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Primary brain regions. Motor cortex is the region in charge of planning, control and execution of voluntary movements. Sensory cortex arranges tactile representation from the toe to mouth. Cerebellum is mainly responsible for motion control. These three regions are more related to Corticomuscular coherence (CMC).
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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 sport...
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... ORTICOMUSCULAR (CMC) is an effective method for understanding how the cortex regulates muscle activity [1], [2], [3]. CMC was initially described between magnetoencephalography (MEG) and electromyography (EMG) [4], [5]. ...
... EEG -EMG coherence has been associated with greater control over motor output, as evidenced by reduced fluctuations in force production and more consistent muscle activity [2]. This suggests that beta-band coherence plays a key role in stabilizing motor output by aligning oscillatory activity in the motor cortex with EMG signals [41]. ...
EEG–EMG coherence (Corticomuscular coherence – CMC) reveals the functional connection between the cortical activity and muscle activity during voluntary movements. During voluntary movements the cortical and muscle activity are synchronized in the beta band range. Age-related deteriorations in the central and peripheral system can impair communication between the brain’s cortical regions and the muscle activity. This study aim to examine the beta band EEG – EMG coherence in older individuals and compare the results with young adults. Twenty-two-channel EEG and two-channel EMG data were collected from twenty healthy young adults aged 20–30 (26.96±2.68) and fourteen older adults aged 58–72 (62.57±3.58). Participants were instructed to hold a handle gently for five seconds, then lift and hold it for an additional five seconds under fixed and free conditions (with the thumb platform either fixed or sliding). EEG – EMG coherence magnitude was lower in the older group compare to the young group. Furthermore, the magnitude of EEG-EMG coherence of the young group was greater in the fixed condition than the free condition. In contrast, no difference in EEG-EMG coherence magnitude was observed in the older group between the task conditions. In summary, older adults exhibit reduced and consistent EEG – EMG coherence across different motor tasks compared to younger adults, reflecting age-related declines in neural synchrony and motor control efficiency. In contrast, younger individuals exhibit task related modulation in EEG-EMG coherence magnitude. This suggests a fundamental difference in motor control mechanisms between younger and older populations during task performance.
... Wavelet Coherence (WC) analysis quantitatively evaluates the physiological correlation between two anatomically independent neural regions. The coherence value ranges from 0 to 1, where a CMC value closer to 1 indicates higher coherence (either positive or negative) [32]. ...
... 2) Factors affecting CMC: The results of CMC can be influenced by a variety of factors, among which participant heterogeneity and experimental paradigms are particularly significant. Participant heterogeneity encompasses factors such as lesion location, severity of impairment, time since stroke onset, and age [32], [40]. Evidence from some studies indicates that the spatial distribution of CMC peak may correlate with the location of stroke lesions [22], [41]. ...
Corticomuscular coupling (CMC) can quantify the information interaction between the brain and muscles during motor control. However, current research regarding changes in CMC after stroke is inconsistent. To address this, this paper propose a novel use of CMCdif as an indicator to assess motor function after stroke. This indicator include WCdif, derived from wavelet coherence analysis and TSEdif, derived from transfer spectral entropy analysis. Twelve stroke patients and twelve healthy controls were included in this study, with an experimental paradigm of upper limb isokinetic push-pull movements. The results revealed that WCdif were significantly higher in the stroke patient group compared to the healthy group. Moreover, the TSEdif of stroke group is higher than healthy group on the efferent pathway, but no difference on the afferent pathway. Utilizing the validated CMCdif indices, we developed a motor function assessment model that showed strong relation with clinical assessment outcomes (R2 = 0.873, p = 0.003). These findings provide a new insight to understand the mechanisms underlying CMC changes after stroke. The combined use of linear and nonlinear indicators enhances the potential of CMC for clinical motor function assessment in stroke patients.
... Each of these methods approaches synchronization from a slightly different perspective. However, NMC is most frequently quantified using coherence, in this case referred to as corticomuscular coherence (CMC) [25]. Coherence can be considered a frequency domain equivalent of Pearson's correlation coefficient. ...
