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Movement-related cortical potentials during muscle fatigue induced by upper limb submaximal isometric contractions Feng Guoa,b, Ji-Ya Wangb, Yong-Jun Sunc, A-Li Yangc and Ri-Hui Zhanga,b The aim of this study was to examine the central neurophysiological mechanisms during fatigue induced by submaximal isometric contractions. A total of 23 individuals participated in the study and were assigned to fatigue and nonfatigue groups. Handgrip force, root mean square (RMS) of surface electromyography (sEMG) signal and movementrelated cortical potentials during self-paced submaximal handgrip isometric contractions were assessed for each participant. The experimental data showed significant decreases in both maximal voluntary contraction [ − 24.3%; F(3, 42) = 19.62, P < 0.001, ηp2 = 0.48] and RMS [ − 30.1%; F(3, 42) = 19.01, P < 0.001, ηp2 = 0.57] during maximal voluntary contractions and a significant increase [F(3, 42) = 14.27, P < 0.001, ηp2 = 0.50] in the average RMS of sEMG over four blocks in the fatigue group. There was no significant difference in the readiness potential between the fatigue and the nonfatigue groups at early stages, and at late stages, significant differences were observed only at the Fp1 and FC1 sites. Motor potential amplitudes were significantly higher in the fatigue group than in the nonfatigue group irrespective of block or electrode positions. Positive waveforms were observed in the prefrontal cortex in states without muscle fatigue, whereas a negative waveform pattern was observed with muscle fatigue. Significant within-subject correlations were
observed between motor potential at the C1 site and RMS of sEMG (r = − 0.439, P = 0.02, ηp2 = 0.11). Neurophysiological evidence indicates that cortical activity increases in the prefrontal cortex, primary motor cortex and supplementary motor cortex with muscle fatigue. Muscle fatigue appears to have considerable effects on the components of movement-related cortical potentials during movement execution, whereas the readiness potential before movement is sensitive to cognitive demands during prolonged exercise. Our results provide additional evidence for a link between central motor command during movement execution and motor unit recruitment. NeuroReport 25:1136–1143 © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins.
Introduction
planning, preparation and initiation of conscious movement [9]. Some researchers have observed that the RP amplitude increases during the fatigue task of repetitive contractions; this increase was proposed to be an attempt by the brain to compensate for peripheral fatigue [3,10, 11]. By contrast, another study observed a decrease in the amplitude of MRCPs during muscle fatigue [12], which can be explained by a decrease in intention and habituation processes. However, Schillings et al. [11] reported that prolonged exercise can induce an increase in RP during repetitive contractions because of the cognitive demands of a physical task, independent of muscle fatigue. Thus, the choice of experimental design for the study of repetitive contractions may have a huge effect on the findings obtained on the effects of muscle fatigue on MRCP components. Studies of the effects of exerciseinduced fatigue on various components of MRCPs have not yielded consistent results and have offered inconsistent explanations for their results. In addition, these studies have focused primarily on the motor cortex and less on the prefrontal areas. It has been proposed that the prefrontal cortex is the brain region that formulates
Fatigue has both mental and physical aspects. Physical fatigue refers to difficulty sustaining maximal voluntary contraction (MVC) or target force [1]. The decrease in force production may originate from different sites within the neural axis, including the sarcolemma, the neuromuscular junction, the spinal cord or the cortex [2]. Thus, muscle fatigue results not only from peripheral processes within the working muscle but also from central processes in sensorimotor pathways. Electroencephalography (EEG) and functional MRI have been used to explore cortical processing during fatigue tasks [2–7]. One approach to study the central mechanisms of fatigue is to examine movement-related cortical potentials (MRCPs) extracted from EEG. The concept of a Bereitschaftpotential or readiness potential (RP), which is a premovement component of MRCPs for self-initiated movements, was originally introduced by Kornhuber and Deecke [8]. The RP typically contains three components: the early RP, the late RP and the motor potential (MP) [8]. These cortical electrical activities reflect the 0959-4965 © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
NeuroReport 2014, 25:1136–1143 Keywords: fatigue, movement-related cortical potentials, neurophysiology a Department of Physiology, College of Basic Medical Sciences, Jilin University, Jilin, bCollege of Human Kinesiology and cDepartment of Physical Education, Shenyang Sport University, Shenyang, China
Correspondence to Feng Guo, PhD, Department of Physiology, College of Basic Medical Sciences, Jilin University, 828 Xinmin Street, Changchun, 130021 Jilin, China Tel: + 86 024 89166557; fax: + 86 024 89166686; e-mail: [email protected] Received 26 April 2014 accepted 8 July 2014
DOI: 10.1097/WNR.0000000000000242
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MRCPs during muscle fatigue Guo et al. 1137
intentions and makes decisions before movements are initiated [13–15]. Thus, activity in the prefrontal cortex may produce significant changes during muscle fatigue. Only Berchicci et al. [4] and Hilty et al. [7] have reported activity in the prefrontal cortex during muscle fatigue, which, in both studies, involved the large muscle masses in the lower limbs. Moreover, Hilty and colleagues reported the spectrum characteristics of the prefrontal cortical EEG, but did not examine MRCP components. In the present study, we sought to explore the central mechanisms of muscle fatigue. Force, surface electromyography (sEMG) and EEG measurements were used to study changes in MRCP components in relevant motor areas and in the prefrontal cortex during fatiguing tasks induced by self-paced submaximal handgrip contractions involving small muscle masses in an upper limb. To study the effect of muscle fatigue on MRCPs, we used a control group to further clarify the roles of different MRCP components during a fatiguing task of repetitive contractions.
Materials and methods Participants
A total of 32 healthy, right-handed individuals (determined by self-report) participated in the study. However, data from nine participants were discarded because of significant EEG artefacts; thus, the final data studied were from 23 participants (fatigue group: 15 participants, age 23.4 ± 1.1 years; nonfatigue group: eight participants, age 22.1 ± 1.7 years). All participants were nonathlete students in university with no exercise experience. The experimental procedures were approved by the Institutional Review Board at Shenyang Sport University. All participants provided written, informed consent before their participation in the study.
Experimental protocol
The 15 participants in the fatigue group performed 200 intermittent voluntary handgrip contractions of the right arm at ∼ 30% MVC in a single session. Each contraction lasted ∼ 6 s, followed by an ∼ 4-s intertrial rest. The 200 contractions were divided into four blocks; each block comprised 50 trials. The MVC was measured during a brief rest between blocks of less than 1 min. Each participant maintained a force level of 30% MVC by observing real-time visual force feedback presented on a computer screen. The eight participants in the nonfatigue group also performed 200 handgrip contractions at the same force level of 30% MVC, but with a longer intertrial rest of ∼ 8 s. To obtain a higher signal-to-noise ratio for the analysis of the MRCPs, the 200 intermittent contractions were divided into two large blocks: early blocks (i.e. blocks 1 and 2) and late blocks (i.e. blocks 3 and 4).
Procedures
Participants sat comfortably on a chair 90 cm away from a computer screen on a table. Each participant’s forearm rested on the arm of the chair while his or her hand was maintained in a position of 90° pronation. All participants were allowed to become familiar with the task, particularly with the level of force required and were asked to avoid eye movements before contractions. MVCs were determined before the formal experiment and after each block. To achieve MVCs, the participants were asked to push against a load cell as hard as possible for ∼ 5 s. To determine the initial MVC, participants performed three trials with rest intervals of 3 min between trials and the largest of the MVCs was selected. However, to reduce the effects of fatigue on MVCs, MVCs were tested only once during each interblock interval. Force measurement and analysis
Handgrip force was measured using a force transducer. For each MVC trial, a mean value was calculated from the data points that represented relatively stable MVC force values. The force and sEMG values of individual MVC trials were calculated after each block. Then, the averaged MVC force and sEMG data were normalized to the corresponding initial MVC values. Finally, the normalized data were averaged across participants. Surface electromyography measurement and analysis
sEMG data for the flexor digitorum profundus were recorded using Neuroscan equipment (Neuro-Scan, El Paso, Texas, USA) (sampling rate: 1000 Hz, bandpass: 0.1–200 Hz). To obtain the recordings, electrodes were placed 1–2 cm medial to the ulna and 3–5 cm distal to the elbow joint because a previous study [16] suggested that better sEMG signals from the flexor digitorum profundus muscle can be obtained at this location. The sEMG data of each muscle trial were rectified by the root mean square (RMS) with a 0.1-s time window and then averaged over the same time period as the force. Electroencephalographic measurement and analysis
Continuous EEG signals were recorded using a 64-channel Neuroscan SynAmps system (version 4.5; Neuro-Scan), with the right mastoid (M2) used as the reference. The ground electrode was located between the FPz and the Fz electrodes. The EEG signal was digitized with a sampling rate of 1000 Hz and amplified with a bandpass from 0.01 to 100 Hz. Horizontal and vertical eye movements were monitored by bipolar recordings. The impedance of all electrodes was maintained below 5 kΩ by adjusting the amount of conductive gel (Quick-Gel; Compumedics Inc., Charlotte, North Carolina, USA) inside the electrodes. EEG signals were analysed offline using Scan 4.5 software (Compumedics Inc.). Raw EEG data were
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inspected manually and epochs with marked EEG artefacts were deleted. The channels with electrooculogram (EOG) artefacts were then corrected using a correlation method [17] in the ocular artefact reduction option of the Scan 4.5 software. With respect to each contraction trial, the sEMG onset was defined as the zero time, in accordance with previous studies [8,18]. Continuous EEG data that had been subjected to primary processing were divided into 4000-ms epochs comprising − 2000 to 2000 ms relative to sEMG onset. For each trial, the baseline was derived by calculating the mean amplitude of all epochs during the initial 500 ms from − 2000 to − 1500 ms. To further reduce the noise level, epochs were filtered with a low-pass cutoff frequency of 30 Hz. Artefacts were then rejected from the filtered epochs with a threshold of ± 80 μV. MRCPs were obtained by averaging the artefact-free epochs. Finally, MRCPs were again re-referenced to the average. Two components of the MRCPs were calculated using approaches on the basis of previous studies [4,16,18]: the RP was calculated as the mean amplitude from − 1500 to − 100 ms relative to sEMG onset and the MP was computed as the average peak amplitude of the MRCPs in the time window from 0 to 150 ms after sEMG onset. These components reflect the distinct stages of preparation for and execution of movement [4,9,18].
