- Academic Editor
Background and Purpose: White
matter hyperintensites (WMHs) , lacunes and brain atrophy have been demonstrated
to be positively related to gait disorder. However, cerebral microbleeds (CMBs)
as a manifestation of cerebral small vessel disease (CSVD) is still
under-investigated. Therefore, correlations between CMBs and
upper extremity, gait and balance performance were investigated in this study.
Methods: A cross-sectional study of middle-aged to
older adults was conducted. CSVD burden was measured with magnetic resonance
imaging (MRI) and the location and number of CMBs were analysed. Gait and balance
functions were evaluated using a four meter walkway, Tinetti,
Timed-Up-and-Go (TUG) and Short Physical Performance Battery
(SPPB) tests. Upper extremity function was measured by 10 repeated
pronation-supination time, 10 repeated finger tapping time, and 10 repeated
opening and closings of the hands. Results: A total of
224 participants were included in this study, with a mean age of 60.6
Gait disturbances are a major issue among elderly patients, and related to functional impairment induced by injury such as fall, institutionalization and of the lead to death [1]. Beside increasing age, there are many contributing factors causing gait and balance disturbances. Many studies have reported associations between poorer motor performance and cerebral small vessel disease (CSVD), especially for white matter hyperintensities (WMHs) [2, 3, 4, 5], lacunes [6, 7], and brain atrophy [8, 9]. Cerebral microbleed (CMB), being a manifestation of CSVD, has been confirmed to play a pivotal role in the development of dementia and vascular cognitive impairment. However, the exploration of the relationship between movement disorder and CMB is still rare and conclusions are controversial. Only two studies have found the number of CMBs is related to gait disturbances [10, 11] with the majority of studies not finding any influences of CMBs on motor performance [3, 12, 13].
With the two exceptions just mentioned, the correlation between the spatial distribution of CMB and gait disturbances remains poorly understood. Normal motor function relies on the integration of information from multiple areas of the brain. Several studies have shown that movement disorders are associated with lesions in the frontal lobe and basal ganglia [6, 11] and that the caudate nucleus plays a significant role in walking speed [9]. Simultaneously, the pathophysiology of CMBs may differ according to their location, with deep or infratentorial CMBs attributable to hypertension, or a history of stroke and lobar (cortical-subcortical) CMBs to cerebral amyloid angiopathy (CAA) [14]. Consequently, a more comprehensive study to investigate the relationship in between different lesions of CMB and movement disorders, is opportune.
In contrast to walking and balance disturbances, upper extremity functions are still underestimated. With the aging of the population, increasing numbers of older adults may be affected by sensorimotor impairment, which negatively impacts their upper extremity performance and independence [15]. Limited study has found an association between WMH and upper extremity movement disorders [12]. As a marker of CSVD, CMB is closely related to WMH and can cause the required destruction in cortical and subcortical regions. But the relationship between the number and location of CMBs and upper extremity function has not been thoroughly studied.
This study, investigates the association between gait, balance and upper extremity functions as measured by quantitative tests and clinical rating scales and the number and location of CMBs to provide new insights into the importance of CMB in movement disorders.
From February 2019 to October 2019, 224 participants were recruited who presented for routine screening at the department of Neurology in Beijing Chao-Yang Hospital, Capital Medical University. All the included participants aged from 45 to 85 years could completed the movement tests and a brain magnetic resonance imaging (MRI) scan. The participants were excluded from the study if they had at least one of the following conditions: (1) dementia, including Alzheimer’s disease, frontotemporal dementia, or dementia with Lewy bodies; Parkinson’s disease (PD) or PD-plus syndrome; other neurodegenerative diseases such as multiple sclerosis or amyotrophic lateral sclerosis; (2) a history of severe stroke, large-vessel cerebrovascular diseases or lacunar syndrome within the previous six months; (3) diabetic neuropathy or neuropathies from other causes; peripheral vascular disease causing gait disturbances and/or upper limb motor function impairment; (4) intracranial space occupying lesion, cancer, toxicity, trauma or infections; (5) recent or current use of acetylcholine-esterase inhibitors, neuroleptic agents and L-dopa; (6) inability to finish the tests because of prominent visual, hearing, language impairment, or psychiatric disease; (7) heart failure, atrial fibrillation, myocardial infarction, severe nephrosis/liver diseases; (8) conditions not related to CSVDs affecting motor function (e.g., joint fusion, severe arthritis, rheumatic disease, joint replacement, or lumbar spondylopathy); (9) participants with poor MRI quality. Ethical approval was obtained from the Committee of Beijing Chao-Yang Hospital, Capital Medical University and was conducted in accordance with the Declaration of Helsinki. All subjects gave written informed for participation in the study.
