Stroke Rehabilitation Research Articles

Somatosensory integration in robot-assisted motor restoration post-stroke

Disruption of somatosensorimotor integration (SMI) after stroke is a significant obstacle to achieving precise motor restoration. Integrating somatosensory input into motor relearning to reconstruct SMI is critical during stroke rehabilitation. However, current robotic approaches focus primarily on precise control of repetitive movements and rarely effectively engage and modulate somatosensory responses, which impedes motor rehabilitation that relies on SMI. This article discusses how to effectively regulate somatosensory feedback from target muscles through peripheral and central neuromodulatory stimulations based on quantitatively measured somatosensory responses in real time during robot-assisted rehabilitation after stroke. Further development of standardized recording protocols and diagnostic databases of quantitative neuroimaging features in response to post-stroke somatosensory stimulations for real-time precise detection, and optimized combinations of peripheral somatosensory stimulations with robot assistance and central nervous neuromodulation are needed to enhance the recruitment of targeted ascending neuromuscular pathways in robot-assisted training, aiming to achieve precise muscle control and integrated somatosensorimotor functions, thereby improving long-term neurorehabilitation after stroke.

Experiences and needs of older patients with stroke in China involved in rehabilitation decision-making: a qualitative study.

Shared decision-making is recommended for stroke rehabilitation. However, the complexity of the rehabilitation modalities exposes patients to decision-making conflicts, exacerbates their disabilities, and diminishes their quality of life. This study aimed to explore the experiences and needs of older patients with stroke in China during rehabilitation decision-making, providing a reference for developing decision-support strategies. A qualitative phenomenological design was used to explore the experiences and needs of older patients with stroke in China. Purposive sampling was used to recruit 31 older Chinese patients with stroke. The participants participated in face-to-face, semi-structured, and in-depth interviews. Data were analyzed using inductive thematic analysis. The key themes identified include (1) mixed feelings in shared decision-making, (2) multiple barriers hinder the possibility of participating in shared decision-making, (3) Delegating rehabilitation decisions to surrogates, (4) gaps between reality and expectation, and (5) decision fatigue from lack of continuity in the rehabilitation health care system. Older patients with stroke in China have complex rehabilitation decision-making experiences and needs and face multiple obstacles when participating in shared decision-making. They lack an effective shared decision-making support system to assist them. Providing patients with comprehensive support (such as emotional and informational), strengthening the construction of a continuous rehabilitation system, alleviating economic pressure, and promoting patient participation in rehabilitation decision-making are necessary.

Electroacupuncture alleviated post-stroke cognitive impairment via the mTOR/NLRP3-mediated autophagy-inflammatory pathway

BackgroundPost-stroke cognitive impairment (PSCI) severely reduces quality of life of patients with stroke. This study aimed to assess the effects of electroacupuncture (EA) on PSCI and the role of the mTOR/NLRP3-mediated autophagy-inflammatory pathway in this process.MethodsThe rat focal cerebral ischemia model was established using middle cerebral artery occlusion (MCAO). Following successful induction of the model, EA was applied to the bilateral Fengchi, Fengfu, and Dazhui acupoints, and brain tissue samples were collected on day 15. Cognitive function was assessed using the Morris water maze test. Cerebral infarct volume was quantified by Triphenyltetrazolium chloride (TTC) staining. Hematoxylin–eosin and TUNEL staining were performed to evaluate pathological changes and apoptosis rates. Apoptosis-, inflammation-, and autophagy-related biomarkers were measured, and autophagosomes were visualized using transmission electron microscopy.ResultsMCAO rats exhibited slower weight gain, reduced mobility, increased infarct size, pathological damage, and apoptosis, confirming successful establishment of the MCAO rat model. Following EA treatment, MCAO rats displayed faster weight gain, improved mobility, and shorter escape latency. EA also reduced the area of cerebral infarction and alleviated pathological damage and apoptosis in MCAO rats. Furthermore, EA downregulated IL-1β, IL-18, NLRP3, and LC3 II/LC3 I expression and upregulated p62, mTOR, and Beclin-1 expression in MCAO rats. EA treatment also decreased the number of autophagosomes in these rats.ConclusionsEA effectively mitigates post-stroke cognitive impairment by reducing apoptosis, inflammation, and autophagy through the regulation of the mTOR/NLRP3-mediated autophagy-inflammatory pathway, offering valuable therapeutic insights for stroke rehabilitation.

