Corticomuscular Coherence and Its Applications: A Review

fnhum-13-00100 March 19, 2019 Time: 9:51 # 1

REVIEW

published: 20 March 2019

doi: 10.3389/fnhum.2019.00100

Edited by:

Camillo Porcaro,

Istituto di Scienze e Tecnologie della

Cognizione (ISTC), Italy

Reviewed by:

Giovanni Assenza,

Campus Bio-Medico University, Italy

Rohan Galgalikar,

Corning Inc., United States

*Correspondence:

Honghai Liu

[email protected]

Received: 13 December 2018

Accepted: 04 March 2019

Published: 20 March 2019

Citation:

Liu J, Sheng Y and Liu H (2019)

Corticomuscular Coherence and Its

Applications: A Review.

Front. Hum. Neurosci. 13:100.

doi: 10.3389/fnhum.2019.00100

Corticomuscular Coherence and Its

Applications: A Review

Jinbiao Liu, Yixuan Sheng and Honghai Liu

State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong

University, Shanghai, China

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.

Keywords: corticomuscular coherence, magnetoencephalography, electroencephalogram, surface

electromyogram, stroke

INTRODUCTION

Preview human studies used positron emission tomography (PET), functional magnetic resonance

imaging (fMRI), transcranial magnetic stimulation (TMS) or electroencephalogram (EEG)

to suggest the underlying mechanisms of motor cortex in patients with strokes (Mima

et al., 2001; Zheng et al., 2017). However, the exact role of how ipsilateral motor cortex

or secondary motor areas control the muscle activity is still to be fully discovered. One

approach to overcome this issue is to measure EEG signals and corresponding EMG signals

simultaneously. This method is known as corticomuscular coherence, which is considered to

be a classic and commonly used approach to assess the synchrony between neural signals

and associated body muscles. Corticomuscular coherence (CMC) was initially reported between

magnetoencephalography (MEG) and electromyography (EMG) (Kilner et al., 2000; Tecchio

et al., 2006, 2008; Porcaro et al., 2008) and is widely detected by techniques such as EEG,

electrocorticography (ECoG), surface electromyography (sEMG), and has thus been validated

across methods and species (Gerloﬀ et al., 2006).

Corticomuscular coherence is a common and useful method to study the mechanism of cerebral

cortex’s control of muscle activity. It reveals functional connection between the cortex and muscles

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Liu et al. Corticomuscular Coherence and Applications

during continuous muscle contractions. The origin of CMC is

the communication in corticospinal pathways between primary

motor cortex and muscles. Normally, cortical events propagate

to the periphery and motor cortex also receives input from the

periphery (Salenius et al., 1997; Gross et al., 2000; Riddle and

Baker, 2005). Horak’s motion control theory emphasizes “Normal

motion control refers to the central nervous system by using

existing and past information to transform neural energy into

kinetic energy and enable it to perform eﬀectively functional

activities” (Horak, 1991). In this process, the interaction between

the two systems of central nervous system and motor muscle

tissue is included. Utilizing hand grip as an example, the

command which is issued by the motor cortex will be carried

down along the motor conduction pathway and dominates

upper body’s peripheral nerves and muscles when motion

occurs (Claudio et al., 2008). The sense of proprioception is

simultaneously conducted along the sensory conduction pathway

to the spinal cord, the brain stem and the cerebellum, and

partly to the cerebral hemisphere. Most of the proprioceptive

information are transmitted to the sensory regions of the brain

for comprehensive analysis and regulate motion commands

(Witham et al., 2011). The study of the cortical-muscle function

coupling can reﬂect the interaction between the cerebral cortex

and the muscle tissue which represents the ﬂow of information

within the motion system and is associated with the cerebral

cortex sending commands to the muscle tissue and the aﬀerent

feedback of muscle contraction. Thus, it serves to understand

how the brain controls muscle tissue, the eﬀects of muscle

movement on brain function and the explanations of the

rooted causes of speciﬁc physiological conditions such as

fatigue. More recent studies using directional coherence analysis

have emphasized that CMC reﬂects both the corticoeﬀerent

descending locomote from motor cortex to muscles as well as

ascending corticoaﬀerent locomote from muscles to motor cortex

in producing the CMC (Hellwig et al., 2001; Fang et al., 2009;

Airaksinen et al., 2015a). Figure 1 indicates the pathway of signal

transmission between cortex and muscle.

Since Conway et al. (1995) reported increased coherence

between MEG signals in the contralateral motor areas and the

surface EMG (sEMG) signal during muscle contractions, CMC

has been extensively studied for the continuous contraction of

limb muscles (Kilner et al., 2000; Krause et al., 2013; Rossiter

et al., 2013; Maezawa, 2016; Matsuya et al., 2017). Based

FIGURE 1 | Schematic diagram of signal transmission in cerebral cortex and

peripheral nerve.

on previous studies, it can be suggested that the magnitude

of CMC reﬂects the indicator of human neurophysiology in

both healthy subjects and sport disorders. However, neural

system is extremely complex and diverse among individuals,

which causes the CMC amplitude would be emerged diﬀerent

correlation results under diﬀerent research conditions. Hellwig

et al. (2001) applied EEG to reveal tremor-correlated cortical

activity in tremor patients. EMG signals of wrist extensor and

ﬂexor muscles were recorded from tremor side of patients.

With EEG recording, CMC was estimated and found that there

was a highly signiﬁcant coherence at the tremor frequency.

Raethjen et al. (2002) discovered that CMC had a signiﬁcant

coherence in the 6–15 Hz range in four out of the six tremor

patients. That because corticomuscular transmission of the

oscillation was in progress between cortex and muscles rather

than peripheral feedback to the cortex. This research pointed

out that the band frequency was an important factor which

may impact CMC. To compare CMC between young and

older adults, Johnson and Shinohara (2012) discussed CMC

and ﬁne motor performance during the unilateral ﬁne motor

task and concurrent motor and cognitive tasks which asked

participants to increase the force from zero to maximum

using the index ﬁnger. From this study, results revealed

that older adults had lower CMC in beta-band and higher

alpha-band than young adults during dual tasks and young

controls, rather than older adults, with greater beta-band CMC

exhibited accurately motor output. Previous researches have

conﬁrmed that CMC magnitude often varies greatly due to

diﬀerent experimental designs, magnitude of exerted force, or

individual diﬀerences. However, there are no relevant studies

to make a detailed investigation of the factors aﬀecting the

amplitude of CMC.

Compare with the coherence between all coupling degrees,

the classiﬁcations which have signiﬁcant coherence could

be observed. These can serve as the research emphasis

in the future. The remaining of this review is organized

as follows: section CMC DEFINITION AND FORMULAE

proposes a generalized deﬁnition of CMC; section FACTORS

AFFECTING CMC provides a detailed overview of the

factors aﬀecting the corticomuscular coordination, including

experimental design (von Carlowitz-Ghori et al., 2015),

band frequencies (Schulz et al., 2014; Maezawa et al., 2016)

and force levels (Dal Maso et al., 2017), age correlation

(Kamp et al., 2013) and diﬀerence between healthy controls

and patients (Krause et al., 2013; Rossiter et al., 2013); the

applications of CMC for patients with various motor disorders

are further presented in section CMC APPLICATIONS;

section DISCUSSION proposes a discussion of CMC

modulation and future directions; the paper is concluded

in section CONCLUSION.