Baseline demographics and laboratory parameters of participants were assessed using structured interviews and laboratory examinations. A comprehensive questionnaire was administered by trained personnel to the patients. Data on demographic profiles (age, sex, height, weight) and a self-reported history of smoking, drinking, hypertension, hyperlipidemia, diabetes mellitus, ischemic stroke, coronary heart disease and medication use were collected and analyzed. Venous blood samples were routinely drawn after an overnight fast. Results of blood tests (white blood cell (WBC), neutrophil, hemoglobin, platelet, triglyceride, cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), homocysteine, aspartate aminotransferase (AST); alanine transaminase (ALT); alkaline phosphatase (ALP); fasting blood glucose, glycosylated hemoglobin levels; urea; creatinine; uric acid; sodium; potassium; prothrombin time (PT); international normalized ratio (INR) and d-dimer) were all recorded.
MRI data were acquired by a 3.0-T MRI scanner (Prisma
2016080316;Siemens AG, Erlangen, Germany) with the following sequences:
T1-weighted imaging (repetition time [TR] = 2000.0 ms, echo time [TE] = 9.2 ms,
slice thickness = 5 mm, and field of view [FOV] = 220
The MRI markers of CSVD including lacunes, WMH, CMB and enlarged perivascular space (EPVS) were rated according to the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) consensus criteria [16]. The degree of periventricular and deep WMH were evaluated separately and combined as Fazekas scores [17]. Severity of EPVS in areas of the centrum semiovale (CSO) and basal ganglia (BG) were assessed according to the semi-quantitative rating scale developed by the Edinburg group [18]. Brain atrophy was assessed via MRI visual ratings [19]. Brain atrophy and lacunes were divided into present or absent.
CMBs were identified as small, rounded hypointense lesions with clear margins and size ranging from 2 to 10 mm on SWI. The definition and rating of CMBs was determined and modified according to the Microbleed Anatomical Rating Scale [20]. The number of CMBs were recorded. The location of CMBs were categorized anatomically into lobar (frontal, parietal, occipital, and temporal), deep (basal ganglia and thalamus) and infratentorial. Two experienced neurologists who were blinded to all clinical data independently rated WMH, lacunes, CMBs, and PVS independently.
The quantitative analysis, recorded velocity (m/s), stride length (cm) and cadence (number of steps on a 4-m walkway), stride width (cm) using 4-m walkway and simultaneously measured the duration of the Timed-Up-and-Go (TUG) test. The participants walked twice at self-selected normal gait speed in low-heeled shoes. The stride length was defined as the distance between the heel points of two consecutive footprints and the stride width was the distance between one midpoint of a footprint and the line of progression of the opposite foot.
Clinical rating of semi-quantitative scale assessments consisted of the Tinetti test with 17 items (9 for balance and 8 for gait) with a maximum score of 28. The maximum score of 28 indicates normal gait and balance, while the lower the score, the poorer the gait and balance performance. Balance was evaluated by a Short Physical Performance Battery (SPPB) test. The SPPB test was incorporated with standing balance, timed walk, and repeated chair stands. Balance was evaluated over a 10 s period by ability to a maintain side-by-side, semi-tandem, and tandem position standing with closed feet together.
Upper extremity function was assessed by 10-repeat pronation–supination time, 10-repeat finger-tapping time and 10-repeat opening and closing hands time.