Statistical analysis
Statistical tests were performed using the software Statistical Program for the Social Sciences (SPSS; IBM, Austin, Texas, USA) 16.0. Force (MVC) and sEMG (RMS) were analysed as dependent variables using a twoway (2 × 4) mixed-model analysis of variance (ANOVA) with respect to group (two levels: fatigue vs. nonfatigue as the between-subject factor) and block (four levels: block 1 vs. block 2 vs. block 3 vs. block 4 as the withinsubject factor). MRCP amplitudes during the course of a physical task were statistically analysed using a general linear mixed model for 2 × 2 × 2 × 3 × 3 ANOVA, with two groups (fatigue and nonfatigue), two blocks (early and late), two epochs of MRCPs (RP and MP) and nine electrode locations, with Fp1, FPz, Fp2, FC1, FCz, FC2, C1, Cz and C2 grouped into coronal planes (three levels: prefrontal, frontocentral and central) and sagittal planes (three levels: ipsilateral, midline and contralateral). For repeated-measures ANOVAs, if the sphericity test was violated, the degrees of freedom were adjusted by the Greenhouse–Geisser correction, which can reduce the risk of type I error. The significance level was set at an α value of 0.05 after the Greenhouse–Geisser correction. In such cases, the uncorrected degrees of freedom, ε values and corrected probability levels are reported. To compare two means, the adjusted least significant difference test was performed. Furthermore, the effect size, which provides an estimate of the extent of association
for the sample, was measured as partial η2 (ηp2) and reported. Correlation analysis was carried out by calculating withinsubject correlation coefficients between sEMG and MRCP amplitudes across blocks during muscle fatigue using the method of Bland and Altman [19]. This method adjusts for the effects of examining variation within participants using an analysis of covariance. The significance was set at 0.05.
Results Force and surface electromyography
For MVCs, there was a significant block × group interaction [F(3, 63) = 11.47, ε = 0.72, P < 0.001, ηp2 = 0.35]. The simple effect indicated that the handgrip task induced a significant reduction in both % MVC force [F(3, 42) = 19.62, ε = 0.72, P < 0.001, ηp2 = 0.48] and the RMS sEMG [F(3, 42) = 19.01, P < 0.001, ηp2 = 0.57] between blocks in the fatigue group, whereas no statistically significant difference was observed in the nonfatigue group (Fig. 1a and b). The results of univariate tests showed that significant differences between the fatigue and nonfatigue groups in % MVC force and sEMG activity occurred after block 2 (Fig. 1a and b). In addition, in the fatigue group, the average RMS over each block significantly increased throughout the physical task [F(3, 42) = 14.27, ε = 0.77, P < 0.001, ηp2 = 0.50]; however, no significant effect of block was observed for the nonfatigue group (Fig. 1c). Movement-related cortical potential waveforms
No quintuplicate or quadruple interactions were identified in five-way mixed-model ANOVAs of MRCP amplitudes. However, quadratic and triple interactions were observed (Table 1). In the present study, the interactions correlated with group were further analysed. In general, MRCP amplitudes increased with muscle fatigue progression (Fig. 2). The simple effect of block on the level of fatigue and nonfatigue is shown in Table 2. Because of the presence of block × group interactions, further analyses were carried out, which showed that there were no significant differences in the RP amplitude across blocks at all electrodes in the nonfatigue group, whereas in the fatigue group, the RP amplitude was larger at the late blocks, as muscle fatigue increased, than at the early blocks at each electrode [F(1, 14) = 58.30, P < 0.001, ηp2 = 0.73]. The MP amplitude was considerably larger at late blocks than at early blocks in the fatigue group [F(1, 14) = 20.46, P < 0.001, ηp2 = 0.49], and these significant differences occurred at each electrode. The results of univariate tests for the simple main effect of group on RP and MP amplitudes are shown in Table 3. Further analysis showed that at the early stages of the fatigue task, significant differences [F(1, 21) = 5.62, P = 0.02, ηp2 = 0.21] between the fatigue and the nonfatigue groups occurred only at the Fp1, FC1,
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MRCPs during muscle fatigue Guo et al. 1139
Fig. 1
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Maximal voluntary contraction (MVC) force (a), root mean square (RMS) of the surface electromyographic signal during MVCs (MVC-RMS) (b) and average RMS across each block (c) in the fatigue and nonfatigue groups. *Significant main effect of group (P < 0.001); #significant main effect of block (P < 0.001); Δinteractions of block × group. The data are presented as mean ± SD.
Table 1
Five-way mixed-model ANOVAs for MRCP amplitudes
Effects COR (coronal electrodes) SAG (sagittal electrodes) Block Epoch Group COR × epoch SAG × epoch Block × group Epoch × group COR × SAG SAG × COR × group SAG × COR × epoch
F
ε
P
ηp
65.83 14.02 8.96 57.03 9.55 30.51 28.44 15.04 8.26 4.29 2.55 5.88
0.69 0.87 1.00 1.00
< 0.001* < 0.001* 0.007* < 0.001* 0.006* < 0.001* < 0.001* 0.001* 0.009* 0.014* 0.045* 0.002*
0.75 0.40 0.29 0.73 0.31 0.59 0.57 0.41 0.28 0.17 0.10 0.21
0.75 0.88 1.00 1.00 0.59 0.59 0.72
2
Only statistically significant effects are shown. ANOVA, analysis of variance; COR, coronal plane; MRCP, movement-related cortical potential; SAG, sagittal plane. *Statistical significance.
FC2 and C2 sites, whereas at the late stages, significant differences were present at all electrodes [F(1, 21) = 16.96, P < 0.001, ηp2 = 0.44], except the FPz site (P = 0.056). In addition, the effect size of block (exercise duration) on the RP amplitude (ηp2 = 0.73) was much higher than that of MP (ηp2 = 0.49) when the muscle was fatigued. However, the effect size of muscle fatigue on the RP (ηp2 = 0.21) was lower than that on MP (ηp2 = 0.44). Correlations analysis
The largest significant within-subject correlation was observed between MP amplitude at the C1 site and RMS sEMG (r = − 0.439, P = 0.02, ηp2 = 0.11).