CMC DEFINITION AND FORMULAE

Coherence is an indicator of the linear connection between

two signals (Grosse et al., 2003) and is an extension of

Pearson correlation coeﬃcient in the frequency domain

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Liu et al. Corticomuscular Coherence and Applications

(Mima and Hallett, 1999). Coherence was obtained from the

normalization of the cross-spectrum (Fang et al., 2009):

Coh

S1,S2







S1,S2



















(1)

S1,S2





i=1





∗





(2)

Where P

S1,S2





is the cross-spectrum density of the signal,





and P





are the auto-spectrum densities of signals

S1 and S2, respectively, at frequency f . Values of coherence is

normalized and will always satisfy 0 to 1 where 1 indicates an

ideal correlation between two signals and 0 indicates a total

absence of association.

Corticomuscular coherence is an implement to understand

how cortical activities control the muscle movements and

examines the functional coupling between brain motor

cortex and associated muscles. Ascending and descending

corticomuscular pathways are two diverse directions which

could both generate coherence, however, descending pathway

are more clearly and certainly than ascending pathway. Hence,

the common deﬁnition of CMC indicates the cortex-muscle

coherence underlying descending pathway.

As the variety of signal collection techniques, CMC shows

a widely research space to analyze diﬀerent types of signals

collecting from diﬀerent approaches. From the recent studies,

the most familiar techniques to collect brain activity signals are

EEG, MEG, and ECoG and muscle activity signals are sEMG and

ultrasound. In the researches of CMC, EEG-EMG, MEG-EMG,

and ECoG-EMG are the most three commonly used methods to

analyze the functional coupling between brain cortex and muscle

activities and these three sets of signals are used to calculate the

coherence parameter. Therefore, in the equations (1) and (2),

two signals S1 and S2 could represent these three types of signal

combinations. At the same time, ultrasound is an unusual signal

format to use in CMC analysis, which could be regarded as a

future research ﬁeld.

Equation (1) and equation (2) oﬀer a basic and intuitive

technique to display the synchronous values. On this basis,

wavelet-based coherence is proposed to enhance the relative level

of the motor cortex and muscle and observes the coherence

in time-frequency domain which estimates the signal spectral

characteristics according to the function of time (Xu et al.,

2015). To overcome the problems of non-stationary signals

like sEMG signal, wavelet analysis is a rational method to

analyze signals with fast-changing spectra (Lachaux et al.,

2002). One primary advantage of wavelet analysis is to observe

the signiﬁcant coherence in diﬀerent time for diﬀerent tasks

intuitively and expediently. Compare to traditional CMC analysis

result, wavelet coherence increases precision when analyzing

temporary activities between two oscillatory neural signals and

is good at dynamic neural interactions.

Morlet wavelet family is a simple and suitable wavelet for

spectral estimations although there are still many wavelets could

be chosen. The signal x

(

)

is decomposed along Morlet wavelet

and under the frequency f and time τ, it could be calculated by

the following formulae:

τ,f

(

)

f · exp



i2πf

(

u − τ

)



· exp



−

(

u − τ

)



(3)

Where ψ

τ,f

(

)

is the product of a sinusoidal wave at

frequency f , with a Gaussian function centered at time τ with

a standard deviation σ proportional to the inverse of frequency

f . The wavelet transform function W



τ, f



of a signal x

(

)

a function given by the convolution of x with Morlet wavelet

family:

(τ, f ) =

(+∞)

(−∞)

x(u) · Ψ

τ,f

∗

(u)du (4)

From the wavelet transform function, the wavelet cross-

spectrum of two signals between brain cortex and muscle is as

follow:

S1,S2



t, f



t+δ/2

t−δ/2



τ, f



· W

∗



τ, f



dτ (5)

Where δ is a scalar that can depend on frequency. Therefore,

the wavelet coherence WCoh

S1,S2





could be deﬁned as follow:

WCoh

S1,S2







S1,S2







S1,S1







S2,S2







(6)

Besides the above two methods to analyze CMC, Fourier

coherence and partial directed coherence are also reported in

some researches (Lachaux et al., 2002; Porcaro et al., 2009;

Mcmanus et al., 2013). Compare Fourier coherence with Wavelet

coherence, these two methods both study non-stationary signals,

however, the window size of wavelet analysis is ﬁxed and it is

more adapted to the frequency of the oscillatory signals. As a

result, wavelet coherence has a more accurate consequence than

Fourier coherence. For partial directed coherence, this technique

could evaluate the ﬂowing direction of neural information

and indicate how cortical signals and muscular signals are

functionally connected compared with ordinary CMC analysis.

And this is a potential technique because the current researches

are most focus on the synchrony between two signals or the

descending corticospinal pathway.

With the intensive study of CMC, related analytical methods

also evolve gradually. Researchers are not satisﬁed with

commonly CMC analysis method; thus, Wavelet coherence has

become a widely used technology in the recent years. Wavelet

coherence could show CMC magnitude during the entire task

time series. Since the strong coherence under speciﬁc movements

could be observed, the discovery of the factors aﬀecting CMC will

be easier to achieve.

FACTORS AFFECTING CMC

Research shows strong correlated area of CMC has been

conﬁrmed by direct electrical stimulation in monkeys and human

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FIGURE 2 | 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).

surgery, including the anterior motor area, the primary motor

area, the primary somatosensory area, the thalamus, the nucleus

of the hypothalamus, and the cerebellum (Salenius and Hari,

2003), Figure 2 shows primary brain regions which related

to CMC. However, several studies suggest that the magnitude

of CMC is not only related to the corresponding regions of

the cerebral cortex, but also directly related to the CMC band

(Cottone et al., 2017; Porcaro et al., 2018). Chakarov et al. (2009)

found that the CMC value of the 15–45 Hz frequency band

increased linearly with the rise of the dynamic force level, that

is, the CMC value was a high-level dynamic synchronization

process. Survey also shows that the CMC value of the beta

band (13–30 Hz) is related to the output of the static force,

and the coherence of the gamma band (31–45 Hz) is related to

the output of the dynamic force (Gwin and Ferris, 2012). The

CMC magnitude is signiﬁcantly lower in the case of unpredictable

low-level force frequency (Mendez-Balbuena et al., 2013). The

unpredictability of the force frequency could lead to the decrease

of the corticospinal tract synchronism, the increase of cortical

and muscle activation, and the decrease of motor performance.

Several studies have demonstrated that beta-band CMC value

is modulated by aﬀerent information (Fisher et al., 2002; Pohja

et al., 2002; Kilner et al., 2004; Riddle and Baker, 2005; Baker et al.,

2006), and visuomotor tasks (Perez et al., 2006).

