDoi":"10.21203/rs.3.rs-8910331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8910331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCerebral small vessel disease (CSVD) is associated with a range of gait disorders that are not entirely accounted for by conventional neuroimaging burden. This study aimed to elucidate the neuroimaging markers related to CSVD, alongside cognitive and oculomotor impairments, to explore potential mediating pathways affecting gait performance. A total of 65 CSVD patients and 35 age-matched healthy controls underwent multi-modal assessments, including quantitative gait analysis, comprehensive neuropsychological tests, and objective eye-tracking measurements. Participants were stratified based on gait status while ensuring a comparable total CSVD load. Correlation analyses revealed significant relationships between neuroimaging markers and measures of gait, cognition, and oculomotor function, while mediation analyses indicated that executive dysfunction, as measured by the Trail Making Test\u0026ndash;B, fully mediated the effect of total CSVD load on gait performance. Moreover, processing speed (Trail Making Test\u0026ndash;A) and specific oculomotor measures, such as saccade latency and fixation stability, played critical mediating roles. These findings suggest that executive dysfunction and oculomotor deficits are pivotal pathways linking CSVD to gait disorder. Consequently, this study supports a dual-pathway model of network inefficiency and underscores the need for early risk stratification and targeted interventions to improve mobility in CSVD.\u003c/p\u003e","manuscriptTitle":"Cognitive and oculomotor impairments mediate gait disorder in cerebral small vessel disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 09:20:44","doi":"10.21203/rs.3.rs-8910331/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca5819ce-9c08-456c-8d9f-ddbaafc8fd45","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T05:54:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 09:20:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8910331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8910331","identity":"rs-8910331","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}