1
|
Nojima I, Sugata H, Takeuchi H, Mima T. Brain-Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis. Neurorehabil Neural Repair 2021; 36:83-96. [PMID: 34958261 DOI: 10.1177/15459683211062895] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain-computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery. METHODS A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model. RESULTS We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies (I2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm. CONCLUSION This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.
Collapse
Affiliation(s)
- Ippei Nojima
- Department of Physical Therapy, 84161Shinshu University School of Health Sciences, Matsumoto, Japan
| | - Hisato Sugata
- Faculty of Welfare and Health Science, 6339Oita University, Oita, Japan
| | - Hiroki Takeuchi
- National Hospital Organization, 73721Higashinagoya National Hospital, Nagoya, Japan
| | - Tatsuya Mima
- Graduate School of Core Ethics and Frontier Sciences, 316844Ritsumeikan University, Kyoto, Japan
| |
Collapse
|
2
|
Bao SC, Chen C, Yuan K, Yang Y, Tong RKY. Disrupted cortico-peripheral interactions in motor disorders. Clin Neurophysiol 2021; 132:3136-3151. [PMID: 34749233 DOI: 10.1016/j.clinph.2021.09.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/08/2021] [Accepted: 09/19/2021] [Indexed: 11/15/2022]
Abstract
Motor disorders may arise from neurological damage or diseases at different levels of the hierarchical motor control system and side-loops. Altered cortico-peripheral interactions might be essential characteristics indicating motor dysfunctions. By integrating cortical and peripheral responses, top-down and bottom-up cortico-peripheral coupling measures could provide new insights into the motor control and recovery process. This review first discusses the neural bases of cortico-peripheral interactions, and corticomuscular coupling and corticokinematic coupling measures are addressed. Subsequently, methodological efforts are summarized to enhance the modeling reliability of neural coupling measures, both linear and nonlinear approaches are introduced. The latest progress, limitations, and future directions are discussed. Finally, we emphasize clinical applications of cortico-peripheral interactions in different motor disorders, including stroke, neurodegenerative diseases, tremor, and other motor-related disorders. The modified interaction patterns and potential changes following rehabilitation interventions are illustrated. Altered coupling strength, modified coupling directionality, and reorganized cortico-peripheral activation patterns are pivotal attributes after motor dysfunction. More robust coupling estimation methodologies and combination with other neurophysiological modalities might more efficiently shed light on motor control and recovery mechanisms. Future studies with large sample sizes might be necessary to determine the reliabilities of cortico-peripheral interaction measures in clinical practice.
Collapse
Affiliation(s)
- Shi-Chun Bao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Cheng Chen
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Kai Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Yuan Yang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Tulsa, OK, USA; Laureate Institute for Brain Research, Tulsa, OK, USA; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Raymond Kai-Yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong.
| |
Collapse
|
3
|
Colamarino E, de Seta V, Masciullo M, Cincotti F, Mattia D, Pichiorri F, Toppi J. Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface. Int J Neural Syst 2021; 31:2150052. [PMID: 34590990 DOI: 10.1142/s0129065721500520] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of "more normal" brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.
Collapse
Affiliation(s)
- Emma Colamarino
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | - Valeria de Seta
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | | | - Febo Cincotti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | - Donatella Mattia
- Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | | | - Jlenia Toppi
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| |
Collapse
|
4
|
Tun NN, Sanuki F, Iramina K. Electroencephalogram-Electromyogram Functional Coupling and Delay Time Change Based on Motor Task Performance. SENSORS 2021; 21:s21134380. [PMID: 34206753 PMCID: PMC8271984 DOI: 10.3390/s21134380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 11/21/2022]
Abstract
Synchronous correlation brain and muscle oscillations during motor task execution is termed as functional coupling. Functional coupling between two signals appears with a delay time which can be used to infer the directionality of information flow. Functional coupling of brain and muscle depends on the type of muscle contraction and motor task performance. Although there have been many studies of functional coupling with types of muscle contraction and force level, there has been a lack of investigation with various motor task performances. Motor task types play an essential role that can reflect the amount of functional interaction. Thus, we examined functional coupling under four different motor tasks: real movement, intention, motor imagery and movement observation tasks. We explored interaction of two signals with linear and nonlinear information flow. The aim of this study is to investigate the synchronization between brain and muscle signals in terms of functional coupling and delay time. The results proved that brain–muscle functional coupling and delay time change according to motor tasks. Quick synchronization of localized cortical activity and motor unit firing causes good functional coupling and this can lead to short delay time to oscillate between signals. Signals can flow with bidirectionality between efferent and afferent pathways.