Statistical analysis was performed using SPSS version 24.0 (IBM Corp., Chicago,
IL, USA). Continuous variables with a normal distribution were presented as the
mean with standard deviation (SD) and variables with a nonnormal distribution
were presented as the median with the interquartile range. First, we compared the
clinical information and imaging characteristics of the present of CMB were
compared using one-way analysis of variance and Kruskal–Wallis
tests for continuous data, and chi-square test or
Fisher exact tests for categorical data. Second,
the relationships between the number of CMB and movement
disorders were investigated using
single and
multiple linear regression analysis adjusted for age, sex,
height. Subsequent adjustments were made for
WMH assessed by Fazekas score, score of BG-EPVS and CSO-EPVS,
the presence of lacunar, brain atrophy, and other risk factors. The relationships
between different locations of CMBs and gait, balance, and upper extremities
disorder were studied and also adjusted for the covariates mentioned above.
Statistical significance was established at p
Characteristics of the participants are given in Table 1. The
mean age was 60.6
Variables | All | CMB (-) | CMB (1–4) | CMB ( |
p |
n = 224 | n = 146 | n = 65 | n = 13 | ||
Age, year | 60.6 |
59.2 |
64.1 |
58.6 |
0.002 |
Sex, male, n (%) | 144.0 (64.3) | 92 (63.0) | 42 (64.6) | 10 (76.9) | 0.603 |
Height, m | 166.6 |
166.4 |
166.4 |
169.7 |
0.348 |
Weight, kg | 71.6 |
71.8 |
70.2 |
77.4 |
0.036 |
BMI, kg/m |
25.8 |
26.1 |
25.3 |
25.5 |
0.317 |
Hypertension, n (%) | 142.0 (63.4) | 87.0 (59.6) | 43 (66.2) | 12 (92.3) | 0.055 |
DM, n (%) | 85.0 (37.9) | 56.0 (38.4) | 26.0 (40.0) | 3 (23.1 | 0.510 |
Hyperlipidemia, n (%) | 41.0 (18.3) | 25 (17.1) | 13 (20.0) | 3 (23.1) | 0.795 |
CAD, n (%) | 39.0 (17.4) | 23 (15.8) | 12 (18.5) | 4 (30.8) | 0.379 |
TIA, n (%) | 26.0 (11.6) | 15 (10.3) | 8 (12.3) | 3 (23.1) | 0.377 |
Smoke, n (%) | 107.0 (47.8) | 68 (46.6) | 31 (47.7) | 8 (61.5) | 0.585 |
Alcohol, n (%) | 86.0 (38.4) | 53 (36.3) | 25 (38.5) | 8 (61.5) | 0.200 |
WMH, Fazekas score | 1.0 (0, 2.0) | 1.0 (0, 2.0) | 2.0 (1.0, 4.0) | 3.0 (2.0, 4.0) | |
BG-EPVS | 2.0 (1.0, 3.0) | 2.0 (1.0, 2.0) | 2.0 (2.0, 3.0) | 3.0 (2.0, 4.0) | |
CSO-EPVS | 2.0 (1.0, 4.0) | 2.0 (1.0, 3.0) | 2.0 (1.0, 4.0) | 3.0 (1.0, 4.0) | 0.146 |
Lacunes, n (%) | 96.0 (43.0) | 48.0 (33.1) | 36.0 (55.4) | 12.0 (92.3) | |
Brain atrophy, n (%) | 116.0 (51.8) | 58 (39.7) | 47 (72.3) | 11 (84.6) |
Data represent number (percentage), mean
CMB, cerebral microbleed; MRI, magnetic resonance imaging; CSVD, cerebral small vessel disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; DM, diabetes mellitus; CAD, coronary artery disease; TIA, transient ischemic attacks; WMH, white matter hyperintensity; BG-EPVS, basal ganglia enlarged perivascular spaces; CSO-EPVS, centrum semiovale enlarged perivascular spaces.
Lobe | Deep | Infratentorial | |||||
41 (18.3) | 29 (13.0) | 34 (15.2) | |||||
Frontal | Parietal | Occipital | Temporal | Basal ganglia | Thalamus | Brainstem | Cerebellum |
11 (4.9) | 7 (3.1) | 10 (4.5) | 19 (8.5) | 28 (12.5) | 28 (12.5) | 23 (10.3) | 16 (7.1) |
Data given as represent number (percentage).
Participants were divided into three groups based on the
number of CMBs 0, 1–4 and
The median gait velocity was 1.1 m/s, stride width 11.0 cm, stride length 63.3 cm and cadence 6.3 steps. The average time for the TUG test was 9.9 seconds. Higher numbers of CMBs had lower gait velocity, wider gait width, shorter gait length, and longer TUG time. As seen in Table 3, they had poor performance in the Tinetti and SPPB tests, as well as significant differences in the balance tests.
