Enhancing Brain Plasticity to Promote Stroke Recovery

Frontiers in Neurology, Journal Year: 2020, Volume and Issue: 11

Published: Oct. 30, 2020

Stroke disturbs both the structural and functional integrity of brain. The understanding stroke pathophysiology has improved greatly in past several decades. However, effective therapy is still limited, especially for patients who are subacute or chronic phase. Multiple novel therapies have been developed to improve clinical outcomes by improving brain plasticity. These approaches either focus on remodeling restoration constructing a neural bypass avoid injury. This review describes emerging therapies, including modern rehabilitation, stimulation, cell therapy, brain-computer interfaces, peripheral nervous transfer, highlights treatment-induced Key evidence from basic studies underlying mechanisms also briefly discussed. insights should lead deeper overall circuit changes, relevance these changes stroke, treatment progress, which will assist development future enhance function after stroke.

Language: Английский

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SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2022, Volume and Issue: 30, P. 1737 - 1744

Published: Jan. 1, 2022

Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI aided devices demonstrated as an effective neural rehabilitation tool to improve function. It is necessary a user of MI-BCI receive long time training, while the usually suffers unsuccessful and unsatisfying results in beginning. To propose another non-invasive paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) was proposed intension detection trigger soft Thirty patients impaired were randomly equally divided into three groups conventional, robotic, BCI-robotic therapy this randomized trial (RCT). Clinical assessment Fugl-Meyer Assessment Upper Limb (FMA-UL), Wolf Function Test (WMFT) Modified Ashworth Scale (MAS) performed at pre-training, post-training months follow-up. In comparing other groups, The group showed significant improvement after training FMA full score (10.05±8.03, p=0.001), shoulder/elbow (6.2±5.94, p=0.0004) wrist/hand (4.3±2.83, p=0.007), WMFT (5.1±5.53, p=0.037). significantly correlated accuracy (r=0.714, p=0.032). Recovery SSVEP-BCI controlled better result solely rehabilitation, equivalent efficacy from previous reported proved feasibility

Language: Английский

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Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2024, Volume and Issue: 21(1)

Published: Jan. 13, 2024

Abstract Objective Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for population. A promising method controlling technologies infer movement intention non-invasively from surface electromyography (EMG). However, existing often limited research settings struggle meet user needs. Approach To address these limitations, we have developed NeuroLife ® EMG System, investigational device which consists a wearable forearm sleeve with 150 embedded electrodes associated hardware software record decode EMG. Here, demonstrate accurate decoding 12 functional hand, wrist, movements in chronic including multiple types grasps participants varying levels impairment. We also collected usability data assess how system meets needs inform future design considerations. Main results Our algorithm trained on historical- within-session produced overall accuracy 77.1 ± 5.6% across rest participants. For individuals severe hand impairment, ability subset two fundamental at 85.4 6.4% accuracy. In online scenarios, survivors achieved 91.34 1.53% three rest, highlighting potential control mechanism assistive technologies. Feedback who tested indicates that sleeve’s various needs, being comfortable, portable, lightweight. The form factor such it can be used home without expert technician worn hours discomfort. Significance System represents platform technology high-resolution real-time devices designed currently by U.S. federal law use.

Language: Английский

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Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial

Med, Journal Year: 2024, Volume and Issue: 5(6), P. 559 - 569.e4

Published: April 19, 2024

Language: Английский

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Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2025, Volume and Issue: 22(1)

Published: Jan. 6, 2025

Abstract Background Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training imagery (MI)-contingent feedback versus MI-independent remain unclear. This study aimed to investigate whether contingent connection between MI-induced brain activity influences functional outcomes. We hypothesized that training, MI-contingent feedback, would result greater improvements upper limb function compared feedback. Methods randomized controlled trial included chronic stroke who underwent involving electrical stimulation on affected wrist extensor. Primary outcomes Medical Research Council (MRC) scale score for muscle strength extensor (MRC-WE) active range motion extension (AROM-WE). Resting-state electroencephalogram recordings were used assess plasticity. Results Compared group, group showed significantly MRC-WE scores (mean difference = 0.52, 95% CI 0.03–1.00, p 0.036) demonstrated increased AROM-WE at 4 weeks post-intervention ( 0.019). Enhanced connectivity hemisphere was observed correlating Fugl-Meyer assessment-distal scores. Improvements also unaffected hemisphere’s connectivity. Conclusions is more effective than improving AROM-WE, MRC, individuals could be a valuable addition conventional rehabilitation survivors, enhancing Trial registration CRIS (KCT0009013).

