Basal Ganglia and Brainstem Located Cerebral Microbleeds Contributed to Gait Impairment in Patients with Cerebral Small Vessel Disease

Abstract

Background:

The mechanism of gait disorder in patients with cerebral small vessel disease (CSVD) remains unclear. Limited studies have compared the effect of cerebral microbleeds (CMBs) and lacunes on gait disturbance in CSVD patients in different anatomical locations.

Objective:

To investigate the relationship of quantitative gait parameters with varied anatomically located MRI imaging markers in patients with CSVD.

Methods:

Quantitative gait tests were performed on 127 symptomatic CSVD patients all with diffuse distributed white matter hyperintensities (WMHs). CMBs and lacunes in regard to anatomical locations and burdens were measured. The correlation between CSVD imaging markers and gait parameters was evaluated using general linear model analysis.

Results:

Presence of CMBs was significantly associated with stride length (β= –0.098, p = 0.0272) and right step length (β= –0.054, p = 0.0206). Presence of CMBs in basal ganglia (BG) was significantly associated with stride length and step length. Presence of CMBs in brainstem was significantly associated with gait parameters including stride length, step length, step height, and step width. Presence of lacunes in brainstem was significantly associated with gait speed (β= –0.197, p = 0.0365). However, presence of lacunes in the other areas was not associated with worse gait performances.

Conclusion:

BG and brain stem located CMBs contributed to gait impairment in symptomatic CSVD patients.

Keywords

Basal ganglia brainstem cerebral microbleeds cerebral small vessel disease quantitative gait parameters

INTRODUCTION

Cerebral small vessel disease (CSVD) is a group of local brain lesions caused by cerebral arterioles, venules, and capillary [1]. Gait disorder is one of the most disabling symptoms of CSVD. It contributes to increased risk of falling and loss of independence [2 –4], while the mechanisms underlying are still unclarified.

There are evidences suggesting that white matter hyperintensities (WMHs), particularly subcortical WMHs in the frontal lobe, were associated with gait disturbance in CSVD patients [5, 6]. It was considered to be related to the impaired executive function by disrupting fiber connections among the frontal, prefrontal, and other brain regions [7 –9]. Except for WMH, lacunes, and CMBs are also typical vascular lesions found in CSVD patients. Since lacunes and CMBs are highly prevalent in basal ganglia and brainstem, two regions which also play crucial roles in the regulation of gait and motor function, one can easily assume that lacunes and CMBs are also related to gait disturbance in CSVD patients. Previous studies did show that the presence of lacunes and CMBs are associated with poor gait performance [10, 11]. However, few studies have compared that lacunes and CMBs, small cavitated infarcts and deposition of hemosiderin respectively [12 –14], which has a greater impact on gait function.

In order to further understand the potential mechanism of vascular lesions related to gait disturbance, we enrolled symptomatic CSVD patients with severe WMH burden, compared the association of lacunes and CMBs in regions of interests with gait disturbance. We hypothesized that differently located CMBs and lacunes have differential effects on gait performance.

METHODS

Study population and clinical data collection

This study is based on a clinical cohort of CSVD patients who visited clinics with complaints of gait or cognitive impairment. Inclusion criteria were: 1) Patients with severe vascular white matter hyperintensity on MRI (Fazekas score = 3 periventricular or at deep white matter); 2) Age ≥18 years old; 3) Modified Rankin score ≤3. Subjects with dementia, Parkinson’s disease, and other unrelated serious mental diseases or serious neurological diseases were excluded. A total of 127 participants who were eligible and have completed all the clinical evaluations were included. All participants have signed an informed consent form. The Medical Review Ethics Committee of Peking Union Medical College Hospital approved the study (reference number: JS-1280).