Variables | All | CMB (-) | CMB (1–4) | CMB ( |
p |
n = 224 | n = 146 | n = 65 | n = 13 | ||
Stride length, cm | 63.3 (58.0, 68.0) | 66.2 (60.0, 68.0) | 60.0 (54.5, 68.0) | 60.2 (60.0, 70.0) | 0.046 |
Stride width, cm | 10.8 (8.0, 18.0) | 10.0 (7.0, 13.0) | 13.0 (10.0, 15.0) | 14.0 (10.0, 17.0) | |
Cadence, steps | 6.3 (5.9, 7.0) | 6.2 (5.9, 6.8) | 6.6 (5.9, 7.5) | 6.1 (5.7, 6.7) | 0.210 |
Gait velocity, m/sec | 1.1 (0.9, 1.2) | 1.1 (1.0, 1.2) | 1.0 (0.9, 1.2) | 1.0 (0.9, 1.1) | 0.008 |
Tinetti test (range 0–28) | 26.0 (24.3, 28.0) | 28.0 (25.0, 28.0) | 25.0 (24.0, 28.0) | 24.0 (24.0, 25.0) | |
Gait (range 0–12) | 12.0 (11.0, 12.0) | 12.0 (11.0, 12.0) | 11.0 (11.0, 12.0) | 11.0 (11.0, 11.0) | |
Body balance (range 0–16) | 15.0 (13.0, 16.0) | 16.0 (14.0, 16.0) | 13.0 (13.0, 14.0) | 12.0 (11.0, 12.0) | |
TUG test, sec | 9.4 (8.3, 11.0) | 8.7 (8.2, 10.6) | 10.1 (8.9, 11.8) | 10.3 (9.5, 12.2) | 0.001 |
SPPB test (range 0–12) | 11.0 (9.0, 12.0) | 12.0 (10.0, 12.0) | 10.0 (9.0, 12.0) | 9.0 (9.0, 11.0) | |
Standing balance (range 0–4) | 3.0 (2.0, 4.0) | 4.0 (2.0, 4.0) | 3.0 (2.0, 4.0) | 2.0 (2.0, 3.0) | |
Timed walk (range 0–4) | 4.0 (4.0, 4.0) | 4.0 (4.0, 4.0) | 4.0 (4.0, 4.0) | 4.0 (4.0, 4.0) | 0.918 |
Repeated chair stands (range 0–4) | 4.0 (3.0, 4.0) | 4.0 (3.0, 4.0) | 4.0 (3.0, 4.0) | 3.0 (2.0, 4.0) | 0.001 |
Repeated chair stands, sec | 10.2 (9.4, 12.1) | 10.0 (9.3, 11.3) | 11.2 (9.8, 12.6) | 12.4 (10.2, 13.1) | |
Pronation–supination, sec | 6.7 (5.3, 7.7) | 6.2 (4.9, 7.2) | 7.2 (6.3, 8.1) | 7.4 (7.3, 8.2) | |
Finger-tapping, sec | 4.6 (3.8, 6.1) | 4.2 (3.7, 5.7) | 5.4 (4.2, 6.8) | 5.5 (4.1, 6.2) | |
Opening and closing hands time, sec | 4.5 (4.1, 5.7) | 4.4 (4.1, 5.2) | 5.1 (4.2, 6.1) | 4.5 (4.3, 5.9) | 0.021 |
Data represent number (percentage), mean
TUG, Timed-Up-and-Go; SPPB, Short Physical Performance Battery.