Language: Английский

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Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation

BioMed Research International, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 11

Published: June 18, 2021

With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering field medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries' strategic plans for innovating this field, and subsequently, major funding talent invested technology. In neurological rehabilitation stroke patients, use BCIs opens up new chapter "top-down" our study, we first reviewed latest BCI technologies, then presented recent research advances landmark findings BCI-based neurorehabilitation patients. Neurorehabilitation was focused on areas motor, sensory, speech, cognitive, environmental interactions. Finally, summarized shortcomings prospects technology

Language: Английский

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Emerging trends in BCI-robotics for motor control and rehabilitation

Current Opinion in Biomedical Engineering, Journal Year: 2021, Volume and Issue: 20, P. 100354 - 100354

Published: Oct. 19, 2021

Language: Английский

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Brain–Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis

Neurorehabilitation and neural repair, Journal Year: 2021, Volume and Issue: 36(2), P. 83 - 96

Published: Dec. 27, 2021

Background Brain–computer interface (BCI) is a procedure involving brain activity in which neural status provided to the participants for self-regulation. The current review aims evaluate effect sizes of clinical studies investigating use BCI-based rehabilitation interventions restoring upper extremity function and effective methods detect motor recovery. Methods A computerized search MEDLINE, CENTRAL, Web Science, PEDro was performed identify relevant articles. We selected trials that used training post-stroke patients assessment scores before after intervention. pooled standardized mean differences were calculated using random-effects model. Results initially identified 655 potentially articles; finally, 16 articles fulfilled inclusion criteria, 382 participants. significant neurofeedback intervention paretic limb observed (standardized difference = .48, [.16-.80], P .006). However, estimates moderately heterogeneous among ( I 2 45%, .03). Subgroup analysis method measurement indicated effectiveness algorithm focusing on sensorimotor rhythm. Conclusion This meta-analysis suggested superior conventional recovery limbs with stroke. results are not conclusive because high risk bias large degree heterogeneity due BCI participants; therefore, further larger cohorts required confirm these results.

Language: Английский

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Leap Motion Controller Video Game-Based Therapy for Upper Extremity Motor Recovery in Patients with Central Nervous System Diseases. A Systematic Review with Meta-Analysis

Sensors, Journal Year: 2021, Volume and Issue: 21(6), P. 2065 - 2065

Published: March 15, 2021

Leap Motion Controller (LMC) is a virtual reality device that can be used in the rehabilitation of central nervous system disease (CNSD) motor impairments. This review aimed to evaluate effect video game-based therapy with LMC on recovery upper extremity (UE) function patients CNSD. A systematic meta-analysis was performed PubMed Medline, Web Science, Scopus, CINAHL, and PEDro. We included five randomized controlled trials (RCTs) CNSD which as experimental compared conventional (CT) restore UE function. Pooled effects were estimated Cohen’s standardized mean difference (SMD) its 95% confidence interval (95% CI). At first, stroke, showed low-quality evidence large mobility (SMD = 0.96; CI 0.47, 1.45). In combination CT, very 1.34; 0.49, 2.19) mobility-oriented task 1.26; 0.42, 2.10). Second, non-acute (cerebral palsy, multiple sclerosis, Parkinson’s disease), medium grip strength (GS) 0.47; 0.03, 0.90) gross dexterity (GMD) 0.73; 0.28, 1.17) most affected UE. high GMD 0.80; 0.06, 1.15) fine (FMD) 0.82; 0.07, 1.57). improved tasks, CNSD, GS FMD when it CT.

Language: Английский

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Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Frontiers in Neuroscience, Journal Year: 2021, Volume and Issue: 15

Published: July 2, 2021

Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, BCI can be used translate neural signals associated with movement intentions into tangible feedback for patient, when they are unable generate functional themselves. Clinical interest in is growing rapidly, as it would facilitate rehabilitation commence earlier following brain damage and provides options patients who partake traditional physical therapy. However, substantial challenges existing implementations have prevented its widespread adoption. Recent advances knowledge technology opportunities change, provided that researchers clinicians using agree on standardisation of guidelines protocols shared efforts uncover mechanisms. We propose addressing speed effectiveness learning control priorities field, which may improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies early stages, before transitioning practical, mobile implementations. Clarification mechanisms give rise improvement function an essential next step towards justifying clinical use BCI. In particular, quantifying unknown contribution non-motor recovery calls stringent conditions experimental work. Here we contemporary viewpoint factors impeding scalability Further, future outlook optimal design best exploit potential, practices research reporting findings.

Language: Английский

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