Moreover, the CMC value of healthy subjects is generally

higher than that of sport disorders. Stroke patients have

a signiﬁcantly lower corticomuscular coherence compare

with healthy controls at both the beta (20–30 Hz) and lower

gamma (30–40 Hz) bands during the movement (Fang et al.,

2009). During light voluntary muscular contraction, beta-band

CMC is markedly reduced in Amyotrophic Lateral Sclerosis

patients compare with healthy controls (Proudfoot et al.,

2018). However, research also indicates that stroke survivors

manifest a more distributed range of cortical locations for

peak CMC than healthy controls, in keeping with plastic

reorganization of sensorimotor functionality (Farmer et al.,

1993; Rossiter et al., 2013). In addition, with the rehabilitation of

motor function, the value of CMC will increase gradually

on sport disorder patients. Motor deﬁcits secondary to

acute stroke are accompanied by a unilateral reduction in

CMC, which then normalizes with good functional recovery

(von Carlowitz-Ghori et al., 2014).

Although the factors that aﬀect the result of CMC amplitude

have not been speciﬁcally counted, the classiﬁcation and

comparison of the current research focus on CMC can provide

a more speciﬁc understanding of the mechanism of CMC.

The factors aﬀecting the corticomuscular coordination are

summarized as follows: experimental design, band frequencies

and force levels, age correlation and diﬀerence between healthy

controls and patients.

Experimental Design

The magnitude of CMC is closely related to the paradigm design.

Diﬀerent experimental paradigms may result in separate CMC

magnitude. Table 1 displays diﬀerent CMC experimental design.

Force is one of the most signiﬁcant indices in CMC experiment

generally which is relevant to the form of muscle contraction

during the experimental design, such as isometric contraction,

isokinetic contraction, isotonic contraction, etc. Most studies

used isometric contraction as a form of muscle contraction

in CMC experiments. Dal Maso et al. (2017) investigated

that whether CMC magnitude diﬀered with torque levels

during isometric knee contractions tasks. The net joint torque,

muscles co-activation and CMC values were comparable when

participants performed complete isometric elbow ﬂexion exercise

with three force output levels (Cremoux et al., 2017). Similarly,

in order to explore whether oscillatory activity could contribute

more to the stability of isometric muscle contraction, the subjects

were required to perform steady isometric contractions, using

two diﬀerent ﬁnger muscles (Lim et al., 2014). Current studies

on isokinetic and isotonic contractions are relatively limited.

Intramuscular Tibialis Anterior (TA) coherence estimation was

investigated within a speciﬁc frequency range during 120

◦

isokinetic movement (Bravo-Esteban et al., 2014), and Yang

et al. (2016) proposed a measure for evaluating non-linear

corticomuscular coupling during isotonic wrist ﬂexion.

Muscle fatigue is an unavoidable problem in the CMC

experiment, therefore the time design of the experiment is an

essential part, including the duration of the continuous force,

the rest time, etc. For example, when subjects performed two

speciﬁc tasks according to the prompts on the monitor, they

needed to rest for 10 min between each trial to prevent muscle

fatigue (Rong et al., 2014). Besides, a study of whether CMC

magnitude in beta-band diﬀered with torque levels required

subjects to perform three 4s knee isometric MVC (maximum

voluntary contraction) and 6s rMVC (relative maximum

voluntary contraction) in both directions of contraction (Dal

Maso et al., 2017). For experimental design with patient’s

participation, such as a stroke patient’s experiment, the design of

time interval between two experiments are required to ensure that

the patient’s aﬀected side has achieved signiﬁcant motor function

recovery (Zheng et al., 2017).

Localization of muscle position by EMG electrodes is the

underlying cause of CMC amplitude variation. From previous

studies, normally, the acquisition of muscle signals derives from

the limbs and hands. For instance, Lou et al. (2013) executed

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TABLE 1 | Different experimental design of CMC.

Reference Contraction form Muscle position Sample CMC results

Dal Maso et al., 2017 Isometric Agonist Antagonist 21 right-footed men CMC magnitude decreased more in antagonist

than in agonist muscles as torque level increased.

Cremoux et al., 2017 Isometric Antagonist 8 SCI patients 10 healthy

participants

Magnitude of CMC and muscle co-activation

decreased with the increase in the force level.

Lim et al., 2014 Isometric FPB FDMB 15 healthy subjects Greater β-band DTF was associated with high

EMG stability levels and greater β-band CMC

strength.

Matsuya et al., 2017 Isometric FDI SOL 16 healthy young adults A signiﬁcant, positive correlation between recurrent

inhibition and peak CMC across individuals.

Rossiter et al., 2013 Isometric Forearm ﬂexors and

extensors

25 stroke patients

23 healthy controls

Peak CMC in the contralesional hemisphere was

found not only in some highly impaired patients, but

also in some patients with good functional recovery.

Rong et al., 2014 Isometric Right EDC 27 healthy subjects CMC might represent a general marker of aging

increased coherence amplitude might denote a

compensatory mechanism to maintain isometric

contraction.

Bravo-Esteban et al., 2014 Isokinetic TA 14 SCI subjects

15 healthy controls

Analysis of intramuscular TA coherence during

isometric activation is related to muscle strength

and gait function following incomplete SCI.

Yang et al., 2016 Isotonic FCR 11 healthy subjects The corticospinal tracks mainly mediate linear

corticomuscular coupling, while non-linear coupling

might relate to sensory feedback pathways.

SCI, spinal cord injury; FPB, ﬂexor pollicis brevis, FDMB, ﬂexor digiti minimi brevis; FDI, ﬁrst dorsal interosseous; SOL, soleus; EDC, extensor digitorum communis; TA,

tibialis anterior; FCR, ﬂexor carpi radialis.

4 hand movement tasks to investigate CMC. Rong et al. (2014)

proved sensorimotor cortex enhanced communication with the

measured muscle in right hand. EMG of biceps brachii muscle

had also been studied to analyze the eﬀects of mechanically

ampliﬁed tremor on CMC (Budini et al., 2014). Otherwise,

some studies have evaluated the inﬂuence of stance width,

vision, and surface compliance on beta CMC during human

stance. The results showed that under the condition of wide-

stance, CMC amplitude is obviously larger than that under the

condition of narrow-stance (Jacobs et al., 2015). However, no

study placed electrodes in human trunk, the reason for this result

is probably that the muscle contraction degree in human trunk

is considerably lower than that in the limbs, and the EMG signal

obtained is not enough to achieve obvious CMC amplitude.

Moreover, some comparative studies have demonstrated the

eﬀects of diﬀerent forms of tasks in CMC. For example,

study found that functional coupling between cortex activity

and muscles was less in position-control task than in force-

control task (Poortvliet et al., 2015). Similarly, research indicated

that motor control strategies diﬀered between force and

position control tasks (Maluf and Enoka, 2005). Comparing

to the position control task, EEG power of beta-range in the

force control task showed greater activity desynchronization

(Pfurtscheller and Lopes da Silva, 1999). For patients, the

comparison of the CMC task between the aﬀected side and

the unaﬀected side is usually adopted. The study found that

the frequency of CMC on the aﬀected side reduced and the

magnitude of CMC on the unaﬀected side increased in acute

stroke (von Carlowitz-Ghori et al., 2014).

