Abstract
Objective
To examine hand motor features in individuals diagnosed with disorder of consciousness (DoC) after severe brain injury, and to investigate the relationship between manual dexterity and levels of consciousness.
Design
Cross-sectional study.
Setting
Rehabilitation hospital.
Participants
Sixty patients with DoC (44 men and 16 women), with a mean age of 55.03±15.28 years and a mean postinjury duration of 10.68±7.98 months.
Interventions
Not applicable (no interventions were administered).
Main Outcome Measures
Manual dexterity (Brunnstrom Recovery Stage [BRS]), spasticity (Modified Ashworth Scale), pain (Nociception Coma Scale-Revised), and consciousness (Coma Recovery Scale-Revised) were assessed. Descriptive, nonparametric, and multivariate analyses were performed.
Results
Evaluation of the best-functioning hands in the 60 patients with DoC showed that 60% (n=36) had low dexterity (BRS<4), and edema was revealed in 28.3% (17/60) of these hands. Compared with patients in minimally conscious state (MCS), those in unresponsive wakefulness syndrome (UWS) had significantly lower manual dexterity (96.2% vs 32.4%) and a higher prevalence of hand edema (42.3% vs 17.6%). BRS scores were significantly higher for hands than for arms (P<.001). Manual dexterity was strongly associated with consciousness diagnosis (adjusted odds ratios=16.03, 95% confidence interval, 2.16-119.11; P=.007). No significant difference was found between right and left hands.
Conclusions
A considerable proportion of patients with DoC experience severe hand complications, which can negatively impact their clinical diagnosis and quality of life. The BRS is effective in detecting subtle hand motor responses that may reflect residual consciousness in DoC. The observed association between manual dexterity and consciousness suggests that hand motor control may engage neural circuits involved in consciousness processing.
Keywords: Complications, Disorder of consciousness, Manual dexterity, Rehabilitation, Upper limb motor
The global population of patients with disorder of consciousness (DoC) caused by severe brain injuries is steadily increasing. Individuals with DoC are entirely dependent on others for activities of daily living.1 After a period of coma, patients with severe brain injuries may transition into an altered state of consciousness, ranging from unresponsive wakefulness syndrome (UWS), which is also known as vegetative state, to minimally conscious state (MCS).2 Patients with UWS typically exhibit only reflexive behaviors, whereas patients with MCS exhibit fluctuating, purposeful behaviors that reflect conscious of themselves or their environment. The Coma Recovery Scale-Revised (CRS-R) is recognized as the most comprehensive, evidence-based behavioral assessment tool for detecting signs of consciousness in patients recovering from coma.3 In addition, advanced imaging and electrophysiology techniques have been developed to identify cognitive-motor dissociation, a condition in which patients appear unresponsive but retain intact consciousness and significant cognitive capabilities.4
However, the clinical application of advanced imaging and electrophysiology techniques remains limited because of its high cost and the complexity of data analysis. Consequently, clinicians rely more on practical and cost-effective behavioral assessments such as the CRS-R while striving to avoid misdiagnosis. Studies have shown that combing CRS-R with the Motor Behavior Tool-Revised can notably improve the detection of intentional movement and signs of awareness. Expanding motor assessment to include limb, face, and ocular movements allows for the identification of subtle, purposeful behaviors even in patients classified as UWS. The hand, which occupies a significant presentation in the cerebral cortex, plays a critical role in activities of daily living. However, despite its importance, hand dexterity in patients with DoC remains underexplored, primarily because of their communication limitations. Existing research involving DoC populations have focused on upper limb spasticity and pain.5,6 Scholars suggested that movement disorders of the limbs have a profound impact on the diagnosis,7,8 treatment,9 and prognosis3,10 of its clinical management. Thibaut et al11, 12, 13 indicated that the prevalence of spasticity in DoC ranged from 59%-89%, with upper limb spasticity being more common than lower limb. Moreover, upper limb spasticity has shown to correlate with the Nociception Coma Scale-Revised (NCS-R) scores. Sattin et al14 also found that patients in vegetative state exhibit lower NCS scores than those with MCS. Impairments in hand dexterity represents some of the most disabling consequences in neurologic and orthopedic conditions.15 Therefore, comprehensive assessment of hand dexterity in patients with DoC is both necessary and clinically important. Hand dexterity refers to skilled, coordinated hand movements, ranging from simple grasping to complex manipulative tasks.16 Although various standardized tools such as the Wolf Motor Function Test, the Fugl Meyer Test, and the Action Research Arm Test17 exist for evaluating upper limb function, these assessments typically require patient consciousness and the ability to follow instructions. Because most patients with DoC experience consciousness impairment and fatigue, these task-oriented scales with numerous items are often unsuitable for this population. The Brunnstrom Recover Stage scale (BRS), first established by Brunnstrom18 in 1966, was originally designed to evaluate motor recovery in patients with stroke. It has since been adapted for use in patients with severe brain injuries. The BRS is simple and practical because it assesses motor recovery based on observable characteristics, and has been demonstrated to have acceptable reliability and validity. For DoC population, the BRS is particularly useful because it allows the staging of motor recovery in both the arms and hands without requiring active patient cooperation. The scale is divided into 6 phases based on the degree of spasticity and the onset of voluntary movements, with higher stages indicating greater motor flexibility.
