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Krueger J, Krauth R, Reichert C, Perdikis S, Vogt S, Huchtemann T, Dürschmid S, Sickert A, Lamprecht J, Huremovic A, Görtler M, Nasuto SJ, Tsai IC, Knight RT, Hinrichs H, Heinze HJ, Lindquist S, Sailer M, Millán JDR, Sweeney-Reed CM. Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery. Sci Rep 2024; 14:18700. [PMID: 39134592 PMCID: PMC11319604 DOI: 10.1038/s41598-024-69037-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/30/2024] [Indexed: 08/15/2024] Open
Abstract
Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery. Using a brain-computer interface (BCI) to classify MC spectral power in electroencephalographic (EEG) signals to trigger FES-delivery with detection of movement attempts improved motor outcomes in chronic stroke patients. We hypothesized that heightened neural plasticity earlier post-stroke would further enhance corticomuscular functional connectivity and motor recovery. We compared subcortical non-dominant hemisphere stroke patients in BCI-FES and Random-FES (FES temporally independent of MC movement attempt detection) groups. The primary outcome measure was the Fugl-Meyer Assessment, Upper Extremity (FMA-UE). We recorded high-density EEG and transcranial magnetic stimulation-induced motor evoked potentials before and after treatment. The BCI group showed greater: FMA-UE improvement; motor evoked potential amplitude; beta oscillatory power and long-range temporal correlation reduction over contralateral MC; and corticomuscular coherence with contralateral MC. These changes are consistent with enhanced post-stroke motor improvement when movement is synchronized with MC activity reflecting attempted movement.
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Affiliation(s)
- Johanna Krueger
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | - Richard Krauth
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | | | - Serafeim Perdikis
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Susanne Vogt
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Psychosomatic Medicine and Psychotherapy, Otto von Guericke University, Magdeburg, Germany
| | - Tessa Huchtemann
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, University Hospital Münster, Münster, Germany
| | - Stefan Dürschmid
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, Magdeburg, Germany
| | - Almut Sickert
- Neurorehabilitation Centre, MEDIAN, Magdeburg, Germany
| | - Juliane Lamprecht
- Neurorehabilitation Centre, MEDIAN, Magdeburg, Germany
- Health and Care Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Almir Huremovic
- Neurorehabilitation Centre, MEDIAN, Magdeburg, Germany
- Department of Neurology, Ingolstadt Hospital, Ingolstadt, Germany
| | - Michael Görtler
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | | | - I-Chin Tsai
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California -Berkeley, Berkeley, USA
- Department of Psychology, University of California -Berkeley, Berkeley, USA
| | - Hermann Hinrichs
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- University Hospital Magdeburg, Otto von Guericke University, Magdeburg, Germany
| | - Sabine Lindquist
- Department of Neurology, Pfeiffersche Stiftung, Magdeburg, Germany
| | | | - Jose Del R Millán
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA
- Department of Neurology, The University of Texas at Austin, Austin, USA
- Mulva Clinic for the Neurosciences, The University of Texas at Austin, Austin, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
| | - Catherine M Sweeney-Reed
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany.
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, Magdeburg, Germany.
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Kim MS, Park H, Kwon I, An KO, Shin JH. Brain-computer interface on wrist training with or without neurofeedback in subacute stroke: a study protocol for a double-blinded, randomized control pilot trial. Front Neurol 2024; 15:1376782. [PMID: 39144712 PMCID: PMC11322049 DOI: 10.3389/fneur.2024.1376782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 07/15/2024] [Indexed: 08/16/2024] Open
Abstract
Background After a stroke, damage to the part of the brain that controls movement results in the loss of motor function. Brain-computer interface (BCI)-based stroke rehabilitation involves patients imagining movement without physically moving while the system measures the perceptual-motor rhythm in the motor cortex. Visual feedback through virtual reality and functional electrical stimulation is provided simultaneously. The superiority of real BCI over sham BCI in the subacute phase of stroke remains unclear. Therefore, we aim to compare the effects of real and sham BCI on motor function and brain activity among patients with subacute stroke with weak wrist extensor strength. Methods This is a double-blinded randomized controlled trial. Patients with stroke will be categorized into real BCI and sham BCI groups. The BCI task involves wrist extension for 60 min/day, 5 times/week for 4 weeks. Twenty sessions will be conducted. The evaluation will be conducted four times, as follows: before the intervention, 2 weeks after the start of the intervention, immediately after the intervention, and 4 weeks after the intervention. The assessments include a clinical evaluation, electroencephalography, and electromyography using motor-evoked potentials. Discussion Patients will be categorized into two groups, as follows: those who will be receiving neurofeedback and those who will not receive this feedback during the BCI rehabilitation training. We will examine the importance of motor imaging feedback, and the effect of patients' continuous participation in the training rather than their being passive.Clinical Trial Registration: KCT0008589.
