Neurorehabilitation and neural repair最新文献

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.

BackgroundPhysical activity (PA) supports physical, cognitive, and mental health, yet is often limited in persons with multiple sclerosis (PwMS) due to mobility, cognitive, and psychological factors. Practical methods to identify those meeting PA recommendations are needed. This study aimed to develop a clinically useful approach combining patient-reported outcomes and objective measures to determine whether PwMS meet step count goals.MethodsParticipants completed mobility assessments (static balance, reactive balance, forward walking [FW], and backward walking [BW]), cognitive testing, and self-report measures. Fitbit tracked PA for 3 months, categorizing participants based on whether they met the MS daily step goal (7500 steps).ResultsForty-five PwMS (age: 51.16 ± 11.12 years; median Patient Determined Disease Steps: 1; 84% female) participated, with 15 meeting the daily step goal. Participants who met the step count goal reported significantly lower mobility limitations (MS Walking Scale-12, P = .01) and concern about falling (Falls Efficacy Scale-International, P < .01) compared to those who did not. Significant differences were also observed for BW at both comfortable and fast speeds (P < .01), FW at both speeds (P = .01), and reactive balance (P = .04). No differences were observed for cognition. Logistic regression identified BW at both comfortable (0.89 m/s) and fast speeds (1.25 m/s) as the strongest predictors of achieving the daily step goal, with predictive accuracies of 80% and 82.2%, respectively.ConclusionBW is a clinically relevant predictor of achieving daily step goals in PwMS. Established cut-off values-0.89 m/s for comfortable and 1.25 m/s for fast BW-demonstrated strong predictive accuracy. These findings highlight the utility of BW as a mobility measure to inform interventions and promote PA in clinical practice.

BackgroundThe Wolf Motor Function Test (WMFT) is a well-recognized measure for assessing upper extremity motor function in stroke rehabilitation. However, prolonged administration time limits the WMFT in clinical use.ObjectiveThis study aimed to reduce the number of WMFT tasks using machine learning and explore its measurement structure and psychometric properties, using data from 3 stroke rehabilitation trials that together engaged 543 participants with a wide range of motor impairment during subacute and chronic recovery phases.MethodsWMFT performance time data were converted to rates and outliers were eliminated using multivariate normality tests. Random forest regression with the elbow method was employed to determine the optimal number of items in the WMFT. Further, a machine learning technique with cross-validation and bootstrapping was used to select items. We used confirmatory factor analysis to determine the measurement structure of the original and shortened version of WMFT. Psychometric properties of the shortened version were also assessed.ResultsMachine learning-based item reduction identified 4 items (Hand to Table, Hand to Box, Extend Elbow Without Weight, and Lift Can) as representative tasks. Factor analysis revealed a 2-factor structure for both original and shorten versions, comprising non-manipulative/transport and manipulative/dexterity factors. WMFT-4 showed strong convergent validity with WMFT-15 (R = 0.98, P < .001) and moderate cross-domain validity with the Fugl-Meyer Assessment of Upper Extremity (FMA-UE) (R = 0.523, P < .001), comparable to the original WMFT-15 (R = 0.526, P < .001).ConclusionThe streamlined WMFT-4 enhances the feasibility of the WMFT for both clinical and research settings while maintaining its original measurement characteristics.

BackgroundStroke is a leading cause of upper-extremity (UE) motor impairments worldwide. Transcranial direct current stimulation (TDCS) may enhance UE recovery, but response variability remains a challenge.ObjectiveThis randomized, double-blinded feasibility and pilot clinical trial evaluated effects of patient-tailored TDCS versus sham on UE recovery in subacute stroke.MethodsPatients with subacute ischemic stroke and UE impairment were randomized to receive either anodal TDCS or sham stimulation, during UE rehabilitation 3 times weekly for 4 weeks. Electrode placement was patient-tailored and optimized using electric field modeling and targeted the ipsilesional primary motor hand area (M1-HAND). Primary outcome was Fugl-Meyer Assessment of UE (FMA-UE) score at end-of-intervention (EOT) and 12-weeks follow-up. Feasibility and exploratory clinical outcomes were also assessed.Results24 participants were randomized into real (n = 12, mean age 63 years) and sham TDCS (n = 12, mean age 68 years). FMA-UE improved at EOT in both groups, but improvement was significantly larger in the real TDCS group (mean difference 4.5 points, 95% confidence interval (CI) -5.34-14.31, P = .011). The differences diminished at 12-week follow-up. Median compliance was 95.8 and 100%, for real- and sham-TDCS groups, respectively, with no severe adverse events.ConclusionsPatient-tailored anodal TDCS over the ipsilesional M1-HAND may boost recovery of UE impairment in subacute stroke versus sham TDCS. This trial identified a clinically feasible framework for optimizing protocols of patient-tailored TDCS for larger-scale stroke trials. Despite the complex trial setup, the favorable safety profile supports future large-scale studies with improved stratification by UE impairment.

