Neurorehabilitation and neural repair最新文献

BackgroundCognitive rehabilitation and exercise training are promising approaches for improving cognition in persons with progressive multiple sclerosis (MS). Identifying heterogeneity of change and factors that influence the effects of cognitive rehabilitation and/or exercise training on cognitive outcomes at the individual level have direct relevance for developing tailored and optimized rehabilitation interventions for improving cognition in progressive MS.ObjectiveThis study involved a secondary data analysis from the CogEx trial in progressive MS. This study first described heterogeneity of change in cognitive processing speed (CPS) across the intervention conditions and then identified possible adherence/compliance, baseline performance, and demographic/clinical variables as correlates of rehabilitation-related CPS changes.MethodsA total of 311 persons with progressive MS who were pre-screened for impaired CPS completed 12 weeks of combined cognitive rehabilitation (or sham) and exercise training (or sham). CPS was measured before and after the 12-week period. As potential correlates of CPS changes, we measured adherence/compliance (ie, treatment exposure), performance outcomes at baseline, as well as demographic and clinical characteristics at baseline.ResultsThere was heterogeneity of change in CPS across the 4 intervention conditions. We further identified baseline learning and memory impairment and premorbid intelligence quotient (IQ), but not adherence/compliance, other baseline performance outcomes, or demographic/clinical characteristics as significant correlates of CPS changes across the 4 intervention conditions.ConclusionsThe overall pattern of results suggests that future trials in this area might account for impaired learning and memory and/or premorbid IQ as potential covariates, or more carefully consider the role of reserve within rehabilitation interventions in progressive MS.

Background: Falls are a common challenge for people with Parkinson's disease (PwPD), driven by balance impairments and misaligned perceptions of balance abilities.

Objective: This study investigated the replicability and generalizability of the relationship between balance ability and perception discordance and fall risk.

Methods: Using baseline data from 2 clinical trials involving 171 PwPD, discordance was calculated using the Activities Specific Balance Confidence Scale and Timed Up and Go (TUG) or the Mini Balance Evaluation System's Test (MiniBEST).

Results: Findings supported the replicability of discordance as a predictor of fall risk, with results consistent across measures. While TUG-derived discordance was statistically significant, MiniBEST-derived discordance showed generalizability without statistical inferiority.

Conclusion: These results emphasize the relevance of balance perception and its misalignment with ability as fall risk predictors.

BackgroundOur understanding of sleep during early stroke care and its impact on rehabilitation outcomes remains limited. The objectives of this work were to (1) evaluate multidimensional sleep health and disruptions during acute inpatient rehabilitation for individuals with stroke, and (2) explore the relationship between sleep health/disruptions and functional recovery.MethodsData from 103 individuals with stroke were analyzed during acute inpatient rehabilitation. Sleep health/disruptions were assessed via patient reports, actigraphy, and biometric sensors. Functional outcomes were measured at admission and discharge. Generalized Linear Models (GLMs) were used to describe changes in sleep health over time, and multivariate regressions analyzed sleep disruptions and sleep-related predictors of functional recovery.ResultsOver inpatient stays, sleep improved with a 23% reduction in wake after sleep onset and 15% fewer multiple overnight disruptions. GLMs revealed that improved sleep quality was associated with reduced overnight activity and increased heart rate over time. Poor initial sleep quality and cognitive status were associated with more overnight disruptions. Lastly, minimal associations were found between sleep health and functional recovery.ConclusionsSleep health during inpatient stroke rehabilitation is generally poor, though improves over time. Sleep is affected by neurological recovery and hospital environment. Overnight activity and autonomic biomarkers were associated with perceived sleep health, and both physiological and environmental factors triggered disruptions. The association between functional recovery and indirect indicators of sleep health requires further investigation. These findings reveal new insights about inpatient sleep which can inform early, targeted sleep interventions to optimize post-stroke outcomes.SIESTA, ClinicalTrials.gov (NCT04254484).

IntroductionImpairments in cognition are prominent for individuals with multiple sclerosis (MS) and have been linked to low levels of physical activity (PA) and walking impairment. However, this relationship remains inconsistent, necessitating the synthesis of current literature to yield collective knowledge.ObjectiveTo investigate the relationship between cognition and measures of PA, mobility, and gait quality in individuals with MS.MethodsRelevant, peer-reviewed research articles were identified through a systematic search of MEDLINE, EMBASE, PsycINFO, SPORTSDiscus, and CINAHL from inception to April 2, 2024. Eligible studies explored the relationship between cognition and measures of PA, mobility, and gait quality. The Standard Quality Assessment Criteria for quantitative studies was employed for quality assessment.ResultsTwenty-six studies with a total of 3248 participants were identified in this review, of which 21 studies indicated strong methodological quality. Our review found that processing speed holds a significant relationship with PA volume, but not PA intensity metrics. Mobility and gait quality outcomes were associated with varying cognitive domains, including processing speed, executive function, verbal memory, and visuospatial memory. The magnitudes of the association between cognition and PA, mobility, and gait quality were mostly weak-to-moderate.ConclusionProcessing speed appears to be collectively associated with PA volume, mobility, and gait quality. However, the evidence supporting this conclusion is largely based on correlational studies involving individuals with mild-to-moderate ambulation disability, warranting future research.