Single linear regression
analysis showed that a number of CMBs was associated
with gait velocity (
CMBs(n) | ||||||
Model 1 | Model 2 | Model 3 | ||||
p | p | p | ||||
Stride length, cm | –0.348 | 0.207 | –0.480 | 0.049 | –0.099 | 0.101 |
Stride width, cm | 0.500 | 0.471 | 0.000 | 0.287 | 0.020 | |
Cadence, steps | 0.016 | 0.295 | 0.043 | 0.409 | 0.062 | 0.428 |
Gait velocity, m/sec | –0.117 | –0.236 | 0.001 | –0.310 | 0.004 | |
Tinetti test | –0.254 | –0.249 | –0.150 | 0.004 | ||
Gait | –0.069 | –0.068 | –0.042 | 0.005 | ||
Body balance | –0.185 | –0.181 | –0.160 | 0.001 | ||
TUG test, sec | 0.172 | 0.024 | 0.173 | 0.015 | 0.162 | 0.018 |
SPPB test | –0.188 | 0.001 | –0.183 | 0.001 | –0.153 | 0.002 |
Standing balance | –0.121 | –0.177 | –0.096 | 0.001 | ||
Timed walk | –0.004 | 0.740 | –0.031 | 0.643 | –0.041 | 0.672 |
repeated chair stands | –0.068 | 0.001 | –0.066 | 0.002 | –0.058 | 0.005 |
repeated chair stands time, sec | 0.238 | 0.001 | 0.232 | 0.001 | 0.209 | 0.001 |
Pronation-supination time, sec | 0.204 | 0.001 | 0.213 | 0.001 | 0.143 | 0.018 |
Finger-tapping time, sec | 0.092 | 0.101 | 0.124 | 0.055 | 0.156 | 0.071 |
Opening and closing hands time, sec | 0.075 | 0.130 | 0.118 | 0.072 | 0.155 | 0.069 |
Wider stride width and lower gait velocity were associated
with CMBs in the frontal (
N of CMBs | Tinetti test | SPPB test | ||||||||||
Length, cm | Stride width, cm | Gait velocity, m/sec | Cadence, steps | Total score | Gait | Balance | TUG test, sec | Total score | Standing balance | Repeated chair stands | Pronation–supination | |
Lobe | –4.146 (0.001) | 1.845 (0.004) | –1.602 (0.001) | 0.042 (0.515) | –0.886 (0.001) | –0.351 ( |
–0.536 (0.015) | 0.809 (0.011) | –0.712 (0.006) | –0.425 (0.008) | 1.128 (0.001) | 0.724 (0.005) |
Frontal | –0.014 (0.805) | 0.111 (0.040) | –0.816 (0.006) | 0.032 (0.627) | –0.073 (0.216) | –0.276 (0.030) | –0.044 (0.454) | 0.051 (0.433) | –0.042 (0.498) | –0.015 (0.887) | 0.136 (0.034) | 0.714 (0.005) |
Parietal | –8.646 (0.002) | 0.097 (0.124) | –0.015 (0.713) | 0.084 (0.192) | –1.291 (0.031) | –0.123 (0.056) | –0.968 (0.042) | 0.089 (0.114) | –1.608 (0.004) | –0.786 (0.023) | 2.073 (0.007) | 1.930 (0.005) |
Occipital | –0.035 (0.533) | 0.018 (0.774) | –0.007 (0.665) | 0.014 (0.527) | –0.090 (0.122) | –0.100 (0.096) | –0.049 (0.381) | 0.011 (0.852) | –0.008 (0.892) | –0.098 (0.112) | –0.001 (0.998) | 0.089 (0.171) |
Temporal | –3.968 (0.002) | 2.172 (0.013) | –1.226 (0.025) | 0.013 (0.838) | –0.871 (0.021) | –0.311 (0.004) | –0.102 (0.071) | 0.062 (0.291) | –0.878 (0.012) | –0.581 (0.017) | –0.117 (0.022) | 0.181 (0.002) |
Deep | –0.027 (0.660) | 1.860 (0.013) | –0.011 (0.065) | 0.031 (0.638) | –0.084 (0.194 | –0.076 (0.192) | –0.100 (0.084) | 0.045 (0.461) | –0.005 (0.092) | –0.016 (0.728) | –0.049 (0.450) | 1.215 (0.001) |
Basal ganglia | –0.019 (0.743) | 2.052 (0.006) | –0.023 (0.125) | 0.017 (0.792) | –0.096 (0.094) | –0.104 (0.098) | –0.112 (0.048) | 0.064 (0.278) | –0.013 (0.832) | –0.008 (0.798) | –0.040 (0.538) | 1.219 (0.001) |
Thalamus | –0.031 (0.591) | 0.036 (0.561) | –0.006 (0.718) | 0.096 (0.142) | –0.015 (0.798) | –0.014 (0.816) | –0.033 (0.568) | 0.005 (0.924) | –0.060 (0.315) | –0.095 (0.114) | –0.039 (0.541) | 0.041 (0.513) |
Infratentorial | –0.091 (0.116) | 1.584 (0.024) | –0.002 (0.213) | 0.009 (0.884) | –0.748 (0.013) | –0.101 (0.105) | –0.609 (0.011) | 0.892 (0.021) | –0.706 (0.011) | –0.476 (0.005) | –1.007 (0.008) | 0.093 (0.148) |