Beyond Pharmacology: The Biological Mechanisms of Remote Ischemic Conditioning in Cerebrovascular Disease

Cerebrovascular diseases (CVDs), comprising predominantly ischemic stroke and chronic cerebral hypoperfusion (CCH), are a significant threat to global health, often leading to disability and mortality. Remote ischemic conditioning (RIC) has emerged as a promising, non-pharmacological strategy to combat CVDs by leveraging the body’s innate defense mechanisms. This review delves into the neuroprotective mechanisms of RIC, categorizing its effects during the acute and chronic phases of stroke recovery. It also explores the synergistic potential of RIC when combined with other therapeutic strategies, such as pharmacological treatments and physical exercise. Additionally, this review discusses the pathways through which peripheral transmission can confer central neuroprotection. This review concludes by addressing the challenges regarding and future directions for RIC, emphasizing the need for standardized protocols, biomarker identification, and expanded clinical trials to fully realize its therapeutic potential.

A longitudinal investigation of the determinants of stroke survivors’ utilisation of a healthy lifestyle for stroke rehabilitation in Australia

This study aimed to determine the longitudinal predictors of lifestyle behaviours among stroke survivors in New South Wales, Australia. This longitudinal study utilised data from the baseline survey (2005–2009) and a sub-study survey (2017) of the 45 and Up Study. Physical activity, alcohol consumption, smoking status, and supplement use were included as dependent variables. Generalised estimating equation models were employed to assess the longitudinal association between the dependent variable and demographic and health status measures. The average age of the participants (n = 576) was 67 (SD = 9) years at baseline and 76 (SD = 9) years at the sub-study survey time, with 54.9% being male. The longitudinal analysis revealed that the likelihood of moderate/high physical activity significantly declined over time and was lower among participants with diabetes, but was higher among those with university education. The likelihood of smoking was significantly higher in females, moderate/high-risk alcohol consumers, and those with depression, but was lower among supplement users. The likelihood of moderate/high-risk alcohol consumption significantly declined with time, and was lower among females, but higher among smokers. The likelihood of supplement use significantly declined over time, but was higher among females and/or those with asthma. Our findings help illustrate that many stroke survivors may benefit from further support in adopting and maintaining a healthy lifestyle as part of their stroke management and long-term rehabilitation, which is crucial to optimising their quality of life and successful secondary stroke prevention.

Attention-based CNN model for motor imagery classification from nonlinear EEG signals

Motor imagery (MI)-based brain-computer interface (BCI) provides a promising solution for the limb rehabilitation of stroke patients. For better rehabilitation performance, high-precision classification of MI-related EEG signals plays a critical role. However, this is still a challenging problem for multi-category MI signals. In this paper, we focus on four commonly used stroke rehabilitation actions, and propose a modular temporal-spatial attention-based CNN (MTSACNN) model for MI classification. In detail, we carry out the MI experiments and acquire the EEG signals related to imagining left/right fist clenching and left/right wrist dorsiflexion. MTSACNN model firstly extracts the low-order MI features through the temporal-spatial feature extraction module (TSFE module). Especially, a group attention mechanism is proposed for intra-group information interaction. Secondly, considering the short- and long-term working characteristics of brain, high-order temporal features are further extracted and fused by the multi-level feature fusion module (MLFF module). Finally, four auxiliary losses are arranged in the classification module (C module) to speed up the model optimization process. The experimental results show that MTSACNN model can achieve good performance in decoding rehabilitation-related MI brain intentions, achieving an average classification accuracy of 72.05% for fourteen subjects. This work is beneficial to promote the construction of high-performance stroke rehabilitation BCI system.