BackgroundPost-brain injury autonomic dysfunction, mediated by frontal-vagal network (FVN) dysregulation, lacks noninvasive tools for functional mapping and targeted neuromodulation.ObjectiveTo characterize autonomic impairment after brain injury, testify left dorsolateral prefrontal cortex (DLPFC) as an FVN hub, and validate a closed-loop intermittent theta-burst stimulation coupled with heart rate variability monitoring (iTBS-HRV) paradigm for FVN assessment.MethodsThis exploratory, secondary analysis integrated data from 3 coordinated investigations conducted using a dual-modality platform that combined structural magnetic resonance imaging (MRI)-guided optical neuronavigation with real-time HRV biofeedback: (1) autonomic profiling through HRV analysis comparing 59 brain-injured patients with 30 healthy controls; (2) A randomized crossover trial using MRI-neuronavigation iTBS to compare left versus right DLPFC stimulation effects on HRV in 15 participants; and (3) a translation study applied closed-loop iTBS-HRV intervention in 17 patients to quantify FVN responsivity. Key HRV metrics: root-mean-square of successive RR intervals differences ([RMSSD]; vagal tone), (high-frequency [HF]), low-frequency (LF)/HF (sympathovagal balance), and standard deviation of RR intervals ([SDNN]; global variability).ResultsPatients showed severe autonomic dysfunction with reduced vagal tone (RMSSD: 18.6 ms vs 36.7 ms, P < .001) and global variability (SDNN: 21.3 ms vs 50.9 ms, P < .001). Left frontal lesions exacerbate sympathovagal imbalance (LF/HF ↑2.40, P < .05). Left DLPFC iTBS selectively enhanced vagal modulation (ΔHF%: +2.73, P < .01; ΔLF/HF: -1.60, P < .001), confirming lateralized hub function, while patients exhibited attenuated HRV responses (ΔRMSSD: 0.50 ms vs 3.34 ms in controls, P < .01).ConclusionThe dual-modality iTBS-HRV framework provides an effective approach for mapping FVN dysfunction and targeting the left DLPFC hub for neuromodulation after brain injury.

ObjectiveExamine cortical activation patterns in Huntington's disease (HD) under single-task (ST) and dual-task (DT) balance conditions compared to controls using portable functional near-infrared spectroscopy (fNIRS).BackgroundIndividuals with HD have difficulty multitasking while performing balance tasks, so previously automatic tasks may require more attentional resources to maintain stability and prevent falls. Our understanding of the neural mechanisms underlying the relationship between impaired cognition and balance in HD is minimal. fNIRS provides a noninvasive means to functionally image the brain under ecologically valid conditions to understand the neural underpinnings of impaired balance in HD.MethodsEighteen HD (56.2 ± 9.8 years) and 20 age-matched control participants (57.4 ± 11.2 years) completed ST/ DT balance testing with eyes open (EO) or eyes closed (EC) wearing inertial sensors and fNIRS to collect spatiotemporal balance variables with concurrent prefrontal (PFC) and posterior parietal (PPC) cortical activity monitoring. The cognitive DT was the Controlled Oral Word Association Test during 3, 30-second trials.ResultsIndividuals with HD had significantly lower PPC activity during the EO DT condition compared to controls (P = .007). Unlike controls, there were no differences in PFC or PPC activation across balance conditions in HD, despite significantly worsening postural sway during DT conditions (P < .0001).ConclusionOur findings suggest that individuals with HD are unable to increase cortical activation during challenging DT conditions suggesting a recruitment ceiling was reached during ST conditions. Furthermore, individuals with HD may not be able to increase cortical recruitment in response to increasing task difficulty.

BackgroundThe Coma Recovery Scale-Revised (CRS-R) is the reference standard for diagnosing disorders of consciousness after severe brain injury. Rating scale categories for the 6 CRS-R items have been operationalized to diagnostic criteria for states of consciousness, but the validity of these diagnostic categories has not been examined in non-traumatic brain injury.

Objective: This study evaluates the hierarchy of CRS-R rating scale categories (RSCs) in individuals with disorders of consciousness due to non-traumatic brain injury.

Methods: We analyzed 4562 CRS-R assessments from 410 individuals using a partial credit Rasch model. We assessed reproducibility, structural validity, measurement accuracy, and conceptual validity by examining RSC alignment with the Aspen Consensus Criteria.

Results: All CRS-R items fit the Rasch model, with high Wright's person separation reliability (0.94) and strata (3.8), indicating strong measurement precision. The Visual and Motor items exhibited disordered rating scale thresholds. Several RSCs currently aligned with the unresponsive wakefulness syndrome showed comparable mean category measures to RSCs aligned with the minimally conscious state.

Conclusions: The CRS-R demonstrated strong reproducibility and validity in patients with non-traumatic brain injury, but may require refinement due to disordered thresholds. Consistent with literature in traumatic brain injury, our findings suggest that diagnostic criteria may need to be revised to better align with the constellation of behavioral features that are actually observed at different levels of neurorecovery. Specifically, RSC 4 on Auditory (consistent command following) and 3 on Arousal (Attention) may indicate emergence from the minimally conscious state.