In this study, we aimed to investigate the manual dexterity of patients with DoC and explore its association with levels of consciousness. In addition, we aimed to identify hand-related complications and their implications for clinical management in DoC, as to provide evidence to support individualized hand management strategies and enhance understanding of hand motor characteristics in the DoC population.
Methods
Participants
Patients were recruited between September 2021 and May 2022 from a rehabilitation hospital. The screening and recruiting flow of participants is shown in figure 1.
Fig 1.
Flowchart of the study. BRS, the Brunnstrom Recover Stage scale; CRS-R, the Coma Recovery Scale-Revised; DoC, Disorder of Consciousness; EMCS, emergence from minimally conscious state; MAS, the Modified Ashworth Scale; MCS, minimally conscious state; NCS-R, the Nociception Coma Scale-Revised.
Information on patient handedness, as documented in the hospital's electronic medical records based on admission interviews with family members, was retrieved for all participants.
Our inclusion and exclusion criteria were inclusion—(1) age≥18 years; (2) stable vital signs; (3) no fractures in either upper limb; (4) no active seizures; and (5) diagnosis of DoC based on 5 CRS-R assessments; exclusion—(1) unstable vital signs and (2) diagnoses of coma or emergence from MCS.
Evaluation process
All evaluators received standardized professional training and were proficient in CRS-R scoring procedures. Eligible patients were screened, and the CRS-R was administered 5 times at random intervals within 10 days to ensure the accurate diagnosis. Before the first CRS-R evaluation, passive flexion and extension spasticity of the upper extremities (elbow, wrist, metacarpophalangeal joint, and proximal interphalangeal joint) was assessed by the same evaluator using the Modified Ashworth Scale. This was done to minimize evaluation time and avoid fatigue in patients with impaired consciousness, which could affect their behavioral responses. Spasticity was categorized based on its impact on daily care and resistance throughout the range of motion. Patients with the Modified Ashworth Scale ≥2 were recorded as having severe spasticity. Standardized stimulation was used during CRS-R evaluations. Specifically, the left- and right-hand nail beds were pressed, and the patient's NCS-R scores were recorded. BRS assessment was conducted simultaneously by observing the patient's upper limb and hand movements during CRS-R evaluations. The patient's hand condition was observed before each assessment. For edema evaluation, the same evaluator pressed the dorsal surface of the patient's left and right hands with equal force. If the area became depressed and did not rebound for a short time, it was recorded as edema.
All assessments were conducted after obtaining informed consent from the legal representatives of the patients with DoC. Clinical data (eg, age, sex, time since onset, traumatic vs nontraumatic injury, and medical diagnostics) of the patients were retrieved from the electronic medical database of the hospital.
Statistical analysis
Our statistical analysis was designed to address distinct research questions at different levels to ensure both clinical relevance and methodological rigor.
Primary analysis strategy
Patient-level analysis: to assess the relationship between a patient's optimal manual dexterity and clinical outcomes, we used a single data point per patient for all inferential analyses: the score from the “best hand” (defined as the hand with the higher BRS score). The incidence rates of complications—including edema, spasticity, pain, and restrictive device use—were calculated based on their presence in at least one hand per patient. This approach captures the patient's preserved functional capacity while ensuring statistical independence of observations.
Within-subject comparison: to specifically investigate differences in manual dexterity between the left and right hands within the same individual, the Wilcoxon signed-rank test was conducted using data from both hands.
Specific statistical methods
Descriptive statistics: demographic and clinical profiles were summarized using medians and interquartile ranges for non-normally distributed continuous data, and percentages for qualitative variables.