2-Acetylacteoside improves recovery after ischemic stroke by promoting neurogenesis via the PI3K/Akt pathway

Ischemic stroke induces adult neurogenesis in the subventricular zone (SVZ), even in elderly patients. Harnessing of this neuroregenerative response presents the therapeutic potential for post-stroke recovery. We found that phenylethanoid glycosides (PhGs) derived from Cistanche deserticola aid neural repair after stroke by promoting neurogenesis. Among these, 2-acetylacteoside had the most potent on the proliferation of neural stem cells (NSCs) in vitro. Furthermore, 2-acetylacteoside was shown to alleviate neural dysfunction by increase neurogenesis both in vivo and in vitro. RNA-sequencing analysis highlighted differentially expressed genes within the PI3K/Akt signaling pathway. The candidate target Akt was validated as being regulated by 2-acetylacteoside, which, in turn, enhanced the proliferation and differentiation of cultured NSCs after oxygen-glucose deprivation/reoxygenation (OGD/R), as evidenced by western blot analysis. Subsequent analysis using cultured NSCs from adult subventricular zones (SVZ) confirmed that 2-acetylacteoside enhanced the expression of phosphorylated Akt (p-Akt), and its effect on NSC neurogenesis was shown to be dependent on the PI3K/Akt pathway. In summary, our findings elucidate for the first time the role of 2-acetylacteoside in enhancing neurological recovery, primarily by promoting neurogenesis via Akt activation following ischemic brain injury, which offers a novel strategy for long-term cerebrological recovery in ischemic stroke.

Enhancing training transfer among stroke specialist nurses: Insights from latent profile analysis

AimUsing Latent Profile Analysis (LPA) to identify distinct categories within stroke specialist nurses training transfer and to explore the factors influencing these categories. BackgroundStroke represents a significant worldwide health challenge. During the treatment and rehabilitation of stroke, stroke specialist nurses play an essential role. However, despite significant resources devoted in the training of stroke specialist nurses, the effectiveness of training transfer is still at a moderate or low level. Therefore, there is an urgent need to identify the factors affecting the training transfer of stroke specialist nurses and to formulate effective strategies for improvement based on these findings. DesignThis is a cross-sectional study. MethodFrom August to September 2023, demographic questionnaires, stroke specialist nurses training transfer scales, nursing work environment scales, and job crafting questionnaire were used to survey 413 stroke specialist nurses in 5 tertiary hospitals in Henan Province, China. LPA was used to analyze the latent characteristics of nurse training transfer and to explore the predictive factors of different categories using univariate analysis and logistic regression. ResultsA total of 397 stroke specialist nurses were included, identifying three categories of training transfer: "low training transfer group" (20.40 %), "medium training transfer group" (31.74 %), and "high training transfer group" (47.86 %). Work experience, employment type, training duration, nursing work environment, and job crafting significantly influence the training transfer categories. ConclusionThe majority of stroke specialist nurses fall into medium and low training transfer group. Contract employment and less work experience are the main reasons for medium and low levels of training transfer. Enhancing work experience, designing effective training courses, ensuring equal pay, improving the nursing environment, and implementing job crafting strategies could effectively improve training transfer for various types of stroke specialist nurses.

Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis

BackgroundAlthough the application of brain-computer interface (BCI) technology in rehabilitation has been extensively studied, a systematic and comprehensive bibliometric analysis of this area remains lacking. Thus, this study aims to analyze the research progress of BCI technology in rehabilitation through bibliometric methods.MethodsThe study retrieved relevant publications on BCI technology in rehabilitation from the Web of Science Core Collection (WoSCC) between January 1, 2004, and June 30, 2024. The search was conducted using thematic queries, and the document types included “original articles” and “review articles.” Bibliometric analysis and knowledge mapping were performed using the Bibliometrix package in R software and CiteSpace software.ResultsDuring the study period, a total of 1,431 publications on BCI technology in rehabilitation were published by 4,932 authors from 1,281 institutions across 79 countries in 386 academic journals. The volume of research literature in this field has shown a steady upward trend. The United States of America (USA) and China are the primary contributors, with Eberhard Karls University of Tübingen being the most active research institution. The journal Frontiers in Neuroscience published the most articles, while the Journal of Neural Engineering was the most cited. Niels Birbaumer not only authored the most articles but also received the highest number of citations. The main research areas include neurology, sports medicine, and ophthalmology. The diverse applications of BCI technology in stroke and spinal cord injury rehabilitation, as well as the evaluation of BCI performance, are current research hotspots. Moreover, deep learning has demonstrated significant potential in BCI technology rehabilitation applications.ConclusionThis bibliometric study provides an overview of the research landscape and developmental trends of BCI technology in rehabilitation, offering valuable reference points for researchers in formulating future research strategies.

