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Neural mechanism of central inhibition during physical fatigue: A magnetoencephalography study
brain research 1537 (2013) 117–124
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Research Report
Neural mechanism of central inhibition during physical fatigue: A magnetoencephalography study Masaaki Tanakaa,n, Akira Ishiia, Yasuyoshi Watanabea,b a
Department of Physiology, Osaka City University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan b RIKEN Center for Life Science Technologies, 6-7-3 Minatojima-minamimachi, Chuo-ku, Hyogo 650-0047, Japan
art i cle i nfo
ab st rac t
Article history:
Central inhibition plays an important role in physical performance during physical fatigue.
Accepted 27 August 2013
We tried to clarify the neural mechanism of central inhibition during physical fatigue using
Available online 3 September 2013
the magnetoencephalography (MEG) and a classical conditioning technique. Twelve right-
Keywords:
handed volunteers participated in this study. Participants underwent MEG recording
Alpha frequency
during the imagery of maximum grips of the right hand guided by metronome sounds
Central inhibition
for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for
Classical conditioning
10 min; the metronome sounds were started 5 min after the beginning of the handgrip
Dorsolateral prefrontal cortex
trials. We used metronome sounds as conditioned stimuli and maximum handgrip trials
Event-related desynchronization
as unconditioned stimuli to cause central inhibition. The next day, MEG recording during
Magnetoencephalography
the imagery of maximum grips of the right hand guided by metronome sounds were
Physical fatigue
measured for 10 min. Levels of the fatigue sensation in the right hand and sympathetic nerve activity on the second day were significantly higher than those on the first day. In the right dorsolateral prefrontal cortex (Brodmann's area 46), the alpha-band eventrelated desynchronization (ERD) of the second MEG session relative to the first session with the time window of 200 to 300 ms after the onset of handgrip cue sounds was identified. The ERD level in this brain region was positively associated with the change in subjective level of right hand fatigue after the conditioning session and was negatively associated with that of the sympathetic nerve activity. We demonstrated that the right dorsolateral prefrontal cortex is involved in the neural substrates of central inhibition during physical fatigue. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Fatigue can be defined as the difficulty in initiating or sustaining voluntary activities (Chaudhuri and Behan, 2004). Fatigue can be classified as physical or mental, and physical fatigue can be classified as peripheral or central. In contrast to peripheral fatigue, central fatigue is caused at sites proximal n
Corresponding author. Fax: þ81 6 6645 3712. E-mail address: [email protected] (M. Tanaka).
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to the peripheral nerves and is defined as a progressive decline in the ability to activate muscles voluntarily (Gandevia et al., 1996; Taylor et al., 1996). Increased inhibition from groups III and IV afferent nerves, which carry sensory information to the central nervous system, to motor neurons in the spinal cord, was reported during physical fatigue (Hayward et al., 1988; Garland et al., 1988;
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Hayward et al., 1991; Garland, 1991; Garland and Kaufman, 1995). The inhibitory input to the motoneuron pool in the spinal cord projects to output neurons in the primary motor cortex (M1) through ascending pathways and the inhibitory input changes the signals of these cells in M1 (Peltier et al., 2005). The inhibitory input from the spinal cord thus seems to contribute to fatigue-related changes in M1. The supraspinal existence of central fatigue was suggested in a neuroimaging study using Ramachandran's mirror box (Tanaka et al., 2011). Ramachandran's mirror box was constructed by placing a vertical mirror inside a cardboard box with the roof of the box removed; the front of the box had two holes in it through which the participants inserted their arms (Ramachandran et al., 1995). The movement-evoked magnetoencephalography (MEG) response to the imagery of maximum voluntary contractions in the contralateral sensorimotor area was reduced by physical fatigue without the mirror box; however, this reduction completely disappeared with the mirror box. These results confirmed that sensory input was channeled to the ipsilateral M1 using the mirror box, and provided evidence for supraspinal existence of central inhibition (Tanaka et al., 2011). Recently, we performed a neuroimaging study of classical conditioning of physical fatigue (Tanaka et al., 2013). In this study, metronome sounds were used as conditioned stimuli and physical task trials were used as unconditioned stimuli to cause physical fatigue. Participants underwent MEG measurements during the imagery of maximum handgrips guided by metronome sounds for 10 min. Thereafter, fatigue-inducing physical task trials were performed for 10 min; metronome sounds were started 5 min after the beginning of the task trials. The next day, neural activities during the imagery of maximum handgrips guided by metronome sounds for 10 min were measured. The level of fatigue sensation caused by listening to the metronome sounds on the second day was higher relative to the first day and the equivalent current dipoles (ECDs) in the insular cortex and posterior cingulate cortex were observed only after the conditioning session. These MEG results showed that classical conditioning of physical fatigue took place, and that these brain regions were involved in the neural substrates of physical fatigue related to classical conditioning. However, some brain regions involved in the neural substrates of physical fatigue were thought to have been missed because of limitations of the ECD method. In addition, these brain regions were reported to contribute to fatigue sensation rather than to central inhibition during physical fatigue (Ishii et al., 2012). Therefore, the neural mechanisms of central inhibition during physical fatigue have not been clarified. The aim of our present study was to identify the neural mechanisms of central inhibition related to classical conditioning during physical fatigue. We tried to identify the neural substrates of central inhibition through the comparison of neural activities during the imagery of maximum handgrips between conditioned and unconditioned states, as the difference between these conditions has been shown to be limited to the presence and absence of central inhibition (Tanaka et al., 2012; Shigihara et al., 2013a; Tanaka et al., 2013). In addition to having a high temporal resolution, MEG has an advantage of measuring brain activity using time–frequency
analyses (Stam, 2010). Oscillatory brain rhythms are considered to originate from synchronous synaptic activities of a large number of neurons (Brookes et al., 2011). Synchronization of neural networks may reflect integration of information processing, and such synchronization processes can be evaluated using MEG time-frequency analyses; multiple, broadly distributed, and continuously interacting dynamic neural networks are achievable through the synchronization of oscillations at particular time–frequency bands (Varela et al., 2001). In particular, event-related desynchronization (ERD) of alpha frequency band (8–13 Hz) was reported to be associated with fatigue in the central nervous system (Shigihara et al., 2013b; Ishii et al., in press). Alterations of the decreased MEG alpha power densities in some brain regions induced by classical conditioning during the imagery of maximum handgrips may provide valuable clues to identify the neural mechanism of central inhibition. In addition, the correlation analyses between the MEG variables and changes of subjective and physiological parameters after classical conditioning may provide important clues regarding the roles of MEG variables on physical fatigue.