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Affiliation(s)
- Myeong Sun Kim
- Translational Research Center for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, Republic of Korea
- Department of Rehabilitative and Assistive Technology, National Rehabilitation Center, Rehabilitation Research Institute, Ministry of Health and Welfare, Seoul, Republic of Korea
| | - Hyunju Park
- Translational Research Center for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, Republic of Korea
- Department of Rehabilitative and Assistive Technology, National Rehabilitation Center, Rehabilitation Research Institute, Ministry of Health and Welfare, Seoul, Republic of Korea
| | - Ilho Kwon
- Translational Research Center for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, Republic of Korea
- Department of Rehabilitative and Assistive Technology, National Rehabilitation Center, Rehabilitation Research Institute, Ministry of Health and Welfare, Seoul, Republic of Korea
| | - Kwang-Ok An
- Department of Healthcare and Public Health Research, National Rehabilitation Center, Rehabilitation Research Institute, Ministry of Health and Welfare, Seoul, Republic of Korea
| | - Joon-Ho Shin
- Translational Research Center for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, Republic of Korea
- Department of Rehabilitation Medicine, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, Republic of Korea
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Wang A, Tian X, Jiang D, Yang C, Xu Q, Zhang Y, Zhao S, Zhang X, Jing J, Wei N, Wu Y, Lv W, Yang B, Zang D, Wang Y, Zhang Y, Wang Y, Meng X. Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial. MED 2024; 5:559-569.e4. [PMID: 38642555 DOI: 10.1016/j.medj.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Upper limb motor dysfunction is a major problem in the rehabilitation of patients with stroke. Brain-computer interface (BCI) is a kind of communication system that converts the "ideas" in the brain into instructions and has been used in stroke rehabilitation. This study aimed to investigate the efficacy and safety of BCI in rehabilitation training on upper limb motor function among patients with ischemic stroke. METHODS This was an investigator-initiated, multicenter, randomized, open-label, blank-controlled clinical trial with blinded outcome assessment conducted at 17 centers in China. Patients were assigned in a 1:1 ratio to the BCI group or the control group based on traditional rehabilitation training. The primary efficacy outcome is the difference in improvement of the Fugl-Meyer Assessment upper extremity (FMA-UE) score between two groups at month 1 after randomization. The safety outcomes were any adverse events within 3 months. FINDINGS A total of 296 patients with ischemic stroke were enrolled and randomly allocated to the BCI group (n = 150) and the control group (n = 146). The primary efficacy outcomes of FMA-UE score change from baseline to 1 month were 13.17 (95% confidence interval [CI], 11.56-14.79) in the BCI group and 9.83 (95% CI, 8.19-11.47) in the control group (mean difference between groups was 3.35; 95% CI, 1.05-5.65; p = 0.0045). Adverse events occurred in 33 patients (22.00%) in the BCI group and in 31 patients (21.23%) in the control group. CONCLUSIONS BCI rehabilitation training can further improve upper limb motor function based on traditional rehabilitation training in patients with ischemic stroke. This study was registered at ClinicalTrials.gov: NCT04387474. FUNDING This work was supported by the National Key R&D Program of China (2018YFC1312903), the National Key Research and Development Program of China (2022YFC3600600), the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), the Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), the Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47).
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Affiliation(s)
- Anxin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xue Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Di Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chengyuan Yang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qin Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yifei Zhang
- Research and Development Center, Shandong Haitian Intelligent Engineering Co., Ltd., Shandong, China
| | - Shaoqing Zhao
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Xiaoli Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ning Wei
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuqian Wu
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Lv
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Banghua Yang
- School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
| | - Dawei Zang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yumei Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Qu H, Zeng F, Tang Y, Shi B, Wang Z, Chen X, Wang J. The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review. Disabil Rehabil Assist Technol 2024; 19:30-41. [PMID: 35450498 DOI: 10.1080/17483107.2022.2060354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/26/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to evaluate the clinical effects of BCI-robot systems. METHODS The articles published from January 2010 to December 2020 have been searched by using the databases (EMBASE, PubMed, CINAHL, EBSCO, Web of Science and manual search). The single-group studies were qualitatively described, and only the controlled-trial studies were included for the meta-analysis. The mean difference (MD) of Fugl-Meyer Assessment (FMA) scores were pooled and the random-effects model method was used to perform the meta-analysis. The PRISMA criteria were followed in current review. RESULTS A total of 897 records were identified, eight single-group studies and 11 controlled-trial studies were included in our review. The systematic analysis indicated that the BCI-robot systems had a significant improvement on motor function recovery. The meta-analysis showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects (p > 0.05). CONCLUSION The use of BCI-robot systems has significant improvement on the motor function recovery of hemiparetic upper-limb, and there is a sustaining effect. The meta-analysis showed no statistical difference between the experimental group (BCI-robot) and the control group (robot). However, there are a few shortcomings in the experimental design of existing studies, more clinical trials need to be conducted, and the experimental design needs to be more rigorous.Implications for RehabilitationIn this review, we evaluated the clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation. After we screened the databases, 19 articles were included in this review. These articles all clinical trial research, they all used non-invasive brain-computer interfaces and upper-limb robot.We conducted the systematic review with nine articles, the result indicated that the BCI-robot system had a significant improvement on motor function recovery. Eleven articles were included for the meta-analysis, the result showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects.We thought the result of meta-analysis which showed no statistic difference was probably caused by the heterogenicity of clinical trial designs of these articles.We thought the BCI-robot systems are promising strategies for post-stroke rehabilitation. And we gave several suggestions for further research: (1) The experimental design should be more rigorous, and describe the experimental designs in detail, especially the control group intervention, to make the experiment replicability. (2) New evaluation criteria need to be established, more objective assessment such as biomechanical assessment, fMRI should be utilised as the primary outcome. (3) More clinical studies with larger sample size, novel external devices, and BCI systems need to be conducted to investigate the differences between BCI-robot system and other interventions. (4) Further research could shift the focus to the patients who are in subacute stage, to explore if the early BCI training can make a positive impact on cerebral cortical recovery.
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Affiliation(s)
- Hao Qu
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Feixiang Zeng
- Department of Rehabilitation Medicine, HuiZhou Third People's Hospital, Huizhou, China
| | - Yongbin Tang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Bin Shi
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhijun Wang
- Department of Rehabilitation Medicine, FoShan Fifth People's Hospital, Guangdong, China
| | - Xiaokai Chen
- Department of Rehabilitation Medicine, HuiZhou Third People's Hospital, Huizhou, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
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Ribeiro TF, Carriello MA, de Paula EP, Garcia AC, da Rocha GL, Teive HAG. Clinical applications of neurofeedback based on sensorimotor rhythm: a systematic review and meta-analysis. Front Neurosci 2023; 17:1195066. [PMID: 38053609 PMCID: PMC10694284 DOI: 10.3389/fnins.2023.1195066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/07/2023] [Indexed: 12/07/2023] Open
Abstract
Background Among the brain-machine interfaces, neurofeedback is a non-invasive technique that uses sensorimotor rhythm (SMR) as a clinical intervention protocol. This study aimed to investigate the clinical applications of SMR neurofeedback to understand its clinical effectiveness in different pathologies or symptoms. Methods A systematic review study with meta-analysis of the clinical applications of EEG-based SMR neurofeedback performed using pre-selected publication databases. A qualitative analysis of these studies was performed using the Consensus tool on the Reporting and Experimental Design of Neurofeedback studies (CRED-nf). The Meta-analysis of clinical efficacy was carried out using Review Manager software, version 5.4.1 (RevMan 5; Cochrane Collaboration, Oxford, UK). Results The qualitative analysis includes 44 studies, of which only 27 studies had some kind of control condition, five studies were double-blinded, and only three reported a blind follow-up throughout the intervention. The meta-analysis included a total sample of 203 individuals between stroke and fibromyalgia. Studies on multiple sclerosis, insomnia, quadriplegia, paraplegia, and mild cognitive impairment were excluded due to the absence of a control group or results based only on post-intervention scales. Statistical analysis indicated that stroke patients did not benefit from neurofeedback interventions when compared to other therapies (Std. mean. dif. 0.31, 95% CI 0.03-0.60, p = 0.03), and there was no significant heterogeneity among stroke studies, classified as moderate I2 = 46% p-value = 0.06. Patients diagnosed with fibromyalgia showed, by means of quantitative analysis, a better benefit for the group that used neurofeedback (Std. mean. dif. -0.73, 95% CI -1.22 to -0.24, p = 0.001). Thus, on performing the pooled analysis between conditions, no significant differences were observed between the neurofeedback intervention and standard therapy (0.05, CI 95%, -0.20 to -0.30, p = 0.69), with the presence of substantial heterogeneity I2 = 92.2%, p-value < 0.001. Conclusion We conclude that although neurofeedback based on electrophysiological patterns of SMR contemplates the interest of numerous researchers and the existence of research that presents promising results, it is currently not possible to point out the clinical benefits of the technique as a form of clinical intervention. Therefore, it is necessary to develop more robust studies with a greater sample of a more rigorous methodology to understand the benefits that the technique can provide to the population.
