Journal of NeuroEngineering and Rehabilitation最新文献

Background: Artificial sensory feedback can improve function and user experience in lower-limb prosthesis users. Non-invasive methods like vibrotactile stimulation are clinically convenient, as they require no surgery. Most studies evaluate single feedback approaches, typically under controlled conditions promoting reliance on feedback. This study presents a flexible framework to compare multiple feedback approaches using microprocessor-controlled prosthesis (MP) sensors during daily-life activities.

Methods: Ten able-bodied participants and one prosthesis user with transfemoral amputation (TFA) tested two feedback locations (waist "Belt", or thigh/residual limb "Socket") to investigate tradeoffs between perception quality and compactness, using Sensation Thresholds (ST), Weber Fraction (WF), Spatial Discrimination (SD), and comfort. TFA then completed an out-of-the-lab walking session with the Socket configuration to evaluate the impact of four feedback approaches on spatiotemporal parameters and kinematics symmetries, cognitive load, and user experience during overground walking and stair climbing. Three approaches used embedded MP sensors, conveying (1) knee angle, (2) hybrid (gait phases overground, knee angle during stairs), and (3) damping (velocity-dependent resistance to flexion/extension) feedback. The fourth method used a sensorized insole, providing (4) force feedback (plantar pressure under the prosthetic foot).

Results: Able-bodied participants perceived the Belt configuration better-lower ST (29.09 ± 0.60% vs. 33.19 ± 0.60%, p < 0.001), lower WF (14.49 ± 7.02% vs. 17.98 ± 5.72%, p < 0.01), better SD at higher task difficulty (four choices: 99.3 ± 2.0% vs. 91.5 ± 2.0%, p < 0.01; eight choices: 96.0 ± 2.0% vs. 78.1 ± 2.0%, p < 0.001)-and found it also more comfortable (9.17 ± 0.3 vs. 8.15 ± 0.3; p < 0.05). Similar trends were observed in TFA. Feedback did not impact the kinematics symmetry but slightly affected stance time/percentage symmetry, with force feedback demonstrating the most consistent benefits. These suggest that incidental feedback provided intrinsically by the prosthesis (e.g., motion, sound, socket pressure, vibration) may already support gait in experienced users. Nevertheless, TFA preferred having feedback, especially force and damping, which reduced cognitive load.

Conclusion: Embedded MP sensors enable flexible, compact feedback solutions, combining internal signals (e.g., damping feedback) with external sensing (e.g., omnidirectional force feedback). Belt-mounted vibromotors are effective for testing complex encoding schemes. Feedback should be co-developed with users, balancing objective performance and subjective experience.

Background: Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the "right intervention, at the right time, in the right setting, for the right person," is essential for optimizing stroke recovery. Although artificial intelligence (AI) has been effectively utilized in other medical fields, no current AI system is designed to tailor and continuously refine rehabilitation plans post-stroke.

Methods: We propose a novel AI-based decision-support system for precision rehabilitation that uses reinforcement learning (RL) to personalize the treatment plan. Specifically, our system iteratively adjusts the sequential treatment plan-timing, dosage, and intensity-to maximize long-term outcomes based on a patient model that includes covariate data (the context). The system collaborates with clinicians and people with stroke to customize the recommended plan based on clinical judgment, constraints, and preferences. To achieve this goal, we propose a contextual Markov decision process (CMDP) framework and a novel hierarchical Bayesian model-based RL algorithm, named posterior sampling for contextual RL (PSCRL), that discovers and continuously adjusts near-optimal sequential treatments by efficiently balancing exploitation and exploration while respecting constraints and preferences.

Results: We implemented and validated our precision rehabilitation system in simulations with 150 diverse, synthetic patients. Simulation results showed the system's ability to continuously learn from both upcoming data from the current patient and a database of past patients via Bayesian hierarchical modeling. Specifically, the algorithm's sequential treatment recommendations became increasingly more effective in improving functional gains for each patient over time and across the synthetic patient population. As a result, the algorithm's treatments were superior to non-adaptive, "one-size-fits-all" dosing schedules (uniform, decreasing, and increasing).