2.
Results
To assess changes in the subjective level of fatigue after the 10min maximum handgrip trials, two-way analyses of variance (ANOVAs) for repeated measures were performed. Significant main effects of hand [F(1,11)¼63.43, Poo0.001] and time course [F(1,11)¼ 35.21, Poo0.001] and a hand time course interaction effect [F(1,11)¼39.18, Poo0.001] were shown. The level of subjective fatigue of the right hand after the handgrip trials was significantly higher than before the handgrip trials (Fig. 1A). However, the level of subjective fatigue of the left hand was not altered after the handgrip trials (Fig. 1A). The handgrip force of the right hand after the handgrip trials was significantly lower than that before the trials (Fig. 1B). To assess changes in the subjective level of fatigue after the conditioning session, comparisons of fatigue scores before and after the first and second MEG sessions were performed. Although the main effect of conditioning [F(1,11)¼2.80, P¼0.122] was not shown, a significant main effect of time course [F(1,11)¼11.83, Poo0.001] and a conditioning time course interaction effect [F(1,11)¼ 7.45, P¼ 0.019] were shown in the right hand. The main effects of conditioning [F(1,11)¼ 0.18, P¼ 0.681] and time course [F(1,11)oo0.01, P¼ 1.000] and a conditioning time course interaction effect [F(1,11)¼3.48, P¼0.089] were not shown in the left hand. Subjective levels of right hand fatigue after the second MEG session was higher than that after the first MEG session (Fig. 2A), although the subjective level of left hand fatigue after the second MEG session was not different from that after the first MEG session (Fig. 2B). We also assessed changes in high-frequency (HF) power and low-frequency (LF) power/HF power ratio after the conditioning session. Although HF was not altered after the conditioning session (Fig. 3A), LF/HF ratio was significantly increased after the conditioning session (Fig. 3B). To identify the brain region associated with the central inhibition during physical fatigue, the decreased oscillatory power, that is, ERD, for alpha frequency band in the second
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Fig. 1 – Visual analog scale (VAS) values of right and left hands for fatigue (A) and handgrip forces (B) immediately before (open columns) and after (closed columns) the 10-min fatigue-inducing handgrip trials. Data are mean and SD. nnPo0.01, nPo0.05, significantly different (paired t-test).
Fig. 2 – Visual analog scale (VAS) values of right (A) and left (B) hands for fatigue immediately before (open columns) and after (closed columns) the first and second magnetoencephalography (MEG) sessions. Data are mean and SD. nPo0.05, nnPo0.01, significant difference (paired t-test).
MEG session relative to the first MEG session was evaluated. These results are shown in Table 1 and Fig. 4. Among the entire brain regions with all the time windows of 0–1000 ms (every 100 ms) after the onset of handgrip cue sounds, only the right dorsolateral prefrontal cortex (Brodmann's area 46) with the time window of 200–300 ms showed a significant ERD (Poo0.05, corrected for multiple comparisons). Finally, to evaluate the relationships between the ERD level of alpha frequency band in the right dorsolateral prefrontal cortex (Brodmann's area 46) and changes in subjective fatigue scores and autonomic nerve activities after the conditioning session, correlation analyses were performed. The ERD level was positively associated with the change of subjective level of right hand fatigue after the conditioning session (Fig. 5A; R¼ 0.643, P¼ 0.024). The ERD level tended to be positively associated with the change of HF (Fig. 5B; R ¼0.548, P ¼0.065) and negatively associated with the change of LF/HF ratio (Fig. 5C; R¼ 0.591, P¼ 0.043) after the conditioning session.
3.
Discussion
The present study showed the alpha-band ERD of the second MEG session relative to the first session with the time
window of 200–300 ms after the onset of handgrip cue sounds in the right dorsolateral prefrontal cortex (Brodmann's area 46) (Fig. 4 and Table 1). In addition, the ERD level in this brain region was positively associated with the subjective level of right hand fatigue (Fig. 5A), and this ERD level was negatively associated with the sympathetic nerve activity (Fig. 5C). Our results therefore provide evidence that the right dorsolateral prefrontal cortex is involved in the neural substrates of central inhibition during physical fatigue. We first tried to confirm whether classical conditioning of central inhibition occurred after the 10-min maximum handgrip trials. Because the handgrip force of the right hand was decreased (Fig. 1A) and the subjective level of the right hand fatigue (Fig. 1B) was increased after the handgrip trials, physical fatigue was induced by the conditioning session. Similar to our previous report (Tanaka et al., 2013), we showed that subjective level of right hand fatigue (Fig. 2A) and sympathetic nerve activity (Fig. 3B) were significantly increased after the conditioning session. Recently, we showed that the subjective level of fatigue and sympathetic nerve activity assessed by the time–frequency analyses of R–R intervals using ECG were higher in a central inhibition condition relative to the control condition, and the subjective level of fatigue and the sympathetic nerve activity were suggested to be closely related to central inhibition (Tanaka et al., 2013).
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Fig. 3 – Autonomic nerve activities assessed by frequency analyses of electrocardiography. High-frequency power (HF; A) and low-frequency power (LF)/HF ratio (B) were assessed during the first (open columns) and second (closed columns) magnetoencephalography (MEG) sessions. Data means and SD. nPo0.05, significantly different from the corresponding values of the first MEG session (paired t-test). Table 1 – Brain region that showed event-related desynchronization of alpha frequency band in the second magnetoencephalography session relative to the first session. Location
Middle frontal gyrus
Side
Right
Brodmann's area
46
Coordinate (mm)
Z-value
x
y
z
47
53
20
3.79
x, y, z: Stereotaxic coordinate of peak of activated cluster. Random-effect analyses of 12 participants (Po0.05, corrected for multiple comparisons).
Fig. 4 – Statistical parametric maps of event-related desynchronization of alpha frequency band (second magnetoencephalography session relative to the first session; random-effect analyses of 12 participants, Po0.05, corrected for multiple comparisons). Statistical parametric maps are superimposed on surface-rendered highresolution MRIs. The color bar indicates T-values.