... It quantifies the degree to which the frequency content of two signals align. Several studies have reported a weak but significant CMC in the beta frequency range [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] between EMG signals from hand or foot muscles and EEG signals over contralateral sensorimotor regions during sustained isometric muscle contractions [26][27][28][29]. With increasing contraction intensities, CMC tends to increase [30,31], while during movement, CMC typically decreases [32,33]. ...
... Most studies examined NMC in the beta [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] and gamma frequency ranges. Thirteen studies examined the alpha frequency range [8][9][10][11][12][13], six studies the theta frequency range [4][5][6][7][8], and four studies the delta frequency range [1][2][3][4][5][6]. ...
This study systematically reviews the role of the cortex in gait control by analyzing connectivity between electroencephalography (EEG) and electromyography (EMG) signals, i.e., neuromuscular connectivity (NMC) during walking. We aim to answer the following questions: (i) Is there significant NMC during gait in a healthy population? (ii) Is NMC modulated by gait task specifications (e.g., speed, surface, and additional task demands)? (iii) Is NMC altered in the elderly or a population affected by a neuromuscular or neurologic disorder? Following PRISMA guidelines, a systematic search of seven scientific databases was conducted up to September 2023. Out of 1308 identified papers, 27 studies met the eligibility criteria. Despite large variability in methodology, significant NMC was detected in most of the studies. NMC was able to discriminate between a healthy population and a population affected by a neuromuscular or neurologic disorder. Tasks requiring higher sensorimotor control resulted in an elevated level of NMC. While NMC holds promise as a metric for advancing our comprehension of brain–muscle interactions during gait, aligning methodologies across studies is imperative. Analysis of NMC provides valuable insights for the understanding of neural control of movement and development of gait retraining programs and contributes to advancements in neurotechnology.
... Corticomuscular coherence measures the degree of statistical dependency between electroencephalography (EEG) and electromyography (EMG) signals, which may suggest a level of coordination between brain and muscle activity, though it does not imply a direct interaction. This can be quantified by calculating coherence between EEG and EMG signals, identical to extending the Pearson correlation coefficient into the frequency domain 11,12 . Several studies have demonstrated corticomuscular coherence, indicating the relationship between EEG and EMG signals in the 15-30 Hz at the beta frequency range of EEG signal during motor tasks 13,14 . ...
Hand dominance has long been associated with differences in neural control and motor performance, with the dominant hand typically exhibiting better coordination in reaching tasks. However, the extent to which this dominance influences performance in finger force control remains unclear. This study aimed to examine the behavioural and neural features of the dominant and non-dominant hands during grasping and lifting tasks in healthy young adults, focusing on the synergy index, EEG band power, and EEG–EMG coherence as key measures. Twenty right-handed adults participated in this study. Participants engaged in an experimental task where they grasped a handle for the initial 5 s, followed by lifting and holding it for an additional 5 s. There were two task conditions: fixed (thumb platform secured) and free (thumb platform movable). It was hypothesized that the dominant hand would exhibit greater finger force coordination and enhanced neural features, including higher EEG band power and increased EEG–EMG coherence, compared to the non-dominant hand. Contrary to the hypothesis, we found statistical equivalence in the synergy index, EEG band power, and EEG–EMG coherence between the dominant and non-dominant hands across both fixed and free task conditions. These findings suggest that both hands can achieve similar levels of performance in tasks emphasizing steady-state force maintenance, despite the typical advantages of the dominant hand in other motor tasks. However, a significant difference was observed between task conditions, with the fixed condition showing higher values than the free condition in both behavioural (synergy index—η² = 0.81, p < 0.0001,) and neural (EEG band power η² = 0.37, p < 0.05 and EEG–EMG coherence—η² = 0.49, p < 0.0001) features. These differences were likely due to changes in friction, yet the adjustments remained consistent between the dominant and non-dominant hands.
... Over the past decade, CMC has become a key tool in motor control research (Boonstra, 2013;Liu et al., 2019). Since then, an increasing number of studies have probed the details of this connection, both in physiological and pathological conditions. ...