The data of subjects’ age, gender, hypertension, diabetes, smoking and drinking history, and medication history were collected by a unified questionnaire. Cardiovascular risk factors were defined as follows: hypertension was defined as blood pressure≥140/90 mmHg; diabetes mellitus was defined as a fasting plasma glucose level≥7.0 mmol/L or 2-h postprandial plasma glucose level≥11.1 mmol/L; hyperlipidemia was defined as total cholesterol > 5.2 mmol/L or low-density lipoprotein > 2.58 mmol/L; current smoker was defined as an individual smoking at least 1 cigarette per day for more than 6 months before enrollment. All of the medical history variables were coded as binary variable, indicating presence (1) or absence (0). The Chinese version of Mini-Mental State Examination (c-MMSE) and Montreal Cognitive Assessment (MoCA) were used to comprehensively evaluate the cognitive ability of the subjects [15, 16].

Measurement of gait

Motor function was evaluated by ReadyGo Gait Measurement Device (a medical device registered with NMPA, China, manufactured by Beijing CAS-Ruiyi Information Technology Co, Ltd.), which automatically recognized the target examinee from the video, extracted the walking segmentations, and acquired quantitative gait parameters using a built-in movement detection model. For motor evaluation of gait parameters, participants were asked to walk at usual pace over a three-meter distance. The parameters in this study included gait speed, stride length, left step length, right step length, left step height, right step height, and step width. Gait speed is defined as the distance between the start point and the end point divided by time. The distance between two landings of the same foot is defined as stride length of a single foot. Take the average of the left and right side as the stride length. Step length is defined as the distance from the landing of one foot to the landing of the other foot, and step height is defined as the highest distance from the ground during the swing of the one foot. Step width is defined as the average of the width of the left and right feet in each image frame.

MRI acquisition and definitions of imaging markers and severity

Images were performed on a single 3-Tesla Siemens Skyra scanner (Siemens, Germany). MRI imaging findings relevant to CSVD were classified based on standard published Standards for Reporting Vascular Changes on Neuroimaging [17]. WMHs on T2-weighted images were defined as hyperintensities without cavitation and on FLAIR scans were defined as being not hypointense. Periventricular white matter hyperintensities (PVWMHs) and deep white matter hyperintensities (DWMHs) were scored on axial FLAIR (fluid-attenuated inversion recovery) images using the Fazekas scale [18]. CMBs were defined as round or ovoid lesions of hypointensity on SWI sequences, ranging from 2–10 mm in size with blooming effect. Lacunes were defined as focal deep infarcts ranging from 3–15 mm in size with the same signal characteristics as cerebrospinal fluid on all sequences situated in the basal ganglia or white matter. Global cerebral atrophy (GCA) is estimated visually rated on scale of 0–3 based on the semi-quantitative rating scale proposed by Pasquier et al on the FLAIR scans [19].

Three well-trained readers who were blinded to all clinical data visually rated WMHs, CMBs, and lacunes independently. Each type of lesion was rated by the same reader. The intra-rater agreement was assessed in a random sample of 50 individuals with an interval of longer than one month between the first and second readings. The κ coefficient was 0.95 for lacunes and 0.90 for CMBs.

Statistical analysis

Continuous variables are expressed as mean and standard deviation (SD), and categorical variables are expressed as frequencies and proportions. The correlation between CSVD imaging markers and gait was evaluated using general linear model analyses with each CSVD marker (CMBs or lacunes) as a determinant and gait performance (gait speed, stride length, left step length, right step length, left step height, right step height, step width) as outcome variables. The models were adjusted for age, gender, height, educational level, MMSE score, and GCA level. Statistical significance was defined as p < 0.05.

All analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC, United States).

RESULTS

Demographic, imaging, and motor characteristics

The participants’ demographic characters and vascular risk factors are shown in Table 1. Of the 127 patients included in the final analysis, 87 (68.5%) were male, and mean age of the participants was 64.6 years. The prevalence of WMHs, CMBs and lacunes, as well as the gait performance are also shown in Table 1. Among them, 117 (92.1%) had Fazekas scores of 3 for both PVWMH and DWMH. Lacunes and CMBs were found in 31 (24.4%) and 96 (75.6%) participants, respectively. The average gait speed was 0.9179 m/s. The distribution of lesions in white matter, BG, and brainstem was summarized in Supplementary Table 1. 66.9% of the participants have lesions in BG, which is the most among the three areas. Patients with CMBs alone account for the largest proportion in all of the three areas (48.08% in patients with white matter-located lesions, 69.41% in patients with basal ganglia-located lesions, 78.00% in patients with brainstem-located lesions). Patients with basal ganglia-located CMBs was the most (61.4%, 78/127), followed by brainstem (34.6%, 44/127).