Brainstem | –0.066 (0.290) | 1.604 (0.049) | –0.001 (0.334) | 0.036 (0.569) | –0.088 (0.121) | –0.080 (0.189) | –0.071 (0.214) | 0.083 (0.164) | –0.108 (0.069) | –0.483 (0.017) | –0.109 (0.089) | 0.025 (0.695) |
Cerebellum | –0.077 (0.172) | 0.089 (0.157) | –0.006 (0.516) | 0.066 (0.299) | –0.107 (0.065) | –0.357 (0.359) | –0.660 (0.047) | 1.101 (0.038) | –0.071 (0.236) | –0.091 (0.132) | –0.085 (0.190) | 0.103 (0.106) |
Adjusted for age, sex, height, WMH, EPVS and the present of lacunar and brain atrophy.
Pronation-supination, finger-tapping and opening and closing
of hands were performed giving medians of 6.7, 4.6 and 4.5 sec, respectively,
with a significant difference between CMB and 10 repeated pronation–supination,
finger-tapping and opening and closing hands time, p
Multiple linear regression analysis showed a significant correlation between the
time of 10-repeat pronation–supinations (
In this study, we reported that CMBs, particularly located in the frontal and temporal lobes, basal ganglia and infratentorial areas, were associated with gait dysfunction and balance problems, regardless of the presence of other CSVD categories. A novel finding was that CMBs located in the lobes (frontal, parietal and temporal) and basal ganglia correlated with motor disorder of the upper extremity. This indicates that CMB can impair gait, balance and upper extremity function and the analysis of different regions reveals the possible pathogenesis of CMBs. This type of CSVD needs to be identified and managed appropriately in the treatment of movement disorders.
In previous studies, the relationships between individual CSVD imaging markers and gait performance remained controversial [3, 8, 12, 21]. The association between WMH and lacunes and gait has consistently been reported [12, 22, 23, 24, 25]. However, just a few studies have shown that the presence of CMB magnifies the effects of the volume of WMH on gait but not on postural stability [25]. In studies focusing specifically on CMBs and movement disorders, it was observed that the presence of CMBs were associated with a slower walking speed [26], and a longer stride time and stance phase [27]. According to another study, a higher number of CMBs, measured by T2*-weighted gradient-echo (GRE) sequences, was associated with shorter stride length and worse performance on Tinetti and TUG tests [11]. In the current research, it was discovered that the number of CMBs had correlations with both gait and balance disorders. To examine the specific reasons for this, previous studies were reviewed and several differences identified that need to be addressed. First, the incidence of CMBs in previous studies was lower than in this study, which may affect the research about the location of CMBs. Second, some studies detected CMBs using GRE, which is not as sensitive in terms of number and distribution as SWI. Third, CMBs in most patients are not severe enough to have a functional impact, so just examining general gait parameters is not sufficient for the detection of early changes in gait performance for people with CMBs. During this study, gait function was measured with both quantitative and semi-quantitative methods which were more comprehensive for the detection of gait disorders. The effect of increased number of CMB on motor function was explored and significant differences were found. Furthermore, the SPPB test measured gait and balance, providing more validity, reliability and responsiveness to assess physical function, increasing accuracy of any relationship between CMBs and motor function [28].
