Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study.

BACKGROUND: Restoring shoulder function is critical for upper-extremity rehabilitation following a stroke. The complex musculoskeletal anatomy of the shoulder presents a challenge for safely assisting elevation movements through robotic interventions. The level of shoulder elevation assistance in rehabilitation is often based on clinical judgment. There is no standardized method for deriving an optimal level of assistance, underscoring the importance of addressing abnormal movements during shoulder elevation, such as abnormal synergies and compensatory actions. This study aimed to investigate the effectiveness and safety of a newly developed shoulder elevation exoskeleton robot by applying a novel optimization technique derived from the muscle synergy index. METHODS: Twelve chronic stroke participants underwent an intervention consisting of 100 robot-assisted shoulder elevation exercises (10 × 10 times, approximately 40 min) for 10 days (4-5 times/week). The optimal robot assist rate was derived by detecting the change points using the co-contraction index, calculated from electromyogram (EMG) data obtained from the anterior deltoid and biceps brachii muscles during shoulder elevation at the initial evaluation. The primary outcomes were the Fugl-Meyer assessment-upper extremity (FMA-UE) shoulder/elbow/forearm score, kinematic outcomes (maximum angle of voluntary shoulder flexion and elbow flexion ratio during shoulder elevation), and shoulder pain outcomes (pain-free passive shoulder flexion range of motion [ROM] and visual analogue scale for pain severity during shoulder flexion). The effectiveness and safety of robotic therapy were examined using the Wilcoxon signed-rank sum test. RESULTS: All 12 patients completed the procedure without any adverse events. Two participants were excluded from the analysis because the EMG of the biceps brachii was not obtained. Ten participants (five men and five women; mean age: 57.0 [5.5] years; mean FMA-UE total score: 18.7 [10.5] points) showed significant improvement in the FMA-UE shoulder/elbow/forearm score, kinematic outcomes, and pain-free passive shoulder flexion ROM (P < 0.05). The shoulder pain outcomes remained unchanged or improved in all patients. CONCLUSIONS: The study presents a method for deriving the optimal robotic assist rate. Rehabilitation using a shoulder robot based on this derived optimal assist rate showed the possibility of safely improving the upper-extremity function in patients with severe stroke in the chronic phase.

Insight into the pathophysiological advances and molecular mechanisms underlying cerebral stroke: current status.

Molecular pathways involved in cerebral stroke are diverse. The major pathophysiological events that are observed in stroke comprises of excitotoxicity, oxidative stress, mitochondrial damage, endoplasmic reticulum stress, cellular acidosis, blood-brain barrier disruption, neuronal swelling and neuronal network mutilation. Various biomolecules are involved in these pathways and several major proteins are upregulated and/or suppressed following stroke. Different types of receptors, ion channels and transporters are activated. Fluctuations in levels of various ions and neurotransmitters have been observed. Cells involved in immune responses and various mediators involved in neuro-inflammation get upregulated progressing the pathogenesis of the disease. Despite of enormity of the problem, there is not a single therapy that can limit infarction and neurological disability due to stroke. This is because of poor understanding of the complex interplay between these pathophysiological processes. This review focuses upon the past to present research on pathophysiological events that are involved in stroke and various factors that are leading to neuronal death following cerebral stroke. This will pave a way to researchers for developing new potent therapeutics that can aid in the treatment of cerebral stroke.

Current evidence of synaptic dysfunction after stroke: Cellular and molecular mechanisms.

BACKGROUND: Stroke is an acute cerebrovascular disease in which brain tissue is damaged due to sudden obstruction of blood flow to the brain or the rupture of blood vessels in the brain, which can prompt ischemic or hemorrhagic stroke. After stroke onset, ischemia, hypoxia, infiltration of blood components into the brain parenchyma, and lysed cell fragments, among other factors, invariably increase blood-brain barrier (BBB) permeability, the inflammatory response, and brain edema. These changes lead to neuronal cell death and synaptic dysfunction, the latter of which poses a significant challenge to stroke treatment. RESULTS: Synaptic dysfunction occurs in various ways after stroke and includes the following: damage to neuronal structures, accumulation of pathologic proteins in the cell body, decreased fluidity and release of synaptic vesicles, disruption of mitochondrial transport in synapses, activation of synaptic phagocytosis by microglia/macrophages and astrocytes, and a reduction in synapse formation. CONCLUSIONS: This review summarizes the cellular and molecular mechanisms related to synapses and the protective effects of drugs or compounds and rehabilitation therapy on synapses in stroke according to recent research. Such an exploration will help to elucidate the relationship between stroke and synaptic damage and provide new insights into protecting synapses and restoring neurologic function.