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Affiliation(s)
- Tatiana Ferri Ribeiro
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Marcelo Alves Carriello
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Eugenio Pereira de Paula
- Physical Education (UFPR)—Invited Colaborador, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Amanda Carvalho Garcia
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Guilherme Luiz da Rocha
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Helio Afonso Ghizoni Teive
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
- Department of Clinical Medicine, UFPR, and Coordinator of the Movement Disorders Sector, Neurology Service, Clinic Hospital, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
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Tanamachi K, Kuwahara W, Okawada M, Sasaki S, Kaneko F. Relationship between resting-state functional connectivity and change in motor function after motor imagery intervention in patients with stroke: a scoping review. J Neuroeng Rehabil 2023; 20:159. [PMID: 37980496 PMCID: PMC10657492 DOI: 10.1186/s12984-023-01282-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/12/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND In clinical practice, motor imagery has been proposed as a treatment modality for stroke owing to its feasibility in patients with severe motor impairment. Motor imagery-based interventions can be categorized as open- or closed-loop. Closed-loop intervention is based on voluntary motor imagery and induced peripheral sensory afferent (e.g., Brain Computer Interface (BCI)-based interventions). Meanwhile, open-loop interventions include methods without voluntary motor imagery or sensory afferent. Resting-state functional connectivity (rs-FC) is defined as a significant temporal correlated signal among functionally related brain regions without any stimulus. rs-FC is a powerful tool for exploring the baseline characteristics of brain connectivity. Previous studies reported changes in rs-FC after motor imagery interventions. Systematic reviews also reported the effects of motor imagery-based interventions at the behavioral level. This study aimed to review and describe the relationship between the improvement in motor function and changes in rs-FC after motor imagery in patients with stroke. REVIEW PROCESS The literature review was based on Arksey and O'Malley's framework. PubMed, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched up to September 30, 2023. The included studies covered the following topics: illusion without voluntary action, motor imagery, action imitation, and BCI-based interventions. The correlation between rs-FC and motor function before and after the intervention was analyzed. After screening by two independent researchers, 13 studies on BCI-based intervention, motor imagery intervention, and kinesthetic illusion induced by visual stimulation therapy were included. CONCLUSION All studies relating to motor imagery in this review reported improvement in motor function post-intervention. Furthermore, all those studies demonstrated a significant relationship between the change in motor function and rs-FC (e.g., sensorimotor network and parietal cortex).
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Affiliation(s)
- Kenya Tanamachi
- Department of Physical Therapy, Graduate School of Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-Ogu, Arakawa-Ku, Tokyo, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Wataru Kuwahara
- Department of Physical Therapy, Graduate School of Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-Ogu, Arakawa-Ku, Tokyo, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Megumi Okawada
- Department of Physical Therapy, Graduate School of Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-Ogu, Arakawa-Ku, Tokyo, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Shun Sasaki
- Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Fuminari Kaneko
- Department of Physical Therapy, Graduate School of Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-Ogu, Arakawa-Ku, Tokyo, Japan.
- Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.
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Shao Z, Dou W, Ma D, Zhai X, Xu Q, Pan Y. A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3375-3383. [PMID: 37581962 DOI: 10.1109/tnsre.2023.3305474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in [Formula: see text], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.
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Ma ZZ, Wu JJ, Hua XY, Zheng MX, Xing XX, Ma J, Shan CL, Xu JG. Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis. Front Neurol 2023; 14:1135466. [PMID: 37346164 PMCID: PMC10281191 DOI: 10.3389/fneur.2023.1135466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/03/2023] [Indexed: 06/23/2023] Open
Abstract
Background Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. Methods Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. Results The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). Conclusion Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies. Clinical Trial Registration The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.
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Affiliation(s)
- Zhen-Zhen Ma
- Department of Rehabilitation Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
| | - Jia-Jia Wu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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9
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Yu J, Zhang X, Yang J, Wang Z, Zhao H, Yuan X, Fan Z, Liu H. A functional near-infrared spectroscopy study of the effects of video game-based bilateral upper limb training on brain cortical activation and functional connectivity. Exp Gerontol 2022; 169:111962. [PMID: 36162532 DOI: 10.1016/j.exger.2022.111962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 12/15/2022]
Abstract
Video game-based therapies are widely used in rehabilitation. Compared with conventional bilateral upper limb training (CBULT), the effects of video game-based bilateral upper limb training (VGBULT) on brain cortical activation and functional connectivity, still not fully clear. We have developed a VGBULT system, and measured the brain activity of 20 elderly subjects (10 male, mean age = 62.4 ± 5.8) while performing CBULT and VGBULT tasks by using functional near infrared spectroscopy (fNIRS). The results showed that the cerebral cortex of the two groups both showed significant activation (p < 0.05), compared with the baseline; In the VGBLUT group, the activation of motor cortex (MC) and prefrontal cortex (PFC) was stronger, and the functional connectivity between PFC and MC was also enhanced. This study showed that VGBULT is potentially more beneficial for the elderly neural activities and cognitive control, and provides a theoretical basis for future research and development of such rehabilitation products. Moreover, fNIRS is a reliable tool for tracking brain activation in the evaluation of retraining regimens.
