Neurorehabilitation and neural repair最新文献

Dr. Steven L. Wolf has been engaged in neurorehabilitation research for 6 decades. During this time, he has published numerous studies, of great breadth and depth, and of substantial impact. Along the way, he has taught many people a number of key lessons, pertaining to subjects such as leadership, tenacity, creativity, and generosity. This editorial explores 10 of the top lessons from the career of Dr. Wolf.

BackgroundStudies show that beat-based cues from music and metronomes benefit gait in persons with progressive multiple sclerosis (PwPMS) during short walking. While useful for controlled assessment, prolonged walking better reflects real-world function. Adaptive beat-alignment algorithms using biofeedback may enhance synchronization and benefit gait dynamics but their effects in PwPMS remain unknown.AimsTo investigate the effect of walking with and without an adaptive beat-alignment algorithm on synchronization consistency during 8 minutes of walking compared to walking in silence, and its effects on gait. We also examined how clinical factors like dynamic balance and cognition affect synchronization consistency during walking with music and metronomes, with and without adaptive beat alignment.MethodsHealthy controls (HCs) and PwPMS walked for 8 minutes under 5 conditions: silence, music, and metronomes, with or without adaptive beat alignment, at their preferred pace. The algorithm adjusted phase and tempo in real time to restore baseline cadence. We measured synchronization, gait parameters, dynamics, and clinical outcomes.ResultsTwenty-one PwPMS and 18 HCs participated. Adaptive beat-alignment improved synchronization consistency and gait dynamics compared to fixed-tempo walking. Cognitive flexibility and working memory explained 10% of variance in the non-adaptive and 6% in the adaptive conditions, while dynamic balance (40%) impaired synchronization across both algorithms. Auditory cues resulted in slower walking and shorter strides compared to silence.ConclusionThis study demonstrates the feasibility of adaptive beat-alignment strategies enhancing synchronization consistency and gait dynamics in PwPMS; however, cognitive flexibility, working memory, and dynamic balance influenced synchronization performance.

ObjectiveTo explore the latent trajectory classes of objective sleep quality in stroke patients and their impact on neurological functional recovery.MethodsA multicenter cluster sampling method was used to recruit 362 stroke patients from the neurology departments of 5 tertiary hospitals in China between November 2023 and July 2024. Baseline data were collected using a general information questionnaire and related scales. Objective sleep data were obtained using ActiGraph GT3X triaxial accelerometers during the acute (T0), recovery (T1), and chronic (T2) phases of stroke. Neurological recovery was assessed at 12 months post-onset (T3) using the modified Rankin Scale. Parallel-process latent class growth modeling was used to identify trajectory classes. Binary logistic regression examined the association between sleep trajectories and neurological recovery.ResultsA total of 306 patients were followed up. Four distinct trajectory classes were identified: Consistently good sleep quality group (34.31%), Short sleep-increased efficiency-improved fragmentation group (49.02%), Long sleep-reduced efficiency-deteriorated fragmentation group (7.84%), and Consistently poor sleep quality group (8.82%). Compared to the consistently good sleep quality group, patients in the Long sleep-reduced efficiency-deteriorated fragmentation group and Consistently poor sleep quality group had 5.728 (95% confidence interval [CI]: 2.124-15.444) and 6.769 (95% CI: 2.580-17.758) times higher risks of poor neurological recovery, respectively.ConclusionStroke patients exhibit heterogeneous sleep quality trajectories, with differential impacts on neurological recovery. Healthcare providers should implement personalized sleep management strategies to optimize both sleep quality and functional outcomes.

Background: Respiratory dysfunction is common after stroke and may negatively affect functional recovery. Inspiratory muscle training (IMT) has been proposed to enhance diaphragmatic function and activity performance in this population.

Objective: To investigate the effects of IMT on diaphragm function and activity performance in patients with subacute ischemic stroke. Methods: In this randomized controlled, single-blind trial, 26 patients with subacute ischemic stroke (>1 month post-onset) were randomly allocated to an intervention group (IG, n = 13) or control group (CG, n = 13). Both groups received standard neurorehabilitation, aerobic training, and the Active Cycle of Breathing Technique (ACBT). The IG additionally underwent IMT, 5 days per week for 6 weeks (30 sessions). Assessments included maximal inspiratory and expiratory pressures (MIP, MEP), diaphragmatic thickness (Ti, Te) and excursion via ultrasonography, 6-minute walk test (6MWT), and Canadian Occupational Performance Measure (COPM).

Results: Twenty-two participants completed the study (IG = 11; CG = 11). Significant within-group improvements were observed in both groups for MIP, MEP, Ti, TF, excursion, 6MWT, and COPM scores (P < .05). Between-group analyses showed significantly greater improvements in the IG for ΔMIP (P < .001), ΔMEP (P = .003), affected-side ΔTi (P = .007), ΔTF (P = .008), and Δexcursion (P = .005). No significant differences were found for 6MWT and COPM (P > .05).

Conclusions: IMT effectively improves respiratory muscle strength, diaphragmatic thickness, and excursion in subacute ischemic stroke and may be a valuable addition to post-stroke rehabilitation programs.

Registration: URL: https://www.

Clinicaltrials: gov; Unique identifier: NCT06210516.

Background and aim: Unilateral spatial neglect (UN) impairs patients' ability to detect and respond to stimuli on the contralesional side, severely limiting functional recovery after right-hemispheric stroke. Neck muscle vibration (NMV) has been shown to be a bottom-up, proprioceptive intervention to modulate spatial neglect. Although preliminary studies found promising effects, the isolated efficacy of NMV for neglect rehabilitation has not yet been tested in a randomized, blinded controlled trial. This study aimed to evaluate whether NMV alone improves neglect symptoms and activities of daily living (ADL).