Collapse
Affiliation(s)
- Nyi Nyi Tun
- Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
- Correspondence: (N.N.T.); (K.I.); Tel.: +81-80-9392-9429 (N.N.T.); Fax: +81-92-802-3581 (N.N.T.)
| | - Fumiya Sanuki
- Graduate School of Systems Life Sciences, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan;
| | - Keiji Iramina
- Faulty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
- Correspondence: (N.N.T.); (K.I.); Tel.: +81-80-9392-9429 (N.N.T.); Fax: +81-92-802-3581 (N.N.T.)
| |
Collapse
|
5
|
Fischer P. Mechanisms of Network Interactions for Flexible Cortico-Basal Ganglia-Mediated Action Control. eNeuro 2021; 8:ENEURO.0009-21.2021. [PMID: 33883192 PMCID: PMC8205496 DOI: 10.1523/eneuro.0009-21.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
In humans, finely tuned γ synchronization (60-90 Hz) rapidly appears at movement onset in a motor control network involving primary motor cortex, the basal ganglia and motor thalamus. Yet the functional consequences of brief movement-related synchronization are still unclear. Distinct synchronization phenomena have also been linked to different forms of motor inhibition, including relaxing antagonist muscles, rapid movement interruption and stabilizing network dynamics for sustained contractions. Here, I will introduce detailed hypotheses about how intrasite and intersite synchronization could interact with firing rate changes in different parts of the network to enable flexible action control. The here proposed cause-and-effect relationships shine a spotlight on potential key mechanisms of cortico-basal ganglia-thalamo-cortical (CBGTC) communication. Confirming or revising these hypotheses will be critical in understanding the neuronal basis of flexible movement initiation, invigoration and inhibition. Ultimately, the study of more complex cognitive phenomena will also become more tractable once we understand the neuronal mechanisms underlying behavioral readouts.
Collapse
Affiliation(s)
- Petra Fischer
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, United Kingdom
| |
Collapse
|
6
|
Mrachacz-Kersting N, Ibáñez J, Farina D. Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation. J Physiol 2021; 599:2361-2374. [PMID: 33728656 DOI: 10.1113/jp281314] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 03/05/2021] [Indexed: 12/11/2022] Open
Abstract
Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.
Collapse
Affiliation(s)
| | - Jaime Ibáñez
- Department of Bioengineering, Centre for Neurotechnologies, Imperial College London, London, UK
- Department of Clinical and Movement Neuroscience, Institute of Neurology, University College London, London, UK
| | - Dario Farina
- Department of Bioengineering, Centre for Neurotechnologies, Imperial College London, London, UK
| |
Collapse
|
7
|
Vidaurre C, Haufe S, Jorajuría T, Müller KR, Nikulin VV. Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance. Front Neurosci 2021; 14:575081. [PMID: 33390877 PMCID: PMC7775663 DOI: 10.3389/fnins.2020.575081] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/16/2020] [Indexed: 12/29/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.
Collapse
Affiliation(s)
- Carmen Vidaurre
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Tania Jorajuría
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - Klaus-Robert Müller
- Department of Machine Learning, Berlin University of Technology, Berlin, Germany.,Department of Artificial Intelligence, Korea University, Seoul, South Korea.,Max Planck Institute for Informatics, Saarbrücken, Germany.,Google Research, Brain Team, Berlin, Germany
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| |
Collapse
|
8
|
Kenville R, Maudrich T, Vidaurre C, Maudrich D, Villringer A, Nikulin VV, Ragert P. Corticomuscular interactions during different movement periods in a multi-joint compound movement. Sci Rep 2020; 10:5021. [PMID: 32193492 PMCID: PMC7081206 DOI: 10.1038/s41598-020-61909-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/05/2020] [Indexed: 11/25/2022] Open
Abstract
While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.
Collapse
Affiliation(s)
- Rouven Kenville
- Institute for General Kinesiology and Exercise Science, Faculty of Sports Science, University of Leipzig, D-04109, Leipzig, Germany. .,Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany.
| | - Tom Maudrich
- Institute for General Kinesiology and Exercise Science, Faculty of Sports Science, University of Leipzig, D-04109, Leipzig, Germany.,Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany
| | - Carmen Vidaurre
- Dpt. of Statistics, Informatics and Mathematics, Public University of Navarre, Pamplona, 31006, Spain.,Machine Learning Group, Faculty of EE and Computer Science, TU Berlin, Berlin, 10587, Germany
| | - Dennis Maudrich
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany.,MindBrainBody Institute at Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin and Humboldt-Universität zu Berlin, Berlin, 10099, Germany.,Clinic for Cognitive Neurology, University Hospital Leipzig, D-04103, Leipzig, Germany
| | - Vadim V Nikulin
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany.,Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, 101000, Russian Federation.,Neurophysics Group, Department of Neurology, Charité-University Medicine Berlin, Campus Benjamin Franklin, Berlin, 10117, Germany
| | - Patrick Ragert
- Institute for General Kinesiology and Exercise Science, Faculty of Sports Science, University of Leipzig, D-04109, Leipzig, Germany.,Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany
| |
Collapse
|
9
|
Sho'ouri N, Firoozabadi M, Badie K. The effect of beta/alpha neurofeedback training on imitating brain activity patterns in visual artists. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101661] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
10
|
Pichiorri F, Mattia D. Brain-computer interfaces in neurologic rehabilitation practice. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:101-116. [PMID: 32164846 DOI: 10.1016/b978-0-444-63934-9.00009-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.