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Affiliation(s)
- Jiulong Yu
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Xin Zhang
- Institute of Medical Biology, Chinese Academy of Medical Science and Peking Union Medical College, Kunming 650118, People's Republic of China
| | - Jie Yang
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Zilin Wang
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - HuaChao Zhao
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Xin Yuan
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Zhijun Fan
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Heshan Liu
- School of Mechanical Engineering, Shandong University, Jinan 250061, People's Republic of China.
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10
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Behboodi A, Lee WA, Hinchberger VS, Damiano DL. Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review. J Neuroeng Rehabil 2022; 19:104. [PMID: 36171602 PMCID: PMC9516814 DOI: 10.1186/s12984-022-01081-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background Brain–computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications? Methods We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes. Results From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n = 13) and the Action Research Arm Test (ARAT; n = 6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose. Conclusion This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.
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Affiliation(s)
- Ahad Behboodi
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | - Walker A Lee
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | | | - Diane L Damiano
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA.
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11
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Mansour S, Giles J, Ang KK, Nair KPS, Phua KS, Arvaneh M. Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface. Sci Rep 2022; 12:16223. [PMID: 36171400 PMCID: PMC9519575 DOI: 10.1038/s41598-022-20345-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/12/2022] [Indexed: 11/09/2022] Open
Abstract
Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI.
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Affiliation(s)
- Salem Mansour
- Department of Automatic Control and Systems Engineering, University of Sheffield, Mapping Street, Sheffield, S13JD, UK.
| | - Joshua Giles
- Department of Automatic Control and Systems Engineering, University of Sheffield, Mapping Street, Sheffield, S13JD, UK
- Agency for Science Technology and Research, Institute for Infocomm Research, Singapore, Singapore
| | - Kai Keng Ang
- Agency for Science Technology and Research, Institute for Infocomm Research, Singapore, Singapore
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Krishnan P S Nair
- Neurology, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust and The University of Sheffield, Sheffield, UK
| | - Kok Soon Phua
- Agency for Science Technology and Research, Institute for Infocomm Research, Singapore, Singapore
| | - Mahnaz Arvaneh
- Department of Automatic Control and Systems Engineering, University of Sheffield, Mapping Street, Sheffield, S13JD, UK
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12
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Xie H, Li X, Huang W, Yin J, Luo C, Li Z, Dou Z. Effects of robot-assisted task-oriented upper limb motor training on neuroplasticity in stroke patients with different degrees of motor dysfunction: A neuroimaging motor evaluation index. Front Neurosci 2022; 16:957972. [PMID: 36188465 PMCID: PMC9523102 DOI: 10.3389/fnins.2022.957972] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionAlthough robot-assisted task-oriented upper limb (UL) motor training had been shown to be effective for UL functional rehabilitation after stroke, it did not improve UL motor function more than conventional therapy. Due to the lack of evaluation of neurological indicators, it was difficult to confirm the robot treatment parameters and clinical efficacy in a timely manner. This study aimed to explore the changes in neuroplasticity induced by robot-assisted task-oriented UL motor training in different degrees of dysfunction patients and extract neurological evaluation indicators to provide the robot with additional parameter information.Materials and methodsA total of 33 adult patients with hemiplegic motor impairment after stroke were recruited as participants in this study, and a manual muscle test divided patients into muscle strength 0–1 level (severe group, n = 10), 2–3 level (moderate group, n = 14), and 4 or above level (mild group, n = 9). Tissue concentration of oxyhemoglobin and deoxyhemoglobin oscillations in the bilateral prefrontal cortex, dorsolateral prefrontal cortex (DLPFC), superior frontal cortex (SFC), premotor cortex, primary motor cortex (M1), primary somatosensory cortex (S1), and occipital cortex were measured by functional near-infrared spectroscopy (fNIRS) in resting and motor training state. The phase information of a 0.01 −0.08 Hz signal was identified by the wavelet transform method. The wavelet amplitude, lateralization index, and wavelet phase coherence (WPCO) were calculated to describe the frequency-specific cortical changes.ResultsCompared with the resting state, significant increased cortical activation was observed in ipsilesional SFC in the mild group and bilateral SFC in the moderate group during UL motor training. Patients in the mild group demonstrated significantly decreased lateralization of activation in motor training than resting state. Moreover, the WPCO value of motor training between contralesional DLPFC and ipsilesional SFC, bilateral SFC, contralesional, S1, and ipsilesional M1 showed a significant decrease compared with the resting state in the mild group.ConclusionRobot-assisted task-oriented UL motor training could modify the neuroplasticity of SFC and contribute to control movements and continuous learning motor regularity for patients. fNIRS could provide a variety of real-time sensitive neural evaluation indicators for the robot, which was beneficial to formulating more reasonable and effective personalized prescriptions during motor training.
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Affiliation(s)
- Hui Xie
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xin Li
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wenhao Huang
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiahui Yin
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Cailing Luo
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- *Correspondence: Zengyong Li
| | - Zulin Dou
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Zulin Dou
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13
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Xie YL, Yang YX, Jiang H, Duan XY, Gu LJ, Qing W, Zhang B, Wang YX. Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials. Front Neurosci 2022; 16:949575. [PMID: 35992923 PMCID: PMC9381818 DOI: 10.3389/fnins.2022.949575] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices. Methods English-language literature published before April 1, 2022, was searched in five electronic databases using search terms including “brain-computer/machine interface”, “stroke” and “upper extremity.” The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence. Results A total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I2 = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I2 = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I2 = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I2 = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I2 = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I2 = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group. Conclusion BMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.
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Affiliation(s)
- Yu-lei Xie
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- Department of Rehabilitation Medicine, Capital Medical University, Beijing, China
| | - Yu-xuan Yang
- Department of Rehabilitation Medicine, The Second Clinical Hospital of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Hong Jiang
- Department of Rehabilitation Medicine, Xichong County People's Hospital, Nanchong Central Hospital, Nanchong, China
| | - Xing-Yu Duan
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Li-jing Gu
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Wu Qing
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Bo Zhang
- Department of Rehabilitation Medicine, The Second Clinical Hospital of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
- Bo Zhang
| | - Yin-xu Wang
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- *Correspondence: Yin-xu Wang
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14
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Le Franc S, Herrera Altamira G, Guillen M, Butet S, Fleck S, Lécuyer A, Bougrain L, Bonan I. Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives. Front Hum Neurosci 2022; 16:917909. [PMID: 35911589 PMCID: PMC9332194 DOI: 10.3389/fnhum.2022.917909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.