Methods: Twenty patients with right-hemispheric stroke and UN were randomly assigned to receive either active or placebo NMV (combined, but not simultaneously with computer-based training) over 2 weeks (5 sessions/week, 20 minutes/day). In the computer training, the placebo NMV group completed neglect-specific modules (standard neglect therapy [SNT], e.g., visual exploration training), while the active NMV group performed only general cognitive tasks unrelated to neglect. This allowed the isolated effect of NMV to be examined. Assessments included standard neglect diagnostics (e.g., Letter Cancellation), the Free Exploration Test (FET), and 2 ADL-based measures (NET, CBS), conducted before, immediately after, and (NMV group only) 1 month post-treatment.

Results: The active NMV group showed significant improvements in 3 of 4 standard neglect tests, exploration behavior (FET), and ADL performance, with effects remaining stable at 1-month follow-up. The SNT group with placebo NMV showed comparable gains in ADL outcomes but improved in 1 standard neglect test only. Between-group analyses revealed no statistically significant differences, suggesting similar efficacy of both interventions.

Conclusion: NMV alone yields clinically meaningful and lasting improvements in neglect symptoms and ADL, comparable to SNT. Its passive nature makes it a promising tool, particularly for early rehabilitation.

Background: Mild traumatic brain injury (mTBI) can lead to persistent balance impairments, affecting daily functioning. While mTBI deficits in static and dynamic balance are well-documented, reactive balance-essential for recovering from perturbations-remains understudied, and the relationship with symptom severity and quality of life remains unclear.

Objective: Examine reactive balance across different stages of mTBI recovery and its association with self-reported symptoms and quality of life.

Methods: This cross-sectional study included 82 participants: 19 with acute (4-14 days post-injury), 11 with sub-acute (3-12 weeks post-injury), 11 with chronic (>12 weeks post-injury) mTBI, and 41 healthy controls. Reactive balance was assessed using the Instrumented-modified Push and Release under both single and cognitive dual-task conditions, measuring time to stability and step latency.

Results: Participants with acute mTBI had longer step latencies (P = .004 single-task; P = .016 dual-task) but no differences in time to stability compared to controls, while people with chronic mTBI exhibited longer time to stability (P = .004 single-task; P = .017 dual-task) but no difference in step latencies compared to controls. Dizziness and total symptom severity were moderately associated with single- and dual-task time to stability and dual-task step latency acutely, and with single-task time to stability and dual-task step latency in the sub-acute phase.

Conclusions: Reactive balance deficits persist chronically after mTBI and differ between people with acute, sub-acute, and chronic symptoms. These differences suggest people with chronic symptoms may prioritize different aspects of balance control compared to the people in the acute stage of recovery related to functional adaptations to self-reported symptoms.

Wearable movement sensing has enormous potential to transform the field of neurorehabilitation and neural repair. This perspective paper discusses: (1) the case for wearable sensing as a compelling, scalable measurement tool, (2) moving from first generation to second generation research in wearable movement sensing, (3) the enormity in the potential range of use cases for wearable technology, and (4) challenges that lie ahead for moving from research space into clinical rehabilitation care. Wearable sensors, as a measurement tool, offer a data-rich avenue for measuring numerous dimensions of motor behavior in the clinic and in daily life, complementing other available tools. Second-generation research questions focus on determining how to quantify, for whom, when, and with what variable(s). Answering these second-generation questions requires substantial evidence at the individual use case level; we provide 1 exemplar variable and its evidence within stroke recovery and rehabilitation. Potential use cases for deployment of wearable movement sensors span developmental, acquired, and degenerative neurological conditions and variables extracted can be intended as digital biomarkers and/or digital clinical outcome assessments. As research progresses, we look forward to the translation of this measurement tool into routine clinical care and welcome implementation challenges related to readiness, approach, and presentation in the busy, complex, healthcare arena. Achieving the promise of wearable movement sensing will require extensive collaboration, as exemplified by Dr. Wolf, across research teams, disciplines, institutions, people with lived experience, and other stakeholders.

BackgroundThe Predict REcovery Potential-2 (PREP2) prediction tool uses clinical assessments and transcranial magnetic stimulation (TMS) within 1 week post-stroke to predict individuals' upper limb functional outcome at 3 months (3M) post-stroke. PREP2 was successfully implemented in clinical care at Auckland City Hospital, New Zealand in 2017.ObjectiveThe primary aim was to evaluate the accuracy of PREP2 predictions made by clinicians during routine clinical care, with a threshold of 70% accuracy for validation. A secondary aim was to identify new baseline predictors that could increase PREP2 accuracy.MethodsEighty-three patients who received PREP2 predictions were recruited within 1 week of stroke and had their upper limb outcome assessed at 3M post-stroke with the Action Research Arm Test. Cognition and sensation were evaluated within 1 week of stroke.ResultsOverall accuracy of the PREP2 prediction tool in clinical practice was 66% (95% confidence interval, 55%-76%). Accuracy was highest for the Excellent (80%) and Poor (100%) categories and lowest for the Good category (36%). Prediction accuracy for Good outcomes was 67% for patients who did not require TMS and 27% for patients who did. Finger extension differentiated participants predicted to have a Good outcome using TMS who did and did not have a favorable upper limb outcome.ConclusionsExcellent and Poor predictions are highly accurate when used in clinical practice, however the full PREP2 tool is not yet validated in clinical practice. Future studies with larger samples could investigate additional measures to enhance accuracy of the Good prediction category.Clinical trial registration number ACTRN12619000225112, https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12619000225112.