Discussion Effects of muscle fatigue on force and surface electromyography
The protocols for the fatigue and nonfatigue groups differed only in the length of the period of intertrial rest allowed. In the fatiguing protocol, which involved 200 6-s intermittent handgrip contractions at the 30% MVC force
level (∼100 N) with an ∼ 4-s intertrial rest, the MVCs after each block and the RMS during MVCs showed significant decreases. Muscle fatigue is defined as an exercise-induced reduction in the ability to produce force with a muscle or a muscle group during an MVC [20]. In our fatiguing task, to maintain the target force level of 30% MVC for all 200 contractions, participants were required to recruit more motor neurons in the spinal cord as muscle fatigue progressed, thereby showing a significant increase in RMS sEMG over each block. This increase in RMS sEMG was also an indicator of a fatiguing state, as described previously [21–23]. The measurements used in this study have been used by others [10,24] to evaluate muscle fatigue. Our fatiguing protocol provided evidence that repeated submaximal handgrip contractions with 30% MVC can induce muscle fatigue [5], whereas in the nonfatigue group, which allowed an ∼8-s intertrial rest, no significant reductions in MVC force and sEMG activity were observed, indicating that no muscle fatigue occurred. In general, our results are in agreement with previous studies of the effects of muscle fatigue on force and sEMG [3,4,10,24]. Effects of muscle fatigue on movement-related cortical potential amplitudes
Although the task participants in the fatigue group experienced significant muscle fatigue at the early stages of the task, there was no difference in the RP amplitude between the fatigue group and the nonfatigue group. At the late stages of the task, the RP amplitude was significantly increased in the fatigue group compared with the nonfatigue group only at the Fp1 and FC1 sites. Some researchers [4,10,11,13] have also observed a significant increase in the RP amplitude when comparing early blocks with late blocks during repeated movements, but in those experiments, muscle fatigue was induced by repetitive submaximal contractions, which could
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Fig. 2
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Grand average movement-related cortical potential waveforms at the prefrontal area (Fp1, FPz and Fp2), frontocentral area (FC1, FCz and FC2) and central area (C1, Cz and C2) electrodes during the early and late blocks in the fatigue (F) and nonfatigue (NF) groups. Time 0 corresponds to surface electromyography (sEMG) onset. Because the sEMG signal always precedes movement onset, the motor potential component was delayed relative to time 0.
Table 2 Epoch RP MP
Simple main effect of exercise duration on RP and MP Group
Early blocks
Late blocks
F
P
Nonfatigue Fatigue Nonfatigue Fatigue
− 1.52 ± 0.51 − 1.63 ± 0.37 − 4.05 ± 0.94 − 6.81 ± 0.68
− 1.45 ± 0.54 − 3.05 ± 0.39 − 3.72 ± 0.95 − 8.56 ± 0.69
0.09 58.30 0.39 20.46
0.76 < 0.001** 0.53 < 0.001**
ηp
2
0.01 0.73 0.02 0.49
Data are presented as mean ± SEM. MP, motor potential; RP, readiness potential. **Significant differences in RP and MP between the fatigue and the nonfatigue groups.
contribute toward an increase in RP amplitude simply because of an increase in cognitive demands during repetitive movements, as has been proposed by Schillings et al. [11]. Schillings and colleagues observed that the RP was significantly increased for central motor areas during repetitive handgrip contractions at 70% MVC accompanied by a small amount of peripheral
muscle fatigue. This change in RP induced by prolonged exercise may be interpreted as representing the effects of the cognitive demands of the physical task to compensate for the decrease in cortical efficiency during repetitive contractions rather than the effects of muscle fatigue. This interpretation is also supported by the findings of Freude and Ullsperger [13], who observed an increase in the area under the curve of the RP across repeated contractions at 80% MVC and 20% MVC, but not at 50% MVC, which they attributed to the increased intentional involvement required for the 80% MVC and 20% MVC tasks because of the greater difficulty of the motor control required for these contractions. We did not observe a significant difference in the RP amplitude between the fatigue and the nonfatigue groups at the early stages of muscle fatigue; this lack of a significant difference between groups may be because of the lack of difference
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MRCPs during muscle fatigue Guo et al. 1141
Table 3 Epoch RP MP
Univariate tests for the simple main effect of fatigue on RP and MP amplitude Block
Nonfatigue
Fatigue
F
P
η p2
Early blocks Late blocks Early blocks Late blocks
− 1.52 ± 0.51 − 1.45 ± 0.54 − 4.05 ± 0.94 − 3.72 ± 0.95
− 1.63 ± 0.37 − 3.05 ± 0.39 − 6.81 ± 0.68 − 8.56 ± 0.69
0.02 5.73 5.62 16.96
0.86 0.02* 0.02* < 0.001**
0.001 0.21 0.21 0.44
Data are presented as mean ± SEM. MP, motor potential; RP, readiness potential. *Significant differences in RP and MP between the fatigue and the nonfatigue groups. **Significant differences in MP at late blocks between the fatigue and the non-fatigue groups.
in cognitive demands at the initial stages of the task other than muscle fatigue. However, the significant differences in the RP amplitude at the late stages at the Fp1 and FC1 sites may be attributable to the increased cognitive demands associated with maintaining a relatively difficult target force during muscle fatigue; these cognitive demands result from the requirement to achieve precise timing, a precise force level and memory processing. Furthermore, our data on effect size indicate that the RP amplitude is sensitive to prolonged exercise duration, which may induce a more cognitive component of effort. By contrast, there were no significant changes in the RP amplitude across blocks for participants in the nonfatigue group, indicating that prolonged exercise during the 30% MVC handgrip contraction with a sufficient 8-s intertrial rest did not induce a more cognitive component of effort for participants. As proposed by Jahanshahi et al. [14], the RP amplitude is sensitive to the experimental conditions. The MP increased significantly over the frontal cortex with the development of muscle fatigue, and the effect size indicated that muscle fatigue has considerable effects on MP amplitude rather than on RP. Several studies have examined the effects of muscle fatigue and weakness on MRCP amplitudes [3,10,25,26]. Shibata et al. [25] induced muscle weakness during task performance by an arterial occlusion technique, equivalent to muscle fatigue, and found that there was no increase in cortical negative activity before muscle contraction, but a significant increase in MP amplitude during movement execution, accompanied by increased sEMG activity. It was concluded that augmented cortical activation during movement execution induced by arterial occlusion may reflect the recruitment of additional motor units to compensate for the loss of force generation. Similarly, Jankelowitz and Colebatch [26] observed only small changes in the RP amplitude when muscles were weakened with peripheral intramuscular anaesthesia, but much larger changes in the MP during movement execution. De Morree et al. [3] observed that participants showed an increase in MRCP amplitudes only at Cz and only during movement execution in a fatiguing task involving lifting a weight with the arms, but the EEG was measured after an experimental manipulation to induce muscle fatigue, which suggests that the effect of fatigue is delayed, resulting in an increase in MP amplitude only
at the Cz site. In a similar study, Berchicci et al. [4] observed a difference in MP amplitude only at C1, with higher negativity in the fatigue group than in the nonfatigue group. Our results are also in agreement with the functional MRI findings of Liu and colleagues, who observed a gradual increase in the activation of the sensorimotor cortex during the first minute of a maximal 2-min isometric handgrip contraction, an activity equivalent to the submaximal isometric handgrip contraction in our protocol. The activity of muscle afferents (groups III and IV) affects central fatigue [27,28]. These afferents, which respond to mechanical, chemical and noxious stimuli, increase their firing rates as metabolites accumulate in fatiguing muscle [29,30]. Some investigators [6,31,32] have suggested that the activity of muscle afferents (groups III and IV) that are sensitive to fatigue affects motor cortical cells. Thus, the increases in MP amplitude observed in the present study may be attributed to the accumulation of metabolites in the fatiguing muscle, which contributes towards increases in sensorimotor cortex activity by group III and IV muscle afferents. Contribution of the prefrontal cortex to movements
The RP amplitude increased significantly in late blocks at the Fp1 and FC1 sites in the fatigue group compared with the nonfatigue group. The prefrontal cortex and the supplementary motor area have been assumed to play a role during preparations for self-initiated movements [18, 33]. In a hierarchical model of voluntary movements [13–15], the prefrontal cortex has been described as the brain region that formulates intentions and makes decisions before initiating movements. Jahanshahi et al. [15] observed that self-paced joystick movements involving decision-making about precise tasks and timing induced a slowly increasing subdural positivity before movement in the dorsolateral and inferior prefrontal cortex in an epileptic patient, as indicated by MRI. In the present study, waveforms of prefrontal electrodes were distinguished from those of motor area electrodes, suggesting that the prefrontal cortex may not be directly responsible for controlling voluntary movement. Consistent with the results of previous studies, participants in the nonfatigue group showed positive neural activity in the prefrontal cortex when performing a
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physical task requiring decision-making to generate a precise force level with precise timing. In addition, the significant changes in the RP amplitude at Fp1 and FC1 in the fatigue group compared with the nonfatigue group in our self-initiated fatiguing task may have resulted from an increase in the cognitive demand of decision-making before initiating movement as muscle fatigue progressed. Berchicci et al. [4] examined prefrontal activity during muscle fatigue involving submaximal voluntary contractions of the lower limbs and found that participants showed higher positive activity in the prefrontal cortex in the fatigue group than in the nonfatigue group. However, in our study, in the fatigue group, the prefrontal cortex showed negative waveforms; this result is supported by the results of Jahanshahi et al. [15], who observed that MRCPs recorded using subdural electrodes placed over the prefrontal cortex yielded either positive or negative waveforms, and by the results of Ikeda et al. [34], who showed that these waveforms were positive or negative depending on the orientation of the dipole and its position relative to the electrode. Our result may suggest that the orientation of the dipole at this cortical position changes during muscle fatigue. Although Hilty et al. [7] did not use MRCPs to examine the effects of fatigue on cortical activity, they observed widespread, significant increases in α and β in Brodmann area 11 (medial orbitofrontal cortex) after an exhausting cycling exercise. It was concluded that in the context of exhausting exercise, a prominent increase in power within the medial orbitofrontal cortex may be interpreted as endurance in response to an increasing perception of fatigue. Correlations between movement-related cortical potentials and the surface electromyography
A significant within-subject correlation was observed between MP amplitude at the C1 site and RMS sEMG. The C1 site overlies the contralateral primary motor cortex, which plays an important role in controlling voluntary movement. The MP component of MRCPs corresponded to the time of movement initiation, reflecting a central command to control the muscle group involved in a movement task, as described by Kristeva et al. [12]. According to Gottlieb et al. [35], a central command activates the α-motor neuron pool in the spinal cord that innervates the target muscle. The increased RMS sEMG amplitude during fatigue observed in the present study is in agreement with the findings of other studies using submaximal fatiguing protocols [21–23] and can be viewed as a reflection of the recruitment of additional motor units and/or an increasing activity level of the active motor units [24]. Thus, the augmented MP amplitude at the cortical level seems to contribute towards the recruitment of additional motor units. In general, the changes produced in both the motor cortex and the α-motor neuron pool during muscle fatigue may reflect central mechanisms for compensating for the loss in force production with muscle fatigue.