Therefore, these observations demonstrate that central inhibition was classically conditioned successfully (Ishii et al., 2013). We tried to identify the neural substrates of central inhibition during physical fatigue through the comparison of the activities during the imagery of maximum handgrips between the conditioned and unconditioned states, as the difference between these conditions was limited to the presence and absence of the central inhibition (Tanaka et al., 2012; Shigihara et al., 2013a; Tanaka et al., 2013). Using
the metronome sounds as the conditioned stimuli and the 10-min maximum handgrip trials as the unconditioned stimuli, our MEG study showed that the right dorsolateral prefrontal cortex (Brodmann's area 46) was specifically activated during the imagery of maximum grips of the right hand after conditioning (Fig. 4 and Table 1), suggesting that this brain region is involved in the neural substrates of central inhibition. The positive relation of the fatigue sensation of right hand (Fig. 5A) and negative relation of the sympathetic nerve activity (Fig. 5C) with the neural activation in the right dorsolateral prefrontal cortex support the inhibitory role of this brain region during physical fatigue (Nugent et al., 2011). The inhibitory role of the dorsolateral prefrontal cortex has been widely reported for factors such as stereotyped response (Kadota et al., 2010), working memory (Daskalakis et al., 2008), emotion (Leyman et al., 2009), cigarette craving (Hayashis et al., 2013), and gamma band activity (Farzan et al., 2010) through the cortical GABA(B) receptor mediated inhibitory neurotransmission (Farzan et al., 2009; Fitzgerald et al., 2009). Because the dorsolateral prefrontal cortex alters motor output from M1 (Kolb and Whishaw, 1983; Goldman-Rakic, 1987; Narayanan and Laubach, 2006), this brain region may attempt to lose the force-generating ability of the fatiguing muscles and even amplify the inhibitory input to the sensorimotor area to decrease the descending motor output from M1. There are two limitations to our study. First, we performed our study with a limited number of participants. In addition, while women are more fatigued than men, the participants in this study are all men. To generalize our results, studies involving a large number of participants including women are essential. Second, it was difficult to assess the neural
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Fig. 5 – Relationships between the event-related desynchronization (ERD) level of alpha frequency band in the right middle frontal gyrus (Brodmann's area 46) and changes of visual analog scale (VAS) of right hand for fatigue (A), high-frequency power (HF; B), and low-frequency power (LF)/HF ratio (C). Linear regression lines, Pearson's correlation coefficients, and P values are shown.
activities of the brain regions located deeply by using MEG. Therefore, some brain regions involved in the neural substrates of central inhibition during physical fatigue might be missed because of the limitations of MEG. For example, the amygdala is reported to be related to the classical conditioning (Sehlmeyer et al., 2009) as well as fatigue (Boksem and Tops, 2008), and the orbitofrontal cortex is reported to be associated with fatigue sensation (Tajima et al., 2010). Future studies using other neuroimaging techniques, such as functional magnetic resonance imaging and positron emission tomography, would address this limitation. In conclusion, we demonstrated that the right dorsolateral prefrontal cortex (Brodmann's area 46) is involved in the neural substrates of central inhibition during physical fatigue. Central inhibition is considered to be associated with the pathophysiology of chronic fatigue in human diseases or syndromes (Tanaka and Watanabe, 2010, 2012a, 2012b). Our findings may help clarify the mechanisms of central fatigue as well as aid in the development of treatment methods for patients suffering from chronic fatigue.
4.
Experimental procedures
4.1.
Participants
Twelve healthy male volunteers (age, 27.0778.8 years [mean7 7SD]) were enrolled. According to the Edinburgh handedness inventory (Oldfield, 1971), all participants were right-handed. Current smokers, participants with a history of mental or brain disorders, and those taking chronic medications that affect the central nervous system were excluded. All participants provided written informed consent before participation. This study was approved by the Ethics Committee of Osaka City University and was conducted in accordance with the principles of the Declaration of Helsinki.
4.2.
Experimental design
The experiment consisted of two MEG sessions and a single conditioning session (Fig. 6). On the first day, MEG recording
Fig. 6 – Experimental design. On the first day, neural activities during the imagery of handgrips guided by the handgrip cues of metronome sounds were measured using magnetoencephalography (MEG) for 10 min (first MEG session). Thereafter, 10-min fatigue-inducing maximum handgrip trials (conditioning session) were performed, in which metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause central inhibition. On the next day, neural activities during the imagery of handgrips guided by the handgrip cues of metronome sounds were measured using MEG for 10 min (second MEG session).
during the imagery of maximum grips of the right hand, guided by metronome sounds, was performed for 10 min (first MEG session). The imagery of maximum grips of the right hand rather than submaximum grips was performed, since it was difficult to correctly imagine the level of submaximum handgrips (such as 80% and 50% of maximum voluntary contraction level). Thereafter, 10-min fatigue-inducing maximum handgrip trials using a device (HAND GRIPS 30 kg; IGNIO, Nagoya, Japan) were performed (conditioning session), in which the metronome sounds (same as the MEG session) were started 5 min after the beginning of the handgrip trials and the sounds were continued until the end of the handgrip trials. We used metronome sounds as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause central inhibition (Tanaka et al., 2013). Participants
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were not informed about the metronome sounds before the task trials. On the next day, MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds was performed for 10 min (second MEG session). Each MEG session consisted of 150 blocks, and each block consisted of three pacing cues followed by one handgrip cue. During the MEG session, participants heard the sound cues every 1 s with their eyes closed, and every 4 s during the handgrip cue period, they were requested to imagine that they were gripping a soft ball with their right hand at a maximal voluntary contraction level for 1 s. The pacing cue consisted of white noise that lasted 33 ms; the handgrip cue consisted of a 1000 Hz tone that lasted 1 s. All cue sounds were produced by Windows Media Player (Microsoft Corporation, Redmond, WA) and were converted to electric signals by a sound card (Creative X-Fi Audio Processor [WDM]; Creative Technology, Singapore, Singapore) installed in a personal computer (DELL Precision 390; Dell, Round Rock, TX). The sound signal was amplified by an audio amplifier (MA-500U; Onkyo Corporation, Tokyo, Japan) outside of the magnetically shielded room. During the conditioning session, participants watched a fixed mark (þ; black mark on white background) on a screen placed in front of their eyes using a video projector (PG-B10S; SHARP, Osaka, Japan). When a handgrip cue mark ( ; black mark on white background) was presented instead of the fixation mark every 4 s, they were requested to perform a handgrip with their right hand at a maximal voluntary contraction level for 1 s by gripping the device. The timing of the visual handgrip cues was same as that of the metronome handgrip cue sounds started 5 min after the beginning of the handgrip trials. Electrocardiography (ECG) was recorded during the MEG sessions. Just before and after the conditioning session and after each MEG session, participants were asked to subjectively rate their fatigued level of the right and left hands on a visual analog scale (VAS) from 0 (minimum) to 100 (maximum) (Lee et al., 1991). In addition, just before and after the conditioning session, handgrip forces of the right hand were measured using a handgrip meter (ST-100, Toei Light Co., Ltd, Saitama, Japan). This study was conducted in a quiet, temperature-, and humidity-controlled, magnetically shielded room. During the experiment, participants lay on a bed in the supine position. For 1 day before each visit, they refrained from intense physical and mental activities and caffeinated beverages, consumed a normal diet, and maintained normal sleeping hours.
