brain research 1542 (2014) 49–55
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
Neural effect of mental fatigue on 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:
We sought to clarify the neural effect of mental fatigue on physical fatigue using
Accepted 10 October 2013
magnetoencephalography (MEG) and classical conditioning techniques. Eleven right-
Available online 24 October 2013
handed volunteers participated in this study. On the first day, participants performed
Keywords:
fatigue-inducing maximum handgrip trials for 10 min; metronome sounds were started
Alpha frequency
5 min after the beginning of the trials. We used metronome sounds as conditioned stimuli
Anterior cingulate cortex
and maximum handgrip trials as unconditioned stimuli to cause physical fatigue. On the
Event-related synchronization
next day, MEG recordings during the imagery of maximum grips of the right hand guided
Magnetoencephalography
by the metronome sounds were performed for 10 min just before (control session) and after
Physical fatigue
(mental fatigue session) a 30-min fatigue-inducing mental task session. In the right
Mental fatigue
anterior cingulate cortex (Brodmann's area 23), the alpha-band event-related synchronization of the mental fatigue session relative to the control session within the time window of 500–600 ms after the onset of handgrip cue sounds was identified. We demonstrated that mental fatigue suppresses activities in the right anterior cingulate cortex during physical fatigue. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Fatigue, which can be primarily classified into mental and physical fatigue, is best defined as a condition or phenomenon of declined ability and efficiency of mental and/or physical activities caused by excessive mental or physical activities, or illness; fatigue is often accompanied by peculiar sense of discomfort, desire to rest, and reduced motivation, referred to as fatigue sensation (translated from Japanese into English by M.T.) (Kitani et al., 2011). It was reported in human studies that mental fatigue impairs physical activity, and this decreased performance is not caused by cardiorespiratory or 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.2013.10.018
muscular mechanisms (Marcora et al., 2009; Pageaux et al., in press), suggesting the neural impact of mental fatigue on physical fatigue. However, the neural mechanism of impaired physical performance induced by mental fatigue has not been clarified. In addition to its high temporal resolution, magnetoencephalography (MEG) can measure brain activity using timefrequency 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
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altered after the mental fatigue-inducing mental trials (Fig. 1A). Similarly, the level of subjective fatigue of the left hand during the MEG recording was not altered after the mental fatigue-inducing mental trials (Fig. 1B). To assess changes in subjective levels of general fatigue after the fatigue-inducing mental task session, comparisons of fatigue scores between the control and mental fatigue sessions were performed. The level of subjective general fatigue during the MEG recording was significantly increased after the mental fatigue-inducing mental trials (Fig. 2). To identify the brain region affected by mental fatigue during physical fatigue, the increased and decreased oscillatory powers, that is, event-related synchronization (ERS) and event-related desynchronization (ERD), respectively, for the alpha-frequency band in the mental fatigue session relative to the control session within the time window of 0–1000 ms (every 100 ms) were evaluated. Results are shown in Table 1 and Fig. 3. Among the entire brain regions within the time windows of 0–1000 ms (every 100 ms), only the right anterior cingulate cortex (Brodmann's area 23) within the time window of 500–600 ms showed a significant ERS (Po0.05, corrected for multiple comparisons). No brain regions showed a significant ERD within the time window assessed.
100 80 VAS for fatigue
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, alterations of alpha-frequency band (8–13 Hz) power were reported to be associated with fatigue in the central nervous system (Shigihara et al., 2013a; Ishii et al., 2013; Tanaka et al., in press). The alterations of the MEG alpha power densities in some brain regions induced by mental fatigue when performing physical trials under the condition of physical fatigue may provide valuable clues to identifying the neural effect of mental fatigue on physical fatigue. However, because physical trials cause a lot of electromagnetic noise, it is difficult to evaluate neural activities when participants perform physical tasks. 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 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 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 using MEG. The level of physical fatigue sensation caused by listening to the metronome sounds on the second day was higher relative to the first day and the MEG recordings and evaluations were successfully performed. These findings suggest that classical conditioning of physical fatigue took place, and the neural activities related to the physical trials under the condition of physical fatigue could be evaluated using the MEG and classical conditioning techniques. The aim of the present study was to identify the neural effect of mental fatigue on physical fatigue using these techniques.