Conclusion Neurophysiological evidence has shown that cortical activation over supplementary motor and sensorimotor areas increases with muscle fatigue progression, as reflected in the amplitudes of MRCP. The present study is the first to show that activity in the prefrontal cortex becomes more negative with the development of muscle fatigue. The sEMG was significantly correlated with the MP amplitude at contralateral primary motor areas during muscle fatigue, suggesting that central mechanisms attempt to compensate for the loss in force production under muscle fatigue. In addition, we observed that muscle fatigue has considerable effects on the components of MRCPs during movement execution, whereas the RP before movement was sensitive to exercise duration. Future studies should utilize imaging techniques to explore the source of activation changes for components of MRCPs during muscle fatigue.
Acknowledgements The authors thank Zai-fu Duan from the Cognitive Psychological Laboratory at Shenyang Sport University for providing equipment. The authors are grateful for funding from the Liaoning Provincial Committee of Education (L2012412), the State General Administration of Sport in China (2012A013) in China and the Company in Shandong Ajiao Co. Ltd in China. Conflicts of interest
There are no conflicts of interest.
References 1
Chaudhuri A, Behan PO. Fatigue in neurological disorders. Lancet 2004; 363:978–988. 2 Zwarts MJ, Bleijenberg G, van Engelen BG. Clinical neurophysiology of fatigue. Clin Neurophysiol 2008; 119:2–10. 3 De Morree HM, Klein C, Marcora SM. Perception of effort reflects central motor command during movement execution. Psychophysiology 2012; 49:1242–1253. 4 Berchicci M, Menotti F, Macaluso A, Di Russo F. The neurophysiology of central and peripheral fatigue during sub-maximal lower limb isometric contractions. Front Hum Neurosci 2013; 7:135. 5 Taylor JL, Gandevia SC. A comparison of central aspects of fatigue in submaximal and maximal voluntary contractions. J Appl Physiol (1985) 2008; 104:542–550. 6 Hilty L, Jäncke L, Luechinger R, Boutellier U, Lutz K. Limitation of physical performance in a muscle fatiguing handgrip exercise is mediated by thalamoinsular activity. Hum Brain Mapp 2011; 32:2151–2160. 7 Hilty L, Langer N, Pascual-Marqui R, Boutellier U, Lutz K. Fatigue-induced increase in intracortical communication between mid/anterior insular and motor cortex during cycling exercise. Eur J Neurosci 2011; 34:2035–2042. 8 Kornhuber HH, Deecke L. Changes in the brain potential in voluntary movements and passive movements in man: readiness potential and reafferent potentials. Pflugers Arch Gesamte Physiol Menschen Tiere 1965; 284:1–17. 9 Bozzacchi C, Giusti MA, Pitzalis S, Spinelli D, Di Russo F. Awareness affects motor planning for goal-oriented actions. Biol Psychol 2012; 89:503–514. 10 Johnston J, Rearick M, Slobounov S. Movement-related cortical potentials associated with progressive muscle fatigue in a grasping task. Clin Neurophysiol 2001; 112:68–77. 11 Schillings ML, Kalkman JS, van der Werf SP, Bleijenberg G, van Engelen BG, Zwarts MJ. Central adaptations during repetitive contractions assessed by the readiness potential. Eur J Appl Physiol 2006; 97:521–526. 12 Kristeva R, Cheyne D, Lang W, Lindinger G, Deecke L. Movement-related potentials accompanying unilateral and bilateral finger movements with
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
MRCPs during muscle fatigue Guo et al. 1143
13
14
15
16 17 18 19 20 21 22
23 24
different inertial loads. Electroencephalogr Clin Neurophysiol 1990; 75:410–418. Freude G, Ullsperger P. Changes in Bereitschaftspotential during fatiguing and non-fatiguing hand movements. Eur J Appl Physiol Occup Physiol 1987; 56:105–108. Jahanshahi M, Jenkins IH, Brown RG, Marsden CD, Passingham RE, Brooks DJ. Self-initiated versus externally triggered movements I. An investigation using measurement of regional cerebral blood flow with PET and movement-related potentials in normal and Parkinson’s disease subjects. Brain 1995; 118:913–933. Jahanshahi M, Dirnberger G, Liasis A, Towell A, Boyd S. Does the pre-frontal cortex contribute to movement-related potentials? Recordings from subdural electrodes. Neurocase 2001; 7:495–501. Liu JZ, Dai TH, Sahgal V, Brown RW, Yue GH. Nonlinear cortical modulation of muscle fatigue: a functional MRI study. Brain Res 2002; 957:320–329. Gratton G, Coles MG, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalogr Clin Neurophysiol 1983; 55:468–484. Shibasaki H, Hallett M. What is the Bereitschaftspotential? Clin Neurophysiol 2006; 117:2341–2356. Bland JM, Altman DG. Calculating correlation coefficients with repeated observations: part 1 – correlation within subjects. BMJ 1995; 310:446. Enoka RM, Duchateau J. Muscle fatigue: what, why and how it influences muscle function. J Physiol 2008; 586:11–23. Moritani T, Muro M, Nagata A. Intramuscular and surface electromyogram changes during muscle fatigue. J Appl Physiol (1985) 1986; 60:1179–1185. Merletti R, Knaflitz M, De Luca CJ. Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. J Appl Physiol (1985) 1990; 69:1810–1820. Fuglevand AJ, Winter DA, Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol 1993; 70:2470–2488. Esposito F, Orizio C, Veicsteinas A. Electromyogram and mechanomyogram changes in fresh and fatigued muscle during sustained contraction in men. Eur J Appl Physiol Occup Physiol 1998; 78:494–501.
25
26
27 28 29
30
31
32
33
34
35
Shibata M, Oda S, Moritani T. The relationships between movement-related cortical potentials and motor unit activity during muscle contraction. J Electromyogr Kinesiol 1997; 7:79–85. Jankelowitz SK, Colebatch JG. Movement related potentials in acutely induced weakness and stroke. Exp Brain Res 2005; 161: 104–113. Gandevia SC. Spinal and supraspinal factors in human muscle fatigue. Physiol Rev 2001; 81:1725–1789. Nybo L, Secher NH. Cerebral perturbations provoked by prolonged exercise. Prog Neurobiol 2004; 72:223–261. Li J, Sinoway LI. ATP stimulates chemically sensitive and sensitizes mechanically sensitive afferents. Am J Physiol Heart Circ Physiol 2002; 283: H2636–H2643. Sinoway LI, Hill JM, Pickar JG, Kaufman MP. Effects of contraction and lactic acid on the discharge of group III muscle afferents in cats. J Neurophysiol 1993; 69:1053–1059. Gandevia SC, Allen GM, Butler JE, Taylor JL. Supraspinal factors in human muscle fatigue: evidence for suboptimal output from the motor cortex. J Physiol 1996; 490:529–536. Taylor JL, Butler JE, Allen GM, Gandevia SC. Changes in motor cortical excitability during human muscle fatigue. J Physiol 1996; 490 (Pt 2): 519–528. Deecke L. Bereitschaftspotential as an indicator of movement preparation in supplementary motor area and motor cortex. Ciba Found Symp 2008; 713:231. Ikeda A, Lüders HO, Burgess RC, Shibasaki H. Movement-related potentials recorded from supplementary motor area and primary motor area. Role of supplementary motor area in voluntary movements. Brain 1992; 115 (Pt 4): 1017–1043. Gottlieb GL, Corcos DM, Agarwal GC. Organizing principles for single-joint movements. I. A speed-insensitive strategy. J Neurophysiol 1989; 62:342–357.