Corticospinal Tract Sparing in Cervical Spinal Cord Injury

Disruptions in the brain’s connections to the hands resulting from a cervical spinal cord injury (cSCI) can lead to severe and persistent functional impairments. The integrity of these connections is an important predictor of upper extremity recovery in stroke and may similarly act as a biomarker in cSCI. In this perspective article, we review recent findings from a large cohort of individuals with cSCI, demonstrating the predictive value of corticospinal tract (CST) integrity in cSCI—CST sparing. This research underscores that, akin to stroke, the integrity of brain-to-hand connections is crucial for predicting upper extremity recovery following cSCI. We address the limitations of commonly used metrics, such as sacral sparing and the concept of central cord syndrome. Furthermore, we offer insights on emerging metrics, such as tissue bridges, emphasizing their potential in assessing the integrity of brain connections to the spinal cord.

Transforming Stroke Recovery: The Bobath Concept in a Case Study of Middle Cerebral Artery Infarction

Stroke remains a major cause of long-term disability, necessitating effective rehabilitation strategies. This case study examines the rehabilitation of a 57-year-old male patient recovering from a middle cerebral artery (MCA) infarction, utilising the Model of Bobath Clinical Practice (MBCP). The patient underwent interventions based on the Bobath concept, focusing on selective activation of trunk muscles, core engagement, and pelvic alignment to enhance postural control. Assessments revealed significant improvements in both sitting and standing postures, with reductions in compensatory fixation strategies. The targeted therapeutic exercises facilitated better trunk control and lower limb weight-bearing, resulting in overall enhanced stability. The findings underscore the importance of individualised approaches in stroke rehabilitation aligned with Bobath principles. While the outcomes are promising, further research is needed to confirm the effectiveness of the MBCP model across varied patient populations. This study highlights the potential of the Bobath Concept in improving functional recovery and quality of life for stroke survivors.

Neuropeptide FF Promotes Neuronal Survival and Enhances Synaptic Protein Expression Following Ischemic Injury

This study explores the neuroprotective effects of neuropeptide FF (NPFF, FLFQPQRFamide) in the context of ischemic injury. Based on transcriptomic analysis in stroke models treated with 5-Aza-dC and task-specific training, we identified significant gene expression changes, particularly involving NPFF. To further explore NPFF’s role in promoting neuronal recovery, recombinant NPFF protein (rNPFF) was used in primary mixed cortical cultures subjected to oxygen-glucose deprivation and reoxygenation. Our results demonstrated that rNPFF significantly reduced lactate dehydrogenase release, indicating decreased cellular damage. It also significantly increased the expression of TUJ1 and MAP2, markers of neuronal survival and dendritic integrity. Additionally, rNPFF significantly upregulated key synaptic proteins, including GAP43, PSD95, and synaptophysin, which are essential for synaptic repair and plasticity. Post-injury rNPFF treatment led to a significant upregulation of pro-brain-derived neurotrophic factor (BDNF) and mature BDNF, which play critical roles in neuronal survival, growth, and synaptic plasticity. Moreover, rNPFF activated the protein kinase Cε isoform, Sirtuin 1, and peroxisome proliferator-activated receptor gamma pathways, which are crucial for regulating cellular stress responses, synaptic plasticity, and energy homeostasis, further promoting neuronal survival and recovery. These findings suggest that rNPFF may play a pivotal role in enhancing neuronal survival and synaptic plasticity after ischemic injury, highlighting its potential as a therapeutic target for stroke recovery.

Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation

The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally derived data to real data to enrich the variability represented in the training set. Traditional augmentation methods, such as rotation, permutation, and time-warping, have shown some benefits in improving classifier performance, but often fail to produce realistic training examples. This study employs Conditional Generative Adversarial Networks (cGANs) to create synthetic kinematic data from a publicly available dataset, closely mimicking the experimentally measured reaching movements of stroke survivors. This approach not only captures the complex temporal dynamics and common movement patterns after stroke, but also significantly enhances the training dataset. By training deep learning models on both synthetic and experimental data, we enhanced task classification accuracy: models incorporating synthetic data attained an overall accuracy of 80.0%, significantly higher than the 66.1% seen in models trained solely with real data. These improvements allow for more precise task classification, offering clinicians the potential to monitor patient progress more accurately and tailor rehabilitation interventions more effectively.

Contribution of cognitive status on admission to mobility and balance at discharge from acute rehabilitation for stroke

Acute inpatient rehabilitation is crucial for improving mobility and balance for individuals with stroke. A potentially important factor in the recovery of mobility and balance is cognition. The purpose of this study was to determine the effect of cognition on mobility and balance in acute stroke rehabilitation. This was a longitudinal cohort study based on an inpatient rehabilitation unit at a large academic medical center. Participants were individuals with stroke admitted to acute rehabilitation after an acute care hospital stay (N = 281). Demographic data and predictor variables were collected on admission to the unit. Outcomes were collected at discharge from the unit. Multiple regression analyses were used to determine the associations between cognition (Montreal Cognitive Assessment) on mobility (Functional Independence Measure mobility subscale) and balance (Berg Balance Scale). Subtests from the Montreal Cognitive Assessment were also examined to determine if specific dimensions of cognition could predict balance after controlling for covariates. Dominance analysis was used to determine the relative importance of baseline predictors. In separate models, cognition was a significant predictor of mobility (B = 0.19) and balance (B = 0.28) at discharge after adjusting for admission mobility and balance, as well as age, sex, and length of stay. The most important predictors in both models were baseline mobility and balance, but cognition contributed to the models independently of baseline scores. Cognition was generally more important than age and sex while about equally important as length of stay. In separate models, the visuospatial/executive (B = 0.42) and the delayed recall (B = 0.37) subtests were also significant predictors of mobility. The models’ most important predictors were baseline mobility and balance scores. Cognition is a clinically relevant predictor of mobility and balance in acute stroke rehabilitation. Specific dimensions of cognition, such as executive function, visuospatial function, and delayed recall, may be especially important. Cognitive challenges and meta-cognitive strategies should be included in mobility and balance tasks when possible. Studies that evaluate the efficacy of dual-task training and meta-cognitive approaches are needed.

Improvements in upper extremity isometric muscle strength, dexterity, and self-care independence during the sub-acute phase of stroke recovery: an observational study on the effects of intensive comprehensive rehabilitation.

Stroke often impairs upper extremity motor function, with recovery in the sub-acute phase being crucial for regaining independence. This study examines changes in isometric muscle strength, dexterity, and self-care independence during this period, and evaluates the effects of a comprehensive intensive rehabilitation (COMIRESTROKE). Individuals in sub-acute stroke recovery and age- and sex-matched controls were assessed for pre- and post-rehabilitation differences in primary outcomes (grip/pinch strength, Nine Hole Peg Test [NHPT], Action Research Arm Test [ARAT]). COMIRESTROKE's effects on primary and secondary outcomes (National Institute of Health Stroke Scale [NIHSS], Modified Rankin Scale [MRS], Functional Independence Measure [FIM]) were evaluated. Outcomes were analyzed for dominant and non-dominant limbs, both regardless of impairment and with a focus on impaired limbs. Fifty-two individuals with stroke (NIHSS 7.51 ± 5.71, age 70.25 ± 12.66 years, 21.36 ± 12.06 days post-stroke) and forty-six controls participated. At baseline, individuals with stroke showed significantly lower strength (dominant grip, key pinch, tip-tip pinch, p adj < 0.05), higher NHPT scores (p adj < 0.05), and lower ARAT scores (p adj < 0.001). COMIRESTROKE led to improvements in dominant key pinch, non-dominant tip-tip pinch, NHPT, and both dominant and non-dominant ARAT (p adj < 0.05). Notably, non-dominant key pinch improved significantly when considering only impaired hands. Pre- and post-test differences between groups were significant only for ARAT (both limbs), even after adjustment (p adj < 0.05). All secondary outcomes (NIHSS, MRS, FIM) showed significant improvement post-COMIRESTROKE (p adj < 0.001). Individuals with stroke exhibit reduced muscle strength and dexterity, impairing independence. However, comprehensive intensive rehabilitation significantly improves these functions. Data are available from the corresponding author upon request and are part of a sub-study of NCT05323916.