*
60 40 20
2.
Results
0 Control session
100
100
80
80
60 40 20
Mental fatigue session
Fig. 2 – Visual analog scale (VAS) values for general fatigue in the control (open columns) and mental fatigue (closed columns) sessions. Data are presented as mean and SD. n Po0.05, significant difference (paired t-test).
VAS for fatigue
VAS for fatigue
To assess changes in subjective levels of right- and left-hand fatigue after a fatigue-inducing mental task session, comparisons of fatigue scores between the control and mental fatigue sessions were performed. The level of subjective fatigue of the right hand during the MEG recording was not
60 40 20
0
0 Control session
Mental fatigue session
Control session
Mental fatigue session
Fig. 1 – Visual analog scale (VAS) values of right (A) and left (B) hands for fatigue in the control (open columns) and mental fatigue (closed columns) sessions. Data are presented as mean and SD.
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brain research 1542 (2014) 49–55
3.
Discussion
The present study identified the alpha-band ERS of the mental fatigue session relative to the control session within the time window of 500 to 600 ms after the onset of handgrip cue sounds in the right anterior cingulate cortex (Brodmann's area 23). Oscillatory brain activity in the alpha-band power is generated in the process of thalamo-cortical interactions (Lopes da Silva, 1991). Although the precise mechanism of the alpha rhythm generation is still a matter of debate, it has been suggested that inhibitory activity induced by GABAergic neurons generates alpha rhythms (Klimesch et al., 2007).
We therefore interpret the increased alpha-band power in the anterior cortex in the present study as follows: mental fatigue suppressed activities in the right anterior cingulate cortex during physical fatigue. Chronic fatigue syndrome is an illness characterized by a profound, disabling, and unexplained sensation of fatigue lasting at least 6 months, which severely impairs daily functioning (Fukuda et al., 1994). Metabolic impairments in the anterior cingulate cortex have been reported in these patients (Kuratsune et al., 2002; Siessmeier et al., 2003; Yamamoto et al., 2004; Tanaka and Watanabe, 2010, 2012a, b,c). This suggests that impairments in this brain region are involved in the pathophysiology of this fatigue-related
Table 1 – Brain region that showed event-related synchronization of the alpha frequency band in the mental fatigue session relative to the control session. Location
Anterior cingulate cortex
Side
Right
Brodmann's area
23
Coordinate (mm)
Z-value
x
y
z
12
28
20
3.99
x, y, z: Stereotaxic coordinate of peak of activated cluster.Random-effect analysis of 11 participants (Po0.05, corrected for multiple comparisons).
Fig. 3 – Statistical parametric maps of event-related synchronization of alpha-frequency band (the mental fatigue session relative to the control session; random-effect analysis of 11 participants, Po0.05, corrected for multiple comparisons). Statistical parametric maps are superimposed on surface-rendered high-resolution MRIs. Sagittal (upper left), coronal (upper right), and axial (lower left) sections passing through the anterior cingulate cortex are shown. The color bar indicates T-values. R, right side.