Table 1

Basic information

Characteristics	Mean (SD) or N (%)
	(N = 127)
Age, y, mean (SD)	64.6 (11.3)
Male, n (%)	87 (68.5)
Weight, kg, mean (SD)	68.1 (11.8)
Height, m, mean (SD)	1.7 (0.1)
High school and above, n (%)	69 (54.3)
Smoking, n (%)	55 (45.5)
Drinking, n (%)	54 (45.4)
Hypertension, n (%)	85 (68.0)
Diabetes mellitus, n (%)	28 (22.4)
Hyperlipidemia, n (%)	51 (42.1)
SBP, mmHg, mean (SD)	134 (16.8)
DBP, mmHg, mean (SD)	78.2 (13.4)
MMSE score, mean (SD)	22.0 (9.8)
MoCA score, mean (SD)	17.3 (8.5)
GCA level, mean (SD)	1.6 (0.8)
WMHs
PVWMHs, Fazekas = 2, n (%)	10 (7.9)
PVWMHs, Fazekas = 3, n (%)	117 (92.1)
DWMHs, Fazekas = 3, n (%)	127 (100)
Lacunes
Presence of lacune, n (%)	31 (24.4)
Presence of lacune-BG, n (%)	26 (20.5)
Presence of lacune-WM, n (%)	27 (21.3)
Presence of lacune-stem, n (%)	11 (8.7)
CMBs
Presence of CMBs, n (%)	96 (75.6)
Presence of CMB-BG, n (%)	78 (61.4)
Presence of CMB-WM, n (%)	37 (29.1)
Presence of CMB-stem, n (%)	44 (34.9)
Presence of CMB-cerebellum, n (%)	38 (29.9)
Presence of CMB-lobe, n (%)	79 (62.2)
Gait parameters
Speed, m/s, mean (SD)	0.9179 (0.3011)
Stride length, m, mean (SD)	0.8670 (0.1954)
Left step length, m, mean (SD)	0.4279 (0.1074)
Right step length, m, mean (SD)	0.4339 (0.0980)
Left height, m, mean (SD)	0.1084 (0.0401)
Right height, m, mean (SD)	0.1127 (0.0413)
Width, m, mean (SD)	0.1529 (0.0312)

Association between CMBs, lacunes, and gait parameters

The presence of CMBs, regardless of distribution, was significantly associated with stride length (standardized β= –0.098, p = –0.272) and right step length (standardized β= –0.054, p = 0.0206) as shown in Table 2. As compared with those without CMBs, presence of CMBs in brainstem was significantly associated with a number of gait parameters, manifesting as a shorter stride length and step length, lower step height and narrower step width in CSVD patients. Presence of CMBs in BG was also found to be associated with stride length and step length. Considering that the presence of lacunes may have affected the results, the presence of lacunes was adjusted as a confounding factor in the model (not shown), and no significant change was found on the correlation between CMBs and gait parameters. CMBs in white matter and lobe did not show significant correlations with any of the gait parameters.