Cerebral autoregulation: A reliable predictor of prognosis in patients receiving intravenous thrombolysis.

AIMS: To investigate the characteristics of dynamic cerebral autoregulation (dCA) after intravenous thrombolysis (IVT) and assess the relationship between dCA and prognosis. METHODS: Patients with unilateral acute ischemic stroke receiving IVT were prospectively enrolled; those who did not were selected as controls. All patients underwent dCA measurements, by quantifying the phase difference (PD) and gain, at 1-3 and 7-10 days after stroke onset. Simultaneously, two dCA-based nomogram models were established to verify the predictive value of dCA for patients with mild-to-moderate stroke. RESULTS: Finally, 202 patients who received IVT and 238 who did not were included. IVT was positively correlated with higher PD on days 1-3 and 7-10 after stroke onset. PD values in both sides at 1-3 days after stroke onset and in the affected side at 7-10 days after onset were independent predictors of unfavorable outcomes in patients who received IVT. Additionally, in patients with mild-to-moderate stroke who received IVT, the dCA-based nomogram models significantly improved the risk predictive ability for 3-month unfavorable outcomes. CONCLUSION: IVT has a positive effect on dCA in patients with acute stroke; furthermore, dCA may be useful to predict the prognosis of patients with IVT.

Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial.

BACKGROUND: Over 80% of patients with stroke experience finger grasping dysfunction, affecting independence in activities of daily living and quality of life. In routine training, task-oriented training is usually used for functional hand training, which may improve finger grasping performance after stroke, while augmented therapy may lead to a better treatment outcome. As a new technology-supported training, the hand rehabilitation robot provides opportunities to improve the therapeutic effect by increasing the training intensity. However, most hand rehabilitation robots commonly applied in clinics are based on a passive training mode and lack the sensory feedback function of fingers, which is not conducive to patients completing more accurate grasping movements. A force feedback hand rehabilitation robot can compensate for these defects. However, its clinical efficacy in patients with stroke remains unknown. This study aimed to investigate the effectiveness and added value of a force feedback hand rehabilitation robot combined with task-oriented training in stroke patients with hemiplegia. METHODS: In this single-blinded randomised controlled trial, 44 stroke patients with hemiplegia were randomly divided into experimental (n = 22) and control (n = 22) groups. Both groups received 40 min/day of conventional upper limb rehabilitation training. The experimental group received 20 min/day of task-oriented training assisted by a force feedback rehabilitation robot, and the control group received 20 min/day of task-oriented training assisted by therapists. Training was provided for 4 weeks, 5 times/week. The Fugl-Meyer motor function assessment of the hand part (FMA-Hand), Action Research Arm Test (ARAT), grip strength, Modified Ashworth scale (MAS), range of motion (ROM), Brunnstrom recovery stages of the hand (BRS-H), and Barthel index (BI) were used to evaluate the effect of two groups before and after treatment. RESULTS: Intra-group comparison: In both groups, the FMA-Hand, ARAT, grip strength, AROM, BRS-H, and BI scores after 4 weeks of treatment were significantly higher than those before treatment (p < 0.05), whereas there was no significant difference in finger flexor MAS scores before and after treatment (p > 0.05). Inter-group comparison: After 4 weeks of treatment, the experimental group's FMA-Hand total score, ARAT, grip strength, and AROM were significantly better than those of the control group (p < 0.05). However, there were no statistically significant differences in the scores of each sub-item of the FMA-Hand after Bonferroni correction (p > 0.007). In addition, there were no statistically significant differences in MAS, BRS-H, and BI scores (p > 0.05). CONCLUSION: Hand performance improved in patients with stroke after 4 weeks of task-oriented training. The use of a force feedback hand rehabilitation robot to support task-oriented training showed additional value over conventional task-oriented training in stroke patients with hand dysfunction. CLINICAL TRIAL REGISTRATION INFORMATION: NCT05841108.

EvolvRehab-MoveWell telerehabilitation for stroke survivors: study protocol for a feasibility with embedded initial proof-of-concept study.