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brain research 1542 (2014) 49–55
human syndrome. In addition to CFS, mental fatigue in healthy persons may also be associated with alterations in the anterior cingulate cortex: During the time course of prolonged mental task trials, the amplitude of an eventrelated potential component of error-related negativity, indexing performance monitoring by the anterior cingulate cortex, was significantly attenuated (Lorist et al., 2005; Boksem and Tops, 2008). These findings suggest that impairments in the anterior cingulate cortex are a characteristic feature of mental fatigue. As active muscle fibers become fatigued, we progressively increase voluntary efforts to increase motor output from the primary motor cortex (M1) to compensate for physical fatigue until the task requires a maximal effort, and a facilitation system increases the motor output from the M1 to overcome physical fatigue (Gandevia et al., 1996; Taylor et al., 1996; Taylor and Gandevia, 2008). A re-entrant neural circuit that interconnects the limbic system, basal ganglia, thalamus, orbitofrontal cortex, prefrontal cortex, anterior cingulate cortex, premotor area, supplementary motor area, and M1, constitutes the facilitation system (Dettmers et al., 1996; Chaudhuri and Behan, 2000; Johnston et al., 2001; Liu et al., 2003; Chaudhuri and Behan, 2004; Korotkov et al., 2005; Liu et al., 2007; Post et al., 2009; Tanaka and Watanabe, 2010, 2012a,b,c). A motivational input to this facilitation system enhances the supplementary motor area and then the M1 to increase the motor output to the peripheral system (Tanaka and Watanabe, 2010, 2012a,b,c). Interestingly, a moderate physical task was reported to improve cognitive function, and enhanced functional near-infrared spectroscopy response in the frontal area coincided with improved cognitive function (Yanagisawa et al., 2010). This finding implies that the physical facilitation system shares common neural substrates with the mental facilitation system; that is, activation of the physical facilitation system may cause enhancement of the mental facilitation system through activation of common neural networks. In fact, the neural circuit or re-entrant loop that interconnects the limbic system, basal, thalamus, orbitofrontal cortex, prefrontal cortex, and anterior cingulate cortex, is considered to constitute the mental facilitation system (Boksem and Tops, 2008; Tanaka and Watanabe, 2010, 2012a, b,c). Hence, mental fatigue may cause not only suppression of the mental facilitation system but also that of the physical facilitation system through the deactivation of common neural networks during physical fatigue, manifested as a decreased level of alpha-band ERS in the anterior cingulate cortex. There are two limitations to our study. First, we had a limited number of participants. To generalize our results, studies involving a larger number of participants are essential. Second, we could not identify the neural mechanisms of the suppression of the activities in the right anterior cingulate cortex. 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 mental fatigue suppresses activities in the right anterior cingulate cortex (Brodmann's area 23) during physical fatigue. Our result may benefit not only endurance athletes, but also people suffering from impaired physical performance. Our findings may help
clarify the neural mechanisms of fatigue as well as aid in the development of evaluation and treatment methods for people suffering from physical fatigue.
4.
Experimental procedures
4.1.
Participants
Eleven healthy male volunteers (age, 23.873.8 years [mean7SD]) were enrolled. All participants were right-handed according to the Edinburgh handedness inventory (Oldfield, 1971). 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, a conditioning session, and a fatigue-inducing mental task session (Fig. 4). On the first day, 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 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 physical fatigue (Tanaka et al., 2013). The participants were not informed about the metronome
Metronome (5 min)
First day
Conditioning session (10 min)
Metronome (10 min)
Second day
Control session
Metronome (10 min)
Fatigue-inducing mental task session (30 min)
Mental fatigue session
Time
Fig. 4 – Experimental design. On the first day, participants performed fatigue-inducing maximum handgrip trials for 10 min (conditioning session); metronome sounds were started 5 min after the beginning of the handgrip trials. We used metronome sounds as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause physical fatigue. On the second day, magnetoencephalography recordings during the imagery of maximum grips of the right hand guided by the metronome sounds were performed for 10 min just before (control session) and after (mental fatigue session) a 30-min fatigue-inducing mental task session.