Conclusions: Our novel AI-based precision rehabilitation system, based on contextual model-based RL, has the potential to play a key role in novel learning health systems in rehabilitation.

Background: Sensorimotor impairments following stroke frequently result in diminished voluntary control of the ankle, contributing to deficits in balance and gait. Robotic training paradigms targeting ankle motor control often use an assist-as-needed strategy, where compliant guidance is provided to assist movements towards a target trajectory. However, interaction with "perfect" reference trajectories may overly constrain movements during training and has been shown to limit learning in many upper-limb contexts; alternatives to robotic assistance have rarely been explored for post-stroke ankle training. Inspired by human-robot-human interaction studies, we investigated whether physical interaction with a therapist-termed human interaction-offers advantages over traditional trajectory guidance regarding short-term learning.

Methods: In a within-subject design, nine individuals with chronic stroke (61.6 ± 14.3 years) performed a 1-DoF visuomotor tracking task while wearing ankle robots designed to train dorsiflexion and plantarflexion movements. Two robotic training methods were evaluated in separate visits: (1) compliant connection to a sinusoidal target trajectory (i.e., trajectory guidance) and (2) compliant connection to a physical therapist who tracked the same target trajectory (i.e., human interaction). In each visit, tracking performance (i.e., errors, movement smoothness) and muscle activation were evaluated during and immediately after training.

Results: Both training types improved tracking accuracy and movement smoothness during training, however random error was more significantly suppressed with trajectory guidance. Immediately after training, we found no significant difference in tracking accuracy or movement smoothness across training types. However, participants demonstrated significantly higher dorsiflexor activation after training with human interaction compared to trajectory guidance.

Conclusion: Our results suggest that human interaction is a viable strategy for training ankle movements in chronic stroke participants, likely by providing assistance without over-constraining an individual's movement smoothness or variability. Training while physically interacting with a partner could serve as an effective alternative to conventional robot-guided therapy for post-stroke ankle rehabilitation, though further studies with larger cohorts are needed to assess the generalization of this approach regarding long-term retention and functional improvement. Registry: clinicaltrials.gov, TRN: NCT04578665, Registration date: 8 October 2020.

Background: Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation. As a purely cognitive process, MI produces low-amplitude, non-stationary EEG. Despite improving accuracies, cross-subject variability and limited generalization continue to motivate approaches that strengthen MI representations and enhance system robustness.

Methods: We designed a task-guided preparatory movement state-based motor imagery (PMS-MI) paradigm that elicits a brief motor preparatory state before MI and captures EEG features from both the preparation and imagery phases. To decode the features effectively, we introduced a multilayer energy decoder (MLED) that integrates graph signal processing (GSP): EEG is modeled as intra- and cross-frequency multilayer brain networks, and a graph Fourier transform (GFT) projects the signals into network energy features before classification. We benchmarked the PMS-MI paradigm and the MLED method across multiple time window lengths using a panel of classical and deep-learning classifiers.

Results: The PMS-MI paradigm elicited significant energy variations during the movement preparation phase and induced earlier event-related desynchronization (ERD) with broader frequency band activation during MI, compared to traditional MI paradigms. Classification performance using CSP in the PMS-MI paradigm surpassed that of the traditional paradigm at all time windows. Further accuracy improvements were achieved with the MLED method. Brain network analysis revealed distinct neural representations between the preparation and MI phases, and MLED effectively captured these differences. Feature fusion of preparation and MI stages resulted in classification accuracies exceeding 85% for both 1 s and 4 s windows. The results demonstrate that both algorithmic design and paradigm choice play important roles in MI EEG decoding, with their relative contributions varying across temporal windows and experimental conditions.

