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Sarabzadeh M, Shariatzadeh M. Electroneuromyography comparison between pre-elderly adult females with and without MS; the potential role of a mind-body therapy in improving neurophysiological profile of MS during pandemic. J Bodyw Mov Ther 2024; 39:489-495. [PMID: 38876673 DOI: 10.1016/j.jbmt.2024.03.026] [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: 07/26/2022] [Revised: 02/28/2024] [Accepted: 03/11/2024] [Indexed: 06/16/2024]
Abstract
INTRODUCTION Imaginary exercises seem to be useful therapeutic approaches to modulate neuromuscular functions due to two main reasons: first, this training would not greatly increase body temperature, and secondly, it can positively affect brain-muscle pathways-which are both primary factors should be considered in rehabilitation programs for patients with multiple sclerosis (MS). METHOD 32 pre-elderly adult females with relapsing-remitting MS (n = 16 - age M (SD): 56.75 (5.07)) and without MS (n = 16 - age M (SD): 56.56 (4.35)) voluntarily recruited. First, they were assigned into two groups: MS patients and healthy controls, to investigate baseline between-group comparison. Then, MS patients were randomly divided into two groups of eight each, designated as experimental and control groups. Recording the nerve conduction velocity (NCV) of tibial nerve and integrated electromyographic muscle activation (IEMG) of gastrocnemius muscle was conducted twice, before and after a six-week mind-body exercise therapy to evaluate its effectiveness on improving neuromuscular function. RESULTS The results showed significant difference in both tibial NCV (P < 0.001) and IEMG (P = 0.001) variables between non-MS group and MS group. Furthermore, there was a significant main effect of intervention (P = 0.05) and time (P < 0.001) on IEMG in the MS group, while there was no significant effect of intervention (P = 0.18) and time (P = 0.23) on NCV (p = 0.89). CONCLUSION Neuromuscular dysfunction were apparent in MS patients, and a mind-body therapy of imagery isometric training was found to be useful on improving the neurological deficit in women with MS. TRIAL REGISTRATION NUMBER UMIN000046935.
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Affiliation(s)
- Mostafa Sarabzadeh
- Research Associate in Exercise Physiology & Neurophysiotherapy, Iran's National Elites Foundation (INEF), Tehran, Iran.
| | - Mohammad Shariatzadeh
- Department of Exercise Physiology, Sport Sciences Research Institute, Tehran, Iran. Tel: 0989122914857.
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Ma P, Dong C, Lin R, Liu H, Lei D, Chen X, Liu H. A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks. Front Neurosci 2024; 18:1306283. [PMID: 38586195 PMCID: PMC10996401 DOI: 10.3389/fnins.2024.1306283] [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: 10/03/2023] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Background The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. Objective This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. Methods The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. Results For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. Conclusion The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.
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Affiliation(s)
- Pengfei Ma
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
| | - Chaoyi Dong
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Ruijing Lin
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Huanzi Liu
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Dongyang Lei
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Xiaoyan Chen
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Huan Liu
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
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Cai G, Xu J, Ding Q, Lin T, Chen H, Wu M, Li W, Chen G, Xu G, Lan Y. Electroencephalography oscillations can predict the cortical response following theta burst stimulation. Brain Res Bull 2024; 208:110902. [PMID: 38367675 DOI: 10.1016/j.brainresbull.2024.110902] [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: 11/30/2023] [Revised: 01/28/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Continuous theta burst stimulation and intermittent theta burst stimulation are clinically popular models of repetitive transcranial magnetic stimulation. However, they are limited by high variability between individuals in cortical excitability changes following stimulation. Although electroencephalography oscillations have been reported to modulate the cortical response to transcranial magnetic stimulation, their association remains unclear. This study aims to explore whether machine learning models based on EEG oscillation features can predict the cortical response to transcranial magnetic stimulation. METHOD Twenty-three young, healthy adults attended two randomly assigned sessions for continuous and intermittent theta burst stimulation. In each session, ten minutes of resting-state electroencephalography were recorded before delivering brain stimulation. Participants were classified as responders or non-responders based on changes in resting motor thresholds. Support vector machines and multi-layer perceptrons were used to establish predictive models of individual responses to transcranial magnetic stimulation. RESULT Among the evaluated algorithms, support vector machines achieved the best performance in discriminating responders from non-responders for intermittent theta burst stimulation (accuracy: 91.30%) and continuous theta burst stimulation (accuracy: 95.66%). The global clustering coefficient and global characteristic path length in the beta band had the greatest impact on model output. CONCLUSION These findings suggest that EEG features can serve as markers of cortical response to transcranial magnetic stimulation. They offer insights into the association between neural oscillations and variability in individuals' responses to transcranial magnetic stimulation, aiding in the optimization of individualized protocols.