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Affiliation(s)
- Salomé Le Franc
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- *Correspondence: Salomé Le Franc,
| | | | - Maud Guillen
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- Neurology Unit, University Hospital of Rennes, Rennes, France
| | - Simon Butet
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Stéphanie Fleck
- Université de Lorraine, CNRS, LORIA, Nancy, France
- EA7312 Laboratoire de Psychologie Ergonomique et Sociale pour l’Expérience Utilisateurs (PERSEUS), Metz, France
| | - Anatole Lécuyer
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | | | - Isabelle Bonan
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
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15
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Chen Q, Shen W, Sun H, Zhang H, Liu C, Chen Z, Yu L, Cai X, Ke J, Li L, Zhang L, Fang Q. The effect of coupled inhibitory-facilitatory repetitive transcranial magnetic stimulation on shaping early reorganization of the motor network after stroke. Brain Res 2022; 1790:147959. [PMID: 35654120 DOI: 10.1016/j.brainres.2022.147959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 11/29/2022]
Abstract
Neural plasticity is a major factor driving cortical reorganization after stroke. This study aimed to evaluate functional connectivity (FC) changes in the cortical motor network after coupled inhibitory-facilitatory repetitive transcranial magnetic stimulation (rTMS) treatment and to assess the correlation between FC changes and functional recovery, further characterizing the neural mechanisms underlying the beneficial effects of rTMS. We randomly divided 63 patients with acute stroke into four groups: (1) Group A received coupled inhibitory-facilitatory rTMS [1 Hz over the contralesional primary motor cortex (M1) and 10 Hz over ipsilesional M1]; (2) Group B received a contralesional sham stimulation and ipsilesional 10 Hz stimulation; (3) Group C received a contralesional 1 Hz rTMS and ipsilesional sham stimulation; and (4) Group D received bilateral sham stimulation only. Standardized rehabilitation therapy was performed immediately after rTMS, and each group was treated with their respective treatment modalities for 4 weeks. Twenty-four hours before and after the intervention, participants underwent resting-state functional magnetic resonance imaging. Additional functional assessments were conducted at baseline, after treatment, and at the 3 month follow-up. The rTMS treatment significantly changed the FCs of intra- and inter-hemispheric cortical motor networks in the rTMS groups (A and B) compared with the sham group (Group D). This effect was more pronounced in Group A, which displayed a changed FC between the contralesional postcentral gyrus and contralesional superior parietal gyrus, between the contralesional precentral gyrus and contralesional postcentral gyrus, and between the ipsilesional postcentral gyrus and contralesional superior parietal gyrus, when compared with Groups B and C. Importantly, FC changes were significantly correlated with improvement of motor function. In the early stages of ischemic stroke, coupled rTMS was more conducive to motor recovery by modulating the FCs of intra-hemispheric and inter-hemispheric motor networks. Our results suggested that FC changes were related to motor function recovery for early-stage cerebral stroke patients treated with coupled rTMS. These findings could help to understand the mechanism of coupled rTMS and further the use of this therapy as an adjunct rehabilitation technique in motor recovery.
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Affiliation(s)
- Qingmei Chen
- Department of Physical Medicine &Rehabilitation, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China; Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Wenjun Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Haiwei Sun
- Department of Emergency Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Hanjun Zhang
- Department of Physical Medicine &Rehabilitation, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Chuandao Liu
- Department of Physical Medicine &Rehabilitation, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Zhiguo Chen
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Liqiang Yu
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Xiuying Cai
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Jun Ke
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China
| | - Li Li
- Department of Physical Medicine &Rehabilitation, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China.
| | - Lichi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
| | - Qi Fang
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu Province, China.
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Li X, Wang L, Miao S, Yue Z, Tang Z, Su L, Zheng Y, Wu X, Wang S, Wang J, Dou Z. Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study. Front Neurosci 2022; 16:808830. [PMID: 35360158 PMCID: PMC8962957 DOI: 10.3389/fnins.2022.808830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/27/2022] [Indexed: 12/02/2022] Open
Abstract
Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8–12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement. Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].
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Affiliation(s)
- Xin Li
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lu Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Si Miao
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Zan Yue
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Zhiming Tang
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liujie Su
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yadan Zheng
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangzhen Wu
- Department of Rehabilitation Medicine, Shenzhen Hengsheng Hospital, Shenzhen, China
| | - Shan Wang
- Air Force Medical Center, PLA, Beijing, China
- *Correspondence: Shan Wang,
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Jing Wang,
| | - Zulin Dou
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Zulin Dou,
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17
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Yang W, Zhang X, Li Z, Zhang Q, Xue C, Huai Y. The Effect of Brain–Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials. Front Neurosci 2022; 15:766879. [PMID: 35197817 PMCID: PMC8859107 DOI: 10.3389/fnins.2021.766879] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background Upper limb motor dysfunction caused by stroke greatly affects the daily life of patients, significantly reduces their quality of life, and places serious burdens on society. As an emerging rehabilitation training method, brain–computer interface (BCI)–based training can provide closed-loop rehabilitation and is currently being applied to the restoration of upper limb function following stroke. However, because of the differences in the type of experimental clinical research, the quality of the literature varies greatly, and debate around the efficacy of BCI for the rehabilitation of upper limb dysfunction after stroke has continued. Objective We aimed to provide medical evidence-based support for BCI in the treatment of upper limb dysfunction after stroke by conducting a meta-analysis of relevant clinical studies. Methods The search terms used to retrieve related articles included “brain-computer interface,” “stroke,” and “upper extremity.” A total of 13 randomized controlled trials involving 258 participants were retrieved from five databases (PubMed, Cochrane Library, Science Direct, MEDLINE, and Web of Science), and RevMan 5.3 was used for data analysis. Results The total effect size for BCI training on upper limb motor function of post-stroke patients was 0.56 (95% CI: 0.29–0.83). Subgroup analysis indicated that the standard mean differences of BCI training on upper limb motor function of subacute stroke patients and chronic stroke patients were 1.10 (95% CI: 0.20–2.01) and 0.51 (95% CI: 0.09–0.92), respectively (p = 0.24). Conclusion Brain–computer interface training was shown to be effective in promoting upper limb motor function recovery in post-stroke patients, and the effect size was moderate.
