Journal of NeuroEngineering and Rehabilitation最新文献

Background: Ankle-foot orthoses (AFOs) are commonly prescribed to improve gait after stroke; however, their effectiveness varies among individuals. Limited evidence exists on how AFOs specifically influence ground reaction force (GRF) patterns during gait. This study investigated how baseline anterior-posterior GRF (A-P GRF) patterns, reflecting braking and propulsive abilities, influence the immediate effects of distinct AFO designs.

Methods: This retrospective cross-sectional study included 66 community-dwelling individuals with hemiparesis who underwent gait analysis under three conditions: without AFO (noAFO), with oil-damper AFO (odAFO), and with plastic AFO (pAFO). A-P GRF impulse and mean were assessed across four stance phase bins (Bin 1: initial double support following heel contact, Bin 2: first half of the single support, Bin 3: second half of the single support, Bin 4: terminal double support preceding toe-off), alongside gait speed and limb kinematics. Hierarchical cluster analysis identified distinct A-P GRF patterns based on the impulse from Bins 1-4 during the baseline noAFO condition; immediate AFO effects were compared across clusters.

Results: Both AFO types significantly but modestly increased gait speed overall, with variable responses across clusters. Three baseline A-P GRF patterns were identified: favorable propulsion (Cluster 1, n = 19), moderate impairment (Cluster 2, n = 27), and poor propulsion with excessive braking (Cluster 3, n = 20). Participants with the poorest gait function (Cluster 3) demonstrated the most significant improvements in gait speed with both AFO types (odAFO: p < 0.001; pAFO: p = 0.006), through different biomechanical mechanisms: odAFO improved propulsive forces in Bin 4 (impulse: p < 0.001; mean: p = 0.012), whereas pAFO reduced excessive braking forces in Bin 1 (impulse: p < 0.001; mean: p = 0.048). Participants with favorable baseline A-P GRF patterns showed minimal immediate effects.

Conclusion: AFO effectiveness depends on baseline A-P GRF patterns, with the greatest benefits observed in participants exhibiting poor propulsive forces and excessive braking, through different biomechanical mechanisms. These findings highlight the importance of considering individual A-P GRF patterns when prescribing orthotic interventions in post-stroke rehabilitation.

Background: Digital health technologies (DHTs) can quantify movements in daily routines but rely heavily on participant adherence over prolonged wear times.

Methods: We analyzed accelerometry data from wrist-worn devices during short at-home episodes of prescribed exercises performed by 329 individuals living with amyotrophic lateral sclerosis (ALS) in a longitudinal study. We developed an automated and interpretable signal processing method to estimate four metrics describing exercise repetitions, i.e., their count, duration, intensity, and similarity. We examined their associations with time elapsed from enrollment and ALS Functional Rating Scale-Revised (ALSFRS-R) using linear mixed effect models. We also compared them with previously validated free-living metrics that require substantial sensor wear-time. Finally, we studied how many repetitions are sufficient to determine participants' upper limb functioning.

Results: Three out of four exercise metrics (all but count) demonstrated significant association with ALSFRS-R outcomes. The duration of exercise repetitions increased, while intensity and similarity of movement decreased over time (all p-value < 0.001), indicating longer but less vigorous and less consistent upper limb movements over time. Exercise intensity was determined as the most robust exercise-based predictor of changes in upper limb function, and it was comparable to free-living metrics, which required at 21 h of sensor wear time (R-squared 0.899 vs. 0.860, respectively). Sensitivity analysis indicated that as few as five exercise repetitions were sufficient to yield statistically significant associations with ALSFRS-R.

Conclusions: These results suggest that prescribed exercise can effectively quantify upper limb function and track longitudinal decline comparably to free-living observation. The proposed method may serve as an alternative that decreases participation burden, increases study adherence, and extends diagnostic accessibility.

Purpose: The primary treatment for lower limb Soft Tissue Sarcoma (LL-STS) consists of wide surgical resection followed by the Free Functional Muscle Transfer (FFMT) when restoration of muscular continuity and contractile function is needed. Despite the promising results, this approach led to the onset of neuromotor disabilities, reducing the patients' sensorimotor capabilities during walking. Nowadays, the role of rehabilitation in neuromuscular recovery after FFMT has not been deeply analyzed. The aim of the study was to evaluate the effect of a customized rehabilitation protocol on walking capabilities of patients with LL-STS who underwent radical resection followed by microsurgery reconstruction using FFMT.