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Integrative systems
Movement-related cortical potentials during muscle fatigue induced by upper limb submaximal isometric contractions Feng Guoa,b, Ji-Ya Wangb, Yong-Jun Sunc, A-Li Yangc and Ri-Hui Zhanga,b The aim of this study was to examine the central neurophysiological mechanisms during fatigue induced by submaximal isometric contractions. A total of 23 individuals participated in the study and were assigned to fatigue and nonfatigue groups. Handgrip force, root mean square (RMS) of surface electromyography (sEMG) signal and movementrelated cortical potentials during self-paced submaximal handgrip isometric contractions were assessed for each participant. The experimental data showed significant decreases in both maximal voluntary contraction [ − 24.3%; F(3, 42) = 19.62, P < 0.001, ηp2 = 0.48] and RMS [ − 30.1%; F(3, 42) = 19.01, P < 0.001, ηp2 = 0.57] during maximal voluntary contractions and a significant increase [F(3, 42) = 14.27, P < 0.001, ηp2 = 0.50] in the average RMS of sEMG over four blocks in the fatigue group. There was no significant difference in the readiness potential between the fatigue and the nonfatigue groups at early stages, and at late stages, significant differences were observed only at the Fp1 and FC1 sites. Motor potential amplitudes were significantly higher in the fatigue group than in the nonfatigue group irrespective of block or electrode positions. Positive waveforms were observed in the prefrontal cortex in states without muscle fatigue, whereas a negative waveform pattern was observed with muscle fatigue. Significant within-subject correlations were
observed between motor potential at the C1 site and RMS of sEMG (r = − 0.439, P = 0.02, ηp2 = 0.11). Neurophysiological evidence indicates that cortical activity increases in the prefrontal cortex, primary motor cortex and supplementary motor cortex with muscle fatigue. Muscle fatigue appears to have considerable effects on the components of movement-related cortical potentials during movement execution, whereas the readiness potential before movement is sensitive to cognitive demands during prolonged exercise. Our results provide additional evidence for a link between central motor command during movement execution and motor unit recruitment. NeuroReport 25:1136–1143 © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins.
Introduction
planning, preparation and initiation of conscious movement [9]. Some researchers have observed that the RP amplitude increases during the fatigue task of repetitive contractions; this increase was proposed to be an attempt by the brain to compensate for peripheral fatigue [3,10, 11]. By contrast, another study observed a decrease in the amplitude of MRCPs during muscle fatigue [12], which can be explained by a decrease in intention and habituation processes. However, Schillings et al. [11] reported that prolonged exercise can induce an increase in RP during repetitive contractions because of the cognitive demands of a physical task, independent of muscle fatigue. Thus, the choice of experimental design for the study of repetitive contractions may have a huge effect on the findings obtained on the effects of muscle fatigue on MRCP components. Studies of the effects of exerciseinduced fatigue on various components of MRCPs have not yielded consistent results and have offered inconsistent explanations for their results. In addition, these studies have focused primarily on the motor cortex and less on the prefrontal areas. It has been proposed that the prefrontal cortex is the brain region that formulates
Fatigue has both mental and physical aspects. Physical fatigue refers to difficulty sustaining maximal voluntary contraction (MVC) or target force [1]. The decrease in force production may originate from different sites within the neural axis, including the sarcolemma, the neuromuscular junction, the spinal cord or the cortex [2]. Thus, muscle fatigue results not only from peripheral processes within the working muscle but also from central processes in sensorimotor pathways. Electroencephalography (EEG) and functional MRI have been used to explore cortical processing during fatigue tasks [2–7]. One approach to study the central mechanisms of fatigue is to examine movement-related cortical potentials (MRCPs) extracted from EEG. The concept of a Bereitschaftpotential or readiness potential (RP), which is a premovement component of MRCPs for self-initiated movements, was originally introduced by Kornhuber and Deecke [8]. The RP typically contains three components: the early RP, the late RP and the motor potential (MP) [8]. These cortical electrical activities reflect the 0959-4965 © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
NeuroReport 2014, 25:1136–1143 Keywords: fatigue, movement-related cortical potentials, neurophysiology a Department of Physiology, College of Basic Medical Sciences, Jilin University, Jilin, bCollege of Human Kinesiology and cDepartment of Physical Education, Shenyang Sport University, Shenyang, China
Correspondence to Feng Guo, PhD, Department of Physiology, College of Basic Medical Sciences, Jilin University, 828 Xinmin Street, Changchun, 130021 Jilin, China Tel: + 86 024 89166557; fax: + 86 024 89166686; e-mail: [email protected] Received 26 April 2014 accepted 8 July 2014
DOI: 10.1097/WNR.0000000000000242
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MRCPs during muscle fatigue Guo et al. 1137
intentions and makes decisions before movements are initiated [13–15]. Thus, activity in the prefrontal cortex may produce significant changes during muscle fatigue. Only Berchicci et al. [4] and Hilty et al. [7] have reported activity in the prefrontal cortex during muscle fatigue, which, in both studies, involved the large muscle masses in the lower limbs. Moreover, Hilty and colleagues reported the spectrum characteristics of the prefrontal cortical EEG, but did not examine MRCP components. In the present study, we sought to explore the central mechanisms of muscle fatigue. Force, surface electromyography (sEMG) and EEG measurements were used to study changes in MRCP components in relevant motor areas and in the prefrontal cortex during fatiguing tasks induced by self-paced submaximal handgrip contractions involving small muscle masses in an upper limb. To study the effect of muscle fatigue on MRCPs, we used a control group to further clarify the roles of different MRCP components during a fatiguing task of repetitive contractions.
Materials and methods Participants
A total of 32 healthy, right-handed individuals (determined by self-report) participated in the study. However, data from nine participants were discarded because of significant EEG artefacts; thus, the final data studied were from 23 participants (fatigue group: 15 participants, age 23.4 ± 1.1 years; nonfatigue group: eight participants, age 22.1 ± 1.7 years). All participants were nonathlete students in university with no exercise experience. The experimental procedures were approved by the Institutional Review Board at Shenyang Sport University. All participants provided written, informed consent before their participation in the study.
Experimental protocol
The 15 participants in the fatigue group performed 200 intermittent voluntary handgrip contractions of the right arm at ∼ 30% MVC in a single session. Each contraction lasted ∼ 6 s, followed by an ∼ 4-s intertrial rest. The 200 contractions were divided into four blocks; each block comprised 50 trials. The MVC was measured during a brief rest between blocks of less than 1 min. Each participant maintained a force level of 30% MVC by observing real-time visual force feedback presented on a computer screen. The eight participants in the nonfatigue group also performed 200 handgrip contractions at the same force level of 30% MVC, but with a longer intertrial rest of ∼ 8 s. To obtain a higher signal-to-noise ratio for the analysis of the MRCPs, the 200 intermittent contractions were divided into two large blocks: early blocks (i.e. blocks 1 and 2) and late blocks (i.e. blocks 3 and 4).
Procedures
Participants sat comfortably on a chair 90 cm away from a computer screen on a table. Each participant’s forearm rested on the arm of the chair while his or her hand was maintained in a position of 90° pronation. All participants were allowed to become familiar with the task, particularly with the level of force required and were asked to avoid eye movements before contractions. MVCs were determined before the formal experiment and after each block. To achieve MVCs, the participants were asked to push against a load cell as hard as possible for ∼ 5 s. To determine the initial MVC, participants performed three trials with rest intervals of 3 min between trials and the largest of the MVCs was selected. However, to reduce the effects of fatigue on MVCs, MVCs were tested only once during each interblock interval. Force measurement and analysis
Handgrip force was measured using a force transducer. For each MVC trial, a mean value was calculated from the data points that represented relatively stable MVC force values. The force and sEMG values of individual MVC trials were calculated after each block. Then, the averaged MVC force and sEMG data were normalized to the corresponding initial MVC values. Finally, the normalized data were averaged across participants. Surface electromyography measurement and analysis
sEMG data for the flexor digitorum profundus were recorded using Neuroscan equipment (Neuro-Scan, El Paso, Texas, USA) (sampling rate: 1000 Hz, bandpass: 0.1–200 Hz). To obtain the recordings, electrodes were placed 1–2 cm medial to the ulna and 3–5 cm distal to the elbow joint because a previous study [16] suggested that better sEMG signals from the flexor digitorum profundus muscle can be obtained at this location. The sEMG data of each muscle trial were rectified by the root mean square (RMS) with a 0.1-s time window and then averaged over the same time period as the force. Electroencephalographic measurement and analysis
Continuous EEG signals were recorded using a 64-channel Neuroscan SynAmps system (version 4.5; Neuro-Scan), with the right mastoid (M2) used as the reference. The ground electrode was located between the FPz and the Fz electrodes. The EEG signal was digitized with a sampling rate of 1000 Hz and amplified with a bandpass from 0.01 to 100 Hz. Horizontal and vertical eye movements were monitored by bipolar recordings. The impedance of all electrodes was maintained below 5 kΩ by adjusting the amount of conductive gel (Quick-Gel; Compumedics Inc., Charlotte, North Carolina, USA) inside the electrodes. EEG signals were analysed offline using Scan 4.5 software (Compumedics Inc.). Raw EEG data were
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inspected manually and epochs with marked EEG artefacts were deleted. The channels with electrooculogram (EOG) artefacts were then corrected using a correlation method [17] in the ocular artefact reduction option of the Scan 4.5 software. With respect to each contraction trial, the sEMG onset was defined as the zero time, in accordance with previous studies [8,18]. Continuous EEG data that had been subjected to primary processing were divided into 4000-ms epochs comprising − 2000 to 2000 ms relative to sEMG onset. For each trial, the baseline was derived by calculating the mean amplitude of all epochs during the initial 500 ms from − 2000 to − 1500 ms. To further reduce the noise level, epochs were filtered with a low-pass cutoff frequency of 30 Hz. Artefacts were then rejected from the filtered epochs with a threshold of ± 80 μV. MRCPs were obtained by averaging the artefact-free epochs. Finally, MRCPs were again re-referenced to the average. Two components of the MRCPs were calculated using approaches on the basis of previous studies [4,16,18]: the RP was calculated as the mean amplitude from − 1500 to − 100 ms relative to sEMG onset and the MP was computed as the average peak amplitude of the MRCPs in the time window from 0 to 150 ms after sEMG onset. These components reflect the distinct stages of preparation for and execution of movement [4,9,18].
