1
|
Xiang YT, Wu JJ, Ma J, Xing XX, Zhang JP, Hua XY, Zheng MX, Xu JG. Peripheral nerve transfers for dysfunctions in central nervous system injuries: a systematic review. Int J Surg 2024; 110:3814-3826. [PMID: 38935818 PMCID: PMC11175768 DOI: 10.1097/js9.0000000000001267] [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: 08/22/2023] [Accepted: 02/21/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND The review highlights recent advancements and innovative uses of nerve transfer surgery in treating dysfunctions caused by central nervous system (CNS) injuries, with a particular focus on spinal cord injury (SCI), stroke, traumatic brain injury, and cerebral palsy. METHODS A comprehensive literature search was conducted regarding nerve transfer for restoring sensorimotor functions and bladder control following injuries of spinal cord and brain, across PubMed and Web of Science from January 1920 to May 2023. Two independent reviewers undertook article selection, data extraction, and risk of bias assessment with several appraisal tools, including the Cochrane Risk of Bias Tool, the JBI Critical Appraisal Checklist, and SYRCLE's ROB tool. The study protocol has been registered and reported following PRISMA and AMSTAR guidelines. RESULTS Nine hundred six articles were retrieved, of which 35 studies were included (20 on SCI and 15 on brain injury), with 371 participants included in the surgery group and 192 in the control group. These articles were mostly low-risk, with methodological concerns in study types, highlighting the complexity and diversity. For SCI, the strength of target muscle increased by 3.13 of Medical Research Council grade, and the residual urine volume reduced by more than 100 ml in 15 of 20 patients. For unilateral brain injury, the Fugl-Myer motor assessment (FMA) improved 15.14-26 score in upper extremity compared to 2.35-26 in the control group. The overall reduction in Modified Ashworth score was 0.76-2 compared to 0-1 in the control group. Range of motion (ROM) increased 18.4-80° in elbow, 20.4-110° in wrist and 18.8-130° in forearm, while ROM changed -4.03°-20° in elbow, -2.08°-10° in wrist, -2.26°-20° in forearm in the control group. The improvement of FMA in lower extremity was 9 score compared to the presurgery. CONCLUSION Nerve transfer generally improves sensorimotor functions in paralyzed limbs and bladder control following CNS injury. The technique effectively creates a 'bypass' for signals and facilitates functional recovery by leveraging neural plasticity. It suggested a future of surgery, neurorehabilitation and robotic-assistants converge to improve outcomes for CNS.
Collapse
Affiliation(s)
- Yun-Ting Xiang
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine
| | - Jia-Jia Wu
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Jie Ma
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Xiang-Xin Xing
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Jun-Peng Zhang
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine
| | - Xu-Yun Hua
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine
| | - Mou-Xiong Zheng
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine
| | - Jian-Guang Xu
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| |
Collapse
|
2
|
Demarest P, Rustamov N, Swift J, Xie T, Adamek M, Cho H, Wilson E, Han Z, Belsten A, Luczak N, Brunner P, Haroutounian S, Leuthardt EC. A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study. Sci Rep 2024; 14:3433. [PMID: 38341457 PMCID: PMC10858946 DOI: 10.1038/s41598-024-53261-3] [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: 05/23/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.
Collapse
Affiliation(s)
- Phillip Demarest
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St Louis, MO, 63130, USA
| | - Nabi Rustamov
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - James Swift
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Tao Xie
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Markus Adamek
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Hohyun Cho
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Elizabeth Wilson
- Division of Clinical and Translational Research, Department of Anesthesiology, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Washington University Pain Center, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Zhuangyu Han
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St Louis, MO, 63130, USA
| | - Alexander Belsten
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Nicholas Luczak
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Peter Brunner
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St Louis, MO, 63130, USA
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Simon Haroutounian
- Division of Clinical and Translational Research, Department of Anesthesiology, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
- Washington University Pain Center, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA
| | - Eric C Leuthardt
- Division of Neurotechnology, Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA.
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St Louis, MO, 63130, USA.