Univariate group comparisons: because of the non-normal distribution of continuous data, group comparisons between the 2 diagnostic cohorts were performed using nonparametric tests: the Mann-Whitney U test for continuous variables, and the chi-square test (or Fisher exact test where appropriate) for categorical variables.
Analysis of ordinal data: for comparisons across the 6 BRS stages, the Kruskal-Wallis H test was employed. When a significant difference was detected, post-hoc pairwise comparisons were conducted with Bonferroni correction.
Multivariate analyses: to identify independent predictors of clinical outcomes, binomial logistic regression was performed. The selection of covariates for the multivariate models was prespecified based on biological plausibility and existing literature, rather than relying on univariate screening. As such, all models were adjusted for the following core set of potential confounders: age, sex, etiology (traumatic vs nontraumatic), and time since injury.1, 2, 3 Variables that showed statistical significance in the univariate analyses were also entered into the models to assess their independent contribution after accounting for these prespecified factors. Results were expressed as adjusted odds ratios (aORs) with their 95% confidence intervals (CIs).
Validation and exploratory analyses
Sensitivity analysis: to validate the robustness of the primary findings, all key models were re-run using data solely from the dominant (right) hand.
Exploratory analysis: the laterality of manual dexterity was compared between patients with UWS and MCS by computing a laterality index (BRS Hand Right – BRS Hand Left), with group comparison performed using the Mann-Whitney U test.
A P value of <.05 was regarded as statistically significant. All statistical analyses were performed using IBM SPSS Statistics, version 25.0a.
Results
A total of 60 patients with DoC were included in this study, median age was 58.5 years and median time since injury was 283 days. A total of 26 patients diagnosed as UWS through multiple CRS-R assessments and 34 classified as MCS after exhibiting conscious behaviors in at least 1 assessment. Etiologic analysis of the 60 enrolled patients with DoC revealed that nontraumatic brain injury (66.7%, 40/60) was the primary cause, with an incidence approximately twice that of traumatic brain injury (33.3%, 20/60). Among the nontraumatic causes, intracerebral hemorrhage was the most common (31.7%, 19/60), followed by cerebral infarction (16.7%, 10/60). Among the traumatic causes, road traffic accidents (18.3%, 11/60) and falls (10.0%, 6/60) were the leading mechanisms. The detailed etiologic classification is provided in supplemental table S1 (available online only at http://www.archives-pmr.org/). Baseline characteristics revealed no significant differences between the 2 groups in age, sex, etiology (traumatic or nontraumatic), or time after injury (table 1).
Table 1.
Demographic characteristics and clinical data of the study sample (N=60)
| Items | UWS | MCS | Z/χ2 | P Value | Total | ||||
|---|---|---|---|---|---|---|---|---|---|
| Age (y) M (IQR) | 51.5 (38.5-65) | 63.5 (45.5-70.3) | −1.531 | .126 | 58.5 (43-66) | ||||
| Time since injury, d, M (IQR) | 315 (116-315) | 281.5 (155-351.3) | −0.500 | .617 | 283 (135-458) | ||||
| Gender, n (%) | |||||||||
| Male | 20 (76.9) | 24 (70.6) | 0.302* | .582 | 44 (73.3) | ||||
| Etiology, n (%) | |||||||||
| TBI | 10 (38.5) | 10 (29.4) | 0.543* | .461 | 20 (33.3) | ||||
| Diagnosis, n (%) | 26 (43.3) | 34 (56.7) | 1.067* | .302 | |||||
| CRS-R total, M (IQR) | 6 (5-6) | 10 (9-13) | −6.416 | <.001 | 7.5 (6-10) | ||||
| LEFT | RIGHT | χ2 | P Value† | ||||||