Experimental design is the initial step of studying CMC.

Only a scientiﬁc and reasonable paradigm is possible

to achieve satisfactory results. Future research on CMC

experimental design should be transferred to isokinetic

contraction and isotonic contraction since isokinetic movement

excludes the muscle force is diﬀerent during dynamic

muscle contraction among individual which is superior to

isometric contraction. Time design requires full consideration

of the characteristics of muscle fatigue of diverse subjects

and more reasonable allocation. Meanwhile, changes of

muscle activity in human trunk are also the direction of

future CMC research.

Band Frequencies and Force Levels

The corticomuscular coherence at a certain frequency is a

function of power spectral density (PSD) and cross-spectral

density (CSD), which indicates that frequency bands aﬀect the

CMC amplitude. Oscillations in the beta-range (14–30 Hz)

are explicitly observed in recording EEG from the cerebral

motor cortex (Budini et al., 2014). Signiﬁcant coherence

of beta-range between sensorimotor cortex and contraction

muscles has been reported for the ﬁrst time. Signiﬁcant beta

band coherent activities between the sensorimotor cortex and

contracting muscle were proposed in both monkeys (Baker

et al., 1997) and humans (Conway et al., 1995) around 20 years

ago. Similar oscillations can also be observed in the EMG

of forearm and medial muscles of hand during sustained

contraction (Conway et al., 1995; Baker et al., 2003). More

prominent beta-range rhythmic EMG burst accompanies with

higher CMC. Ushiyama et al. (2011a) described that the

amplitude of CMC was positively correlated with the beta-

range oscillation of EMG signal. Besides, the experimental

data also showed that there was a signiﬁcant correlation

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between the CMC amplitude and the beta-range intensity of

EMG. CMC was noteworthy in both 13–21 and 21–31 Hz

frequency bands in ﬂexors and extensors regardless of subject

group, torque level or direction of contraction (Dal Maso

et al., 2017). In addition, research on alpha-band (8–13 Hz)

and gamma (30–80 Hz) was also being undertaken. Alpha-

range coherence showed advanced EMG reﬂecting ascending

or feedback interactions and gamma-range coherence revealed

delayed EMG activity indicated descending or feedforward

interactions (Mehrkanoon et al., 2014).

Studies in participants with Parkinson’s disease and essential

tremor, however, have observed signiﬁcant coherence between

cerebral cortex and peripheral EMG activities in the alpha-range

and the frequency range of pathological tremor (4–6 Hz) (Hellwig

et al., 2000; Timmermann et al., 2003; Raethjen et al., 2007). In

addition, signiﬁcant peak CMC has been revealed at 8–12 Hz

when healthy subjects imitated Parkinsonian resting tremor at 3–

6 Hz (Pollok et al., 2004). From the present study, it has been

proved that beta band is the focus of CMC research. CMC has

stronger volatility and more obvious amplitude in this frequency

band. However, the CMC amplitude of sport disorder patients at

lower frequencies are easy to be observed.

In current CMC researches, diverse force levels are normally

studied along with diﬀerent band frequencies. Table 2 shows

some researches concerning force and bands. The muscular force

level and the movement type can aﬀect the CMC amplitude

(Lattari et al., 2010). It has been further shown that the level of

CMC increases with the strengthen EMG in healthy individuals

(Kilner et al., 2000), which indicates that muscle output is

dependent on CMC intensity. For example, to investigate the

correlation of CMC in diﬀerent MVC (maximum voluntary

contraction) levels in both static and dynamic task of hand

movement, the participants should reach 4, 8, and 16% MVC

and the result showed that the amplitude of CMC tended to

increase with the force increasing in static task and dynamic

ﬁnger moving task and the CMC mainly concentrated in beta

band (Fu et al., 2014). In the same way, Witte et al. (2007)

also demonstrated a signiﬁcant increase in CMC values of beta-

range from 4 to 16% MVC which was associated with better

performance. Thus, it could be seen that the force input level

is closely related to the CMC frequency band, and prior studies

have indicated that steady force is accompanied by beta-range

CMC (Pfurtscheller and Neuper, 1992; Baker et al., 1997; Halliday

et al., 1998; Kilner et al., 1999; Feige et al., 2000; Mima et al., 2000;

Fu et al., 2014). Besides, studies demonstrated that CMC within

the gamma band can be observed (Brown et al., 1998) during

slow movements (Mima et al., 1999) and phasic movements

for Previous studies also observed that gamma-range CMC has

been related with isometric compensation of low dynamic force

(4% MVC) and a markedly broad-band CMC (15–45 Hz) which

composed of beta- and gamma- range was associated with the

force level (Chakarov et al., 2009). These results show that the

function of beta-range CMC is not limited to low-level steady

forces. In addition, the sensorimotor system may resort to higher

and also extended frequency range of CMC would generate

stable corticospinal interaction during rising force standard

(Lattari et al., 2010).

Furthermore, Lattari et al. (2010) also showed that greater

corrective movements in the 4% MVC condition might reduce

CMC. In line with that, the ﬁndings of Andrykiewicz et al.

(2007) demonstrated that the amplitude of dynamic force did

not modulate the gamma-range CMC, which suggested that

changes in proprioceptive input during dynamic forces in the

range from 1.6 to 4% MVC were insuﬃcient for this modulation.

In view of this, there is an explanation that weakening of cortical-

muscular coupling may be the main neural mechanism induce

to muscle fatigue and associate with performance impairment

(Yang et al., 2009). Rong et al. (2014) found that when grip

force increased, the sensorimotor cortex reduced communication

in gamma band to keep stabilization. Besides, there are also

studies consider that with force increasing, the CMC tends

to shift to gamma-range (Omlor et al., 2007). For patients

with diﬀerent force levels, CMC is also signiﬁcant in a certain

frequency band. For example, in humans with cervical spinal

cord injury, participants had an increased muscle co-activation

associated with a decreased magnitude of the CMC in 10 Hz

with antagonist muscles (Cremoux et al., 2017). Some authors

proposed that lower limbs CMC was signiﬁcantly reduced

in SCA2 (spinocerebellar ataxia type 2) patients compared

to healthy participants during repeated simultaneous ﬂexion

movements of ﬁngers and wrist at a constant contraction level

of 30% MVC (Velázquez-Pérez et al., 2017a).

Corticomuscular coherence is a key measurement to clarify

the neural mechanism which is associated with an individual

TABLE 2 | Diverse force level and bands of CMC.