This study investigates the role of the premotor area (PMA) in motor planning during decision-making, focusing on differences between brain hemispheres. A cross-sectional assessment was conducted involving seventeen right-handed participants who performed tasks requiring responses with either hand to visual stimuli. Motion capture, EEG and EMG signals were collected to analyze corticomuscular coherence (CMC) in the beta and gamma bands across four motor-related cortical areas. Findings revealed significant beta-band CMC between anterior deltoids and contralateral PMA before stimulus onset in simple reaction tasks. Moreover, significant beta-band CMC was observed between the left anterior deltoid and the right PMA during the motor planning
phase, prior to the onset of muscle contraction, corresponding with shorter planning times. This connectivity pattern was consistent across both simple and complex reaction tasks, indicating that the PMA plays a crucial role during decision-making. Notably, motor planning for the right hand did not exhibit the same connectivity pattern, suggesting more complex cognitive processes. These results emphasize the distinct functional roles of the left and right hemispheres in motor planning and underscore the importance of CMC in understanding the neural mechanisms underlying motor control. This study contributes to the theoretical framework of motor decision-making and offers insights for future research on motor planning and rehabilitation strategies.
... The analysis of response times provides valuable insights into the cognitive demands of the control processes. However, more detailed neurophysiological measurements, such as corticomuscular coherence (Liu et al. 2019), would offer a deeper understanding of the dynamics in functional connectivity driving these processes. ...
Proficient use of multiple fingers across both hands enables complex interactions with the world. Developing dexterity for delicate bimanual tasks can take weeks to years to master. This study examines whether a simple mechanical abstraction can enhance bimanual response times to visual stimuli using index fingers and thumbs. A plate placed over the buttons was introduced to shift task conceptualization—rather than pressing buttons, subjects rocked the plate over them. Following this mechanical augmentation, we observed improved bimanual performance in middle‐aged participants but no effect on unimanual actions. Conversely, younger adults showed no performance improvement. A control group of middle‐aged participants confirmed that the observed improvements resulted from the augmentation. We hypothesize that highly similar afferent sensory signals from the fingers of both hands alter the motor control strategy. This work provides a foundation for further research into the diverse mechanisms of multi‐fingered bimanual behavior in both abstract and practical contexts.
... Early studies on cortico-muscular interactions primarily utilized CMC to quantify spectral coherence between EEG and EMG signals [25]. CMC, an extension of the Pearson correlation coefficient to the frequency domain, is derived by computing the normalized cross-spectrum density between two signals [26]. However, due to limitations such as linear nature, weak coupling strength and insignificant coherence estimation, several extensions have been developed to address these issues, focusing on nonlinear mapping [14], delay compensation [16,17] and frequency decomposition [27]. ...
Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions. Our approach leverages an embedded optimization framework by integrating a partial least squares (PLS)-based objective function, a sparsity constraint and a connectivity-based structured constraint, addressing the generalizability, interpretability and spatial structure. To solve the optimization problem, we develop an efficient alternating iterative algorithm within a unified framework and prove its convergence experimentally. Extensive experimental results from one synthetic and several real-world datasets have demonstrated that ssPLSC can achieve competitive or better performance over some representative multivariate cortico-muscular fusion methods, particularly in scenarios characterized by limited sample sizes and high noise levels. This study provides a novel multivariate fusion method for cortico-muscular analysis, offering a transformative tool for the evaluation of corticospinal pathway integrity in neurological disorders.
... Figure 3a shows CMC as a function of frequency in the time window just before reach onset. In line with previous findings (Liu et al., 2019), there is a peak in the beta band, at 20 Hz. We focused on averaged CMC in frequencies between 10 and 40 Hz (gray area in Figure 3) for further analysis. ...
Evidence accumulation processes during decision‐making are thought to continuously feed into the motor system, preparing multiple competing motor plans, of which one is executed when the evidence is complete. Previously, the state of this accumulation process has been studied by reading out the preparatory state of the motor system with evoked responses, once per trial. In this study, we aim to continuously track the sensorimotor decision during the trial using corticomuscular (CMC) and intermuscular coherence (IMC). We recorded EEG and EMG of healthy young adults (n = 34) who viewed random dot motion stimuli, with varying strengths across trials, and indicated their perceived motion direction by reaching towards one of two targets, requiring either flexion or extension of the elbow. Coherence was computed in the beta band. After stimulus presentation, both CMC and IMC show an initial phasic pattern, which is followed by sustained coherence patterns at a level that depends on stimulus strength for CMC. Prior to reach onset, the CMC for different stimulus strengths had a tendency to settle at similar levels. This tendency tentatively marks a stimulus‐independent decision bound. We conclude that CMC, and to a lesser extent IMC, track the evidence accumulation process on a single trial.