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18
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Borisova VA, Isakova EV, Kotov SV. [Possibilities of the brain-computer interface in the correction of post-stroke cognitive impairments]. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:60-66. [PMID: 36582163 DOI: 10.17116/jnevro202212212260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In recent years, brain-computer interfaces have been widely used in neurorehabilitation, and an extensive database of results from clinical studies conducted around the world has been accumulated, demonstrating their effectiveness in restoring motor function after a stroke. Currently, their use in post-stroke cognitive impairment is expanding. This article discusses the potential and prospects for using brain-computer interfaces for the treatment of cognitive disorders, reviews the experience of using it, presents the results of clinical studies in stroke patients, evaluates the possibilities of using this technology, describes the prospects, new directions of work on studying its effects.
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Affiliation(s)
- V A Borisova
- Vladimirskii Moscow Regional Research Clinical Institute, Moscow, Russia
| | - E V Isakova
- Vladimirskii Moscow Regional Research Clinical Institute, Moscow, Russia
| | - S V Kotov
- Vladimirskii Moscow Regional Research Clinical Institute, Moscow, Russia
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19
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Mansour S, Ang KK, Nair KP, Phua KS, Arvaneh M. Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Clin EEG Neurosci 2022; 53:79-90. [PMID: 33913351 PMCID: PMC8619716 DOI: 10.1177/15500594211009065] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/03/2021] [Accepted: 03/12/2021] [Indexed: 11/15/2022]
Abstract
Background. A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.
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Affiliation(s)
- Salem Mansour
- Department of Automatic Control and Systems Engineering, University
of Sheffield, UK
| | - Kai Keng Ang
- Agency for Science Technology and
Research, Institute for Infocomm Research, Singapore, Singapore
- School of Computer Science and Engineering, Nanyang Technological
University, Singapore
| | - Krishnan P.S. Nair
- School of Computer Science and Engineering, Nanyang Technological
University, Singapore
| | - Kok Soon Phua
- Agency for Science Technology and
Research, Institute for Infocomm Research, Singapore, Singapore
| | - Mahnaz Arvaneh
- Department of Automatic Control and Systems Engineering, University
of Sheffield, UK
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20
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Emerging trends in BCI-robotics for motor control and rehabilitation. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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21
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Tavazzi E, Cazzoli M, Pirastru A, Blasi V, Rovaris M, Bergsland N, Baglio F. Neuroplasticity and Motor Rehabilitation in Multiple Sclerosis: A Systematic Review on MRI Markers of Functional and Structural Changes. Front Neurosci 2021; 15:707675. [PMID: 34690670 PMCID: PMC8526725 DOI: 10.3389/fnins.2021.707675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/03/2021] [Indexed: 01/10/2023] Open
Abstract
Background: Motor rehabilitation is routinely used in clinical practice as an effective method to reduce progressive disability gain in multiple sclerosis (MS), but rehabilitation approaches are typically unstandardized, and only few studies have investigated the impact of rehabilitation on brain neuroplasticity. Objective: To summarize and critically analyze studies applying MRI markers of functional connectivity and structural changes to assess the effect of motor rehabilitation on brain neuroplasticity in MS. Methods: Literature search was performed using PubMed and EMBASE, selecting studies having as a subject motor rehabilitation and advanced MRI techniques investigating neuroplasticity in adult patients affected by MS. Results: Seventeen out of 798 papers were selected, of which 5 applied structural MRI (4 diffusion tensor imaging, 1 volumetric measurements), 7 applied functional fMRI (5 task-related fMRI, 2 resting-state fMRI) whereas the remaining 5 applied both structural and functional imaging. Discussion: The considerable data heterogeneity and the small sample sizes characterizing the studies limit interpretation and generalization of the results. Overall, motor rehabilitation promotes clinical improvement, paralleled by positive adaptive brain changes, whose features and extent depend upon different variables, including the type of rehabilitation approach. MRI markers of functional and structural connectivity should be implemented in studies testing the efficacy of motor rehabilitation. They allow for a better understanding of neuroplastic mechanisms underlying rehabilitation-mediated clinical achievements, facilitating the identification of rehabilitation strategies tailored to patients' needs and abilities.
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Affiliation(s)
- Eleonora Tavazzi
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy.,Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Marta Cazzoli
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | | | - Valeria Blasi
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Marco Rovaris
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Niels Bergsland
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy.,Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
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22
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Bezmaternykh DD, Kalgin KV, Maximova PE, Mel'nikov MY, Petrovskii ED, Predtechenskaya EV, Savelov AA, Semenikhina AA, Tsaplina TN, Shtark MB, Shurunova AV. Application of fMRI and Simultaneous fMRI-EEG Neurofeedback in Post-Stroke Motor Rehabilitation. Bull Exp Biol Med 2021; 171:379-383. [PMID: 34292446 DOI: 10.1007/s10517-021-05232-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Indexed: 11/30/2022]
Abstract
This article discusses the contribution of fMRI- and fMRI-EEG-neurofeedback into recovery of motor function in two subacute stroke patients during the early post-stroke period. Premotor and supplementary motor zones of the cortex were chosen as the targets of voluntary control. Patient 1 received 6 sessions of motor imagery-based fMRI neurofeedback of secondary motor areas activity and Patient 2 received a similar course with the addition of μ- and β-EEG activity suppression. Both reduced the motor deficit severity, improved on the quality of life, and increased the C3/C4 coherence to other central leads within EEG μ-band. Patient 1 reliably increased the fMRI signal in target areas and improved on the strength and speed of hand movements. Patient 2 (fMRI-EEG) mastered the EEG activity regulation to a greater degree. The authors conclude that pure fMRI neurofeedback and bi-modal fMRI-EEG neurofeedback produce different clinical effects in motor rehabilitation, which confirms the prospect of the closed-loop stroke treatment.
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Affiliation(s)
- D D Bezmaternykh
- Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia.,Novosibirsk National Research State University, Novosibirsk, Russia
| | - K V Kalgin
- Novosibirsk National Research State University, Novosibirsk, Russia
| | - P E Maximova
- Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - M Ye Mel'nikov
- Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia.
| | - E D Petrovskii
- International Tomography Center Institute of the Siberian Division of Russian Academy of Sciences, Novosibirsk, Russia
| | | | - A A Savelov
- International Tomography Center Institute of the Siberian Division of Russian Academy of Sciences, Novosibirsk, Russia
| | - A A Semenikhina
- Novosibirsk National Research State University, Novosibirsk, Russia
| | - T N Tsaplina
- Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - M B Shtark
- Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - A V Shurunova
- Novosibirsk National Research State University, Novosibirsk, Russia
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23
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Simon C, Bolton DAE, Kennedy NC, Soekadar SR, Ruddy KL. Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation. Front Neurosci 2021; 15:699428. [PMID: 34276299 PMCID: PMC8282929 DOI: 10.3389/fnins.2021.699428] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 12/18/2022] Open
Abstract
Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.