| BRS hand, M (IQR) | 1 (1-2) | 4 (3-5) | −5.370 | <.001 | 2 (1-3) | 2 (1-3.75) | -1.359 | .174 | 3 (1-4) |
| BRS arm, M (IQR) | 1 (1-1) | 3 (1-4) | −4.536 | <.001 | 1 (1-2) | 1 (1-2) | -1.117 | .264 | 1 (1-3.8) |
| NCS-R, M (IQR) | 2 (2-3) | 3 (2-5) | −3.976 | <.001 | 2 (1.5-3) | 2 (2-3) | –.961 | .336 | 3 (2-4) |
| 1. Manual dexterity (BRS Hand < 4), n (%) | 25 (96.2) | 11 (32.4) | 24.989* | <.001 | 49 (81.7) | 45 (75.0) | .523 | 36 (60) | |
| 2. Arm dexterity (BRS UL < 4), (n (%) | 26 (100) | 19 (55.9) | 15.294* | <.001 | 50 (83.3) | 54 (90.0) | .424 | 45 (75) | |
| 3. Pain (NCS-R ≥ 4), n (%) | 1 (3.8) | 16 (47.1) | 13.549* | <.001 | 14 (23.3) | 10 (16.7) | .344 | 17 (28.3) | |
| 4. Edema, n (%) | 11 (42.3) | 6 (17.6) | 4.413* | .036 | 8 (13.3) | 14 (23.3) | .146 | 17 (28.3) | |
| 5. Spasticity (elbow), n (%) | 8 (30.8) | 21 (61.8) | 5.668* | .017 | 20 (33.3) | 18 (30) | .824 | 29 (48.3) | |
| 6. Spasticity (wrist), n (%) | 8 (30.8) | 13 (38.2) | 0.361* | .548 | 11 (18.3) | 15 (25) | .454 | 21 (35) | |
| 7. Spasticity (MP), n (%) | 12 (46.2) | 16 (47.1) | 0.005* | .944 | 22 (36.7) | 22 (36.7) | 1.000 | 28 (46.7) | |
| 8. Spasticity (PIP), n (%) | 10 (38.5) | 16 (47.1) | 0.443* | .505 | 22 (36.7) | 20 (33.3) | .754 | 26 (43.3) | |
| 9. Restricted movement band, n (%) | 0 (0) | 12 (35.3) | 11.471* | .001 | 8 (13.3) | 6 (10) | .549‡ | 13 (21.7) |
Abbreviations: MP, metacarpophalangeal joint; NCS-R, the nociception coma scale-revised; PIP, proximal interphalangeal joint; TBI, traumatic brain injury.
a Chi-square test;
the McNemar test;
refers to the comparison between the left group and the right group.
All patients completed 5 CRS-R evaluations and 1 specialized hand function assessment, ensuring robust data completeness. The study flowchart is detailed in figure 1, with exclusion criteria encompassing unstable vital signs, refusal to participate, or diagnoses of coma or emergence from MCS. Manual dexterity assessments demonstrated that the median BRS stage for hands across all 60 patients with DoC was 3, with 60% exhibiting limited bilateral hand flexibility (BRS<4). In contrast, the median BRS stage for proximal for arms was 1, with 75% of patients showing reduced arm flexibility (BRS<4) (fig 2).
Fig 2.
Percentage of BRS staging in 60 DoC.
Subgroup comparisons revealed significantly worse hand flexibility in the UWS group compared with the MCS group (low flexibility: 96.2% vs 32.4%), with an even more pronounced difference in arm flexibility (100.0% vs 55.9%). Comorbidity analysis identified severe elbow spasticity in 48.3% of patients, with 28.3% experiencing hand pain or edema. The MCS group exhibited significantly higher rates of elbow spasticity (61.8% vs 30.8%) and pain sensitivity (47.1% vs 3.8%) than the UWS group, potentially linked to residual motor intentions causing abnormal muscle tone. In addition, 13 patients with MCS required restrictive bandages to manage involuntary movements, whereas the UWS group showed a higher prevalence of edema (42.3% vs 17.6%) because of reduced mobility (table 1).
To address potential laterality bias, a sensitivity analysis was conducted using data from the right side only. The differences between UWS and MCS groups remained statistically significant for the BRS Hand, BRS Arm, and NCS-R scores. However, the previously observed group differences in the incidence of hand edema and severe elbow spasticity in the best side analysis were no longer significant when the analysis was restricted to the right side (table 2). These findings support using robust core scales (BRS and NCS-R) for diagnosis, while advising that assessments of lateralized complications should specify the side and method of evaluation.
Table 2.