4.3.
MEG recordings
MEG recordings were performed using a 160-channel wholehead type MEG system (MEG vision; Yokogawa Electric Corporation, Tokyo, Japan) with a magnetic field resolution of 4 fT/Hz1/2 in the white-noise region. The sensor and reference coils were gradiometers 15.5 mm in diameter and 50 mm at baseline, and each pair of sensor coils was separated at a distance of 23 mm. The sampling rate was 1000 Hz with a 200 Hz hard low-pass filter and a 0.3 Hz hard high-pass filter.
4.4.
MEG data analyses
MEG signal data were analyzed offline after analog-to-digital conversion. Magnetic noise originating from outside the shield room was eliminated by subtracting the data obtained from reference coils using a software program (MEG 160; Yokogawa Electric Corporation) followed by artifact rejection using careful visual inspection. The MEG data were split into segments of 1000 ms length (from 0 to 1000 ms after the onset of each handgrip cue sound), and the segments were averaged. After averaging, the data were band-pass filtered by a fast Fourier transform using Frequency Trend (Yokogawa Electric Corporation) to obtain time–frequency band signals using a software Brain Rhythmic Analysis for MEG (BRAM; Yokogawa Electric Corporation) (Dalal et al., 2008). Localization and intensity of the time–frequency power of cortical activities were estimated by using BRAM software, which used narrow-band adaptive spatial filtering methods as an algorithm (Dalal et al., 2008). These data were then analyzed using statistical parametric mapping (SPM8, Wellcome Department of Cognitive Neurology, London, UK), implemented in Matlab (Mathworks, Sherbon, MA). The MEG anatomical/spatial parameters used to warp the volumetric data were transformed into the Montreal Neurological Institute (MNI) template of T1-weighed images (Evans et al., 1994) and applied to the MEG data. The anatomically normalized MEG data were filtered with a Gaussian kernel of 20 mm (full-width at half-maximum) in the x, y, and z axes (voxel dimension was 5.0 mm 5.0 mm 5.0 mm). The decreased oscillatory power, that is, ERD, for alpha band (8–13 Hz) within the time window of 0–1000 ms (every 100 ms) in the second MEG session relative to the first MEG session was measured on a region-of-interest basis to obtain the neural activation pattern of central inhibition during physical fatigue. The resulting set of voxel values for each comparison constituted a SPM of the t statistics (SPM{t}). The SPM{t} was transformed to the unit of normal distribution (SPM{Z}). The threshold for the SPM{Z} of individual analyses was set at Poo0.05 (corrected for multiple comparisons). The weighted sum of the parameters estimated in the individual analyses consisted of “contrast” images, which were used for the group analyses (Friston et al., 1999). Individual data were summarized and incorporated into a random-effect model so that inferences could be made at a population level (Friston et al., 1999). SPM{t} and SPM{Z} for the contrast images were created as described above. Significant signal changes for each contrast were assessed by means of t statistics on a voxel-byvoxel basis (Friston et al., 1999). The threshold for the SPM{Z} of group analyses was set at Poo0.05 (corrected for multiple comparisons). Anatomical localization of significant voxels within cluster were done using Talairach Demon software (Lancaster et al., 2000).
4.5.
Magnetic resonance imaging overlay
Anatomic magnetic resonance imaging (MRI) was performed using a Philips Achieva 3.0TX (Royal Philips Electronics, Eindhoven, The Netherlands) for all participants to permit registration of magnetic source locations with their respective anatomic locations. Before MRI scanning, five adhesive
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markers (Medtronic Surgical Navigation Technologies Inc., Broomfield, CO) were attached to the skin of each participant's head (the first and second ones were located 10 mm in front of the left tragus and right tragus, the third at 35 mm above the nasion, and the fourth and fifth at 40 mm right and left of the third one). MEG data were superimposed on MRI scans using information obtained from these markers and MEG localization coils.
4.6.
ECG
To evaluate autonomic nerve activities, ECG was recorded during the MEG sessions. ECG data were analyzed using MemCalc for Windows (Global Medical Solution Inc., Tokyo, Japan). R–R wave variability was measured as an indicator of autonomic nerve activity. For frequency domain analyses of the R–R wave intervals, LF power was calculated as the power within the frequency range of 0.04–0.15 Hz, and HF power was calculated as that within the frequency range of 0.15–0.4 Hz. LF and HF were measured in absolute units (ms2). The average power densities within these frequency ranges were log-transformed (ln) for normalization (Mizuno et al., 2011). The HF is vagally mediated (Akselrod et al., 1981; Pomeranz et al., 1985; Malliani et al., 1991), but the LF originates from a variety of sympathetic and vagal mechanisms (Akselrod et al., 1981; Appel et al., 1989). The LF/HF ratio represents sympathetic nerve activity (Pagani et al., 1997).
4.7.
Statistical analyses
Values are presented as mean77SD, unless otherwise stated. ANOVA for repeated measures were performed to assess the effects of the hand (right or left) and time course within the conditioning session as well as the effects of the conditioning and time course within the MEG session on the subjective level of fatigue. A paired t-test was used to evaluate significant differences between the two conditions. Pearson's correlation analyses were conducted to evaluate the relationships between the MEG responses and changes of subjective scores and autonomic nerve activities after the conditioning session. All P values were two-tailed, and values less than 0.05 were considered statistically significant. Statistical analyses were performed using IBM SPSS 20.0 (IBM, Armonk, NY).
Acknowledgments We thank Forte Science Communications for editorial help with the manuscript and Manryoukai Imaging Clinic for MRI scans. This work was supported in part by the Grant-in-Aid for Scientific Research B (KAKENHI: 23300241) from Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan and by the Health Labor Sciences Research Grant of Japan.