Collapse
Affiliation(s)
- Floriana Pichiorri
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - Donatella Mattia
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
| |
Collapse
|
11
|
Liu J, Sheng Y, Liu H. Corticomuscular Coherence and Its Applications: A Review. Front Hum Neurosci 2019; 13:100. [PMID: 30949041 PMCID: PMC6435838 DOI: 10.3389/fnhum.2019.00100] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 03/04/2019] [Indexed: 12/11/2022] Open
Abstract
Corticomuscular coherence (CMC) is an index utilized to indicate coherence between brain motor cortex and associated body muscles, conventionally. As an index of functional connections between the cortex and muscles, CMC research is the focus of neurophysiology in recent years. Although CMC has been extensively studied in healthy subjects and sports disorders, the purpose of its applications is still ambiguous, and the magnitude of CMC varies among individuals. Here, we aim to investigate factors that modulate the variation of CMC amplitude and compare significant CMC between these factors to find a well-developed research prospect. In the present review, we discuss the mechanism of CMC and propose a general definition of CMC. Factors affecting CMC are also summarized as follows: experimental design, band frequencies and force levels, age correlation, and difference between healthy controls and patients. In addition, we provide a detailed overview of the current CMC applications for various motor disorders. Further recognition of the factors affecting CMC amplitude can clarify the physiological mechanism and is beneficial to the implementation of CMC clinical methods.
Collapse
Affiliation(s)
- Jinbiao Liu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yixuan Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Honghai Liu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
12
|
Shourie N, Firoozabadi M, Badie K. Fuzzy adaptive neurofeedback training: An efficient neurofeedback training procedure providing a more accurate progress rate for trainee. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
13
|
Yilmaz G, Ungan P, Türker KS. EEG-like signals can be synthesized from surface representations of single motor units of facial muscles. Exp Brain Res 2018; 236:1007-1017. [DOI: 10.1007/s00221-018-5194-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/01/2018] [Indexed: 11/30/2022]
|
14
|
Yang Y, Dewald JPA, van der Helm FCT, Schouten AC. Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity. Eur J Neurosci 2017; 48:2407-2415. [PMID: 28887885 PMCID: PMC6221113 DOI: 10.1111/ejn.13692] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 08/18/2017] [Accepted: 09/05/2017] [Indexed: 01/09/2023]
Abstract
Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending somatosensory feedback and descending motor commands. However, corticomuscular coherence cannot separate this bidirectionality. Furthermore, the sensorimotor system is nonlinear, resulting in cross‐frequency coupling. Cross‐frequency oscillations cannot be assessed nor exploited by linear measures. Here, we emphasise the need of novel coupling measures, which provide directionality and acknowledge nonlinearity, to unveil neural coupling in the sensorimotor system. We highlight recent advances in the field and argue that assessing directionality and nonlinearity of neural coupling will break new ground in the study of the control of movement in healthy and neurologically impaired individuals.
Collapse
Affiliation(s)
- Yuan Yang
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Julius P A Dewald
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.,Department of Biomedical Engineering, McCormick school of Engineering, Northwestern University, Evanston, IL, USA
| | - Frans C T van der Helm
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Alfred C Schouten
- Neuromuscular Control Laboratory, Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.,MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| |
Collapse
|
15
|
Buyukturkoglu K, Porcaro C, Cottone C, Cancelli A, Inglese M, Tecchio F. Simple index of functional connectivity at rest in Multiple Sclerosis fatigue. Clin Neurophysiol 2017; 128:807-813. [DOI: 10.1016/j.clinph.2017.02.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 02/02/2017] [Accepted: 02/14/2017] [Indexed: 11/28/2022]
|
16
|
Nazarova M, Blagovechtchenski E. Modern Brain Mapping - What Do We Map Nowadays? Front Psychiatry 2015; 6:89. [PMID: 26136692 PMCID: PMC4468863 DOI: 10.3389/fpsyt.2015.00089] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Accepted: 05/27/2015] [Indexed: 12/18/2022] Open
Affiliation(s)
- Maria Nazarova
- Centre for Cognition and Decision Making, National Research University Higher School of Economics , Moscow , Russia ; Research Center of Neurology , Moscow , Russia
| | - Evgeny Blagovechtchenski
- Centre for Cognition and Decision Making, National Research University Higher School of Economics , Moscow , Russia ; Institute of Translational Biomedicine, St. Petersburg State University , St. Petersburg , Russia
| |
Collapse
|