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Affiliation(s)
- Guiyuan Cai
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Jiayue Xu
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Qian Ding
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China; Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 519041 China
| | - Tuo Lin
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Hongying Chen
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Manfeng Wu
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Wanqi Li
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Gengbin Chen
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China; Postgraduate Research Institute, Guangzhou Sport University, Guangzhou, 510500 China
| | - Guangqing Xu
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 519041 China.
| | - Yue Lan
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China; Guangzhou Key Laboratory of Aging Frailty and Neurorehabilitation, Guangzhou 510013, China.
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Rosanne O, Alves de Oliveira A, Falk TH. EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs. SENSORS (BASEL, SWITZERLAND) 2023; 23:9352. [PMID: 38067725 PMCID: PMC10708818 DOI: 10.3390/s23239352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023]
Abstract
Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.
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Affiliation(s)
- Olivier Rosanne
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
| | - Alcyr Alves de Oliveira
- Graduate Program in Psychology and Health, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil;
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
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Inamoto T, Ueda M, Ueno K, Shiroma C, Morita R, Naito Y, Ishii R. Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review. Brain Sci 2023; 13:brainsci13050751. [PMID: 37239223 DOI: 10.3390/brainsci13050751] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/23/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023] Open
Abstract
Mu rhythm, also known as the mu wave, occurs on sensorimotor cortex activity at rest, and the frequency range is defined as 8-13Hz, the same frequency as the alpha band. Mu rhythm is a cortical oscillation that can be recorded from the scalp over the primary sensorimotor cortex by electroencephalogram (EEG) and magnetoencephalography (MEG). The subjects of previous mu/beta rhythm studies ranged widely from infants to young and older adults. Furthermore, these subjects were not only healthy people but also patients with various neurological and psychiatric diseases. However, very few studies have referred to the effect of mu/beta rhythm with aging, and there was no literature review about this theme. It is important to review the details of the characteristics of mu/beta rhythm activity in older adults compared with young adults, focusing on age-related mu rhythm changes. By comprehensive review, we found that, compared with young adults, older adults showed mu/beta activity change in four characteristics during voluntary movement, increased event-related desynchronization (ERD), earlier beginning and later end, symmetric pattern of ERD and increased recruitment of cortical areas, and substantially reduced beta event-related desynchronization (ERS). It was also found that mu/beta rhythm patterns of action observation were changing with aging. Future work is needed in order to investigate not only the localization but also the network of mu/beta rhythm in older adults.
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Affiliation(s)
- Takashi Inamoto
- Graduate School of Comprehensive Rehabilitation, Osaka Prefecture University, Osaka 583-8555, Japan
- Faculty of Health Sciences, Kansai University of Health Sciences, Osaka 590-0482, Japan
| | - Masaya Ueda
- Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Keita Ueno
- Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - China Shiroma
- Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Rin Morita
- Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Yasuo Naito
- Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Ryouhei Ishii
- Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
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Zhang H, Liu Q, Yao M, Zhang Z, Chen X, Luo H, Ruan L, Liu T, Chen Y, Ruan J. Neural oscillations during acupuncture imagery partially parallel that of real needling. Front Neurosci 2023; 17:1123466. [PMID: 37090802 PMCID: PMC10115979 DOI: 10.3389/fnins.2023.1123466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Introduction Tasks involving mental practice, relying on the cognitive rehearsal of physical motors or other activities, have been reported to have similar patterns of brain activity to overt execution. In this study, we introduced a novel imagination task called, acupuncture imagery and aimed to investigate the neural oscillations during acupuncture imagery. Methods Healthy volunteers were guided to watch a video of real needling in the left and right KI3 (Taixi point). The subjects were then asked to perform tasks to keep their thoughts in three 1-min states alternately: resting state, needling imagery left KI3, and needling imagery right KI3. Another group experienced real needling in the right KI3. A 31-channel-electroencephalography was synchronously recorded for each subject. Microstate analyses were performed to depict the brain dynamics during these tasks. Results Compared to the resting state, both acupuncture needling imagination and real needling in KI3 could introduce significant changes in neural dynamic oscillations. Moreover, the parameters involving microstate A of needling imagery in the right KI3 showed similar changes as real needling in the right KI3. Discussion These results confirm that needling imagination and real needling have similar brain activation patterns. Needling imagery may change brain network activity and play a role in neural regulation. Further studies are needed to explore the effects of acupuncture imagery and the potential application of acupuncture imagery in disease recovery.