Reference Force level Bands Sample Signiﬁcant CMC

Hori et al., 2013 1.96 N–3.92 N Alpha/Theta 9 healthy subjects Yes

Budini et al., 2014 20% MVC Alpha 13 healthy subjects Partially

Lim et al., 2014 20% MVC Beta 15 healthy subjects Yes

Ushiyama et al., 2017 30% MVC Beta 22 healthy subjects Yes

Mehrkanoon et al., 2014 target 1: 0.5–0.9 N target 2: 1.1–1.5 N Alpha/Gamma 12 healthy subjects No

Dal Maso et al., 2017 20,40, 60, and 80% of rMVC Beta 10 ST subjects 11 ET subjects CMC decreased

Rong et al., 2014 25 % MGF and 75 % MGF Alpha/Beta/Gamma 14 healthy subjects Alpha/Beta increased

Gamma decreased

Fu et al., 2014 4, 8, and 16% MVC Beta 8 healthy subjects Yes

MVC, maximum voluntary contractions; ST, strength-trained; ET, endurance-trained subjects; rMVC, relative MVC; MGF, maximum grip force.

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ability to stabilize muscle force output. CMC comparison

between individuals with diﬀerent force input level may provide a

deeper understanding of the mechanisms. To sum up, no matter

whether it is healthy subjects or patients, classiﬁcation of force

levels is one of the critical factors aﬀecting CMC amplitude.

Age Correlation

Aging is also associated with neuromuscular changes that can

impair corticomuscular communication (Yoshida et al., 2017).

These changes include decreasing in the recruited motor neurons

(Kawamura et al., 1977a,b; Tomlinson and Irving, 1977) and the

white matter volume of the posterior limbs of the internal capsule

that contain the corticospinal tracts (Good et al., 2001; Salat et al.,

2005). To be exact, previous studies have proposed age-related

reduction in the amplitude of motor evoked potentials (i.e.,

corticospinal excitability) (Eisen et al., 1996) and CMC during

sustained contractions of upper limb muscles (Graziadio et al.,

2010; Bayram et al., 2015).

Age as one of the factors aﬀects corticomuscular

communication during movements should not be ignored.

There was evidence for CMC in all age groups and lager,

more distributed cortical networks in the children and elderly

compared with young adults (Graziadio et al., 2010). James et al.

(2008) compared the CMC among subjects in ages from infancy

to elderly and showed prominent CMC diﬀerences during motor

development in children compared to adults. CMC changed

in functional connection with increasing force output helps to

explain muscle weakness in elderly subjects. It has been reported

that there was a strong link between cortical-muscular coherence

and force output in the elderly individuals during abnormal

walking (Clark et al., 2013). Bayram et al. (2015) investigated the

functional CMC values in the elderly participants by calculating

CMC during voluntary motor performance. The result showed

that the CMC was signiﬁcantly lower in older compared with

young participants at diﬀerent levels of elbow ﬂexion force.

Johnson and Shinohara (2012) investigated the diﬀerences of

CMC between young and older adults during unilateral ﬁne

motor task, concurrent motor and cognitive tasks. They found

that CMC was increasing in older adults with a signiﬁcant

inﬂuence of an additional cognitive task in alpha-range and

young adults with greater beta-range CMC may exhibit more

accurate motor than elderly adults. Besides, beta-range CMC in

the motor cognitive task was negatively correlated with motor

output error across young but not elderly adults. CMC changed

in functional connection with increasing force output could help

explain muscle weakness in elderly subjects.

Task dependency is a critical insight into the eﬀects of aging

on neural activity and motor performance. From previous studies

(James et al., 2008; Graziadio et al., 2010), elderly subjects

usually used simple unilateral tasks that required less awareness

of attention. Beta-range CMC was suggested to be attenuated

with reduced attention to a motor task (Kristeva-Feige et al.,

2002; Johnson et al., 2011). The current research intends to

examine the CMC in elderly adults in view of the importance

of attention to tasks. For example, the alpha-band CMC on

aging was increased with awareness on the task. That is because

signiﬁcant CMC was observed only during attention focusing

or cognitive processing (Kristeva-Feige et al., 2002). Tasks that

require distracting tasks reduce performance and are more

common in elderly adults (Beauchet et al., 2005; Zijdewind et al.,

2006; Voelcker-Rehage and Alberts, 2007; Hiraga et al., 2009).

However, study also found a signiﬁcant negative correlation

between beta-range CMC and EMG variability across multiple

trials which were observed within young adults rather than

elderly adults (Graziadio et al., 2010).

The current research is mainly comparing CMC between

young people and the elderly which employed unilateral tasks and

relatively simple dual tasks. For instance, during unilateral task,

beta-range CMC increased with aging from childhood (0 years

old) to middle age (35 and 59 years old), but not to senior age (55–

80 years old) (Graziadio et al., 2010). Alpha-band CMC during

unilateral task was observed in elderly adults (55–80 years old) in

more cases than in young people (21–35 years old) (Graziadio

et al., 2010). By summarizing the signiﬁcance of age in CMC

on elderly people, we found that age has been gradually valued

as a factor which could aﬀect the magnitude of CMC. However,

for functional signiﬁcance of CMC, future study requires more

awareness of attention to the comparison of CMC between young

adults and elderly under the condition of bilateral complex tasks.

Healthy Controls and Patients

To research the eﬀects of corticomuscular coupling on motor

injury and the possibility of clinical practice of CMC, some

studies compare CMC between healthy subjects and patients

with dyskinesia (i.e., stroke, Parkinson). Table 3 displays the

comparison of healthy controls and patients of CMC. In terms

of signiﬁcant areas, the evaluation of CMC strength of healthy

controls and patients provides evidence that corticomuscular

coupling could apply in the rehabilitative evaluation of dyskinesia

(Gao et al., 2017). CMC was implemented early in the Parkinson’s

disease course which subsequent symptomatic relief with L-Dopa

by CMC modulation (Salenius et al., 2002; Mckeown et al., 2006;

Pollok et al., 2012). Usually, the CMC amplitude of patients

on the aﬀected side is lower than that of healthy subjects.

For example, beta-range CMC was reduced dramatically in

Amyotrophic Lateral Sclerosis (ALS) patients compared with

healthy subjects during light voluntary muscular contraction

(Proudfoot et al., 2018). Similarly, CMC was signiﬁcantly lower

in stroke patients compared with healthy participants for the

anterior deltoid and brachii muscles at both beta (20–30 Hz)

and lower gamma (30–40 Hz) ranges during the movement

(Fang et al., 2009). Mima et al. (2001) and Fang et al. (2009)

pointed that the functional coupling between cortex commands

and corresponding muscular activities of stroke subjects was

weaker than healthy subjects. Riquelme et al. (2014) investigated

CMC during planning and execution of isotonic contractions

in cerebral palsy (CP) patients and healthy subjects. The result

showed that CP patients group displayed longer EMG onset

latency and duration than healthy group and CMC in beta band

of EEG was overall greater in CP than that in healthy controls.

CMC in gamma-range was lower in CP group than healthy

group, and brain functioning during movement initiation was

altered in CP only at the beginning of muscular contraction.

CMC is normally restored in patients with motor function

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TABLE 3 | Health controls and patients of CMC.

Reference Type Sample CMC result

Gao et al., 2017 EEG-EMG

EMG-EEG

7 healthy controls

5 stroke patients

Patients had lower CMC than healthy subjects

Velázquez-Pérez et al.,

2017a

EEG-EMG 24 healthy controls

19 SCA2 patients

Lower limbs CMC was signiﬁcantly reduced in SCA2

patients as compared to healthy participants.