Statistical analysis
Statistical tests were performed using the software Statistical Program for the Social Sciences (SPSS; IBM, Austin, Texas, USA) 16.0. Force (MVC) and sEMG (RMS) were analysed as dependent variables using a twoway (2 × 4) mixed-model analysis of variance (ANOVA) with respect to group (two levels: fatigue vs. nonfatigue as the between-subject factor) and block (four levels: block 1 vs. block 2 vs. block 3 vs. block 4 as the withinsubject factor). MRCP amplitudes during the course of a physical task were statistically analysed using a general linear mixed model for 2 × 2 × 2 × 3 × 3 ANOVA, with two groups (fatigue and nonfatigue), two blocks (early and late), two epochs of MRCPs (RP and MP) and nine electrode locations, with Fp1, FPz, Fp2, FC1, FCz, FC2, C1, Cz and C2 grouped into coronal planes (three levels: prefrontal, frontocentral and central) and sagittal planes (three levels: ipsilateral, midline and contralateral). For repeated-measures ANOVAs, if the sphericity test was violated, the degrees of freedom were adjusted by the Greenhouse–Geisser correction, which can reduce the risk of type I error. The significance level was set at an α value of 0.05 after the Greenhouse–Geisser correction. In such cases, the uncorrected degrees of freedom, ε values and corrected probability levels are reported. To compare two means, the adjusted least significant difference test was performed. Furthermore, the effect size, which provides an estimate of the extent of association
for the sample, was measured as partial η2 (ηp2) and reported. Correlation analysis was carried out by calculating withinsubject correlation coefficients between sEMG and MRCP amplitudes across blocks during muscle fatigue using the method of Bland and Altman [19]. This method adjusts for the effects of examining variation within participants using an analysis of covariance. The significance was set at 0.05.
Results Force and surface electromyography
For MVCs, there was a significant block × group interaction [F(3, 63) = 11.47, ε = 0.72, P < 0.001, ηp2 = 0.35]. The simple effect indicated that the handgrip task induced a significant reduction in both % MVC force [F(3, 42) = 19.62, ε = 0.72, P < 0.001, ηp2 = 0.48] and the RMS sEMG [F(3, 42) = 19.01, P < 0.001, ηp2 = 0.57] between blocks in the fatigue group, whereas no statistically significant difference was observed in the nonfatigue group (Fig. 1a and b). The results of univariate tests showed that significant differences between the fatigue and nonfatigue groups in % MVC force and sEMG activity occurred after block 2 (Fig. 1a and b). In addition, in the fatigue group, the average RMS over each block significantly increased throughout the physical task [F(3, 42) = 14.27, ε = 0.77, P < 0.001, ηp2 = 0.50]; however, no significant effect of block was observed for the nonfatigue group (Fig. 1c). Movement-related cortical potential waveforms
No quintuplicate or quadruple interactions were identified in five-way mixed-model ANOVAs of MRCP amplitudes. However, quadratic and triple interactions were observed (Table 1). In the present study, the interactions correlated with group were further analysed. In general, MRCP amplitudes increased with muscle fatigue progression (Fig. 2). The simple effect of block on the level of fatigue and nonfatigue is shown in Table 2. Because of the presence of block × group interactions, further analyses were carried out, which showed that there were no significant differences in the RP amplitude across blocks at all electrodes in the nonfatigue group, whereas in the fatigue group, the RP amplitude was larger at the late blocks, as muscle fatigue increased, than at the early blocks at each electrode [F(1, 14) = 58.30, P < 0.001, ηp2 = 0.73]. The MP amplitude was considerably larger at late blocks than at early blocks in the fatigue group [F(1, 14) = 20.46, P < 0.001, ηp2 = 0.49], and these significant differences occurred at each electrode. The results of univariate tests for the simple main effect of group on RP and MP amplitudes are shown in Table 3. Further analysis showed that at the early stages of the fatigue task, significant differences [F(1, 21) = 5.62, P = 0.02, ηp2 = 0.21] between the fatigue and the nonfatigue groups occurred only at the Fp1, FC1,
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MRCPs during muscle fatigue Guo et al. 1139
Fig. 1
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Maximal voluntary contraction (MVC) force (a), root mean square (RMS) of the surface electromyographic signal during MVCs (MVC-RMS) (b) and average RMS across each block (c) in the fatigue and nonfatigue groups. *Significant main effect of group (P < 0.001); #significant main effect of block (P < 0.001); Δinteractions of block × group. The data are presented as mean ± SD.
Table 1
Five-way mixed-model ANOVAs for MRCP amplitudes
Effects COR (coronal electrodes) SAG (sagittal electrodes) Block Epoch Group COR × epoch SAG × epoch Block × group Epoch × group COR × SAG SAG × COR × group SAG × COR × epoch
F
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P
ηp
65.83 14.02 8.96 57.03 9.55 30.51 28.44 15.04 8.26 4.29 2.55 5.88
0.69 0.87 1.00 1.00
< 0.001* < 0.001* 0.007* < 0.001* 0.006* < 0.001* < 0.001* 0.001* 0.009* 0.014* 0.045* 0.002*
0.75 0.40 0.29 0.73 0.31 0.59 0.57 0.41 0.28 0.17 0.10 0.21
0.75 0.88 1.00 1.00 0.59 0.59 0.72
2
Only statistically significant effects are shown. ANOVA, analysis of variance; COR, coronal plane; MRCP, movement-related cortical potential; SAG, sagittal plane. *Statistical significance.
FC2 and C2 sites, whereas at the late stages, significant differences were present at all electrodes [F(1, 21) = 16.96, P < 0.001, ηp2 = 0.44], except the FPz site (P = 0.056). In addition, the effect size of block (exercise duration) on the RP amplitude (ηp2 = 0.73) was much higher than that of MP (ηp2 = 0.49) when the muscle was fatigued. However, the effect size of muscle fatigue on the RP (ηp2 = 0.21) was lower than that on MP (ηp2 = 0.44). Correlations analysis
The largest significant within-subject correlation was observed between MP amplitude at the C1 site and RMS sEMG (r = − 0.439, P = 0.02, ηp2 = 0.11).
Discussion Effects of muscle fatigue on force and surface electromyography
The protocols for the fatigue and nonfatigue groups differed only in the length of the period of intertrial rest allowed. In the fatiguing protocol, which involved 200 6-s intermittent handgrip contractions at the 30% MVC force
level (∼100 N) with an ∼ 4-s intertrial rest, the MVCs after each block and the RMS during MVCs showed significant decreases. Muscle fatigue is defined as an exercise-induced reduction in the ability to produce force with a muscle or a muscle group during an MVC [20]. In our fatiguing task, to maintain the target force level of 30% MVC for all 200 contractions, participants were required to recruit more motor neurons in the spinal cord as muscle fatigue progressed, thereby showing a significant increase in RMS sEMG over each block. This increase in RMS sEMG was also an indicator of a fatiguing state, as described previously [21–23]. The measurements used in this study have been used by others [10,24] to evaluate muscle fatigue. Our fatiguing protocol provided evidence that repeated submaximal handgrip contractions with 30% MVC can induce muscle fatigue [5], whereas in the nonfatigue group, which allowed an ∼8-s intertrial rest, no significant reductions in MVC force and sEMG activity were observed, indicating that no muscle fatigue occurred. In general, our results are in agreement with previous studies of the effects of muscle fatigue on force and sEMG [3,4,10,24]. Effects of muscle fatigue on movement-related cortical potential amplitudes
Although the task participants in the fatigue group experienced significant muscle fatigue at the early stages of the task, there was no difference in the RP amplitude between the fatigue group and the nonfatigue group. At the late stages of the task, the RP amplitude was significantly increased in the fatigue group compared with the nonfatigue group only at the Fp1 and FC1 sites. Some researchers [4,10,11,13] have also observed a significant increase in the RP amplitude when comparing early blocks with late blocks during repeated movements, but in those experiments, muscle fatigue was induced by repetitive submaximal contractions, which could
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Fig. 2
Fp1 −15
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Grand average movement-related cortical potential waveforms at the prefrontal area (Fp1, FPz and Fp2), frontocentral area (FC1, FCz and FC2) and central area (C1, Cz and C2) electrodes during the early and late blocks in the fatigue (F) and nonfatigue (NF) groups. Time 0 corresponds to surface electromyography (sEMG) onset. Because the sEMG signal always precedes movement onset, the motor potential component was delayed relative to time 0.