Table 2

Association between CMBs and gait parameters

CSVD imaging	Gait speed		Stride length		Left step length		Right step length		Left step height		Right step height		Step width
markers	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p
Presence of CMBs	–0.136 (0.073)	0.0653	–0.098 (0.044)	0.0272*	–0.042 (0.023)	0.0647	–0.054 (0.023)	0.0206*	–0.003 (0.011)	0.7787	–0.012 (0.011)	0.2757	0.004 (0.007)	0.5565
Presence of CMBs-lobe	–0.070 (0.062)	0.2597	–0.038 (0.037)	0.3103	–0.017 (0.019)	0.3912	–0.021 (0.020)	0.2796	–0.007 (0.009)	0.4240	–0.005 (0.009)	0.5958	0.001 (0.006)	0.9045
Presence of CMBs-WM	0.028 (0.068)	0.6782	0.015 (0.041)	0.7115	0.004 (0.021)	0.8499	0.003 (0.022)	0.8759	0.018 (0.010)	0.0633	0.019 (0.010)	0.0670	0.007 (0.007)	0.3059
Presence of CMBs-BG	–0.103 (0.061)	0.0929	–0.101 (0.036)	0.0060**	–0.051 (0.019)	0.0067**	–0.055 (0.019)	0.0045**	–0.009 (0.009)	0.2912	–0.007 (0.009)	0.4411	0.001 (0.006)	0.8444
Presence of CMBs-stem	–0.080 (0.061)	0.1938	–0.100 (0.036)	0.0064**	–0.055 (0.018)	0.0035**	–0.047 (0.019)	0.0152*	–0.019 (0.009)	0.0268*	–0.019 (0.009)	0.0398*	0.016 (0.006)	0.0070**
Presence of CMBs-cerebellum	–0.106 (0.062)	0.0894	–0.040 (0.038)	0.2864	–0.020 (0.019)	0.3027	–0.018 (0.020)	0.3595	–0.004 (0.009)	0.6553	–0.004 (0.009)	0.6474	0.00 4(0.006)	0.4914

β, standardized β coefficient; CSVD, cerebral small vessel disease; CMB, cerebral microbleeds; WM, white matter; BG, basal ganglia; stem, brainstem. Adjusted for age, gender, height, education level, MMSE score, and GCA level; *p < 0.05; **p < 0.01.

Surprisingly, presence of lacunes was not associated with gait speed (standardized β= –0.039, p = 0.5523), stride length (standardized β= 0.034, p = 0.3977), left step length (standardized β= 0.017, p = 0.3952), right step length (standardized β= 0.010, p = 0.6397), left step height (standardized β= 0.017, p = 0.0658), right step height (standardized β= 0.011, p = 0.2342), or step width (standardized β= 0.009, p = 0.1796), as shown in Table 3. Presence of lacunes in brainstem was associated with gait speed (standardized β= –0.197, p = 0.0365). However, presence of lacunes in the other areas did not show an apparent association with the gait parameters as well. After adjusting for the presence of CMBs, the association becomes no longer significant (standardized β= –0.185, p = 0.0521).

Table 3

Association between lacunes and gait parameters

CSVD	Gait speed		Stride length		Left step length		Right step length		Left step height		Right step height		Step width
imaging markers	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p	β (SE)	p
Presence of lacunes	–0.039 (0.066)	0.5523	0.034 (0.040)	0.3977	0.017 (0.020)	0.3952	0.010 (0.021)	0.6397	0.017 (0.009)	0.0658	0.011 (0.009)	0.2342	0.009 (0.006)	0.1796
Presence of lacunes-WM	–0.074 (0.067)	0.2752	0.012 (0.041)	0.7687	0.008 (0.021)	0.6995	–0.003 (0.022)	0.8867	0.005 (0.010)	0.5862	0.002 (0.010)	0.8025	0.010 (0.007)	0.1215
Presence of lacunes-BG	–0.120 (0.069)	0.0873	0.002 (0.042)	0.9616	–0.000 (0.022)	0.9917	0.000 (0.022)	0.9923	0.016 (0.010)	0.1227	0.001 (0.010)	0.9520	0.011 (0.007)	0.1179
Presence of lacunes-stem	–0.197 (0.093)	0.0365*	–0.064 (0.057)	0.2677	–0.036 (0.029)	0.2204	–0.030 (0.030)	0.3274	–0.020 (0.014)	0.1437	–0.025 (0.014)	0.0697	0.016 (0.009)	0.0851

β, standardized β coefficient; CSVD, cerebral small vessel disease; CMB, cerebral microbleeds; -WM, in white matter; -BG, in basal ganglia; -stem, in brainstem. Adjusted for age, gender, height, education level, MMSE score, and GCA level; *p < 0.05; **p < 0.01.