Shariﬁ et al., 2017 EEG-EMG 18 healthy controls

18 essential tremor patients

CMC remained a relatively high level in healthy subjects.

CMC level frequently dropped below the conﬁdence level in

patients.

Riquelme et al., 2014 EEG-EMG 15 healthy controls

14 CP patients

CMC in gamma-band was lower in CP than in healthy

controls

Proudfoot et al., 2018 EEG-EMG 17 healthy controls

17 ALS patients

Beta-band CMC was signiﬁcantly reduced in ALS patients

compared to healthy controls.

Fang et al., 2009 EEG-EMG 8 healthy subjects

21 stroke patients

Stroke patients had signiﬁcantly lower CMC compared with

healthy subjects for the anterior deltoid and brachii muscles.

EMG, electromyography; EEG, electroencephalogram; SCA2, spinocerebellar ataxia type 2; CP, cerebral palsy; ALS, amyotrophic lateral sclerosis.

recovery. One study reported that motor deﬁcits secondary

to acute stroke were attendant by a unilateral reduction in

CMC (Nielsen et al., 2008), but the CMC magnitude was

normalized with favorable functional recovery (Proudfoot et al.,

2018). Research also demonstrated that the CMC strength

was increasing with the restoration of motor function of

the paretic limb. The measurement of CMC can reﬂect the

recovery of motor function after stroke through quantifying

interactions between the motor cortex and controlled muscle

activities (Zheng et al., 2017).

Although relatively obvious diﬀerences on CMC analysis

between patients and healthy individuals could be displayed

in some studies, the consequences of CMC magnitude are

varying. For instance, the signiﬁcant CMC was only reported

in a selection of patients (Hellwig et al., 2001), which indicated

that the participation of the cortex in patients was not robust

(Raethjen et al., 2007). Stroke patients manifest much more

dispersive extent of cortical locations for peak CMC than healthy

subjects, which purpose to keep with plastic reorganization of

sensorimotor function (Rossiter et al., 2013; Farmer et al., 1993).

In addition, it has been proved that CMC strength is modiﬁed

in healthy subjects after immobilization (Lundbye-Jensen and

Nielsen, 2008) or in neurological conditions such as essential

(Muthuraman et al., 2010), neuropathic tremor (Weiss et al.,

2010) and Parkinson disease (Weiss et al., 2012). Diﬀerent choice

of patients, analysis techniques and recording methods, types

of sport duties, and possibly cognitive state (e.g., awareness

of tremor) might be explained the reasons for inconsistent

results (Shariﬁ et al., 2017). Through the comparative study

of CMC between healthy controls and patients, we can ﬁnd

the potential clinical application of CMC, and the most direct

application is motor rehabilitation. However, most of the current

comparative studies could not give a quantitative index of CMC.

There is only a simple comparison of CMC values between

healthy controls and patients. If the CMC is to be clinically

applied in the future, a more detailed classiﬁcation of the

aﬀected CMC in patients with diﬀerent movement disorders

must be discussed and using CMC as a characteristic value

to achieve a uniﬁed clinical measurement standard should

also be studied.

CMC APPLICATIONS

As mentioned in the section of factors aﬀecting CMC, the

application and development trend of CMC should be the clinic

rehabilitation of patients with sports disorders, even though most

of the current CMC studies are still limited to the laboratory.

The latest CMC studies focus on the types of patients, including

stroke, Parkinson, tremor, etc. In the present review, a detailed

overview of the current applications of CMC for patients with

various motor disorders was provided.

CMC Applications for Stroke Patients

For stroke patients at diﬀerent stages, the performance of

muscle contraction can be approximated as an indicator of

stroke rehabilitation level. Therefore, it is extremely common

to try CMC experiments in stroke patients. Table 4 shows

the correlations of CMC and stroke. Normally, muscle atrophy

in stroke patients cause a decrease in CMC. Some studies

have also conﬁrmed that stroke patients had dramatically

lower CMC compared with healthy subjects for the anterior

deltoid and brachii muscles (Fang et al., 2009). Similar result

of restored CMC was also reported with well recovered

patients with both Transcranial Magnetic Stimulation (TMS)

and Magnetoencephalography (MEG) investigation (Braun et al.,

2007). Rossiter et al. (2013) discovered that peak CMC in the

contralesional hemisphere was found both in highly impaired

patients and stroke patients with good functional recovery.

This discovery provides evidence directly that brain regions

in the contralesional hemisphere are participated in activities

with the aﬀected muscles in stroke patients. Zheng et al. (2017)

demonstrated that the recovery level of motor function after

stroke could be reﬂected by the measurement of CMC by

quantifying interactions between the motor cortex and controlled

muscle activities. Graziadio et al. (2012) proposed that the

degree of global recovery after unilateral stroke in the chronic

phase correlated with the degree symmetry achieved between the

interdependent lesioned and non-lesioned corticospinal systems

at CMC level. In addition to evaluation as rehabilitation indicator

during the recovery of the stroke, CMC was also applied to

distinguished types of stroke patients. Study investigated CMC in

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TABLE 4 | Correlations of CMC and Stroke.

Reference Number of Patients Stroke Type CMC value (Patients vs Controls)

Zheng et al., 2017 1 Hemorrhage Peak CMC in Beta Band (only patients)

Fang et al., 2009 21 17/21 Ischemia 4/21 Hemorrhage Patients < Controls

Gao et al., 2017 5 1/5 Ischemia 4/5 Hemorrhage Patients > Controls

Rossiter et al., 2013 25 Patients < Controls

von Carlowitz-Ghori et al., 2014 11 Ischemia Patients < Controls

Mima et al., 2001 6 Patients < Controls

Larsen et al., 2017 19 Ischemia Patients < Controls

Pan et al., 2018 12 ES CMC > sham ES CMC (only patients)

Chen et al., 2018 8 5/8 Ischemia 3/8 Hemorrhage Patients < Controls

Belardinelli et al., 2017 8 3/8 Ischemia 5/8 Hemorrhage Peak CMC in Beta Band (only patients)

ES, electrical stimulation.

the chronic and acute stroke through following up the recovery

courses, the results indicated CMC amplitude was increased

on the unaﬀected side and CMC frequency was decreased on

the aﬀected side in acute stroke, however, there was no inter-

hemispheric diﬀerence in CMC parameters of the chronic stroke.

The dynamical changes of interaction between cortical cortex

and muscle both at acute and chronic stage of stroke may be a

characteristic parameter for clinical application of CMC.