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Affiliation(s)
- Colin Simon
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David A. E. Bolton
- Department of Kinesiology and Health Science, Utah State University, Logan, UT, United States
| | - Niamh C. Kennedy
- School of Psychology, Ulster University, Coleraine, United Kingdom
| | - Surjo R. Soekadar
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum, Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Kathy L. Ruddy
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
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24
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Abstract
Spinal cord injury (SCI) destroys the sensorimotor pathway and blocks the information flow between the peripheral nerve and the brain, resulting in autonomic function loss. Numerous studies have explored the effects of obstructed information flow on brain structure and function and proved the extensive plasticity of the brain after SCI. Great progress has also been achieved in therapeutic strategies for SCI to restore the "re-innervation" of the cerebral cortex to the limbs to some extent. Although no thorough research has been conducted, the changes of brain structure and function caused by "re-domination" have been reported. This article is a review of the recent research progress on local structure, functional changes, and circuit reorganization of the cerebral cortex after SCI. Alterations of structure and electrical activity characteristics of brain neurons, features of brain functional reorganization, and regulation of brain functions by reconfigured information flow were also explored. The integration of brain function is the basis for the human body to exercise complex/fine movements and is intricately and widely regulated by information flow. Hence, its changes after SCI and treatments should be considered.
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Affiliation(s)
- Can Zhao
- Institute of Rehabilitation Engineering, China Rehabilitation Science Institute, Beijing, China
- School of Rehabilitation, Capital Medical University, Beijing, China
| | - Shu-Sheng Bao
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Meng Xu
- Department of Orthopedics, The First Medical Center of PLA General Hospital, Beijing, China
| | - Jia-Sheng Rao
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
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25
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Wu Q, Ge Y, Ma D, Pang X, Cao Y, Zhang X, Pan Y, Zhang T, Dou W. Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study. Front Neurol 2021; 12:661816. [PMID: 34177767 PMCID: PMC8222567 DOI: 10.3389/fneur.2021.661816] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Upper limb (UL) motor function recovery, especially distal function, is one of the main goals of stroke rehabilitation as this function is important to perform activities of daily living (ADL). The efficacy of the motor-imagery brain-computer interface (MI-BCI) has been demonstrated in patients with stroke. Most patients with stroke receive comprehensive rehabilitation, including MI-BCI and routine training. However, most aspects of MI-BCI training for patients with subacute stroke are based on routine training. Risk factors for inadequate distal UL functional recovery in these patients remain unclear; therefore, it is more realistic to explore the prognostic factors of this comprehensive treatment based on clinical practice. The present study aims to investigate the independent risk factors that might lead to inadequate distal UL functional recovery in patients with stroke after comprehensive rehabilitation including MI-BCI (CRIMI-BCI). Methods: This prospective study recruited 82 patients with stroke who underwent CRIMI-BCI. Motor-imagery brain-computer interface training was performed for 60 min per day, 5 days per week for 4 weeks. The primary outcome was improvement of the wrist and hand dimensionality of Fugl-Meyer Assessment (δFMA-WH). According to the improvement score, the patients were classified into the efficient group (EG, δFMA-WH > 2) and the inefficient group (IG, δFMA-WH ≤ 2). Binary logistic regression was used to analyze clinical and demographic data, including aphasia, spasticity of the affected hand [assessed by Modified Ashworth Scale (MAS-H)], initial UL function, age, gender, time since stroke (TSS), lesion hemisphere, and lesion location. Results: Seventy-three patients completed the study. After training, all patients showed significant improvement in FMA-UL (Z = 7.381, p = 0.000**), FMA-SE (Z = 7.336, p = 0.000**), and FMA-WH (Z = 6.568, p = 0.000**). There were 35 patients (47.9%) in the IG group and 38 patients (52.1%) in the EG group. Multivariate analysis revealed that presence of aphasia [odds ratio (OR) 4.617, 95% confidence interval (CI) 1.435-14.860; p < 0.05], initial FMA-UL score ≤ 30 (OR 5.158, 95% CI 1.150-23.132; p < 0.05), and MAS-H ≥ level I+ (OR 3.810, 95% CI 1.231-11.790; p < 0.05) were the risk factors for inadequate distal UL functional recovery in patients with stroke after CRIMI-BCI. Conclusion: We concluded that CRIMI-BCI improved UL function in stroke patients with varying effectiveness. Inferior initial UL function, significant hand spasticity, and presence of aphasia were identified as independent risk factors for inadequate distal UL functional recovery in stroke patients after CRIMI-BCI.
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Affiliation(s)
- Qiong Wu
- School of Rehabilitation Medicine, China Rehabilitation Research Center, Capital Medical University, Beijing, China.,Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yunxiang Ge
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Di Ma
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xue Pang
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yingyu Cao
- School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xiaofei Zhang
- Department of Clinical Epidemiology and Biostatistics, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Yu Pan
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Tong Zhang
- School of Rehabilitation Medicine, China Rehabilitation Research Center, Capital Medical University, Beijing, China
| | - Weibei Dou
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
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26
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Gao Z, Dang W, Wang X, Hong X, Hou L, Ma K, Perc M. Complex networks and deep learning for EEG signal analysis. Cogn Neurodyn 2021; 15:369-388. [PMID: 34040666 PMCID: PMC8131466 DOI: 10.1007/s11571-020-09626-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/20/2020] [Accepted: 08/16/2020] [Indexed: 12/13/2022] Open
Abstract
Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.