The association between CMBs burdens and gait parameters were further investigated, as presented in Supplementary Table 2. Higher CMBs burdens (CMBs≥2) showed a significant correlation with stride length (standardized β= –0.104, p = 0.0253) and right step length (standardized β= –0.055, p = 0.0248). To assess the impact of CMBs burdens in different anatomical locations on gait parameters, the data was further analyzed. Higher CMBs burden in BG (CMBs≥2) showed a correlation with stride length (standardized β= –0.111, p = 0.0040) and bilateral step length (left side: standardized β= –0.059, p = 0.0030; right side: standardized β= –0.058, p = 0.0044). Moreover, higher CMBs burden in brainstem (CMBs≥2) showed significant correlation with almost all of the gait parameters.

DISCUSSION

Our study showed that CMBs, but not lacunes, have a significant correlation with gait disturbance in a group of symptomatic CSVD patients with severe WMH. Presence of CMBs in BG was significantly associated with stride length and step length. Presence of CMBs in brainstem was significantly associated with multiple gait parameters included in the current study.

CMBs and lacunes are two distinct vascular parenchymal lesions, as one is of hemorrhagic and the other is of ischemic feature. Compared to CMBs, lacunes represent a more remarkable loss of brain tissues, which is more likely to lead to functional deterioration by theory. However, in this CSVD population with balanced severe WMH, CMB burden but not lacunes showed significant correlation with gait performance. The result suggests that there may be a heterogeneous mechanism behind vascular lesions and gait performances. Clearly, ischemic brain tissues loss was not the only pathway. Recent study showed that the hemosiderin causing CMBs may originates not only from red blood cells extravasated from blood vessels, but also from local neural structures, such as oligodendrocytes rich in ferritin, suggesting that CMBs may be associated with more extensive brain structural damage [20]. In addition, abnormal accumulation of iron-containing substance are capable of generating free radicals and associated oxidative injury of brain tissue, potentially contributing to radical-related neurodegenerative and inflammatory disorders [21 –23]. Considering that CMBs may be able to cause greater damage than expected, more attention needs to be paid to those who suffered from higher CMBs burden.

Compared with other regions, CMBs in the BG and brainstem showed a stronger correlation with gait performance. Several studies constructed on different cohorts have unveiled the correlations between gait and posture dysfunction with imaging markers of CSVD including CMBs, lacunes, as well as enlarged perivascular spaces [10 , 24–26]. However, few studies have particularly focused on the relationship between gait performance and lesions in the brainstem. Our results are supported by neuroanatomical theories which highlighted the importance of BG and brainstem in motor control [27 –32]. BG is involved not only in motor planning and programming, but also in the coordination of visual, cognitive, and emotional processes, which are necessary for motor control. Brainstem was considered responsible for the automatic programming of gait pattern under the regulation of the cerebellum. Moreover, both of BG and brainstem are rich in dense nuclei and abundant small blood vessels [20]. It is reasonable to speculate that, compared to white matter and cortex, abundant small lesions within BG or brainstem may have a greater impact on integrity of the neural networks.

Although the correlation between lacunes and gait disturbance was not significant in our study, some previous studies have shown that lacunes were also associated with motor functions such as gait performance in CSVD [33 –35]. However, the results may also by affected by population characteristics. Our population had a relatively higher CMB burden (96 of 127, 75.6%) compared with some other cohorts (prevalence of CMBs ranging from 4.7–38.3%; prevalence of lacunes ranging from 12.3–34.2% [10 , 36–39]). Nevertheless, existing studies are mostly based on community populations or extensive CSVD patient populations. The absence of population screening for gait impairment may be one of the reasons for the low prevalence of CMBs in previous studies.