Background and ObjectivesProsthetic hand development is undergoing a transformative phase, blending biomimicry and neural interface technologies to redefine functionality and sensory feedback. This article explores the symbiotic relationship between biomimetic design principles and neural interface technology (NIT) in advancing prosthetic hand capabilities.MethodsDrawing inspiration from biological systems, researchers aim to replicate the intricate movements and capabilities of the human hand through innovative prosthetic designs. Central to this endeavor is NIT, facilitating seamless communication between artificial devices and the human nervous system. Recent advances in fabrication methods have propelled brain-computer interfaces, enabling precise control of prosthetic hands by decoding neural activity.ResultsAnatomical complexities of the human hand underscore the importance of understanding biomechanics, neuroanatomy, and control mechanisms for crafting effective prosthetic solutions. Furthermore, achieving the goal of a fully functional cyborg hand necessitates a multidisciplinary approach and biomimetic design to replicate the body's inherent capabilities. By incorporating the expertise of clinicians, tissue engineers, bioengineers, electronic and data scientists, the next generation of the implantable devices is not only anatomically and biomechanically accurate but also offer intuitive control, sensory feedback, and proprioception, thereby pushing the boundaries of current prosthetic technology.ConclusionBy integrating machine learning algorithms, biomechatronic principles, and advanced surgical techniques, prosthetic hands can achieve real-time control while restoring tactile sensation and proprioception. This manuscript contributes novel approaches to prosthetic hand development, with potential implications for enhancing the functionality, durability, and safety of the prosthetic limb.

BackgroundPost-stroke fatigue, as one of the long-lasting physical and mental symptoms accompanying stroke survivors, will seriously affect the daily living ability and quality of life of stroke patients.ObjectiveThe aim of this study was to develop machine learning (ML) algorithms to predict early post-stroke fatigue among patients with stroke.MethodsA longitudinal study of 702 patients with stroke followed for 3 months. Twenty-three clinical features were obtained from medical records and questionnaires before discharge. Early post-stroke fatigue was assessed using the Fatigue Severity Scale. The dataset was randomly divided into a training group (70%) and an internal validation group (30%), applied oversampling, 10-fold cross-validation, and grid search to optimize the hyperparameter. Feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Sixteen ML algorithms were performed to predict early post-stroke fatigue in this study. Accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and brier score were used to evaluate the models performance.ResultsAmong the 16 ML algorithms, the Bagging model was the optimal model for predicting early post-stroke fatigue in patients with stroke (AUC = 0.8479, accuracy = 0.7518, precision = 0.5741, recall = 0.7209, F1 score = 0.6392, brier score = 0.1490). The feature selection based on LASSO revealed that risk factors for early post-stroke fatigue in patients with stroke included anxiety, sleep, social support, family care, pain, depression, neural-functional defect, quit/no drinking, balance function, type of stroke, sex, heart disease, smoking, and hemiplegia.ConclusionsIn this study, the Bagging model proved to be effective in predicting early post-stroke fatigue.

BackgroundSpatial neglect (SN) is a risk factor of in-hospital falls among stroke survivors. Our prior study showed that receiving more sessions of prism adaptation treatment (PAT) in inpatient rehabilitation facilities (IRFs) predicted greater SN reduction and functional improvement.ObjectiveTo identify circumstances of falls specific to SN and explore whether increasing PAT sessions may reduce fall incidence.MethodsThe present study was a retrospective analysis of fall-related documentation, clinical data and notes regarding SN assessment, and treatment as part of standard care. Records of 3020 patients admitted to an IRF after stroke were reviewed, and 1489 (49%) had SN based on the Catherine Bergego Scale (CBS).ResultsA total of 276 patients (9% of all patients) fell at least once during their IRF stay, and 173 fallers (67% of all fallers) had SN. SN increased fall incidence (incident rate ratio [IRR] = 1.44, P = .005) after controlling for 5 covariates including age, sex, cognitive and motor functional level at admission, and length of stay. While independent of type or cause of falls, SN was associated with location of falls-SN increased fall incidence in hospital rooms (IRR = 1.55, P = .024), after controlling for the 5 covariates. 62 (36%) of fallers with SN received a median of 4.5 PAT sessions (range = 1-11; interquartile range = 2-10). Increased PAT sessions were associated with fewer falls after PAT (IRR = 0.82, P = .022), controlling for the 5 covariates and 2 additional factors including CBS and number of falls before PAT.ConclusionsTreatment for SN such as PAT should be considered to reduce the risk of falls in these patients. Future research is needed to determine fall prevention measures for stroke survivors with SN, especially in their hospital rooms.