Data-Driven Prognostication in Distal Medium Vessel Occlusions Using Explainable Machine Learning.

Distal medium vessel occlusions (DMVOs) are estimated to cause acute ischemic stroke (AIS) in 25-40% of cases. Prognostic models can inform patient counseling and research by enabling outcome predictions. However, models designed specifically for DMVOs are lacking. This retrospective study developed a machine learning model to predict 90-day unfavorable outcome [defined as a modified Rankin Scale (mRS) score of 3-6] in 164 primary DMVO patients. A model developed with the TabPFN algorithm utilized selected clinical, laboratory, imaging, and treatment data with the Least Absolute Shrinkage and Selection Operator feature selection. Performance was evaluated via 5-repeat 5-fold cross-validation. Model discrimination and calibration were evaluated. SHapley Additive Explanations (SHAP) identified influential features. A web application deployed the model for individualized predictions. The model achieved an area under the receiver operating characteristic curve of 0.815 (95% CI: 0.79-0.841) for predicting unfavorable outcome, demonstrating good discrimination, and a Brier score of 0.19 (95% CI: 0.177-0.202), demonstrating good calibration. SHAP analysis ranked admission National Institutes of Health Stroke Scale (NIHSS) score, premorbid mRS, type of thrombectomy, modified thrombolysis in cerebral infarction score, and history of malignancy as top predictors. The web application enables individualized prognostication. Our machine learning model demonstrated good discrimination and calibration for predicting 90-day unfavorable outcomes in primary DMVO strokes. This study demonstrates the potential for personalized prognostic counseling and research to support precision medicine in stroke care and recovery. ABC = definition; XYZ = definition. DMVO = Distal medium vessel occlusion; AIS = acute ischemic stroke; mRS = modified Rankin Scale; SHAP = SHapley Additive Explanations; NIHSS = National Institutes of Health Stroke Scale; ST = stroke thrombectomy; TRIPOD = Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis; CT = computed tomography; CTP = CT perfusion; MRI = magnetic resonance imaging; CTA = CT angiography; DVT = deep vein thrombosis; PE = pulmonary emboli; TIA = transient ischemic attack; BMI = body mass index; ALP = alkaline phosphatase; ALT = alanine transaminase; AST = aspartate aminotransferase; NCCT-ASPECTS = Alberta Stroke Program Early CT Score; IVT = intravenous thrombolysis; mTICI = modified thrombolysis in cerebral infarction; ER = emergency room; kNN = k-nearest neighbor; LASSO = Least Absolute Shrinkage and Selection Operator; PDPs = partial dependence plots; ROC = receiver operating characteristic; PRC = precision-recall curve; AUROC = area under the ROC curve; AUPRC = area under the PRC; CI = confidence interval.

Sensor-Acquired Reachable Workspace (RWS) Correlates with Activities of Daily Living (ADL) Function in Stroke as Measured by Functional Independence Measure (FIM) Self-Care

Optimal upper extremity motor control and range of motion are necessary to achieve even the basic activities of daily living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide an assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, the sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at the 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3 months were noted (r = 0.619, r = 0.661, p &lt; 0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r = 0.690, r = 0.815, p &lt; 0.05). Multiple linear regression analyses revealed a change in average bilateral total RSA of 0.1 unit from admission to the 3-month follow-up correlated with a respective change in the FIM self-care score of 2.011 points (95%CI: 0.663–3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.