Sensitivity analysis: demographic characteristics and clinical data of the study sample (N=60)
| Items | UWS | MCS | Z/χ2 | P Value | Total |
|---|---|---|---|---|---|
| Age (y), M (IQR) | 51.5 (38.5-65) | 63.5 (45.5-70.3) | −1.531 | .126 | 58.5 (43-66) |
| Time since injury, (d), M (IQR) | 315 (116-315) | 281.5 (155-351.3) | −0.500 | .617 | 283 (135-458) |
| Gender, n (%) | |||||
| Male | 20 (76.9) | 24 (70.6) | 0.302* | .582 | 44 (73.3) |
| Etiology, n (%) | |||||
| TBI | 10 (38.5) | 10 (29.4) | 0.543* | .461 | 20 (33.3) |
| Diagnosis, n (%) | 26 (43.3) | 34 (56.7) | 1.067* | .302 | |
| CRS-R total, M (IQR) | 6 (5-6) | 10 (9-13) | −6.416 | <.001 | 7.5 (6-10) |
| BRS right hand, M (IQR) | 1 (1-2) | 3 (2-4.3) | −3.974 | <.001 | 2 (1-3.8) |
| BRS right arm, M (IQR) | 1 (1-1) | 1 (1-3) | −2.849 | .004 | 1 (1-2) |
| NCS-R-right M, (IQR) | 2 (1-3) | 3 (2-4) | −2.330 | .020 | 2 (2-3) |
| 1. Manual dexterity (BRS right hand < 4), n (%) | 25 (96.2) | 20 (58.8) | 10.950* | .001 | 45 (75) |
| 2. Arm dexterity (BRS Right UL < 4), n (%) | 26 (100) | 28 (82.4) | FET | .031 | 54 (90) |
| 3. Pain (NCS-R-right ≥4), n (%) | 1 (3.8) | 9 (26.5) | FET | .033 | 10 (16.7) |
| 4. Edema right, n (%) | 9 (34.6) | 5 (14.7) | 3.265* | .071 | 14 (23.3) |
| 5. Spasticity (right elbow), n (%) | 5 (19.2) | 13 (38.2) | 2.534* | .111 | 18 (30) |
| 6. Spasticity (right wrist), n (%) | 7 (26.9) | 8 (23.5) | 0.090* | .764 | 15 (25) |
| 7. Spasticity (right MP), n (%) | 10 (38.5) | 12 (35.3) | 0.064* | .801 | 22 (36.7) |
| 8. Spasticity (right PIP), n (%) | 9 (34.6) | 11 (32.4) | 0.034* | .854 | 20 (33.3) |
| 9. Right restricted movement band, n (%) | 0 (0) | 5 (14.7) | FET | .063 | 5 (8.3) |
Abbreviations: FET, Fisher exact test; TBI, traumatic brain injury.
a Chi-square test.
Binomial logistic regression analysis, adjusted for clinically relevant confounders including age, gender, days after injury, and etiology, identified a significant independent association between better hand motor function and higher consciousness levels in patients with DoC (aOR=16.03; 95% CI, 2.16-119.11; P=.007) (table 3). In contrast, arm motor function was not significantly associated with consciousness diagnosis.
Table 3.
Predictors of consciousness diagnosis (UWS vs MCS)
| Variable | Univariable |
Multivariable |
||||
|---|---|---|---|---|---|---|
| P uni | OR | 95% CI | P adj | OR | 95% CI | |
| BRS hand | <.001 | 4.10 | 2.10-7.98 | .007* | 16.03 | 2.16-119.11 |
| BRS arm | .001 | 4.36 | 1.90-10.05 | .824 | 0.84 | 0.18-3.96 |
| NCS-R | .001 | 2.86 | 1.54-5.30 | .613 | 0.73 | 0.21-2.50 |
| Edema | .040 | 3.42 | 1.06-11.09 | .094 | 11.34 | 0.66-194.73 |
| Elbow SP | .019 | 0.28 | 0.09-0.81 | .320 | 0.20 | 0.01-4.87 |
| Age | .159 | 1.03 | 0.99-1.06 | .491 | 1.03 | 0.95-1.11 |
| Days after injury | .294 | 1.00 | 0.99-1.00 | .043* | 0.99 | 0.99-1.00 |
| Gender | .583 | 0.72 | 0.22-2.33 | .817 | 0.74 | 0.06-9.66 |
| Etiology | .462 | 0.67 | 0.23-1.97 | .024* | 0.03 | 0.01-0.62 |
NOTE. The multivariable model was adjusted for all listed variables.
Abbreviations: CI, confidence interval; OR, odds ratio; SP, severe spasticity.
P values indicate significance (P<.05).
This finding was further supported by a sensitivity analysis restricted to the right side, in which BRS Right Hand remained significantly associated with the consciousness diagnosis (aOR=5.59; 95% CI, 1.66-18.81; P=.005) (table 4), whereas right-hand edema and severe elbow spasticity lost statistical significance. Together, these results strengthen the observed link between preserved hand motor function and consciousness levels. This association is consistent with the hypothesis that preserved hand function may reflect partial integrity of higher-order neural networks. Our findings also underscore the importance of adjusting for established demographic and injury-related variables in consciousness research.
Table 4.