The strength of the study is the quantitative evaluation of multiple gait parameters. In addition, we balanced the severity of WMH through patient selection in order to especially investigate the impact of lacunes and CMBs to motor performances. Our study also has several limitations. As mentioned above, the population has a relative high prevalence of WMHs and CMBs. Although this makes the gait impairment in the population relatively prominent and facilitates the discovery of the correlation between CMBs and gait disorders, it also limits the generalizability of the findings. To control the potential biases, we have adjusted some of the confounders. However, the results still need to be validated in differential populations. Furthermore, the study only investigated three of the CSVD imaging markers based on a cross-sectional data. The imaging assessments in the study were mainly relied on the visual measurement of professional physicians, including the assessment of cerebral atrophy. Using software packages to do brain region segmentation and volume extraction will make the results more reliable. Well-conducted, prospective cohort studies which include more imaging markers would help inform the issue.

In conclusion, we found that BG and brainstem located CMBs may have a great impact on gait disturbance of CSVD patients. Patients with CMBs in the brainstem or basal ganglia seen on imaging should be especially evaluated for gait impairment.

Footnotes

ACKNOWLEDGMENTS

We are grateful to all the participants for their cooperation.

FUNDING

This study was supported by the National Natural Science Foundation of China (No. 81901224, 81971138), National High Level Hospital Clinical Research Funding of China (2022-PUMCH-A-256), “the 13th Five Year Plan” National Key Research and Development Program (2016YFC0901004), the Strategic Priority Research Program “Biological basis of aging and therapeutic strategies” of the Chinese Academy of Sciences (No. XDB39040300), and the project of China Foundation for International Medical Exchange “Innovative thinking research funding” (No. CIMF-Z-2016-20-1801).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The supplementary material is available in the electronic version of this article: .

References

, Yang

, Reis

, Tao

, Li

, Zhang

(2018) Cerebral small vessel disease. Cell Transplant 27, 1711–1722.

Veronese

, Stubbs

, Volpato

, Zuliani

, Maggi

, Cesari

, Lipnicki

, Smith

, Schofield

, Firth

, Vancampfort

, Koyanagi

, Pilotto

, Cereda

(2018) Association between gait speed with mortality, cardiovascular disease and cancer: A systematic review and meta-analysis of prospective cohort studies. J Am Med Dir Assoc 19, 981–988.e7.

Perera

, Patel

, Rosano

, Rubin

, Satterfield

, Harris

, Ensrud

, Orwoll

, Lee

, Chandler

, Newman

, Cauley

, Guralnik

, Ferrucci

, Studenski

(2016) Gait speed predicts incident disability: A pooled analysis. J Gerontol A Biol Sci Med Sci 71, 63–71.

Studenski

, Perera

, Patel

, Rosano

, Faulkner

, Inzitari

, Brach

, Chandler

, Cawthon

, Connor

, Nevitt

, Visser

, Kritchevsky

, Badinelli

, Harris

, Newman

, Cauley

, Ferrucci

, Guralnik

(2011) Gait speed and survival in older adults. JAMA 305, 50–58.

van der Holst

, Tuladhar

, Zerbi

, van Uden

IWM

, de Laat

, van Leijsen

EMC

, Ghafoorian

, Platel

, Bergkamp

, van Norden

AGW

, Norris

, van Dijk

, Kiliaan

, de Leeuw

(2018) White matter changes and gait decline in cerebral small vessel disease. Neuroimage Clin 17, 731–738.

Murray

, Senjem

, Petersen

, Hollman

, Preboske

, Weigand

, Knopman

, Ferman

, Dickson

, Jack

Jr (2010) Functional impact of white matter hyperintensities in cognitively normal elderly subjects. Arch Neurol 67, 1379–1385.

Chen

, Wang

, Shan

, Cai

, Liu

, Hu

, Liao

, Huang

, Zhang

, Wang

, Lu

(2019) Cerebral small vessel disease: Neuroimaging markers and clinical implication. J Neurol 266, 2347–2362.

Kim

, Kwon

, Lee

, Cho

, Kim

, Park

, Jung

, San Lee

, Lee

, Jang

, Kim

, Lee

, Choe

, Kim

, Na

, Seo

(2016) Gray and white matter changes linking cerebral small vessel disease to gait disturbances. Neurology 86, 1199–1207.