... To address this, we aimed to analyze the EEG signals and corresponding EMG signals simultaneously during MVF in patients with deafferentation pain after brachial plexus injury, assessing their synchronyin other words, CMC. To date, numerous studies on corticomuscular coherence (CMC) have been reported, along with review articles summarizing these findings (Brambilla et al., 2021;Gao et al., 2024;Lattari et al., 2010;Liu et al., 2019). CMC studies allow for the evaluation of the functional connectivity between the cerebral cortex and muscles, and it has been demonstrated that the generation of CMC reflects both descending neural information from the motor cortex to the muscles and ascending neural information from the muscles to the cerebral cortex (Lim et al., 2014;Witham et al., 2011). ...
Background
Mirror visual feedback (MVF) has shown promise as a treatment for deafferentation pain following brachial plexus injury, yet the underlying mechanisms remain unclear. This study aimed to assess MVF’s effect on two patients with deafferentation pain by analyzing cortico-muscular coherence (CMC), a measure of functional connectivity between the brain and muscles.
Methods
Two patients with brachial plexus injuries performed wrist movements with and without a mirror, accompanied by electromyography (EMG) and electroencephalography (EEG). CMC was calculated during each condition to determine changes in the sensorimotor network.
Results
In Patient 1, CMC increased in the beta band in the extensor carpi radialis and surrounding parietal regions during the mirror condition. In Patient 2, beta-band CMC decreased in the compensatory muscle (biceps brachii) but increased in the primary muscle (flexor carpi ulnaris) when the mirror was used. These findings suggest MVF promotes sensorimotor integration, reducing pain intensity.
Conclusion
Mirror visual feedback (MVF) effectively enhances CMC in the contralateral sensorimotor cortex in the beta frequency band, accompanied by pain relief in the affected limb. This suggests that CMC analysis could refine deafferentation pain rehabilitation using MVF, providing a better understanding of its neural mechanisms and optimizing therapeutic outcomes. Our study underscores the potential of CMC as a valuable biomarker for monitoring and tailoring MVF interventions.
... During rehabilitation, the restoration of interhemispheric balance and shifts in power bands in the affected hemisphere have been documented and these changes are considered as biomarkers indicative of motor recovery capacity (Casula et al. 2021;Lefaucheur et al. 2020). At corticomuscular level, functional connectivity during movement execution between cortex and muscle in descending and ascending pathways can be elucidated through directed corticomuscular coherence (dCMC) (Liu, Sheng, and Liu 2019). Studies have reported the up-regulation of descending dCMC was the main reason for the motor function recovery (Khademi et al. 2022) and an augmentation in ascending dCMC to compensate the reduction in descending dCMC (Zhou et al. 2021). ...
Individualized training improved post-stroke motor function rehabilitation efficiency. However, the mechanisms of how individualized training facilitates recovery is not clear. This study explored the cortical and corticomuscular rehabilitative effects in post-stroke motor function recovery during individualized training. Sprague-Dawley rats with intracerebral hemorrhage (ICH) were randomly distributed into two groups: forced training (FOR-T, n=13) and individualized fatigue-controlled training (FAT-C, n=13) to receive training respectively from day 2 to day 14 post-stroke. The FAT-C group exhibited superior motor function recovery and less central fatigue compared to the FOR-T group. EEG PSD slope analysis demonstrated a better inter-hemispheric balance in FAT-C group compare to the FOR-T group. The dCMC analysis indicated that training-induced fatigue led to a short-term down-regulation of descending corticomuscular coherence (dCMC) and an up-regulation of ascending dCMC. In the long term, excessive fatigue hindered the recovery of descending control in the affected hemisphere. The individualized strategy of peripheral fatigue-controlled training achieved better motor function recovery, which could be attributed to the mitigation of central fatigue, optimization of inter-hemispheric balance and enhancement of descending control in the affected hemisphere.