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Research Report
Neural mechanism of central inhibition during physical fatigue: A magnetoencephalography study Masaaki Tanakaa,n, Akira Ishiia, Yasuyoshi Watanabea,b a
Department of Physiology, Osaka City University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan b RIKEN Center for Life Science Technologies, 6-7-3 Minatojima-minamimachi, Chuo-ku, Hyogo 650-0047, Japan
art i cle i nfo
ab st rac t
Article history:
Central inhibition plays an important role in physical performance during physical fatigue.
Accepted 27 August 2013
We tried to clarify the neural mechanism of central inhibition during physical fatigue using
Available online 3 September 2013
the magnetoencephalography (MEG) and a classical conditioning technique. Twelve right-
Keywords:
handed volunteers participated in this study. Participants underwent MEG recording
Alpha frequency
during the imagery of maximum grips of the right hand guided by metronome sounds
Central inhibition
for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for
Classical conditioning
10 min; the metronome sounds were started 5 min after the beginning of the handgrip
Dorsolateral prefrontal cortex
trials. We used metronome sounds as conditioned stimuli and maximum handgrip trials
Event-related desynchronization
as unconditioned stimuli to cause central inhibition. The next day, MEG recording during
Magnetoencephalography
the imagery of maximum grips of the right hand guided by metronome sounds were
Physical fatigue
measured for 10 min. Levels of the fatigue sensation in the right hand and sympathetic nerve activity on the second day were significantly higher than those on the first day. In the right dorsolateral prefrontal cortex (Brodmann's area 46), the alpha-band eventrelated desynchronization (ERD) of the second MEG session relative to the first session with the time window of 200 to 300 ms after the onset of handgrip cue sounds was identified. The ERD level in this brain region was positively associated with the change in subjective level of right hand fatigue after the conditioning session and was negatively associated with that of the sympathetic nerve activity. We demonstrated that the right dorsolateral prefrontal cortex is involved in the neural substrates of central inhibition during physical fatigue. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Fatigue can be defined as the difficulty in initiating or sustaining voluntary activities (Chaudhuri and Behan, 2004). Fatigue can be classified as physical or mental, and physical fatigue can be classified as peripheral or central. In contrast to peripheral fatigue, central fatigue is caused at sites proximal n
Corresponding author. Fax: þ81 6 6645 3712. E-mail address: [email protected] (M. Tanaka).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.08.054
to the peripheral nerves and is defined as a progressive decline in the ability to activate muscles voluntarily (Gandevia et al., 1996; Taylor et al., 1996). Increased inhibition from groups III and IV afferent nerves, which carry sensory information to the central nervous system, to motor neurons in the spinal cord, was reported during physical fatigue (Hayward et al., 1988; Garland et al., 1988;
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Hayward et al., 1991; Garland, 1991; Garland and Kaufman, 1995). The inhibitory input to the motoneuron pool in the spinal cord projects to output neurons in the primary motor cortex (M1) through ascending pathways and the inhibitory input changes the signals of these cells in M1 (Peltier et al., 2005). The inhibitory input from the spinal cord thus seems to contribute to fatigue-related changes in M1. The supraspinal existence of central fatigue was suggested in a neuroimaging study using Ramachandran's mirror box (Tanaka et al., 2011). Ramachandran's mirror box was constructed by placing a vertical mirror inside a cardboard box with the roof of the box removed; the front of the box had two holes in it through which the participants inserted their arms (Ramachandran et al., 1995). The movement-evoked magnetoencephalography (MEG) response to the imagery of maximum voluntary contractions in the contralateral sensorimotor area was reduced by physical fatigue without the mirror box; however, this reduction completely disappeared with the mirror box. These results confirmed that sensory input was channeled to the ipsilateral M1 using the mirror box, and provided evidence for supraspinal existence of central inhibition (Tanaka et al., 2011). Recently, we performed a neuroimaging study of classical conditioning of physical fatigue (Tanaka et al., 2013). In this study, metronome sounds were used as conditioned stimuli and physical task trials were used as unconditioned stimuli to cause physical fatigue. Participants underwent MEG measurements during the imagery of maximum handgrips guided by metronome sounds for 10 min. Thereafter, fatigue-inducing physical task trials were performed for 10 min; metronome sounds were started 5 min after the beginning of the task trials. The next day, neural activities during the imagery of maximum handgrips guided by metronome sounds for 10 min were measured. The level of fatigue sensation caused by listening to the metronome sounds on the second day was higher relative to the first day and the equivalent current dipoles (ECDs) in the insular cortex and posterior cingulate cortex were observed only after the conditioning session. These MEG results showed that classical conditioning of physical fatigue took place, and that these brain regions were involved in the neural substrates of physical fatigue related to classical conditioning. However, some brain regions involved in the neural substrates of physical fatigue were thought to have been missed because of limitations of the ECD method. In addition, these brain regions were reported to contribute to fatigue sensation rather than to central inhibition during physical fatigue (Ishii et al., 2012). Therefore, the neural mechanisms of central inhibition during physical fatigue have not been clarified. The aim of our present study was to identify the neural mechanisms of central inhibition related to classical conditioning during physical fatigue. We tried to identify the neural substrates of central inhibition through the comparison of neural activities during the imagery of maximum handgrips between conditioned and unconditioned states, as the difference between these conditions has been shown to be limited to the presence and absence of central inhibition (Tanaka et al., 2012; Shigihara et al., 2013a; Tanaka et al., 2013). In addition to having a high temporal resolution, MEG has an advantage of measuring brain activity using time–frequency
analyses (Stam, 2010). Oscillatory brain rhythms are considered to originate from synchronous synaptic activities of a large number of neurons (Brookes et al., 2011). Synchronization of neural networks may reflect integration of information processing, and such synchronization processes can be evaluated using MEG time-frequency analyses; multiple, broadly distributed, and continuously interacting dynamic neural networks are achievable through the synchronization of oscillations at particular time–frequency bands (Varela et al., 2001). In particular, event-related desynchronization (ERD) of alpha frequency band (8–13 Hz) was reported to be associated with fatigue in the central nervous system (Shigihara et al., 2013b; Ishii et al., in press). Alterations of the decreased MEG alpha power densities in some brain regions induced by classical conditioning during the imagery of maximum handgrips may provide valuable clues to identify the neural mechanism of central inhibition. In addition, the correlation analyses between the MEG variables and changes of subjective and physiological parameters after classical conditioning may provide important clues regarding the roles of MEG variables on physical fatigue.