- Department of Neurosurgery, Washington University in St. Louis School of Medicine, St Louis, MO, 63110, USA.
| |
Collapse
|
3
|
Nagarajan A, Robinson N, Ang KK, Chua KSG, Chew E, Guan C. Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface. J Neural Eng 2024; 21:016007. [PMID: 38091617 DOI: 10.1088/1741-2552/ad152f] [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: 05/19/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Objective.Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. This study aims to assess the feasibility of applying MI-BCI models pre-trained using data from healthy individuals to detect MI in stroke patients.Approach.We introduce a new transfer learning approach where features from two-class MI data of healthy individuals are used to detect MI in stroke patients. We compare the results of the proposed method with those obtained from analyses within stroke data. Experiments were conducted using Deep ConvNet and state-of-the-art subject-specific machine learning MI classifiers, evaluated on OpenBMI two-class MI-EEG data from healthy subjects and two-class MI versus rest data from stroke patients.Main results.Results of our study indicate that through domain adaptation of a model pre-trained using healthy subjects' data, an average MI detection accuracy of 71.15% (±12.46%) can be achieved across 71 stroke patients. We demonstrate that the accuracy of the pre-trained model increased by 18.15% after transfer learning (p<0.001). Additionally, the proposed transfer learning method outperforms the subject-specific results achieved by Deep ConvNet and FBCSP, with significant enhancements of 7.64% (p<0.001) and 5.55% (p<0.001) in performance, respectively. Notably, the healthy-to-stroke transfer learning approach achieved similar performance to stroke-to-stroke transfer learning, with no significant difference (p>0.05). Explainable AI analyses using transfer models determined channel relevance patterns that indicate contributions from the bilateral motor, frontal, and parietal regions of the cortex towards MI detection in stroke patients.Significance.Transfer learning from healthy to stroke can enhance the clinical use of BCI algorithms by overcoming the challenge of insufficient clinical data for optimal training.
Collapse
Affiliation(s)
- Aarthy Nagarajan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
| | - Neethu Robinson
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
| | - Kai Keng Ang
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
- Institute for Infocomm Research, Agency of Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore 138632, Singapore
| | - Karen Sui Geok Chua
- Department of Rehabilitation Medicine, Tan Tock Seng Hospital, 11 Jln Tan Tock Seng, Singapore 308433, Singapore
| | - Effie Chew
- National University Health System, 1E Kent Ridge Road, Singapore 119228, Singapore
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
| |
Collapse
|
4
|
Lim RY, Ang KK, Chew E, Guan C. A Review on Motor Imagery with Transcranial Alternating Current Stimulation: Bridging Motor and Cognitive Welfare for Patient Rehabilitation. Brain Sci 2023; 13:1584. [PMID: 38002544 PMCID: PMC10670393 DOI: 10.3390/brainsci13111584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/26/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
Research has shown the effectiveness of motor imagery in patient motor rehabilitation. Transcranial electrical stimulation has also demonstrated to improve patient motor and non-motor performance. However, mixed findings from motor imagery studies that involved transcranial electrical stimulation suggest that current experimental protocols can be further improved towards a unified design for consistent and effective results. This paper aims to review, with some clinical and neuroscientific findings from literature as support, studies of motor imagery coupled with different types of transcranial electrical stimulation and their experiments onhealthy and patient subjects. This review also includes the cognitive domains of working memory, attention, and fatigue, which are important for designing consistent and effective therapy protocols. Finally, we propose a theoretical all-inclusive framework that synergizes the three cognitive domains with motor imagery and transcranial electrical stimulation for patient rehabilitation, which holds promise of benefiting patients suffering from neuromuscular and cognitive disorders.
Collapse
Affiliation(s)
- Rosary Yuting Lim
- Institute for Infocomm Research, Agency for Science Technology and Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632, Singapore;
| | - Kai Keng Ang
- Institute for Infocomm Research, Agency for Science Technology and Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632, Singapore;
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave., #32 Block N4 #02a, Singapore 639798, Singapore;
| | - Effie Chew
- Division of Rehabilitation Medicine, Department of Medicine, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore;
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave., #32 Block N4 #02a, Singapore 639798, Singapore;
| |
Collapse
|