Table 2 Epoch RP MP
Simple main effect of exercise duration on RP and MP Group
Early blocks
Late blocks
F
P
Nonfatigue Fatigue Nonfatigue Fatigue
− 1.52 ± 0.51 − 1.63 ± 0.37 − 4.05 ± 0.94 − 6.81 ± 0.68
− 1.45 ± 0.54 − 3.05 ± 0.39 − 3.72 ± 0.95 − 8.56 ± 0.69
0.09 58.30 0.39 20.46
0.76 < 0.001** 0.53 < 0.001**
ηp
2
0.01 0.73 0.02 0.49
Data are presented as mean ± SEM. MP, motor potential; RP, readiness potential. **Significant differences in RP and MP between the fatigue and the nonfatigue groups.
contribute toward an increase in RP amplitude simply because of an increase in cognitive demands during repetitive movements, as has been proposed by Schillings et al. [11]. Schillings and colleagues observed that the RP was significantly increased for central motor areas during repetitive handgrip contractions at 70% MVC accompanied by a small amount of peripheral
muscle fatigue. This change in RP induced by prolonged exercise may be interpreted as representing the effects of the cognitive demands of the physical task to compensate for the decrease in cortical efficiency during repetitive contractions rather than the effects of muscle fatigue. This interpretation is also supported by the findings of Freude and Ullsperger [13], who observed an increase in the area under the curve of the RP across repeated contractions at 80% MVC and 20% MVC, but not at 50% MVC, which they attributed to the increased intentional involvement required for the 80% MVC and 20% MVC tasks because of the greater difficulty of the motor control required for these contractions. We did not observe a significant difference in the RP amplitude between the fatigue and the nonfatigue groups at the early stages of muscle fatigue; this lack of a significant difference between groups may be because of the lack of difference
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MRCPs during muscle fatigue Guo et al. 1141
Table 3 Epoch RP MP
Univariate tests for the simple main effect of fatigue on RP and MP amplitude Block
Nonfatigue
Fatigue
F
P
η p2
Early blocks Late blocks Early blocks Late blocks
− 1.52 ± 0.51 − 1.45 ± 0.54 − 4.05 ± 0.94 − 3.72 ± 0.95
− 1.63 ± 0.37 − 3.05 ± 0.39 − 6.81 ± 0.68 − 8.56 ± 0.69
0.02 5.73 5.62 16.96
0.86 0.02* 0.02* < 0.001**
0.001 0.21 0.21 0.44
Data are presented as mean ± SEM. MP, motor potential; RP, readiness potential. *Significant differences in RP and MP between the fatigue and the nonfatigue groups. **Significant differences in MP at late blocks between the fatigue and the non-fatigue groups.
in cognitive demands at the initial stages of the task other than muscle fatigue. However, the significant differences in the RP amplitude at the late stages at the Fp1 and FC1 sites may be attributable to the increased cognitive demands associated with maintaining a relatively difficult target force during muscle fatigue; these cognitive demands result from the requirement to achieve precise timing, a precise force level and memory processing. Furthermore, our data on effect size indicate that the RP amplitude is sensitive to prolonged exercise duration, which may induce a more cognitive component of effort. By contrast, there were no significant changes in the RP amplitude across blocks for participants in the nonfatigue group, indicating that prolonged exercise during the 30% MVC handgrip contraction with a sufficient 8-s intertrial rest did not induce a more cognitive component of effort for participants. As proposed by Jahanshahi et al. [14], the RP amplitude is sensitive to the experimental conditions. The MP increased significantly over the frontal cortex with the development of muscle fatigue, and the effect size indicated that muscle fatigue has considerable effects on MP amplitude rather than on RP. Several studies have examined the effects of muscle fatigue and weakness on MRCP amplitudes [3,10,25,26]. Shibata et al. [25] induced muscle weakness during task performance by an arterial occlusion technique, equivalent to muscle fatigue, and found that there was no increase in cortical negative activity before muscle contraction, but a significant increase in MP amplitude during movement execution, accompanied by increased sEMG activity. It was concluded that augmented cortical activation during movement execution induced by arterial occlusion may reflect the recruitment of additional motor units to compensate for the loss of force generation. Similarly, Jankelowitz and Colebatch [26] observed only small changes in the RP amplitude when muscles were weakened with peripheral intramuscular anaesthesia, but much larger changes in the MP during movement execution. De Morree et al. [3] observed that participants showed an increase in MRCP amplitudes only at Cz and only during movement execution in a fatiguing task involving lifting a weight with the arms, but the EEG was measured after an experimental manipulation to induce muscle fatigue, which suggests that the effect of fatigue is delayed, resulting in an increase in MP amplitude only
at the Cz site. In a similar study, Berchicci et al. [4] observed a difference in MP amplitude only at C1, with higher negativity in the fatigue group than in the nonfatigue group. Our results are also in agreement with the functional MRI findings of Liu and colleagues, who observed a gradual increase in the activation of the sensorimotor cortex during the first minute of a maximal 2-min isometric handgrip contraction, an activity equivalent to the submaximal isometric handgrip contraction in our protocol. The activity of muscle afferents (groups III and IV) affects central fatigue [27,28]. These afferents, which respond to mechanical, chemical and noxious stimuli, increase their firing rates as metabolites accumulate in fatiguing muscle [29,30]. Some investigators [6,31,32] have suggested that the activity of muscle afferents (groups III and IV) that are sensitive to fatigue affects motor cortical cells. Thus, the increases in MP amplitude observed in the present study may be attributed to the accumulation of metabolites in the fatiguing muscle, which contributes towards increases in sensorimotor cortex activity by group III and IV muscle afferents. Contribution of the prefrontal cortex to movements
The RP amplitude increased significantly in late blocks at the Fp1 and FC1 sites in the fatigue group compared with the nonfatigue group. The prefrontal cortex and the supplementary motor area have been assumed to play a role during preparations for self-initiated movements [18, 33]. In a hierarchical model of voluntary movements [13–15], the prefrontal cortex has been described as the brain region that formulates intentions and makes decisions before initiating movements. Jahanshahi et al. [15] observed that self-paced joystick movements involving decision-making about precise tasks and timing induced a slowly increasing subdural positivity before movement in the dorsolateral and inferior prefrontal cortex in an epileptic patient, as indicated by MRI. In the present study, waveforms of prefrontal electrodes were distinguished from those of motor area electrodes, suggesting that the prefrontal cortex may not be directly responsible for controlling voluntary movement. Consistent with the results of previous studies, participants in the nonfatigue group showed positive neural activity in the prefrontal cortex when performing a
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1142 NeuroReport 2014, Vol 25 No 14
physical task requiring decision-making to generate a precise force level with precise timing. In addition, the significant changes in the RP amplitude at Fp1 and FC1 in the fatigue group compared with the nonfatigue group in our self-initiated fatiguing task may have resulted from an increase in the cognitive demand of decision-making before initiating movement as muscle fatigue progressed. Berchicci et al. [4] examined prefrontal activity during muscle fatigue involving submaximal voluntary contractions of the lower limbs and found that participants showed higher positive activity in the prefrontal cortex in the fatigue group than in the nonfatigue group. However, in our study, in the fatigue group, the prefrontal cortex showed negative waveforms; this result is supported by the results of Jahanshahi et al. [15], who observed that MRCPs recorded using subdural electrodes placed over the prefrontal cortex yielded either positive or negative waveforms, and by the results of Ikeda et al. [34], who showed that these waveforms were positive or negative depending on the orientation of the dipole and its position relative to the electrode. Our result may suggest that the orientation of the dipole at this cortical position changes during muscle fatigue. Although Hilty et al. [7] did not use MRCPs to examine the effects of fatigue on cortical activity, they observed widespread, significant increases in α and β in Brodmann area 11 (medial orbitofrontal cortex) after an exhausting cycling exercise. It was concluded that in the context of exhausting exercise, a prominent increase in power within the medial orbitofrontal cortex may be interpreted as endurance in response to an increasing perception of fatigue. Correlations between movement-related cortical potentials and the surface electromyography
A significant within-subject correlation was observed between MP amplitude at the C1 site and RMS sEMG. The C1 site overlies the contralateral primary motor cortex, which plays an important role in controlling voluntary movement. The MP component of MRCPs corresponded to the time of movement initiation, reflecting a central command to control the muscle group involved in a movement task, as described by Kristeva et al. [12]. According to Gottlieb et al. [35], a central command activates the α-motor neuron pool in the spinal cord that innervates the target muscle. The increased RMS sEMG amplitude during fatigue observed in the present study is in agreement with the findings of other studies using submaximal fatiguing protocols [21–23] and can be viewed as a reflection of the recruitment of additional motor units and/or an increasing activity level of the active motor units [24]. Thus, the augmented MP amplitude at the cortical level seems to contribute towards the recruitment of additional motor units. In general, the changes produced in both the motor cortex and the α-motor neuron pool during muscle fatigue may reflect central mechanisms for compensating for the loss in force production with muscle fatigue.