2.
Results
To assess changes in the subjective level of fatigue after the 10min maximum handgrip trials, two-way analyses of variance (ANOVAs) for repeated measures were performed. Significant main effects of hand [F(1,11)¼63.43, Poo0.001] and time course [F(1,11)¼ 35.21, Poo0.001] and a hand time course interaction effect [F(1,11)¼39.18, Poo0.001] were shown. The level of subjective fatigue of the right hand after the handgrip trials was significantly higher than before the handgrip trials (Fig. 1A). However, the level of subjective fatigue of the left hand was not altered after the handgrip trials (Fig. 1A). The handgrip force of the right hand after the handgrip trials was significantly lower than that before the trials (Fig. 1B). To assess changes in the subjective level of fatigue after the conditioning session, comparisons of fatigue scores before and after the first and second MEG sessions were performed. Although the main effect of conditioning [F(1,11)¼2.80, P¼0.122] was not shown, a significant main effect of time course [F(1,11)¼11.83, Poo0.001] and a conditioning time course interaction effect [F(1,11)¼ 7.45, P¼ 0.019] were shown in the right hand. The main effects of conditioning [F(1,11)¼ 0.18, P¼ 0.681] and time course [F(1,11)oo0.01, P¼ 1.000] and a conditioning time course interaction effect [F(1,11)¼3.48, P¼0.089] were not shown in the left hand. Subjective levels of right hand fatigue after the second MEG session was higher than that after the first MEG session (Fig. 2A), although the subjective level of left hand fatigue after the second MEG session was not different from that after the first MEG session (Fig. 2B). We also assessed changes in high-frequency (HF) power and low-frequency (LF) power/HF power ratio after the conditioning session. Although HF was not altered after the conditioning session (Fig. 3A), LF/HF ratio was significantly increased after the conditioning session (Fig. 3B). To identify the brain region associated with the central inhibition during physical fatigue, the decreased oscillatory power, that is, ERD, for alpha frequency band in the second
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Fig. 1 – Visual analog scale (VAS) values of right and left hands for fatigue (A) and handgrip forces (B) immediately before (open columns) and after (closed columns) the 10-min fatigue-inducing handgrip trials. Data are mean and SD. nnPo0.01, nPo0.05, significantly different (paired t-test).
Fig. 2 – Visual analog scale (VAS) values of right (A) and left (B) hands for fatigue immediately before (open columns) and after (closed columns) the first and second magnetoencephalography (MEG) sessions. Data are mean and SD. nPo0.05, nnPo0.01, significant difference (paired t-test).
MEG session relative to the first MEG session was evaluated. These results are shown in Table 1 and Fig. 4. Among the entire brain regions with all the time windows of 0–1000 ms (every 100 ms) after the onset of handgrip cue sounds, only the right dorsolateral prefrontal cortex (Brodmann's area 46) with the time window of 200–300 ms showed a significant ERD (Poo0.05, corrected for multiple comparisons). Finally, to evaluate the relationships between the ERD level of alpha frequency band in the right dorsolateral prefrontal cortex (Brodmann's area 46) and changes in subjective fatigue scores and autonomic nerve activities after the conditioning session, correlation analyses were performed. The ERD level was positively associated with the change of subjective level of right hand fatigue after the conditioning session (Fig. 5A; R¼ 0.643, P¼ 0.024). The ERD level tended to be positively associated with the change of HF (Fig. 5B; R ¼0.548, P ¼0.065) and negatively associated with the change of LF/HF ratio (Fig. 5C; R¼ 0.591, P¼ 0.043) after the conditioning session.
3.
Discussion
The present study showed the alpha-band ERD of the second MEG session relative to the first session with the time
window of 200–300 ms after the onset of handgrip cue sounds in the right dorsolateral prefrontal cortex (Brodmann's area 46) (Fig. 4 and Table 1). In addition, the ERD level in this brain region was positively associated with the subjective level of right hand fatigue (Fig. 5A), and this ERD level was negatively associated with the sympathetic nerve activity (Fig. 5C). Our results therefore provide evidence that the right dorsolateral prefrontal cortex is involved in the neural substrates of central inhibition during physical fatigue. We first tried to confirm whether classical conditioning of central inhibition occurred after the 10-min maximum handgrip trials. Because the handgrip force of the right hand was decreased (Fig. 1A) and the subjective level of the right hand fatigue (Fig. 1B) was increased after the handgrip trials, physical fatigue was induced by the conditioning session. Similar to our previous report (Tanaka et al., 2013), we showed that subjective level of right hand fatigue (Fig. 2A) and sympathetic nerve activity (Fig. 3B) were significantly increased after the conditioning session. Recently, we showed that the subjective level of fatigue and sympathetic nerve activity assessed by the time–frequency analyses of R–R intervals using ECG were higher in a central inhibition condition relative to the control condition, and the subjective level of fatigue and the sympathetic nerve activity were suggested to be closely related to central inhibition (Tanaka et al., 2013).
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Fig. 3 – Autonomic nerve activities assessed by frequency analyses of electrocardiography. High-frequency power (HF; A) and low-frequency power (LF)/HF ratio (B) were assessed during the first (open columns) and second (closed columns) magnetoencephalography (MEG) sessions. Data means and SD. nPo0.05, significantly different from the corresponding values of the first MEG session (paired t-test). Table 1 – Brain region that showed event-related desynchronization of alpha frequency band in the second magnetoencephalography session relative to the first session. Location
Middle frontal gyrus
Side
Right
Brodmann's area
46
Coordinate (mm)
Z-value
x
y
z
47
53
20
3.79
x, y, z: Stereotaxic coordinate of peak of activated cluster. Random-effect analyses of 12 participants (Po0.05, corrected for multiple comparisons).
Fig. 4 – Statistical parametric maps of event-related desynchronization of alpha frequency band (second magnetoencephalography session relative to the first session; random-effect analyses of 12 participants, Po0.05, corrected for multiple comparisons). Statistical parametric maps are superimposed on surface-rendered highresolution MRIs. The color bar indicates T-values.