Right-hand sensitivity analysis: predictors of consciousness diagnosis
| Variable | Univariable |
Multivariable |
||||
|---|---|---|---|---|---|---|
| UWS vs MCS | P uni | OR | 95% CI | P adj | OR | 95% CI |
| BRS right hand | .001 | 2.69 | 1.52-4.74 | .005* | 5.59 | 1.66-18.81 |
| BRS right arm | .016 | 2.97 | 1.23-7.161 | .846 | 0.84 | 0.14-4.97 |
| NCS-R total-right | .018 | 1.72 | 1.10-2.68 | .932 | 1.03 | 0.50-2.12 |
| Right-hand edema | .078 | 3.07 | 0.88-10.68 | .104 | 4.98 | 0.72-34.52 |
| Right elbow SP | .117 | 0.39 | 0.12-1.27 | .480 | 0.38 | 0.03-5.60 |
| Age | .159 | 1.03 | 0.99-1.06 | .044* | 1.07 | 1.00-1.14 |
| Days after injury | .294 | 1.00 | 0.99-1.00 | .014* | 1.00 | 0.99-1.00 |
| Gender | .583 | 0.72 | 0.22-2.33 | .992 | 1.01 | 0.16-6.30 |
| Etiology | .462 | 0.67 | 0.23-1.97 | .075 | 0.19 | 0.03-1.18 |
NOTE. The multivariable model was adjusted for all listed variables.
Abbreviations: CI, confidence interval; OR, odds ratio, SP, severe spasticity.
P values indicate significance (P<.05).
Comparisons of BRS stages between proximal (arm) and distal (hand) upper extremities revealed significant differences in both the UWS and MCS groups, with higher BRS stages observed in the hands (table 5).
Table 5.
Comparison of BRS between hands and arms in patients with DoC
| Group | BRS Hand M (IQR) |
BRS Arm M (IQR) |
Z | P Value |
|---|---|---|---|---|
| Total | 3 (1-4) | 1 (1-3.8) | −5.019b | <.001 |
| UWS | 1 (1-1) | 1 (1-1) | −2.484b | .013 |
| MCS | 4 (3-5) | 3 (1-4) | −4.361b | <.001 |
Nonparametric testing combined with BRS stratification analysis indicated that hand BRS≥4 showed a strong association with higher consciousness levels (fig 3).
Fig 3.
Multiple comparisons for the diagnosis of consciousness in different groups of BRS.
An exploratory analysis was conducted to investigate whether the pattern of manual dexterity laterality differed between patients with UWS and MCS. The laterality index was calculated as the difference between the right and left hand BRS scores (BRS Hand Right – BRS Hand Left). The Mann-Whitney U test revealed no statistically significant difference in the laterality index between the UWS (Mean Rank=28.06, n=26) and MCS (Mean Rank=32.37, n=34) groups (U=378.5, P=.299). This indicates that no significant difference in the degree of lateralization was found between these 2 diagnostic subgroups.
Discussion
To our knowledge, this study represents the first systematic investigation of hand motor function lateralization, complication profiles, and their clinical implications in patients with DoC. By integrating assessments of hand flexibility, pain perception, and complication rates, we delineated distinct motor and sensory phenotypes between patients with UWS and MCS and identified hand dexterity as a promising biomarker for consciousness stratification. The absence of significant lateralization in hand flexibility challenges conventional assumptions about hemispheric dominance in motor recovery after severe brain injury, suggesting instead a diffuse or bilateral neural substrate for residual motor function in DoC. This observation is consistent with the interpretation that preserved corticospinal tract integrity, even in nondominant hemispheres, may underpin residual motor capabilities in minimally conscious states.
The robust association between hand flexibility (BRS≥4) and higher consciousness levels underscores the potential role of corticofugal pathways in sustaining conscious awareness. Unlike proximal arm movements, which rely heavily on brainstem-spinal circuits, fine hand control is thought to depend on intact cortical and thalamocortical networks. Thus, preserved hand dexterity in patients with MCS may reflect selective sparing of these higher-order networks, which are also implicated in conscious processing. This duality suggests motor function as both a marker and potential mediator of consciousness, providing a rationale for incorporating hand-specific motor assessments into diagnostic protocols, particularly in differentiating MCS from UWS.