Conclusion Neurophysiological evidence has shown that cortical activation over supplementary motor and sensorimotor areas increases with muscle fatigue progression, as reflected in the amplitudes of MRCP. The present study is the first to show that activity in the prefrontal cortex becomes more negative with the development of muscle fatigue. The sEMG was significantly correlated with the MP amplitude at contralateral primary motor areas during muscle fatigue, suggesting that central mechanisms attempt to compensate for the loss in force production under muscle fatigue. In addition, we observed that muscle fatigue has considerable effects on the components of MRCPs during movement execution, whereas the RP before movement was sensitive to exercise duration. Future studies should utilize imaging techniques to explore the source of activation changes for components of MRCPs during muscle fatigue.
Acknowledgements The authors thank Zai-fu Duan from the Cognitive Psychological Laboratory at Shenyang Sport University for providing equipment. The authors are grateful for funding from the Liaoning Provincial Committee of Education (L2012412), the State General Administration of Sport in China (2012A013) in China and the Company in Shandong Ajiao Co. Ltd in China. Conflicts of interest
There are no conflicts of interest.
References 1
Chaudhuri A, Behan PO. Fatigue in neurological disorders. Lancet 2004; 363:978–988. 2 Zwarts MJ, Bleijenberg G, van Engelen BG. Clinical neurophysiology of fatigue. Clin Neurophysiol 2008; 119:2–10. 3 De Morree HM, Klein C, Marcora SM. Perception of effort reflects central motor command during movement execution. Psychophysiology 2012; 49:1242–1253. 4 Berchicci M, Menotti F, Macaluso A, Di Russo F. The neurophysiology of central and peripheral fatigue during sub-maximal lower limb isometric contractions. Front Hum Neurosci 2013; 7:135. 5 Taylor JL, Gandevia SC. A comparison of central aspects of fatigue in submaximal and maximal voluntary contractions. J Appl Physiol (1985) 2008; 104:542–550. 6 Hilty L, Jäncke L, Luechinger R, Boutellier U, Lutz K. Limitation of physical performance in a muscle fatiguing handgrip exercise is mediated by thalamoinsular activity. Hum Brain Mapp 2011; 32:2151–2160. 7 Hilty L, Langer N, Pascual-Marqui R, Boutellier U, Lutz K. Fatigue-induced increase in intracortical communication between mid/anterior insular and motor cortex during cycling exercise. Eur J Neurosci 2011; 34:2035–2042. 8 Kornhuber HH, Deecke L. Changes in the brain potential in voluntary movements and passive movements in man: readiness potential and reafferent potentials. Pflugers Arch Gesamte Physiol Menschen Tiere 1965; 284:1–17. 9 Bozzacchi C, Giusti MA, Pitzalis S, Spinelli D, Di Russo F. Awareness affects motor planning for goal-oriented actions. Biol Psychol 2012; 89:503–514. 10 Johnston J, Rearick M, Slobounov S. Movement-related cortical potentials associated with progressive muscle fatigue in a grasping task. Clin Neurophysiol 2001; 112:68–77. 11 Schillings ML, Kalkman JS, van der Werf SP, Bleijenberg G, van Engelen BG, Zwarts MJ. Central adaptations during repetitive contractions assessed by the readiness potential. Eur J Appl Physiol 2006; 97:521–526. 12 Kristeva R, Cheyne D, Lang W, Lindinger G, Deecke L. Movement-related potentials accompanying unilateral and bilateral finger movements with
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
MRCPs during muscle fatigue Guo et al. 1143
13
14
15
16 17 18 19 20 21 22
23 24
different inertial loads. Electroencephalogr Clin Neurophysiol 1990; 75:410–418. Freude G, Ullsperger P. Changes in Bereitschaftspotential during fatiguing and non-fatiguing hand movements. Eur J Appl Physiol Occup Physiol 1987; 56:105–108. Jahanshahi M, Jenkins IH, Brown RG, Marsden CD, Passingham RE, Brooks DJ. Self-initiated versus externally triggered movements I. An investigation using measurement of regional cerebral blood flow with PET and movement-related potentials in normal and Parkinson’s disease subjects. Brain 1995; 118:913–933. Jahanshahi M, Dirnberger G, Liasis A, Towell A, Boyd S. Does the pre-frontal cortex contribute to movement-related potentials? Recordings from subdural electrodes. Neurocase 2001; 7:495–501. Liu JZ, Dai TH, Sahgal V, Brown RW, Yue GH. Nonlinear cortical modulation of muscle fatigue: a functional MRI study. Brain Res 2002; 957:320–329. Gratton G, Coles MG, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalogr Clin Neurophysiol 1983; 55:468–484. Shibasaki H, Hallett M. What is the Bereitschaftspotential? Clin Neurophysiol 2006; 117:2341–2356. Bland JM, Altman DG. Calculating correlation coefficients with repeated observations: part 1 – correlation within subjects. BMJ 1995; 310:446. Enoka RM, Duchateau J. Muscle fatigue: what, why and how it influences muscle function. J Physiol 2008; 586:11–23. Moritani T, Muro M, Nagata A. Intramuscular and surface electromyogram changes during muscle fatigue. J Appl Physiol (1985) 1986; 60:1179–1185. Merletti R, Knaflitz M, De Luca CJ. Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. J Appl Physiol (1985) 1990; 69:1810–1820. Fuglevand AJ, Winter DA, Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol 1993; 70:2470–2488. Esposito F, Orizio C, Veicsteinas A. Electromyogram and mechanomyogram changes in fresh and fatigued muscle during sustained contraction in men. Eur J Appl Physiol Occup Physiol 1998; 78:494–501.
25
26
27 28 29
30
31
32
33
34
35
Shibata M, Oda S, Moritani T. The relationships between movement-related cortical potentials and motor unit activity during muscle contraction. J Electromyogr Kinesiol 1997; 7:79–85. Jankelowitz SK, Colebatch JG. Movement related potentials in acutely induced weakness and stroke. Exp Brain Res 2005; 161: 104–113. Gandevia SC. Spinal and supraspinal factors in human muscle fatigue. Physiol Rev 2001; 81:1725–1789. Nybo L, Secher NH. Cerebral perturbations provoked by prolonged exercise. Prog Neurobiol 2004; 72:223–261. Li J, Sinoway LI. ATP stimulates chemically sensitive and sensitizes mechanically sensitive afferents. Am J Physiol Heart Circ Physiol 2002; 283: H2636–H2643. Sinoway LI, Hill JM, Pickar JG, Kaufman MP. Effects of contraction and lactic acid on the discharge of group III muscle afferents in cats. J Neurophysiol 1993; 69:1053–1059. Gandevia SC, Allen GM, Butler JE, Taylor JL. Supraspinal factors in human muscle fatigue: evidence for suboptimal output from the motor cortex. J Physiol 1996; 490:529–536. Taylor JL, Butler JE, Allen GM, Gandevia SC. Changes in motor cortical excitability during human muscle fatigue. J Physiol 1996; 490 (Pt 2): 519–528. Deecke L. Bereitschaftspotential as an indicator of movement preparation in supplementary motor area and motor cortex. Ciba Found Symp 2008; 713:231. Ikeda A, Lüders HO, Burgess RC, Shibasaki H. Movement-related potentials recorded from supplementary motor area and primary motor area. Role of supplementary motor area in voluntary movements. Brain 1992; 115 (Pt 4): 1017–1043. Gottlieb GL, Corcos DM, Agarwal GC. Organizing principles for single-joint movements. I. A speed-insensitive strategy. J Neurophysiol 1989; 62:342–357.
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