Pinter

, Ritchie

, Doubal

, Gattringer

, Morris

, Bastin

, Del C Valdés Hernández

, Royle

, Corley

, MuñozManiega

, Pattie

, Dickie

, Staals

, Gow

, Starr

, Deary

, Enzinger

, Fazekas

, Wardlaw

(2017) Impact of small vessel disease in the brain on gait and balance. Sci Rep 7, 41637.

10.

de Laat

, van den Berg

HAC

, van Norden

AGW

, Gons

RAR

, Olde Rikkert

MGM

, de Leeuw

(2011) Microbleeds are independently related to gait disturbances in elderly individuals with cerebral small vessel disease. Stroke 42, 494–497.

11.

, Wang

, Jiang

, Zhang

, Yang

, Yuan

, Zhu

, Tang

, Fan

, Ye

, Dong

, Jin

, Ding

, Cui

, Chen

(2020) Cerebral small vessel disease is associated with gait disturbance among community-dwelling elderly individuals: The Taizhou imaging study. Aging (Albany NY) 12, 2814–2824.

12.

Shoamanesh

, Kwok

, Benavente

(2011) Cerebral microbleeds: Histopathological correlation of neuroimaging. Cerebrovasc Dis 32, 528–534.

13.

Fisher

, French

, Ji

, Kim

(2010) Cerebral microbleeds in the elderly. Stroke 41, 2782–2785.

14.

Caplan

(2015) Lacunar infarction and small vessel disease: Pathology and pathophysiology. J Stroke 17, 2–6.

15.

Zhang

, Hong

, Li

(1999) The mini-mental state examination in the Chinese residents population aged 55 years and over in the urban and rural areas of Beijing. Chin J Neurol 32, 149–153.

16.

Wen

, Zhang

, Niu

, Li

(2008) [The application of Montreal cognitive assessment in urban Chinese residents of Beijing.]. Zhonghua Nei Ke Za Zhi 47, 36–39.

17.

Wardlaw

, Smith

, Biessels

, Cordonnier

, Fazekas

, Frayne

, Lindley

, O’Brien

, Barkhof

, Benavente

, Black

, Brayne

, Breteler

, Chabriat

, Decarli

, de Leeuw

, Doubal

, Duering

, Fox

, Greenberg

, Hachinski

, Kilimann

, Mok

, Oostenbrugge

, Pantoni

, Speck

, Stephan

, Teipel

, Viswanathan

, Werring

, Chen

, Smith

, van Buchem

, Norrving

, Gorelick

, Dichgans

; STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1) (2013) Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 12, 822–838.

18.

Fazekas

, Chawluk

, Alavi

, Hurtig

, Zimmerman

(1987) MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am J Roentgenol 149, 351–356.

19.

Pasquier

, Leys

, Weerts

JGE

, Mounier-Vehier

, Barkhof

, Scheltens

(1996) Inter-and intraobserver reproducibility of cerebral atrophy assessment on MRI scans with hemispheric infarcts. Eur Neurol 36, 268–272.

20.

Janaway

, Simpson

, Hoggard

, Highley

, Forster

, Drew

, Gebril

, Matthews

, Brayne

, Wharton

, Ince

; MRC Cognitive Function and Ageing Neuropathology Study (2014) Brain haemosiderin in older people: Pathological evidence for an ischaemic origin of magnetic resonance imaging (MRI) microbleeds. Neuropathol Appl Neurobiol 40, 258–269.

21.

Schipper

(2004) Brain iron deposition and the free radical-mitochondrial theory of ageing. Ageing Res Rev 3, 265–301.

22.

, He

, Guan

, Liu

, Han

, Nie

(2012) Disrupted iron metabolism and ensuing oxidative stress may mediate cognitive dysfunction induced by chronic cerebral hypoperfusion. Biol Trace Elem Res 150, 242–248.

23.