The complication profiles further illuminate the pathophysiological divergence between groups. The elevated elbow spasticity (61.8% vs 30.8%) and pain sensitivity (47.1% vs 3.8%) in patients with MCS are consistent with hyperexcitable spinal reflexes triggered by intermittent volitional motor attempts, a phenomenon absent in UWS because of profound cortical disconnection. Conversely, the higher hand edema prevalence in UWS (42.3% vs 17.6%) may arise from autonomic dysregulation and prolonged immobility, hallmarks of severe thalamocortical dysfunction. These findings support condition-specific management: patients with MCS may benefit from antispasmodic therapies and pain modulation, whereas UWS requires rigorous circulatory monitoring to prevent secondary tissue damage.
Clinically, this study positions hand function as a window into residual consciousness. The ability to detect subtle hand movements or flexibility changes could enhance early diagnosis, prognostication, and therapeutic targeting. For instance, hand-focused rehabilitation (eg, sensorimotor stimulation and constraint-induced therapy) might not only mitigate complications but also exploit neuroplasticity to facilitate recovery. Furthermore, the lack of lateralization differences suggests that bilateral hand assessments should be prioritized in clinical evaluations to avoid underestimating motor potential.
Hands in patients with DoC
The observed differences in BRS hand scores, BRS arm scores, NCS-R total scores, severe elbow spasm, and edema between diagnostic groups indicate that individualized hand assessments are feasible and valuable for patients with DoC. The results of binomial logistic regression analysis and Kruskal-Wallis test showed that hand BRS scores were significantly associated with consciousness, with BRS≥4 serving as a promising marker of consciousness. The presence of thumb pinch or independent finger separation movements can help clinicians assess the patient's consciousness more efficiently. Notably, hand complications such as motor paralysis, sensory loss, edema, and severe spasticity can reduce hand flexibility and interfere with the assessment of consciousness. The mean NCS-R score in this study was 2.83±1.38, lower than the 4.77±1.84 reported by Chatelle et al.19 This discrepancy might be because of the inclusion of patients with upper limb paraplegia in our study. Moreover, the incidence of severe spasms in the upper limbs of DoC in our cohort was 65%, aligning with the 60%∼89%6 range reported by Martens et al6 and Thibaut et al.11 These findings emphasize the need for improved upper limb spasticity management in medical care. In addition, prolonged edema, which has notable impacts on joint range of motion, soft tissue mobility, scar tissue formation, and hand esthetics, can further limit the activities of daily living.20,21 Our study found a 28% incidence of hand edema in patients with DoC, comparable with the 23.5%∼37% reported in previous studies with hemiplegic stroke patients.21 In addition, we also found that the onset of edema was inversely correlated with time after injury in patients with DoC (r=-.287, P<.05). Given the unique characteristics of hand function in patients with DoC, individualized hand assessment can have a positive impact on the overall consciousness evaluation, clinical classification, and prognosis.
Proximal and distal upper extremity motor control
Our research revealed that hand dexterity surpassed arm dexterity in patients with DoC. Among the included 60 patients, 40% had a BRS hand score ≥4, whereas 28.3% were at BRS stage 1 for both hands. In contrast, only 25% of the patients reached a BRS arm score ≥4, whereas 53.3% were at BRS stage 1 for both arms. In stroke populations, Saito et al22 found that among a cohort of 32 patients with stroke without DoC, 71.9% of the hemiplegic hands scored ≥4 on the BRS within 2-37 days after injury, with only 0.03% scoring 1. Similarly, Naghdi et al23 reported higher arm BRS scores (≥4) in 66.7% of patients with stroke 2-60 months after injury. In contrast to patients with DoC, stroke survivors without consciousness impairments exhibit faster and more pronounced recovery in proximal upper limb movements (arms) compared with distal hand motor function over time.24, 25, 26, 27 Importantly, our binomial logistic regression models found a significant correlation between hand dexterity and consciousness level, whereas arm flexibility showed no such association. This result may be explained by the distinct motor control strategies used by the arms and hands.16,28,29 Our results suggest that the motor control neural circuits of the hand correlate with the level of consciousness. Patients with DoC have severe brain damage,3 whereas the hand has a greater motor projection in the brain and retains more residual motor capacity than the arm extremities.30,31 Another possibility is that they reflect differences in effector biomechanics: arms are much heavier than fingers.16,32 Future studies should combine more brain neuroimaging techniques to analyze possible causes.