BackgroundPeople with Parkinson's disease (PwPD) have difficulty adapting their gait to asymmetrical conditions. Objective. We investigated cortical activity between 42 PwPD (HY 2-3) and 42 healthy controls using functional near-infrared spectroscopy during tied-belt (TB) and split-belt (SB) treadmill walking.MethodsOxygenated hemoglobin (HbO2) was measured in the prefrontal cortex, supplementary motor area (SMA), premotor cortex (PMC), and posterior parietal cortex (PPC) during 3 blocks of treadmill walking: (1) with the belts moving at the same speed (TB) and (2) when the speed of 1 side was reduced by 50% (SB; 2 blocks). The ability to adjust gait to asymmetric conditions was quantified by step length asymmetry and its variability.ResultsAdaptive gait was worse during the last 5 steps of SB versus TB in PwPD compared to controls. PwPD showed higher HbO2 in the PMC (P = .005) and PPC (P = .004) relative to controls, regardless of condition. However, an increase in HbO2 in the SMA during SB was shown relative to TB in PwPD, a change not observed in controls (group × condition interaction P = .048; pairwise post hoc P = .032). Interestingly, increased PPC activity in PwPD was associated with poorer adapted gait.ConclusionsBoth regular and adaptive gait required enhanced cortical processing in PwPD, as evidenced by the increased activation in the PMC and PPC. However, this heightened cortical activity did not correlate with a reduction in gait asymmetry, suggesting that these changes might be maladaptive. Instead, the elevated cortical activity may reflect the challenges PwPD face in adapting to asymmetrical walking conditions. Careful interpretation is warranted given the relatively small sample of mildly affected PwPD, limiting generalizability to the broader population and the measurement errors inherent to functional near-infrared spectroscopy .

BackgroundAfter a stroke, the use of the paretic arm is determined by its capacity (what it can or cannot do). When both arms have capacity to perform a task, the choice of which arm to use must be based on another criterion, probably by comparing the efficiency of each arm. Two numerical models account for this: the capacity model (the paretic arm is chosen in preference) and the efficiency model (the most efficient arm is chosen).ObjectiveTo numerically determine whether capacity or efficiency best predict the use of the paretic arm in activities of daily living.MethodsWe performed numerical simulations to predict paretic arm use with either the capacity model or the efficiency model. We used the Bayesian Information Criterion (BIC) to compare the adequacy of the 2 models in predicting clinical and accelerometric data collected from 30 patients with chronic stroke.ResultsThe efficiency model predicted arm use in activities of daily living better than the capacity model (BIC = -66.95 vs -5.89; root mean square error = 0.26 vs 0.72).ConclusionsThe study highlights the importance of considering efficiency when assessing paretic arm non-use. Assessing individuals' arm efficiency should help personalize rehabilitation strategies after stroke.

BackgroundMotor imagery is adversely affected by various factors in individuals with multiple sclerosis (MS). However, the impact of MS-related fatigue on motor imagery remains unclear. Our study aimed to compare motor imagery abilities between fatigued and non-fatigued individuals with MS without cognitive impairment.MethodsThis study included 73 individuals with MS, with Expanded Disability Status Scale scores from 0 to 4.5. Participants were divided into 2 groups based on Fatigue Severity Scale scores: ≥4 for Fatigued Group and <4 for Non-fatigued Group. Assessment of motor imagery vividness was done through Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20). The Box and Block Test (BBT) and the Timed Up and Go (TUG) were employed for the temporal congruence component.ResultsThe mean ages of the Fatigued Group (30.4 ± 9.2 years) and the Non-fatigued Group (31.5 ± 9.8 years) were similar (P = .650). The fatigued Group exhibited significantly lower kinesthetic imagery scores on the KVIQ-20 (P = .028) and significantly lower performance in the BBT (upper extremities) mental chronometry test for both the most affected and least affected sides of the upper extremities (P = .007 and .028, respectively). Additionally, the Fatigued Group showed significantly lower performance in the TUG (lower extremities) mental chronometry test (P = .006).ConclusionIn fatigued individuals with MS, there is a greater impact on both temporal congruence components and kinesthetic motor imagery abilities. The difference in the temporal congruence component was observed in tests involving both TUG (lower extremities) and BBT (upper extremities), independent of the affected side.