Therefore, these observations demonstrate that central inhibition was classically conditioned successfully (Ishii et al., 2013). We tried to identify the neural substrates of central inhibition during physical fatigue through the comparison of the activities during the imagery of maximum handgrips between the conditioned and unconditioned states, as the difference between these conditions was limited to the presence and absence of the central inhibition (Tanaka et al., 2012; Shigihara et al., 2013a; Tanaka et al., 2013). Using
the metronome sounds as the conditioned stimuli and the 10-min maximum handgrip trials as the unconditioned stimuli, our MEG study showed that the right dorsolateral prefrontal cortex (Brodmann's area 46) was specifically activated during the imagery of maximum grips of the right hand after conditioning (Fig. 4 and Table 1), suggesting that this brain region is involved in the neural substrates of central inhibition. The positive relation of the fatigue sensation of right hand (Fig. 5A) and negative relation of the sympathetic nerve activity (Fig. 5C) with the neural activation in the right dorsolateral prefrontal cortex support the inhibitory role of this brain region during physical fatigue (Nugent et al., 2011). The inhibitory role of the dorsolateral prefrontal cortex has been widely reported for factors such as stereotyped response (Kadota et al., 2010), working memory (Daskalakis et al., 2008), emotion (Leyman et al., 2009), cigarette craving (Hayashis et al., 2013), and gamma band activity (Farzan et al., 2010) through the cortical GABA(B) receptor mediated inhibitory neurotransmission (Farzan et al., 2009; Fitzgerald et al., 2009). Because the dorsolateral prefrontal cortex alters motor output from M1 (Kolb and Whishaw, 1983; Goldman-Rakic, 1987; Narayanan and Laubach, 2006), this brain region may attempt to lose the force-generating ability of the fatiguing muscles and even amplify the inhibitory input to the sensorimotor area to decrease the descending motor output from M1. There are two limitations to our study. First, we performed our study with a limited number of participants. In addition, while women are more fatigued than men, the participants in this study are all men. To generalize our results, studies involving a large number of participants including women are essential. Second, it was difficult to assess the neural
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Fig. 5 – Relationships between the event-related desynchronization (ERD) level of alpha frequency band in the right middle frontal gyrus (Brodmann's area 46) and changes of visual analog scale (VAS) of right hand for fatigue (A), high-frequency power (HF; B), and low-frequency power (LF)/HF ratio (C). Linear regression lines, Pearson's correlation coefficients, and P values are shown.
activities of the brain regions located deeply by using MEG. Therefore, some brain regions involved in the neural substrates of central inhibition during physical fatigue might be missed because of the limitations of MEG. For example, the amygdala is reported to be related to the classical conditioning (Sehlmeyer et al., 2009) as well as fatigue (Boksem and Tops, 2008), and the orbitofrontal cortex is reported to be associated with fatigue sensation (Tajima et al., 2010). Future studies using other neuroimaging techniques, such as functional magnetic resonance imaging and positron emission tomography, would address this limitation. In conclusion, we demonstrated that the right dorsolateral prefrontal cortex (Brodmann's area 46) is involved in the neural substrates of central inhibition during physical fatigue. Central inhibition is considered to be associated with the pathophysiology of chronic fatigue in human diseases or syndromes (Tanaka and Watanabe, 2010, 2012a, 2012b). Our findings may help clarify the mechanisms of central fatigue as well as aid in the development of treatment methods for patients suffering from chronic fatigue.
4.
Experimental procedures
4.1.
Participants
Twelve healthy male volunteers (age, 27.0778.8 years [mean7 7SD]) were enrolled. According to the Edinburgh handedness inventory (Oldfield, 1971), all participants were right-handed. Current smokers, participants with a history of mental or brain disorders, and those taking chronic medications that affect the central nervous system were excluded. All participants provided written informed consent before participation. This study was approved by the Ethics Committee of Osaka City University and was conducted in accordance with the principles of the Declaration of Helsinki.
4.2.
Experimental design
The experiment consisted of two MEG sessions and a single conditioning session (Fig. 6). On the first day, MEG recording
Fig. 6 – Experimental design. On the first day, neural activities during the imagery of handgrips guided by the handgrip cues of metronome sounds were measured using magnetoencephalography (MEG) for 10 min (first MEG session). Thereafter, 10-min fatigue-inducing maximum handgrip trials (conditioning session) were performed, in which metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause central inhibition. On the next day, neural activities during the imagery of handgrips guided by the handgrip cues of metronome sounds were measured using MEG for 10 min (second MEG session).
during the imagery of maximum grips of the right hand, guided by metronome sounds, was performed for 10 min (first MEG session). The imagery of maximum grips of the right hand rather than submaximum grips was performed, since it was difficult to correctly imagine the level of submaximum handgrips (such as 80% and 50% of maximum voluntary contraction level). Thereafter, 10-min fatigue-inducing maximum handgrip trials using a device (HAND GRIPS 30 kg; IGNIO, Nagoya, Japan) were performed (conditioning session), in which the metronome sounds (same as the MEG session) were started 5 min after the beginning of the handgrip trials and the sounds were continued until the end of the handgrip trials. We used metronome sounds as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause central inhibition (Tanaka et al., 2013). Participants
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were not informed about the metronome sounds before the task trials. On the next day, MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds was performed for 10 min (second MEG session). Each MEG session consisted of 150 blocks, and each block consisted of three pacing cues followed by one handgrip cue. During the MEG session, participants heard the sound cues every 1 s with their eyes closed, and every 4 s during the handgrip cue period, they were requested to imagine that they were gripping a soft ball with their right hand at a maximal voluntary contraction level for 1 s. The pacing cue consisted of white noise that lasted 33 ms; the handgrip cue consisted of a 1000 Hz tone that lasted 1 s. All cue sounds were produced by Windows Media Player (Microsoft Corporation, Redmond, WA) and were converted to electric signals by a sound card (Creative X-Fi Audio Processor [WDM]; Creative Technology, Singapore, Singapore) installed in a personal computer (DELL Precision 390; Dell, Round Rock, TX). The sound signal was amplified by an audio amplifier (MA-500U; Onkyo Corporation, Tokyo, Japan) outside of the magnetically shielded room. During the conditioning session, participants watched a fixed mark (þ; black mark on white background) on a screen placed in front of their eyes using a video projector (PG-B10S; SHARP, Osaka, Japan). When a handgrip cue mark ( ; black mark on white background) was presented instead of the fixation mark every 4 s, they were requested to perform a handgrip with their right hand at a maximal voluntary contraction level for 1 s by gripping the device. The timing of the visual handgrip cues was same as that of the metronome handgrip cue sounds started 5 min after the beginning of the handgrip trials. Electrocardiography (ECG) was recorded during the MEG sessions. Just before and after the conditioning session and after each MEG session, participants were asked to subjectively rate their fatigued level of the right and left hands on a visual analog scale (VAS) from 0 (minimum) to 100 (maximum) (Lee et al., 1991). In addition, just before and after the conditioning session, handgrip forces of the right hand were measured using a handgrip meter (ST-100, Toei Light Co., Ltd, Saitama, Japan). This study was conducted in a quiet, temperature-, and humidity-controlled, magnetically shielded room. During the experiment, participants lay on a bed in the supine position. For 1 day before each visit, they refrained from intense physical and mental activities and caffeinated beverages, consumed a normal diet, and maintained normal sleeping hours.