Methods: Three patients after wide surgical resection and microsurgical reconstruction followed a personalized rehabilitation protocol according to the site of the lesion (hamstrings or quadriceps). Their ambulation performance was evaluated at the beginning, at the end of rehabilitation, and at long-term follow-up using an optoelectronic system, surface and invasive electromyography. Simultaneously, a clinical survey on physical limitations, post-operative neuropathic pain, and perceived quality of life was submitted to the patients.

Results: The patients showed progressive improvements in lower limb joint kinematics and spatio-temporal parameters for both limbs. These results were confirmed by the electromyography analysis demonstrating a complete reinnervation of the flap in all cases, with muscle activation patterns close to physiological one. Indeed, the patients developed coordinated activation patterns and compensatory strategies in the hamstrings and quadriceps femoris that supported limb stability and joint control during movement. The clinical scales demonstrated both a reduction in neuropathic pain and an improvement in physical functionalities and perceived quality of life.

Conclusion: The proposed rehabilitation approach was effective in enhancing ambulation performance of patients following FFMT. These results highlight the critical role of rehabilitation in maximizing functional outcomes after complex oncologic-musculoskeletal surgeries. Trial registration ClinicalTrials.gov (ID NCT06282237, Registration date: 2024-02-28).

Background: Early Parkinson's disease (PD) presents with subtle symptoms and lacks specific diagnostic methods. Clinical diagnosis primarily relies on subjective assessment, with confirmation often occurring at mid-to-late stages. Therefore, identifying objective and quantifiable biomarkers to assist in early PD diagnosis and intervention is of significant clinical value.

Method: This study recruited 20 early PD patients and 18 healthy controls who performed a visually guided motor-cognitive task (VMC) under three visual feedback gain conditions (low, medium, and high), while electroencephalography (EEG) and grip strength data were recorded simultaneously. Subsequently, EEG microstate analysis was used to extract temporal dynamic parameters (Duration, Occurrence, Coverage, and Transition probability), while grip strength parameters (RMSE, CV) were computed. Finally, machine learning models were developed using EEG microstate parameters, grip strength parameters, and their multimodal fusion features to systematically evaluate classification performance in distinguishing early PD patients from healthy individuals.

Result: In the resting-state, PD patients exhibited a significant increase in the Occurrence and Coverage of microstate A, while the Coverage of microstate B significantly decreased. During the VMC task, PD patients exhibited a microstate pattern significantly different from that of HC. PD patients exhibited significantly lower temporal parameters for microstate B but higher values for microstate A and C under medium/high gain conditions, which was exactly the opposite of the performance at low gain. Moreover, PD patients exhibited poorer grip performance across all gain conditions. Finally, compared with the grip strength parameter model and the multidimensional parameter model, the microstate parameter model based on the VMC task demonstrated higher sensitivity in identifying early PD patients. At low, medium, and high visual feedback gains, the highest classification accuracy reached 100% in all cases. Notably, under high-gain conditions, all classification algorithms achieved 100% accuracy.

Conclusion: This study, through EEG microstate analysis, revealed abnormal brain network dynamics in early PD patients under resting-state and various visual feedback gain conditions. The EEG microstate parameter model based on the VMC task exhibited high sensitivity in identifying early PD patients, particularly under high gain conditions. These findings suggest that EEG microstate parameters during the VMC task hold potential as objective biomarkers for early PD diagnosis and have significant clinical value.

Accurate assessment of traumatic brain injury (TBI) is critical for customization of neurorehabilitation treatments and clinical decision-making. Existing monitoring approaches either rely on subjective evaluation or isolated physiological signals, limiting real-time responsiveness and multimodal insight. This study introduces a novel framework integrating electroencephalography (EEG) and electrocardiography (ECG) to explore heart-brain synchronization as a biomarker for neurological state in TBI patients. We first define a synchronization metric using EEG delta power and heart rate variability (HRV), capturing both the degree and direction of synchronization. A two-stage contrastive learning approach is then proposed: Clinically Consistent Contrastive Learning (CCCL) leverages clinical metrics to guide positive sample selection, while Multimodal Heart-brain Contrastive Learning (MHCL) aligns synchronization features with clinical outcomes. Applied to long-term ICU recordings, the proposed approach identifies distinct synchronization patterns associated with recovery trajectories. Although the sample size (N=11) is expected to be extended, this work offers an exploratory, proof-of-concept demonstration of heart-brain synchronization as a potential real-time biomarker for neurophysiological recovery in severe TBI.