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Affiliation(s)
- Zhongke Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Weidong Dang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Xinmin Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Xiaolin Hong
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Linhua Hou
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Kai Ma
- Tencent Youtu Lab, Malata Building, 9998 Shennan Avenue, Shenzhen, 518057 Guangdong Province China
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia
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27
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Ding Q, Lin T, Wu M, Yang W, Li W, Jing Y, Ren X, Gong Y, Xu G, Lan Y. Influence of iTBS on the Acute Neuroplastic Change After BCI Training. Front Cell Neurosci 2021; 15:653487. [PMID: 33776653 PMCID: PMC7994768 DOI: 10.3389/fncel.2021.653487] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 02/22/2021] [Indexed: 12/21/2022] Open
Abstract
Objective: Brain-computer interface (BCI) training is becoming increasingly popular in neurorehabilitation. However, around one third subjects have difficulties in controlling BCI devices effectively, which limits the application of BCI training. Furthermore, the effectiveness of BCI training is not satisfactory in stroke rehabilitation. Intermittent theta burst stimulation (iTBS) is a powerful neural modulatory approach with strong facilitatory effects. Here, we investigated whether iTBS would improve BCI accuracy and boost the neuroplastic changes induced by BCI training. Methods: Eight right-handed healthy subjects (four males, age: 20-24) participated in this two-session study (BCI-only session and iTBS+BCI session in random order). Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) and single-pulse transcranial magnetic stimulation (TMS). In BCI-only session, fNIRS was measured at baseline and immediately after BCI training. In iTBS+BCI session, BCI training was followed by iTBS delivered on the right primary motor cortex (M1). Single-pulse TMS was measured at baseline and immediately after iTBS. fNIRS was measured at baseline, immediately after iTBS, and immediately after BCI training. Paired-sample t-tests were used to compare amplitudes of motor-evoked potentials, cortical silent period duration, oxygenated hemoglobin (HbO2) concentration and functional connectivity across time points, and BCI accuracy between sessions. Results: No significant difference in BCI accuracy was detected between sessions (p > 0.05). In BCI-only session, functional connectivity matrices between motor cortex and prefrontal cortex were significantly increased after BCI training (p's < 0.05). In iTBS+BCI session, amplitudes of motor-evoked potentials were significantly increased after iTBS (p's < 0.05), but no change in HbO2 concentration or functional connectivity was observed throughout the whole session (p's > 0.05). Conclusions: To our knowledge, this is the first study that investigated how iTBS targeted on M1 influences BCI accuracy and the acute neuroplastic changes after BCI training. Our results revealed that iTBS targeted on M1 did not influence BCI accuracy or facilitate the neuroplastic changes after BCI training. Therefore, M1 might not be an effective stimulation target of iTBS for the purpose of improving BCI accuracy or facilitate its effectiveness; other brain regions (i.e., prefrontal cortex) are needed to be further investigated as potentially effective stimulation targets.
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Affiliation(s)
- Qian Ding
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Tuo Lin
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Manfeng Wu
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wenqing Yang
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wanqi Li
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yinghua Jing
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiaoqing Ren
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yulai Gong
- Sichuan Provincial Rehabilitation Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guangqing Xu
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yue Lan
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Tang Y, Wang L, He J, Xu Y, Huang S, Fang Y. Optimal Method of Electrical Stimulation for the Treatment of Upper Limb Dysfunction After Stroke: A Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials. Neuropsychiatr Dis Treat 2021; 17:2937-2954. [PMID: 34552328 PMCID: PMC8450164 DOI: 10.2147/ndt.s332967] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/01/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The obstacle of limb motor caused by stroke, especially the decline of motor function of upper limbs, can directly affect the activities of daily living of stroke patients with hemiplegia. Based on long-term clinical practice, the treatment effect of electrical stimulation methods for stroke limb dysfunction has been widely recognized and supported by authoritative guidelines and systematic reviews. However, which electrical stimulation method is the optimum in the treatment of stroke limb dysfunction is still a controversial issue. OBJECTIVE In this paper, we adopted Network Meta-Analysis (NMA) to rank the priorities of various electrical stimulation methods, so as to select the optimal electrical stimulation method and discuss its rationality in guiding clinical practice. METHODS We carried out a systematic review by searching a total of 6806 studies from 8 databases and 2 clinical trial registries, and finally screened out 34 studies for further investigation. Then, pairwise meta-analysis and Bayesian network meta-analysis were employed to evaluate the effectiveness and ranking of various interventions. The primary outcome measure was Fugl-Meyer Assessment Upper Extremity (FMA-UE), and the secondary outcome measures were Modified Barthel Index (MBI) and Modified Ashworth Scale (MAS). Finally, the risk of bias, publication bias and sensitivity of the Randomized Controlled Trials (RCTs) were evaluated. RESULTS On the basis of comprehensive rehabilitation treatment (RT), the Functional Electrical Stimulation (FES) was superior than other electrical stimulation methods in improving both FMA-UE and MBI. Meanwhile, the results indicated that the Transcutaneous Electrical Acupoint Stimulation (TEAS) was the only electrical stimulation method that showed treatment advantages in reducing MAS. CONCLUSION The study showed that FES had the optimal overall rehabilitation effect on upper limb dysfunction of stroke patients based on the comprehensive RT, while the treatment effect of TEAS on upper limb spasticity after stroke was the most significant.
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Affiliation(s)
- Yuqi Tang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, People's Republic of China
| | - Linjia Wang
- School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, People's Republic of China
| | - Jinxi He
- Department of Pain, Sichuan Provincial Transportation Department Road Bureau Hospital, Chengdu, 611731, People's Republic of China
| | - Yipeng Xu
- School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, People's Republic of China
| | - Shijie Huang
- School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, People's Republic of China
| | - Yu Fang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, People's Republic of China
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29
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Su F, Xu W. Enhancing Brain Plasticity to Promote Stroke Recovery. Front Neurol 2020; 11:554089. [PMID: 33192987 PMCID: PMC7661553 DOI: 10.3389/fneur.2020.554089] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 10/08/2020] [Indexed: 12/11/2022] Open
Abstract
Stroke disturbs both the structural and functional integrity of the brain. The understanding of stroke pathophysiology has improved greatly in the past several decades. However, effective therapy is still limited, especially for patients who are in the subacute or chronic phase. Multiple novel therapies have been developed to improve clinical outcomes by improving brain plasticity. These approaches either focus on improving brain remodeling and restoration or on constructing a neural bypass to avoid brain injury. This review describes emerging therapies, including modern rehabilitation, brain stimulation, cell therapy, brain-computer interfaces, and peripheral nervous transfer, and highlights treatment-induced plasticity. Key evidence from basic studies on the underlying mechanisms is also briefly discussed. These insights should lead to a deeper understanding of the overall neural circuit changes, the clinical relevance of these changes in stroke, and stroke treatment progress, which will assist in the development of future approaches to enhance brain function after stroke.
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Affiliation(s)
| | - Wendong Xu
- Department of Hand Surgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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