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Affiliation(s)
- Hao Zhang
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Qingxia Liu
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Menglin Yao
- School of Integrated Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou, China
| | - Zhiling Zhang
- School of Integrated Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou, China
| | - Xiu Chen
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Hua Luo
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Lili Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Tianpeng Liu
- Shaanxi University of Chinese Medicine, Xianyang, China
| | - Yingshuang Chen
- School of Integrated Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou, China
| | - Jianghai Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
- *Correspondence: Jianghai Ruan,
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Repetitive Peripheral Magnetic Stimulation Combined with Motor Imagery Changes Resting-State EEG Activity: A Randomized Controlled Trial. Brain Sci 2022; 12:brainsci12111548. [DOI: 10.3390/brainsci12111548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022] Open
Abstract
Repetitive peripheral magnetic stimulation is a novel non-invasive technique for applying repetitive magnetic stimulation to the peripheral nerves and muscles. Contrarily, a person imagines that he/she is exercising during motor imagery. Resting-state electroencephalography can evaluate the ability of motor imagery; however, the effects of motor imagery and repetitive peripheral magnetic stimulation on resting-state electroencephalography are unknown. We examined the effects of motor imagery and repetitive peripheral magnetic stimulation on the vividness of motor imagery and resting-state electroencephalography. The participants were divided into a motor imagery group and motor imagery and repetitive peripheral magnetic stimulation group. They performed 60 motor imagery tasks involving wrist dorsiflexion movement. In the motor imagery and repetitive peripheral magnetic stimulation group, we applied repetitive peripheral magnetic stimulation to the extensor carpi radialis longus muscle during motor imagery. We measured the vividness of motor imagery and resting-state electroencephalography before and after the task. Both groups displayed a significant increase in the vividness of motor imagery. The motor imagery and repetitive peripheral magnetic stimulation group exhibited increased β activity in the anterior cingulate cortex by source localization for electroencephalography. Hence, combined motor imagery and repetitive peripheral magnetic stimulation changes the resting-state electroencephalography activity and may promote motor imagery.
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Ma ZZ, Wu JJ, Hua XY, Zheng MX, Xing XX, Ma J, Li SS, Shan CL, Xu JG. Brain Function and Upper Limb Deficit in Stroke With Motor Execution and Imagery: A Cross-Sectional Functional Magnetic Resonance Imaging Study. Front Neurosci 2022; 16:806406. [PMID: 35663563 PMCID: PMC9160973 DOI: 10.3389/fnins.2022.806406] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMotor imagery training might be helpful in stroke rehabilitation. This study explored if a specific modulation of movement-related regions is related to motor imagery (MI) ability.MethodsTwenty-three patients with subcortical stroke and 21 age-matched controls were recruited. They were subjectively screened using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). They then underwent functional magnetic resonance imaging (fMRI) while performing three repetitions of different motor tasks (motor execution and MI). Two separate runs were acquired [motor execution tasks (ME and rest) and motor imagery (MI and rest)] in a block design. For the different tasks, analyses of cerebral activation and the correlation of motor/imagery task-related activity and KVIQ scores were performed.ResultsDuring unaffected hand (UH) active grasp movement, we observed decreased activations in the contralateral precentral gyrus (PreCG), contralateral postcentral gyrus (PoCG) [p < 0.05, family wise error (FWE) corrected] and a positive correlation with the ability of FMA-UE (PreCG: r = 0.46, p = 0.028; PoCG: r = 0.44, p = 0.040). During active grasp of the affected hand (AH), decreased activation in the contralateral PoCG was observed (p < 0.05, FWE corrected). MI of the UH induced significant activations of the contralateral superior frontal gyrus, opercular region of the inferior frontal gyrus, and ipsilateral ACC and deactivation in the ipsilateral supplementary motor area (p < 0.05, AlphaSim correction). Ipsilateral anterior cingulate cortex (ACC) activity negatively correlated with MI ability (r = =–0.49, p = 0.022). Moreover, we found significant activation of the contralesional middle frontal gyrus (MFG) during MI of the AH.ConclusionOur results proved the dominant effects of MI dysfunction that exist in stroke during the processing of motor execution. In the motor execution task, the enhancement of the contralateral PreCG and PoCG contributed to reversing the motor dysfunction, while in the MI task, inhibition of the contralateral ACC can increase the impaired KVIQ ability. The bimodal balance recovery model can explain our results well. Recognizing neural mechanisms is critical to helping us formulate precise strategies when intervening with electrical or magnetic stimulation.
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Affiliation(s)
- Zhen-Zhen Ma
- Department of Rehabilitation Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Si-Si Li
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Chun-Lei Shan,
| | - Jian-Guang Xu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jian-Guang Xu,
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