CMC Applications for Parkinson Patients

Parkinson’s disease (PD) is related to pathologically alter

oscillatory activity (Krause et al., 2013). Table 5 shows the

correlations of CMC and Parkinson disease. CMC as a neuro-

physiological indicator of functional coupling between the

primary motor cortex (M1) and peripheral muscles (Hari and

Salenius, 1999; Krause et al., 2013) was applied as an index for

PD symptoms variation early. Airaksinen et al. (2015b) found

CMC decreased when they investigated defective cortical drive

to muscle in PD. Similarly, CMC is a therapeutic indicator, PD

patients had anomalously weak CMC after levodopa treatment

during isometric contraction (Krause et al., 2013). In the

recent Parkinson study, deep brain stimulation (DBS) of the

subthalamic nucleus (STN) has the eﬀects of improving motor

symptoms and normalizing pathologically altered oscillations

and applied to trace the rehabilitation of Parkinson patients

with CMC. For example, STN-DBS was increasing the CMC

amplitude of 10–30 Hz range for the tremorous hand because of

the improvement of tremor by DBS (Park et al., 2009). Similar to

this result, a slight increase of CMC during DBS was observed

in eight patients on the average of 8 days studied after DBS

implantation (Weiss et al., 2012). In addition, Airaksinen et al.

(2015b) also showed DBS improved the CMC in advanced PD

with large interindividual variability. Despite the diﬀerences in

research results, it can be considered that CMC may be associated

with the therapeutic eﬀects of DBS. Similar to DBS, transcranial

alternating current stimulation (tACS) can modulate cortical

brain activity, some researchers were combined with tACS to

study the CMC of PD patients. Study showed that decreased beta-

range CMC and variability of fast lateral movements were due to

motor cortex tACS at 20 Hz in PD patients (Krause et al., 2013).

CMC Applications for Tremor and Other

Patients

In addition to stroke and Parkinson diseases, CMC may be

possible regarded as an index to value some other movement

disorders. Table 6 shows the correlations of CMC and other

diseases. Tremor is one of the most common disorders. To

conﬁrm the motor cortex involved in essential tremor and

factors that aﬀect CMC strength, Shariﬁ et al. (2017) collected

18 essential tremor patients and the result showed that essential

tremor CMC is desultory and subject to diﬀerent functional

duties. This result may serve to standardize tremor classiﬁcation

and the explanation of the analysis in clinical research. Proudfoot

et al. (2018) aimed to measure pathological alteration to CMC

resulting from ALC during steady force production. During

light voluntary muscular contraction, beta-range CMC was

dramatically reduced in ALS patients and propagation of motoric

rhythms across the cortical cortex was also impaired. Velázquez-

Pérez et al. (2017b) purposed to assess dysfunction of the

corticospinal tract in spinocerebellar ataxia type 2 (SCA2) using

CMC. Signiﬁcant reductions of CMC in SCA2 patients showed an

evidence of corticospinal tract dysfunction. The abnormal CMC

TABLE 5 | Correlations of CMC and Parkinson.

Reference Number of Patients Stimulation CMC value (Patients vs Controls)

Airaksinen et al., 2015b 19 yes DBS modiﬁes patients’ CMC (only patients)

Krause et al., 2013 10 yes Patients < Controls

Yoshida et al., 2017 10 no Patients < Controls

Pollok et al., 2012 20 no Patients < Controls

DBS, deep brain stimulation.

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TABLE 6 | Correlations of CMC and other diseases.

Reference Number of Patients Disease Type CMC value (Patients vs Controls)

Shariﬁ et al., 2017 18 ET Patients < Controls

Raethjen et al., 2013 37 ET Patients > Controls

Hellwig et al., 2001 10 7/10 ET 3/10 EPT Signiﬁcant CMC at the tremor frequency in ET patients (only patients)

Velázquez-Pérez et al., 2017b 19 SCA2 Patients < Controls

Velázquez-Pérez et al., 2017a 15 SCA2 Patients < Controls

Proudfoot et al., 2018 17 ALS Patients < Controls

Cremoux et al., 2017 8 SCI Patients < Controls

Bravo-Esteban et al., 2014 14 SCI Patients < Controls

ET, essential tremor; EPT, enhanced physiological tremor; SCA2, spinocerebellar ataxia type 2; ALS, amyotrophic lateral sclerosis; SCI, spinal cord injury.

could only be detected in lower limbs experiments rather than

upper limbs experiments may result from the corticospinal tract

length on the chrono dispersion of action potential conduction.

Cerebral palsy (CP) is a motor impairment which could aﬀect

the muscular contractions and neural connections between

motor cortex and relative muscle. Many researches indicated

CP inﬂuenced the normal muscular activities such as reduced

voluntary-contraction force (Barber et al., 2012; Braendvik and

Roeleveld, 2012; De et al., 2012). Riquelme et al. (2014) compared

CMC during planning and execution of hand movements in CP

patients and healthy subjects. From the results, CP patients were

characterized by an altered functional coupling through CMC

analysis and CMC may consider as a tool for exploring deﬁcits

during early brain damage.

Current literatures show that the applications of CMC in

patients are simply regarded as a pathological indicator and are

not clearly deﬁned as clinically reliable parameters. The statistical

comparison of diﬀerent disorders use for the CMC study was

collected form the published CMC papers as Figure 3 shown.

This reﬂects that Stroke, Parkinson and Tremor are the top three

diseases that be used to study CMC and explore the physiological

variations during patients’ rehabilitation process. Rehabilitation

diagnosis and treatment system based on CMC is the direction

FIGURE 3 | Corticomuscular coherence application areas distribution for the

disorders.

of future research. In addition, the design of CMC paradigm for

patients with diﬀerent diseases needs careful consideration.

DISCUSSION

The aim of this study is to explore the impacts of corticomuscular

coherence and how these impacts aﬀect the cortex-muscle

coherence. The experimental paradigms almost include all parts

of human body to study whether the coupling strength between

the relevant cortex and muscles would provide potential values

and contributions. In this research, the ﬁndings appropriately

display the CMC in diverse parts have the eﬀects on recovery

monitoring, motion changing and otherwise. Experimental

protocol is a signiﬁcant cause to inﬂuence the results of CMC.

Muscle contraction forms and muscle fatigue are the most

important limit conditions during experiments (Siemionow et al.,

2010; Ushiyama et al., 2010, 2011b; Bayraktaroglu et al., 2011).

At the same time, similar oscillations may be detected from

adjacent muscles result in motion decoding confusion, thus

discrimination of body parts in CMC has a potential research

signiﬁcance. Oscillatory activities in both cortex and muscles

are commonly appeared in diﬀerent band frequencies (alpha-

band, beta-band and gamma-band) (Muthukumaraswamy, 2011;

Schoﬀelen et al., 2011). Alpha-band and beta-band CMC

contribute more to actual motor function, while the peak CMC

is usually observed within beta-band in healthy subjects and

within alpha-band in functional disorder patients (Caviness

et al., 2006). Furthermore, in some ﬁndings, alpha-band CMC is

related to precise control of movements like ﬁnger movements

and for steady isometric or isotonic contractions, beta-band

normally associates with these kind of movements (Omlor

et al., 2011). In general, diverse band frequencies in CMC

represent diﬀerent modes of neural communication between

cerebral cortex and spinal cord. Muscle activation is modulated

by cortical activity which may result in voluntary contractions.