The Role of Bio-Inspired Soft Robotic Gloves in Stroke Rehabilitation: A Comprehensive Analytical Review

Stroke is a leading cause of long-term disability, with many survivors experiencing impaired motor function. Traditional rehabilitation methods often face limitations in achieving full functional recovery. In recent years, bio-inspired soft robotic gloves have emerged as a promising solution for enhancing stroke rehabilitation, mimicking the natural movement and adaptability of biological systems. This paper provides a comprehensive analytical review of bio-inspired soft robotic gloves designed for stroke rehabilitation, focusing on their development, fabrication methods and therapeutic applications. The primary objective of this review is to evaluate the role of bio-inspired designs in the creation of soft robotic gloves and assess their effectiveness in facilitating motor recovery post-stroke. A systematic analysis of existing literature is conducted, exploring various fabrication techniques, including soft material integration, actuators and sensors, as well as the application of these gloves in clinical and therapeutic settings. Findings from this review reveal that bio-inspired soft robotic gloves show significant potential in improving patient outcomes by enhancing finger and hand movement through assisted motor control. These devices offer advantages in terms of comfort, flexibility and adaptability, allowing for prolonged and intensive rehabilitation sessions. However, limitations remain, such as the high cost of fabrication, challenges in achieving precise motor control and the lack of large-scale clinical trials to validate long-term efficacy. The research has several implications: (1) Theoretical: advancing the understanding of bio-inspired design principles in soft robotics; (2) Methodological: highlighting fabrication techniques that enhance glove performance and user adaptability. This review offers original value by consolidating and analysing recent developments in bio-inspired soft robotic gloves, contributing to both the academic literature and practical applications in stroke rehabilitation. It emphasizes the necessity of future research to address current limitations, especially in optimizing performance and reducing production costs, while enhancing patient outcomes.

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

BackgroundFollowing a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.MethodsTwenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.Results and conclusionsKinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

Efficacy and safety of very early rehabilitation for acute ischemic stroke: a systematic review and meta-analysis.

Early rehabilitation after acute ischemic stroke (AIS) contributes to functional recovery. However, the optimal time for starting rehabilitation remains a topic of ongoing investigation. This article aims to shed light on the safety and efficacy of very early rehabilitation (VER) initiated within 48 h of stroke onset. A systematic search in PubMed, Embase, Cochrane Library, and Web of Science databases was conducted from inception to January 20, 2024. Relevant literature on VER in patients with AIS was reviewed and the data related to favorable and adverse clinical outcomes were collected for meta-analysis. Subgroup analysis was conducted at different time points, namely at discharge and at three and 12 months. Statistical analyses were performed with the help of the Meta Package in STATA Version 15.0. A total of 14 randomized controlled trial (RCT) studies and 3,039 participants were included in the analysis. VER demonstrated a significant association with mortality [risk ratio (RR) = 1.27, 95% confidence interval (CI) (1.00, 1.61)], ability of daily living [weighted mean difference (WMD) = 6.90, 95% CI (0.22, 13.57)], and limb motor function [WMD = 5.02, 95% CI (1.63, 8.40)]. However, no significant difference was observed between the VER group and the control group in adverse events [RR = 0.89, 95% CI (0.79, 1.01)], severity of stroke [WMD = 0.52, 95% CI (-0.04, 1.08)], degree of disability [RR = 1.06, 95% CI (0.93, 1.20)], or recovery of walking [RR = 0.98, 95% CI (0.94, 1.03)] after stroke. Subgroup analysis revealed that VER reduced the risk of adverse events in the late stage (at three and 12 months) [RR = 0.86, 95% CI (0.74, 0.99)] and degree of disability at 12 months [RR = 1.28, 95% CI (1.03, 1.60)], and improved daily living ability at 3 months [WMD = 4.26, 95% CI (0.17, 8.35)], while increasing severity of stroke during hospitalization [WMD = 0.81, 95% CI (0.01, 1.61)]. VER improves activities of daily living (ADLs) and lowers the incidence of long-term complications in stroke survivors. However, premature or overly intense rehabilitation may increase mortality in patients with AIS during the acute phase. PROSPERO registration number: CRD42024508180. This systematic review was registered with PROSPERO (https://www.crd.york.ac.uk/PROSPERO/). PROSPERO registration number: CRD42024508180.