4.3.
MEG recordings
MEG recordings were performed using a 160-channel wholehead type MEG system (MEG vision; Yokogawa Electric Corporation, Tokyo, Japan) with a magnetic field resolution of 4 fT/Hz1/2 in the white-noise region. The sensor and reference coils were gradiometers 15.5 mm in diameter and 50 mm at baseline, and each pair of sensor coils was separated at a distance of 23 mm. The sampling rate was 1000 Hz with a 200 Hz hard low-pass filter and a 0.3 Hz hard high-pass filter.
4.4.
MEG data analyses
MEG signal data were analyzed offline after analog-to-digital conversion. Magnetic noise originating from outside the shield room was eliminated by subtracting the data obtained from reference coils using a software program (MEG 160; Yokogawa Electric Corporation) followed by artifact rejection using careful visual inspection. The MEG data were split into segments of 1000 ms length (from 0 to 1000 ms after the onset of each handgrip cue sound), and the segments were averaged. After averaging, the data were band-pass filtered by a fast Fourier transform using Frequency Trend (Yokogawa Electric Corporation) to obtain time–frequency band signals using a software Brain Rhythmic Analysis for MEG (BRAM; Yokogawa Electric Corporation) (Dalal et al., 2008). Localization and intensity of the time–frequency power of cortical activities were estimated by using BRAM software, which used narrow-band adaptive spatial filtering methods as an algorithm (Dalal et al., 2008). These data were then analyzed using statistical parametric mapping (SPM8, Wellcome Department of Cognitive Neurology, London, UK), implemented in Matlab (Mathworks, Sherbon, MA). The MEG anatomical/spatial parameters used to warp the volumetric data were transformed into the Montreal Neurological Institute (MNI) template of T1-weighed images (Evans et al., 1994) and applied to the MEG data. The anatomically normalized MEG data were filtered with a Gaussian kernel of 20 mm (full-width at half-maximum) in the x, y, and z axes (voxel dimension was 5.0 mm 5.0 mm 5.0 mm). The decreased oscillatory power, that is, ERD, for alpha band (8–13 Hz) within the time window of 0–1000 ms (every 100 ms) in the second MEG session relative to the first MEG session was measured on a region-of-interest basis to obtain the neural activation pattern of central inhibition during physical fatigue. The resulting set of voxel values for each comparison constituted a SPM of the t statistics (SPM{t}). The SPM{t} was transformed to the unit of normal distribution (SPM{Z}). The threshold for the SPM{Z} of individual analyses was set at Poo0.05 (corrected for multiple comparisons). The weighted sum of the parameters estimated in the individual analyses consisted of “contrast” images, which were used for the group analyses (Friston et al., 1999). Individual data were summarized and incorporated into a random-effect model so that inferences could be made at a population level (Friston et al., 1999). SPM{t} and SPM{Z} for the contrast images were created as described above. Significant signal changes for each contrast were assessed by means of t statistics on a voxel-byvoxel basis (Friston et al., 1999). The threshold for the SPM{Z} of group analyses was set at Poo0.05 (corrected for multiple comparisons). Anatomical localization of significant voxels within cluster were done using Talairach Demon software (Lancaster et al., 2000).
4.5.
Magnetic resonance imaging overlay
Anatomic magnetic resonance imaging (MRI) was performed using a Philips Achieva 3.0TX (Royal Philips Electronics, Eindhoven, The Netherlands) for all participants to permit registration of magnetic source locations with their respective anatomic locations. Before MRI scanning, five adhesive
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markers (Medtronic Surgical Navigation Technologies Inc., Broomfield, CO) were attached to the skin of each participant's head (the first and second ones were located 10 mm in front of the left tragus and right tragus, the third at 35 mm above the nasion, and the fourth and fifth at 40 mm right and left of the third one). MEG data were superimposed on MRI scans using information obtained from these markers and MEG localization coils.
4.6.
ECG
To evaluate autonomic nerve activities, ECG was recorded during the MEG sessions. ECG data were analyzed using MemCalc for Windows (Global Medical Solution Inc., Tokyo, Japan). R–R wave variability was measured as an indicator of autonomic nerve activity. For frequency domain analyses of the R–R wave intervals, LF power was calculated as the power within the frequency range of 0.04–0.15 Hz, and HF power was calculated as that within the frequency range of 0.15–0.4 Hz. LF and HF were measured in absolute units (ms2). The average power densities within these frequency ranges were log-transformed (ln) for normalization (Mizuno et al., 2011). The HF is vagally mediated (Akselrod et al., 1981; Pomeranz et al., 1985; Malliani et al., 1991), but the LF originates from a variety of sympathetic and vagal mechanisms (Akselrod et al., 1981; Appel et al., 1989). The LF/HF ratio represents sympathetic nerve activity (Pagani et al., 1997).
4.7.
Statistical analyses
Values are presented as mean77SD, unless otherwise stated. ANOVA for repeated measures were performed to assess the effects of the hand (right or left) and time course within the conditioning session as well as the effects of the conditioning and time course within the MEG session on the subjective level of fatigue. A paired t-test was used to evaluate significant differences between the two conditions. Pearson's correlation analyses were conducted to evaluate the relationships between the MEG responses and changes of subjective scores and autonomic nerve activities after the conditioning session. All P values were two-tailed, and values less than 0.05 were considered statistically significant. Statistical analyses were performed using IBM SPSS 20.0 (IBM, Armonk, NY).
Acknowledgments We thank Forte Science Communications for editorial help with the manuscript and Manryoukai Imaging Clinic for MRI scans. This work was supported in part by the Grant-in-Aid for Scientific Research B (KAKENHI: 23300241) from Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan and by the Health Labor Sciences Research Grant of Japan.
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