INTRODUCTION: Stroke is a leading cause of disability throughout the world. Unilateral upper limb impairment is common in people who have had a stroke. As a result of impaired upper limb function, people who have had a stroke often employ abnormal 'compensatory' movements. In the short term, these compensatory movements allow the individual to complete tasks, though long-term movement in this manner can lead to limitations. Telerehabilitation offers the provision of rehabilitation services to patients at a remote location using information and communication technologies. 'EvolvRehab' is one such telerehabilitation system, which uses activities to assess and correct compensatory upper body movements, although the feasibility of its use is yet to be determined in National Health Service services. Using EvolvRehab, we aim to assess the feasibility of 6 weeks telerehabilitation in people after a stroke. METHODS AND ANALYSIS: A multisite feasibility study with embedded design phase. Normally distributed data will be analysed using paired samples t-tests; non-normally distributed data will be analysed using related samples Wilcoxon signed rank tests. Thematic content analysis of interview transcripts will be used to investigate the usability and perceived usefulness of the EvolvRehab kit. ETHICS AND DISSEMINATION: This study has received ethical approval from Solihull Research Ethics Committee (REC reference: 23/WM/0054). Dissemination will be carried out according to the dissemination plan co-written with stroke survivors, including academic publications and presentations; written reports; articles in publications of stakeholder organisations; presentations to and publications for potential customers. TRIAL REGISTRATION NUMBER: NCT05875792.

Stroke walking and balance characteristics via principal component analysis.

Balance impairment is associated gait dysfunction with several quantitative spatiotemporal gait parameters in patients with stroke. However, the link between balance impairments and joint kinematics during walking remains unclear. Clinical assessments and gait measurements using motion analysis system was conducted in 44 stroke patients. This study utilised principal component analysis to identify key joint kinematics characteristics of patients with stroke during walking using average joint angles of pelvis and bilateral lower limbs in every gait-cycle percentile related to balance impairments. Reconstructed kinematics showed the differences in joint kinematics in both paretic and nonparetic lower limbs that can be distinguished by balance impairment, particularly in the sagittal planes during swing phase. The impaired balance group exhibited greater joint variability in both the paretic and nonparetic limbs in the sagittal plane during entire gait phase and during terminal swing phase respectively compared with those with high balance scores. This study provides a more comprehensive understanding of stroke hemiparesis gait patterns and suggests considering both nonparetic and paretic limb function, as well as bilateral coordination in clinical practice. Principal component analysis can be a useful assessment tool to distinguish differences in balance impairment and dynamic symmetry during gait in patients with stroke.

Trainability of affordance judgments in right and left hemisphere stroke patients.

Whenever we are confronted with action opportunities in everyday life, e.g., when passing an opening, we rely on our ability to precisely estimate our own bodily capabilities in relation to the environmental conditions. So-called affordance judgments can be affected after brain damage. Previous studies with healthy adults showed that such judgments appeared to be trainable within one session. In the current study, we examined whether stroke patients with either right brain damage (n = 30) or left brain damage (n = 30) may similarly profit from training in an aperture task. Further, the role of neuropsychological deficits in trainability was investigated. In the administered task, stroke patients decided whether their hand would fit into a presented opening with varying horizontal width (Aperture Task). During one training session, patients were asked to try to fit their hand into the opening and received feedback on their decisions. We analyzed accuracy and the detection theory parameters perceptual sensitivity and judgment tendency. Both patients with right brain damage and patients with left brain damage showed improved performance during training as well as post training. High variability with differential profiles of trainability was revealed in these patients. Patients with impaired performance in a visuo-spatial or motor-cognitive task appeared to profit considerably from the target-driven action phase with feedback, but the performance increase in judgments did not last when the action was withdrawn. Future studies applying lesion analysis with a larger sample may shed further light on the dissociation in the trainability of affordance judgments observed in patients with versus without visuo-spatial or motor-cognitive deficits.

Early functional factors for predicting outcome of independence in daily living after stroke: a decision tree analysis.

OBJECTIVE: This study aimed to investigate the predictive functional factors influencing the acquisition of basic activities of daily living performance abilities during the early stages of stroke rehabilitation using classification and regression analysis trees. METHODS: The clinical data of 289 stroke patients who underwent rehabilitation during hospitalization (164 males; mean age: 62.2 ± 13.9 years) were retrospectively collected and analysed. The follow-up period between admission and discharge was approximately 6 weeks. Medical records, including demographic characteristics and various functional assessments with item scores, were extracted. The modified Barthel Index on discharge served as the target outcome for analysis. A "good outcome" was defined as a modified Barthel Index score ≥ 75 on discharge, while a modified Barthel Index score < 75 was classified as a "poor outcome." RESULTS: Two classification and regression analysis tree models were developed. The first model, predicting activities of daily living outcomes based on early motor functions, achieved an accuracy of 92.4%. Among patients with a "good outcome", 70.9% exhibited (i) ≥ 4 points in the "sitting-to-standing" category in the motor assessment scale and (ii) 32 points on the Berg Balance Scale score. The second model, predicting activities of daily living outcome based on early cognitive functions, achieved an accuracy of 82.7%. Within the "poor outcome" group, 52.2% had (i) ≤ 21 points in the "visuomotor organization" category of Lowenstein Occupational Therapy Cognitive Assessment, (ii) ≤ 1 point in the "time orientation" category of the Mini Mental State Examination. CONCLUSION: The ability to perform "sitting-to-standing" and visuomotor organization functions at the beginning of rehabilitation emerged as the most significant predictors for achieving successful basic activities of daily living on discharge after stroke.

Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.

Globally, stroke is the third-leading cause of mortality and disability combined, and one of the costliest diseases in society. More accurate predictions of stroke outcomes can guide healthcare organizations in allocating appropriate resources to improve care and reduce both the economic and social burden of the disease. We aim to develop and evaluate the performance and explainability of three supervised machine learning models and the traditional multinomial logistic regression (mLR) in predicting functional dependence and death three months after stroke, using routinely-collected data. This prognostic study included adult patients, registered in the Swedish Stroke Registry (Riksstroke) from 2015 to 2020. Riksstroke contains information on stroke care and outcomes among patients treated in hospitals in Sweden. Prognostic factors (features) included demographic characteristics, pre-stroke functional status, cardiovascular risk factors, medications, acute care, stroke type, and severity. The outcome was measured using the modified Rankin Scale at three months after stroke (a scale of 0-2 indicates independent, 3-5 dependent, and 6 dead). Outcome prediction models included support vector machines, artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and mLR. The models were trained and evaluated on 75% and 25% of the dataset, respectively. Model predictions were explained using SHAP values. The study included 102,135 patients (85.8% ischemic stroke, 53.3% male, mean age 75.8 years, and median NIHSS of 3). All models demonstrated similar overall accuracy (69%-70%). The ANN and XGBoost models performed significantly better than the mLR in classifying dependence with F1-scores of 0.603 (95% CI; 0.594-0.611) and 0.577 (95% CI; 0.568-0.586), versus 0.544 (95% CI; 0.545-0.563) for the mLR model. The factors that contributed most to the predictions were expectedly similar in the models, based on clinical knowledge. Our ANN and XGBoost models showed a modest improvement in prediction performance and explainability compared to mLR using routinely-collected data. Their improved ability to predict functional dependence may be of particular importance for the planning and organization of acute stroke care and rehabilitation.

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.

BACKGROUND: Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. METHODS: We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. RESULTS: The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. CONCLUSION: This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Collaterals and stroke reperfusion: Too few leads to too much.

Adequate reperfusion after ischemic stroke is a major determinant of functional outcome yet remains unpredictable and insufficient for most survivors. In this issue of Neuron, Binder et al.1 identify leptomeningeal collaterals (LMCs) in mice and human patients as a key factor in regulating reperfusion and hemorrhagic transformation following stroke.

Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation.

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

Modulation of the Association Between Corticospinal Tract Damage and Outcome After Stroke by White Matter Hyperintensities.

BACKGROUND AND OBJECTIVES: Motor outcomes after stroke relate to corticospinal tract (CST) damage. The brain leverages surviving neural pathways to compensate for CST damage and mediate motor recovery. Thus, concurrent age-related damage from white matter hyperintensities (WMHs) might affect neurologic capacity for recovery after CST injury. The role of WMHs in post-stroke motor outcomes is unclear. In this study, we evaluated whether WMHs modulate the relationship between CST damage and post-stroke motor outcomes. METHODS: We used data from the multisite ENIGMA Stroke Recovery Working Group with T1 and T2/fluid-attenuated inversion recovery imaging. CST damage was indexed with weighted CST lesion load (CST-LL). WMH volumes were extracted with Freesurfer's SAMSEG. Mixed-effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment, controlling for age, days after stroke, and stroke volume. RESULTS: A total of 223 individuals were included. WMH volume related to motor impairment above and beyond CST-LL (ß = 0.178, 95% CI 0.025-0.331, p = 0.022). Relationships varied by WMH severity (mild vs moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (ß = 0.888, 95% CI 0.604-1.172, p < 0.001) with a CST-LL × WMH interaction (ß = -0.211, 95% CI -0.340 to -0.026, p = 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (ß = 0.299, 95% CI 0.008-0.590, p = 0.044), but did not significantly relate to CST-LL or a CST-LL × WMH interaction. DISCUSSION: WMHs relate to motor outcomes after stroke and modify relationships between motor impairment and CST damage. WMH-related damage may be under-recognized in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury.

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

Atrial Fibrillation and Dementia: Pathophysiological Mechanisms and Clinical Implications.