Conclusions: Integration of preparatory movement states into the movement imagery process can generate distinguishable features at different stages and improve the classification performance of BCI systems. The proposed PMS-MI paradigm, combined with the MLED decoding method, provides a promising direction for developing more accurate and robust BCIs, particularly in the context of neurorehabilitation.

Background: Virtual Reality (VR) Serious Games (SGs) can provide a functionally relevant framework to capture cognitive and motor dynamics. Their interactive and engaging nature improves compliance, measurement reliability and allows for more frequent evaluations. Additionally, VR SGs enable the parallel collection of multiple types of data within a single session. We present REAsmash-ET, an immersive VR adaptation of the REAsmash SG, grounded in Feature Integration Theory (FIT) and integrating eye-tracking (ET) and upper limb kinematic (UL) analyses. REAsmash-ET introduces a novel methodological framework for the simultaneous assessment of attentional and motor functions in VR.

Methods: REAsmash is an interactive search-and-reach task designed to elicit structured visual exploration and UL motor responses under varying target-distractor saliency conditions. Custom algorithms extract metrics on visual search strategies and UL motor efficiency. Three age groups of adult healthy participants (n = 15 each) were included to test the feasibility and methodological consistency of the task and its metrics. Relative Response Time (RRT) and ET metrics were analyzed using ANOVA with factors: age group (20-39, 40-59, 60-80 years), target-distractor saliency (high vs. low), and number of distractors (11, 17, 23). Kinematic metrics were analyzed by age group and response hand (dominant vs. non-dominant).

Results: REAsmash-ET differentiated visuomotor performance across task conditions. RRT and ET metrics showed significant effects of saliency, number of distractors, and their interaction, consistent with FIT. Age-related differences emerged in both RRT and visual search efficiency. Kinematic analyses revealed slower and less efficient movements in older participants, with effects of hand dominance.. The results support the robustness and feasibility of REAsmash-ET as a methodological framework.

Conclusions: The results support the robustness and internal consistency of REAsmash-ET as a methodological framework for the integrated assessment of visual attention and UL motor control in immersive VR. The task's ability to capture visuomotor variability and its multidimensional approach highlight its potential for future research and clinical applications in both healthy and clinical populations.

Registry number: This study was registered at ClinicalTrials.gov (NCT04694833).

Objective: Previous animal models of post-stroke dysphagia (PSD) have limitations-these models are primarily induced by cortical strokes. Compared to cortical strokes, brainstem strokes are more likely to cause dysphagia, and the pathological mechanisms underlying dysphagia differ by focal infarction location. This study aimed to create a novel rat dysphagia model via brainstem ischemia (BSI) and explore modified pharyngeal electrical stimulation (mPES) therapy.

Methods: Rat brainstem ischemia was induced by photochemical embolization, confirmed by MRI and pathology. Swallowing function was assessed using Videofluoroscopic Swallowing Study (VFSS), while motor and neurobehavioral changes were evaluated through behavioral tests. Post-mPES treatment, VFSS was conducted to evaluate the swallowing function and single-cell transcriptomics was performed to explore therapeutic mechanisms.

Results: The BSI model showed stable dysphagia characteristics, with prolonged pharyngeal transit time, increased inter-swallowing interval and smaller bolus size area. mPES significantly improved these parameters. Behavioral tests revealed BSI caused anxiety-like behavior and worse motor performance in rats. Single-cell transcriptomics indicated mPES treatment involved multiple biological mechanisms, possibly exerting therapeutic effects by influencing oligodendrocyte differentiation and myelin, or synapse regeneration and repair.

Conclusion: The novel BSI-induced dysphagia model exhibited stable swallowing function deficits. mPES effectively improved these deficiencies, with therapeutic mechanisms potentially associated with oligodendrocytes.