Schrag

, Crofton

, Zabel

, Jiffry

, Kirsch

, Dickson

, Mao

, Vinters

, Domaille

, Chang

, Kirsch

(2011) Effect of cerebral amyloid angiopathy on brain iron, copper, and zinc in Alzheimer’s disease. J Alzheimers Dis 24, 137–149.

24.

Chiu

, Chan

, Wu

, Ko

, Chen

, Hong

(2018) Cerebral microbleeds are associated with postural instability and gait disturbance subtype in people with Parkinson’s disease. Eur Neurol 80, 335–340.

25.

Chen

, Zhang

, Liu

, Wang

, Ma

, Pan

, Wang

, Feng

(2020) Effect of small vessel disease burden and lacunes on gait/posture impairment in Parkinson’s disease. Neurol Sci 41, 3617–3624.

26.

Yang

, Li

, Qin

, Yang

, Hu

(2022) Association between large numbers of enlarged perivascular spaces in basal ganglia and motor performance in elderly individuals: A cross-sectional study. Clin Interv Aging 17, 903–913.

27.

Nielsen

, Crone

, Hultborn

(2007) The spinal pathophysiology of spasticity – from a basic science point of view. Acta Physiol (Oxf) 189, 171–180.

28.

Takakusaki

(2017) Functional neuroanatomy for posture and gait control. J Mov Disord 10, 1–17.

29.

, Francisco

, Zhou

(2018) Post-stroke hemiplegic gait: New perspective and insights. Front Physiol 9, 1021.

30.

Zhao

, You

, Jiang

, Zhang

, Yang

, Zhang

(2019) Effect of Pro-kin visual feedback balance training system on gait stability in patients with cerebral small vessel disease. Medicine (Baltimore) 98, e14503.

31.

Wilson

, Allcock

, Mc Ardle

, Taylor

, Rochester

(2019) The neural correlates of discrete gait characteristics in ageing: A structured review. Neurosci Biobehav Rev 100, 344–369.

32.

Wei

, Zou

, Lv

, Fan

(2020) Functional MRI reveals locomotion-control neural circuits in human brainstem. Brain Sci 10, 757.

33.

de Laat

, van Norden

, Gons

, van Oudheusden

, van Uden

, Bloem

, Zwiers

, de Leeuw

(2010) Gait in elderly with cerebral small vessel disease. Stroke 41, 1652–1658.

34.

Smith

, O’Donnell

, Dagenais

, Lear

, Wielgosz

, Sharma

, Poirier

, Stotts

, Black

, Strother

, Noseworthy

, Benavente

, Modi

, Goyal

, Batool

, Sanchez

, Hill

, McCreary

, Frayne

, Islam

, DeJesus

, Rangarajan

, Teo

, Yusuf

; PURE Investigators (2015) Early cerebral small vessel disease and brain volume, cognition, and gait. Ann Neurol 77, 251–261.

35.

Wang

, Allali

, Kesavadas

, Noone

, Pradeep

, Blumen

, Verghese

(2016) Cerebral small vessel disease and motoric cognitive risk syndrome: Results from the Kerala-Einstein Study. J Alzheimers Dis 50, 699–707.

36.

Choi

, Ren

, Phan

, Callisaya

, Ly

, Beare

, Chong

, Srikanth

(2012) Silent infarcts and cerebral microbleeds modify the associations of white matter lesions with gait and postural stability. Stroke 43, 1505–1510.

37.

Jeerakathil

, Wolf

, Beiser

, Hald

, Au

, Kase

, Massaro

, DeCarli

(2004) Cerebral microbleeds. Stroke 35, 1831–1835.

38.

Poels

MMF

, Vernooij

, Ikram

, Hofman

, Krestin

, van der Lugt

, Breteler

MMB

(2010) Prevalence and risk factors of cerebral microbleeds. Stroke 41, S103–S106.

39.

Hilal

, Mok

, Youn

, Wong

, Ikram

, Chen

CL-H

(2017) Prevalence, risk factors and consequences of cerebral small vessel diseases: Data from three Asian countries. J Neurol Neurosurg Psychiatry 88, 669–674.