Numerous longitudinal studies suggest a strong association between cardiovascular risk factors and cognitive impairment. Individuals with atrial fibrillation are at higher risk of dementia and cognitive dysfunction, as atrial fibrillation increases the risk of cerebral hypoperfusion, inflammation, and stroke. The lack of comprehensive understanding of the observed association and the complex relationship between these two diseases makes it very hard to provide robust guidelines on therapeutic indications. With this review, we attempt to shed some light on how atrial fibrillation is related to dementia, what we know regarding preventive interventions, and how we could move forward in managing those very frequently overlapping conditions.

A Sensor-Based Upper Limb Treatment in Hemiplegic Patients: Results from a Randomized Pilot Study.

In post-stroke patients, the disabling motor deficit mainly affects the upper limb. The focus of rehabilitation is improving upper limb function and reducing long-term disability. This study aims to evaluate the feasibility of using the Gloreha Aria (R-Lead), a sensor-based upper limb in-hospital rehabilitation, compared with conventional physiotherapist-led training in subacute hemiplegic patients. Twenty-one patients were recruited and randomised 1:1 to a sensor-based group (treatment group TG) or a conventional group (control group, CG). All patients performed 30 sessions of 30 min each of dedicated upper limb rehabilitation. The Fugl-Meyer Assessment for Upper Extremity (FMA-UE) was the primary evaluation., both as a motor score and as individual items. Secondary evaluations were Functional Independence Measure; global disability assessed with the Modified Barthel Index; Motor Evaluation Scale for UE in stroke; power grip; and arm, shoulder, and hand disability. All the enrolled patients, 10 in the TG and 11 in the CG, completed all hand rehabilitation sessions during their hospital stay without experiencing any adverse events. FMA-UE scores in upper limb motor function improved in both groups [delta change CG (11.8 ± 9.2) vs. TG (12.7 ± 8.6)]. The score at T1 for FMA joint pain (21.8 vs. 24 best score) suggests the use of the Gloreha Aria (R-Lead) as feasible in improving arm function abilities in post-stroke patients.

Increased sensitivity in detection of deficits following two commonly used animal models of stroke.

Stroke is a leading cause of death and disability in the United States. Most strokes are ischemic, resulting in both cognitive and motor impairments. Animal models of ischemic stroke such as the distal middle cerebral artery occlusion (dMCAO) and photothrombotic stroke (PTS) procedures have become invaluable tools, with their own advantages and disadvantages. The dMCAO model is clinically relevant as it occludes the artery most affected in humans, but yields variability in the infarct location as well as the behavioral and cognitive phenotypes disrupted. The PTS model has the advantage of allowing for targeted location of infarct, but is less clinically relevant. The present study evaluates phenotype disruption over time in mice subjected to either dMCAO, PTS, or a sham surgery. Post-surgery, animals were tested over 28 days on standard motor tasks (grid walk, cylinder, tapered beam, and rotating beam), as well as a novel odor-based operant task; the 5:1 Odor Discrimination Task (ODT). Results demonstrate a significantly greater disturbance of motor control with PTS as compared with Sham and dMCAO. Disruption of the PTS group was detected up to 28 days post-stroke on the grid walk, and up to 7 days on the rotating and tapered beam tasks. PTS also led to significant short-term disruption of ODT performance (1-day post-surgery), exclusively in males, which appeared to be driven by motoric disruption of the lick response. Together, this data provides critical insights into the selection and optimization of animal models for ischemic stroke research. Notably, the PTS procedure was best suited for producing disruptions of motor behavior that can be detected with common behavioral assays and are relatively enduring, as is observed in human stroke.

The effects of visual information deprivation and feedback balance training on balance in patients with stroke.

BACKGROUND: Patients with stroke depend on visual information due to balance deficits. Therefore, it is believed that appropriate visual deprivation training could have an impact on improving balance abilities. OBJECTIVE: The purpose of this study was to compare the effects of balance training performed in visual deprivation and feedback conditions on balance in stroke survivors. METHODS: The 39 participants were randomly assigned to either the Visual Deprivation Group (VDG; n = 13), the Visual Feedback Group (VFG; n = 13), or the Control Group (CG; n = 13). The training sessions were conducted five times a week for three weeks. Participants completed the Berg Balance Scale (BBS), Timed Up and Go test (TUG), Four Square Step Test (FSST), and Limit of Stability (LOS) assessments. RESULTS: The VDG showed significant improvements in BBS, FSST, TUG, and LOS. In VFG, significant improvements were observed in BBS and TUG. There were statistically significant differences among the groups in all variables related to balance. CONCLUSION: The results of this study suggest that balance training under visual deprivation is effective in improving static and dynamic balance and gait in patients with stroke. In other words, patients with stroke need to reduce their over-reliance on visual information.