Background: Postural instability in Parkinson's disease (PD) is a major contributor to falls and functional decline, yet remains poorly responsive to dopaminergic therapy. Noisy galvanic vestibular stimulation (nGVS), a non-invasive neuromodulation technique, has shown potential to enhance postural control by augmenting multisensory integration. However, the neural mechanisms and temporal dynamics underlying its effects remain incompletely understood.

Objective: To evaluate the behavioral and neurophysiological effects of a single session of nGVS in patients with mild-to-moderate PD using standardized clinical measures and resting-state functional MRI (rs-fMRI).

Methods: Forty-one idiopathic PD patients underwent clinical assessments and rs-fMRI at three time points: pre-nGVS, immediately post-stimulation, and one week later. Postural function was quantified using the Berg Balance Test (BBT), Functional Reach Test (FRT), Timed Up and Go (TUG), and Numeric Rating Scale (NRS) for discomfort. Imaging analyses included seed-based connectivity, independent component analysis (ICA), and amplitude of low-frequency fluctuation (ALFF/fALFF).

Results: nGVS elicited significant improvements in static and anticipatory balance, with BBT gains persisting after one week. These behavioral effects were accompanied by transient increases in visual and sensorimotor network activity, short-lived ALFF/fALFF enhancement in occipital and parietal regions, and sustained connectivity between the pedunculopontine nucleus (PPN) and superior frontal gyrus. Gait-related outcomes showed more modest and delayed changes.

Conclusion: nGVS selectively engages intrinsic networks supporting static postural control, producing partially sustained behavioral benefits and circuit-specific neuroplasticity. These findings support its potential as a non-invasive intervention targeting axial symptoms in PD and suggest imaging-based biomarkers for future stratified therapeutic application.

Trial registration: This study was registered with the Clinical Research Information Service (CRIS), Republic of Korea, under the identifier KCT0007058, registered on Mar 04, 2022.

Background: Motor imagery (MI) has garnered significant interest as a novel rehabilitation method for stroke. Additionally, task-oriented robot training has been shown to enhance lower limb motor function in patients with early-stage stroke. However, the therapeutic effects of combining these two approaches remain unclear, and the underlying mechanisms are not yet understood. This study aims to investigate the effects of MI combined with task-oriented robot training on the lower limb motor function of post-stroke patients.

Methods: First-ever stroke patients meeting the inclusion criteria were recruited and randomly allocated eligible participants to the control group (n = 91) or the experimental group (n = 91). Based on routine conventional physical therapy, the experimental group received task-oriented robot training combined with MI training, whereas the control group received task-oriented robot training combined with muscle relaxation training. The outcome indicators are the Fugl-Meyer Assessment of Lower Extremity (FMA-LE), Berg Balance Scale (BBS), and spatio-temporal gait parameters, which reflect the patients' lower limb motor function. The functional connectivity between regions is measured by functional near-infrared spectroscopy (fNIRS).

Results: Significant improvements in FMA-LE and BBS were observed in the experimental group compared with the control group (p < 0.05). Although no significant differences were observed between groups post-treatment (p > 0.05), both groups demonstrated improved step frequency and gait speed scores and reduced gait cycle scores following intervention (p < 0.05). In addition, the experimental group showed significantly enhanced functional connectivity between the prefrontal cortex and motor-related regions compared to the control group (p < 0.05).

Conclusions: Combining MI training with task-oriented robotic training can enhance lower limb motor function and enhance the brain's functional connectivity. Changes in functional connectivity within the prefrontal cortex (PFC) and motor-related cortex may serve as a potential therapeutic target for promoting motor recovery in stroke patients. Future studies should incorporate task-based functional Magnetic Resonance Imaging (fMRI) data to elucidate the directionality of information flow between these brain regions, thereby advancing our understanding of causal interactions underlying functional improvements in post-stroke gait rehabilitation.

Trial registration: It was retrospectively registered at the Chinese Clinical Trial Registry on 8 July 2025 (Registration No. ChiCTR2500105631).