Dexterity in the left and right hands of patients with DoC
Handedness is a well-documented phenomenon, with approximately 90% of adults preferring their right hand.33 However, in our study of 60 right-handed patients with DoC, no statistically significant difference were found regarding flexibility between the left and right hands. This aligns with previous findings that finger movement personalization is similar between dominant and nondominant hands in simple tasks.34, 35, 36 Nonetheless, in more complex tasks such as writing, dominant hands usually tend to outperform nondominant hands.37,38 Patients with DoC, who experience severe cognitive and motor impairment, are typically capable of only simple gross movements and generally unable to perform complex, cognitively directed hand tasks.39 Our exploratory analysis further revealed no significant difference in manual laterality between patients with UWS and MCS, which suggests that profound brain injury may disrupt established lateralization patterns. Consequently, the fact that we do not see clear hand dominance in patients with DoC supports the view that consistent right-hand preference is associated with learned, culturally shaped behavior. Therefore, whether the recovery of such fine motor asymmetry depends on a level of cortical integration exceeding that of MCS presents a key question for future research using more sensitive quantification tools.
Limitations
To our knowledge, evidence on hand flexibility in patients with DoC is limited. Our study found that it significantly correlates with patients’ consciousness levels. However, the study’s generalizability might be limited by its single-center design and moderate sample size. Future research should involve multicenter, large-sample, and longitudinal studies. Second, although we addressed the concern of statistical nonindependence by using the “best hand” for primary analysis and performed sensitivity analyses, the potential for unmeasured confounding persists. Third, behavioral scale surveys were conducted, which were susceptible to the subjectivity of evaluators. In the future, objective assessment techniques such as posture assessment and sensor technology,16 as well as brain imaging and neuroelectrophysiological techniques for DoC,1,3 are needed to further understand the contribution of hand flexibility in key brain regions of consciousness. Finally, the observed variations in hand movement, pain, edema, and spasticity across diagnostic groups underscore the need to develop hand assessment scales tailored specifically for patients with DoC to guide individualized clinical management.
Conclusions
This study bridges a critical gap in DoC research by elucidating the interplay between hand motor function, consciousness level, and associated complications. Our findings suggest that hand dexterity may serve not only as a clinical indicator of consciousness but also as a therapeutic target. Future studies should validate these findings with longitudinal neuroimaging and electrophysiological investigations. By advancing both mechanistic understanding and clinical practice, this study underscores the importance of viewing motor function not merely as a symptom but also as a dynamic biomarker and therapeutic lever in management of DoC.
Suppliers
a. IBM SPSS Statistics, version 25.0.
Disclosures
The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. This study was approved by the Ethics Committee of Hangzhou Normal University (Ethical approval number: 2023010401), and written informed consent was obtained from all legal agents. We followed the principle of Strengthening Epidemiological Observational Studies (STROBE) when reporting the current research.
Acknowledgments
Acknowledgment
We want to express our sincere gratitude to Aurore Thibaut for her valuable contributions to this study. She actively participated in interpreting the study results and provided critical insights while preparing the manuscript.
Authorship contributions/CRediT statements
Lingxiu Sun and Anqi Wang drafted the first draft of the manuscript, performed the statistical analysis of the collected data, and interpreted the results. Steven Laureys, Siyu Dai, and Haibo Di conceived and designed the clinical study, and revised it critically for important intellectual content. Ziqiang Yang and Lijuan Cheng contributed to the study design and provided expertise in clinical trial methodology. Meiqi Li and Jie Huang assisted in patient recruitment and clinical assessments, ensuring adherence to study protocols and maintaining the quality of collected data. Jie Huang, Steven Laureys, Siyu Dai, and Haibo Di served as the principal investigator for the study, securing funding and overseeing the overall conduct of the research, facilitated collaboration between study sites, and ensured the timely progress of the research. Yan Chen, Nantu Hu, Xiaochen Liu, Yufei Xue, and Feng Chen reviewed and provided feedback on the manuscript, ensuring it met the scientific and ethical standards for publication.
Footnotes
Funded by the Intergovernmental International Science and Technology Innovation Cooperation & Strategic Science and Technology Innovation Cooperation (no. 2022YFE0141300); Hangzhou Normal University Seed Fund (no. 8015C52725101091); Hangzhou Medical and Health Science and Technology Project (no. A20251077 and no. B20252204); Hangzhou Biomedicine and Health Industry Development Supporting Science and Technology Special Project (no. 2024WJC012) and Theoretical Research Project on Civil Administration Policies of Zhejiang Province (no. ZMKT2024102).
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.arrct.2025.100576.
Contributor Information
Steven Laureys, Email: stevenlaureys01@gmail.com.
Siyu Dai, Email: daisiyu@hznu.edu.cn.
Haibo Di, Email: 20020192@hznu.edu.cn.
Appendix. Supplementary materials
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