Underlying the co-activation of antagonist and agonist muscles,

reducing cortical inﬂuences on inhibiting antagonist muscles

is supposed to increase the muscles co-activation. Maximum

voluntary contractions (MVC) is a standard to limit force level

during the experiments in common use. With the force level

increasing, the magnitude of corticomuscular coherence seems

to enhance and muscles co-activation would decrease and these

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situations commonly appear in beta-band frequency rather than

other band frequencies. The law of aging in CMC is also found. As

age increases, the CMC would decrease gradually. However, the

studies of aging are less than other factors correspondingly. To

compare the CMC between healthy subjects and patients, healthy

subjects have a higher CMC level than patients on account of

nervous transmission damage (Patino et al., 2008; Meng et al.,

2009). Meanwhile, peak CMC for healthy subjects emerges in

beta-band while for patients, that shows in alpha-band.

In the present study, researchers are inclined to emphasize

variance of corticomuscular coherence underlying distinct force

levels and frequency bands (alpha, beta and gamma bands).

Basically, a majority of researches of CMC would be able

to consider band eﬀects especially beta band and the eﬀects

of diﬀerent bands in CMC have been studied thoroughly.

CMC researches currently aim to seek a relationship or a

correlation between CMC and other responsible factors which

may modulate the amplitude of CMC. The challenges in the

future focus on a greater depth of understanding the relationship

between cortical and muscular activities and the applications in

rehabilitation ﬁeld and clinical ﬁeld. The motion information

decoding underlying the in-depth study of functional mechanism

and CMC is possible to detect voluntary hand movements

and more accurate than EEG only classiﬁcation (Lou et al.,

2013). If CMC is able to become a standard of motion

decoding, it will eventually help exploit a new rehabilitation

protocol. In general, the variance in CMC possible is related

to communication issue between motor cortex and relative

muscles. For instance, patients of cerebral lesion commonly have

lower and intermittent CMC compare to healthy subjects, so

they could not present a desirable movement since the motor

impairment aﬀects muscular contractions. Therefore, the further

study, such as force controls or task complexity in both upper

and lower limbs, would provide a precise CMC modulation

protocols and a large number of experimental data basis for

neuromuscular disorders. Furthermore, in rehabilitation ﬁeld,

experimental data basis might be beneﬁt to establish a more

reasonable and advanced rehabilitation programs for paralyzed

patients and the improvement of CMC analysis could also

provide a monitoring during the rehabilitation process. On the

contrast, CMC monitoring could not only be used in patients’

rehabilitation processing but also be applied in healthy inspection

for the healthy person. If CMC has an abnormal change during a

period, there may be a risk of corticospinal tract degeneration and

a timely diagnosis is helpful in the prevention of such diseases.

Comparing with CMC as a physiological index, many other

metrics have been used for disorder detection. EMG amplitude

and EMG median power frequency are usually good indicators of

fatigue in multiple sclerosis (Tomasevic et al., 2013). Functional

neuroimaging techniques such as fMRI, TMS and PET have

been used to assess neural correlates of motor impairment and

recovery over the past decades. Rehme et al. (2012) discovered

that patients with stroke showed more task-related brain

activation in both the aﬀected and the unaﬀected hemisphere

from PET and fMRI assessments. Volz et al. (2015) combined

TMS, MRI, and connectivity analyses to investigate corticospinal

tract (CST) injury in patients. Cortical excitability and motor

network were eﬀective connectivity for hand function recovery

in chronic stroke patients.

Bourguignon et al. (2015) proposed that corticokinematic

coherence (CKC) to reﬂect coupling between magnetoencephalo-

graphic (MEG) signals and hand kinematics. It provided a

reliable tool to monitor proprioceptive input to the cortex

(Bourguignon et al., 2015). Intermuscular coherence (IMC)

could quantify the strength of the coupling between cortex

and the muscles. It was related to CMC in the beta band

(Kilner et al., 1999) and reduced in the acute phase after

stroke (Larsen et al., 2017). Dal Maso et al. (2018) explored

correlations between event-related desynchronization (ERD),

functional connectivity (FC) and CMC and skill retention,

and suggested that cardiovascular exercise initiates signiﬁcant

changes in FC and CMC during motor memory consolidation

(van Wijk et al., 2012). These metrics could be used as indicators

of physiological activities through various forms of measurement,

more or less relevant to the CMC.

Functional coupling between the motor cortex and muscle

activity usually occurs with a time delay, which reﬂects signal

propagation time between the brain and the muscle and

information interaction (Xu et al., 2017). Perfect coherence

(without temporal lags) does not exist which is only an

ideal hypothesis in theory since multiple features inﬂuence

the delays estimated using corticomuscular coherence, such as

extra delays caused by the motor unit action potential, the

duration of the corticomotoneuronal excitatory postsynaptic

potential (EPSP), and a phase advance produced by motoneuron

properties (Williams and Baker, 2008). The variety of temporal

lags is the limitation of CMC as a physiological index.

Some authors improved CMC by estimating the delay time.

Govindan et al. (2005) used the method of maximizing

coherence to obtain the time delay between two signals

that were suitable for time delay estimation of narrow

band coherence signals. Xu et al. (2017) proposed a CMC

with time lag (CMCTL) function, which was the coherence

displaced from a central observation point between segments

of motor cortex EEG and EMG signals, and showed that

it enhanced the CMC level and provided a more depth

information on the temporal structure of CMC interaction than

traditional CMC.

In summary, CMC research is still in a relatively early

stage. Further exploration is needed in application, not only in

rehabilitation and clinic for patients, but also in development of

physical mechanism for healthy subjects.

CONCLUSION

Corticomuscular coherence is a method to evaluate the coherence

ability between motor cortex and muscles. For a more

comprehensive understanding of the mechanism of CMC, the

comparison between related factors shows that the peak CMC

amplitude has a great probability to emerge under relatively high

force level, beta frequency band. As age increases, CMC decreases

under various degrees, which is also in line with the natural trend

of muscle aging. The amplitude of CMC in healthy subjects is

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Liu et al. Corticomuscular Coherence and Applications

higher than that in patients in most cases. However, with the

recovery of motor function of patients, CMC levels usually return

to normal condition.

Current applications of CMC in patients is simply regarded

as a pathological indicator and is not clearly deﬁned as clinically

reliable parameters. Further investigation is needed for a more

complete understanding of enhancing CMC. Considering the

use of diﬀerent forms of muscle contraction to achieve superior

results, scientiﬁc and reasonable paradigms are arranged to

realize the target. Meanwhile, accurate classiﬁcation of CMC on

the aﬀected side is needed to make CMC as an indicator in

clinical application.

AUTHOR CONTRIBUTIONS

JL and YS wrote the body content and reviewed the whole article.

HL reviewed the whole article and decided the ﬁnal version.

FUNDING

This work was supported by the National Natural Science

Foundation of China (Nos. 51575338, 51575407, 51475427, and

61733011) and the Fundamental Research Funds for the Central

Universities (17JCYB03).

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Conﬂict of Interest Statement: The authors declare that the research was

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be construed as a potential conﬂict of interest.

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