When compared to the lower extremity, the association between CMBs and upper extremity has not been investigated. In this study, it was demonstrated that CMBs in the frontal, parietal, temporal and basal ganglia, were associated with the function of pronation-supination. Many studies have concluded that people with mild cognitive impairment have a greater reduction in finger dexterity [29]. A further study from the Rehabilitative Impairment Study of the Elderly (RISE) study indicated participants with mild cognition impairment had significantly affected reaction on reactions times and pronation-supination of the hands [30]. As one important cause of cognitive impairment, CSVD may also have an important impact on hand function. Currently investigations between the upper extremity and CSVD are still uncommon. One of the studies involving 30 individuals from the Austrian Stroke Prevention Study, Purdue’s Pegboard Test was used to assess upper extremity function. The results showed a positive correlation between finger movement and increasing WMH in the frontal lobe, but the correlation was not significant [2]. Another study found that WMH was associated with motor deficits in pronation–supination, as well as brain atrophy associated with both pronation–supination and finger-tapping, but other types of CSVD were not adjusted [8, 13]. In this study, we examined the relationship between CMB and upper extremity function independently and the sample size appropriately expanded.
CMBs seem to play an important role in motor function, but
the mechanisms are still not to be fully elucidated. The
direct damage of CMB lesions on brain tissues may cause myelin loss, neuronal
loss, variable extent of gliosis, and lead to disordered brain function [31]. The
products derived from blood extravasations, especially ions, can also lead to a
series of secondary brain injuries, such as blood brain barrier (BBB) breakdown
and inflammatory activation associated with other categories of CSVD and
neurodegenerative pathologies [32]. Additional to direct damage to brain, CMBs
are also found to have associations with the disruption of white matter and
impairment of brain networks, including longer path length, and less global
efficiency with diffusion tensor imaging (DTI) and
Takakusaki [38] has pointed out that the upper extremity had sophisticated abilities depending on integrated visual, somatosensory, and action systems of motor cortex, as well as its subcortical connections with brainstem and cerebellum. The process of pronation–supination involves hand movements with visuospatial and coordination components, strength, and speed, and may require the overall function of an intact nervous system. In agreement with the findings reported here, CMBs involved multiple brain regions that controlled motor, sensory, and visual functions associated with hand movements.
The strength of this study includes the use of high-resolution MRI, evaluating both the number and location of CMBs and improved accuracy of imaging markers of CMBs. Another strength was the exploring of relationships between CMBs and motor performances in gait, balance and upper extremity which are rarely discussed. There are limitations to this study. First, its cross-sectional design precluded assumptions of causality. Longitudinal studies are needed to further explore this and to obtain a better insight into optimal timing with respect to prevention and disease progression. Second, although more accurate MRI was employed, visual ratings of CSVD imaging might have introduced errors which could not be overruled, better imaging equipment and software packages are needed to assess CSVD quantitatively so as to explore the correlations more rigorously. Third, the measuring equipment was not accurate enough which might induce error of movement data and more advanced equipment should be used. Fourth, considering lobar CMBs indicate CAA are associated with Alzheimer’s disease and vascular cognitive impairment. Therefore, in exploring the relationship between CMB and movement disorder, cognitive impairment should be considered and the influence of cognitive function should be ruled out in future research. Finally, only one hospital provided patients which limited the generalizability of the results.
The present study indicated that CMBs, particularly in the frontal lobe, temporal lobe, basal ganglia and infratentorial areas, were associated with gait and balance dysfunction independent of other coexisting markers of CSVD. It is also the first study to demonstrate that CMBs contribute to upper extremity disorder. The influence of CMBs on movement impairment should be confirmed in future explorations and these factors should be considered during the development of new interventions.
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
Y-TH drafted the manuscript and conducted the statistical analyses. W-LH managed the database and provided additional statistical expertise. Y-TH, S-NY, Y-L, W-Q and L-Y contributed to the conception and design of the study and interpretation of the data. Y-L provided expertise on brain imaging analysis. W-LH was the principal investigator of the study and was responsible for the study conception and interpretation of data and had final responsibility for the decision to submit for publication. All authors contributed to editorial changes in the manuscript. All authors provided final approval for the version of the manuscript submitted for publication and agree to be accountable for the work.
Ethical approval (2020-3-27-1) was obtained from the Committee of Beijing Chao-Yang Hospital, Capital Medical University. All methods were carried out in accordance with the Declaration of Helsinki and informed consent was obtained from all the study participants or their legal guardians for participation in the study.
Not applicable.
This research received no external funding.
The authors declare no conflict of interest.
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