brain research 1542 (2014) 49–55
sounds before the task trials. On the next day, MEG recordings during the imagery of maximum grips of the right hand guided by the metronome sounds (same as the conditioning session) were performed for 10 min just before (control session) and after (mental fatigue session) a 30-min fatigueinducing mental task session. During the conditioning session, the 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. During the fatigue-inducing mental task session, the participants performed 2-back test trials (Braver et al., 1997) for 30 min. The reliability and validity of these tests to cause mental fatigue have been confirmed (Tanaka et al., 2009, 2012a,b; Shigihara et al., 2013b). One of four types of white letters was continually presented on a black background in the display of a laptop computer every 3 s. The letter size was 30 mm 30 mm. During the trials, they had to judge whether the target letter presented at the center of the screen was the same as the one that had appeared two presentations earlier. If it was, they were to press the right button with their right middle finger, and if it was not, they were to press the left button. They were instructed to perform the task trials as quickly and as correctly as possible. The result of each 2-back trial, that is, a correct response or error, was continually presented on the display of the laptop computer. 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 desktop 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. Just after the control and mental fatigue sessions, the participants were asked to subjectively rate their fatigued levels (right hand fatigue, left hand fatigue, and general fatigue) on a visual analog scale (VAS) from 0 (minimum) to 100 (maximum) (Lee et al., 1991). This study was conducted in a quiet, temperature-, and humidity-controlled, magnetically shielded room. During the experiment, the 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
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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 transformer 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 using BRAM software, which used narrow-band adaptive spatial filtering methods as an algorithm (Dalal et al., 2008). 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 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 halfmaximum) in the x, y, and z axes (voxel dimension was 5.0 5.0 5.0 mm3). The increased and decreased oscillatory powers, that is, ERS and ERD, respectively, for alpha bands within the time window of 0–1000 ms (every 100 ms) in the mental fatigue session relative to the control session were measured on a region-of-interest basis to obtain the change of the neural activation pattern caused by mental fatigue during physical fatigue. The resulting set of voxel values for each comparison constituted a Statistical Parametric Mapping (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 Po0.05 (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
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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 Po0.05 (corrected for multiple comparisons). Anatomical localization of significant voxels within each cluster was 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 markers (Medtronic Surgical Navigation Technologies Inc., Broomfield, CO) were attached to the skin of each participant's head (the first and second markers were located 10 mm anterior the left tragus and right tragus, the third at 35 mm superior 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.
Statistical analysis
A paired t-test was used to evaluate significant differences between the two conditions. All P values were two-tailed, and values less than 0.05 were considered significant. Statistical analyses were performed using IBM SPSS 20.0 (IBM, Armonk, NY).
Acknowledgments We thank the Forte Science Communications for editorial help with the manuscript and Manryoukai Imaging Clinic for MRI scans. This work was supported by the Grant-in-Aid for Scientific Research B (KAKENHI: 23300241) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, the Senryakutekikenkyu (Hoga kenkyu) of Osaka City University, and by the Health Labor Sciences Research Grant of Japan.
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Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
Neural effect of mental fatigue on 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
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ab st rac t
Article history:
We sought to clarify the neural effect of mental fatigue on physical fatigue using
Accepted 10 October 2013
magnetoencephalography (MEG) and classical conditioning techniques. Eleven right-
Available online 24 October 2013
handed volunteers participated in this study. On the first day, participants performed
Keywords:
fatigue-inducing maximum handgrip trials for 10 min; metronome sounds were started
Alpha frequency
5 min after the beginning of the trials. We used metronome sounds as conditioned stimuli
Anterior cingulate cortex
and maximum handgrip trials as unconditioned stimuli to cause physical fatigue. On the
Event-related synchronization
next day, MEG recordings during the imagery of maximum grips of the right hand guided
Magnetoencephalography
by the metronome sounds were performed for 10 min just before (control session) and after
Physical fatigue
(mental fatigue session) a 30-min fatigue-inducing mental task session. In the right
Mental fatigue
anterior cingulate cortex (Brodmann's area 23), the alpha-band event-related synchronization of the mental fatigue session relative to the control session within the time window of 500–600 ms after the onset of handgrip cue sounds was identified. We demonstrated that mental fatigue suppresses activities in the right anterior cingulate cortex during physical fatigue. & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Fatigue, which can be primarily classified into mental and physical fatigue, is best defined as a condition or phenomenon of declined ability and efficiency of mental and/or physical activities caused by excessive mental or physical activities, or illness; fatigue is often accompanied by peculiar sense of discomfort, desire to rest, and reduced motivation, referred to as fatigue sensation (translated from Japanese into English by M.T.) (Kitani et al., 2011). It was reported in human studies that mental fatigue impairs physical activity, and this decreased performance is not caused by cardiorespiratory or 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.2013.10.018
muscular mechanisms (Marcora et al., 2009; Pageaux et al., in press), suggesting the neural impact of mental fatigue on physical fatigue. However, the neural mechanism of impaired physical performance induced by mental fatigue has not been clarified. In addition to its high temporal resolution, magnetoencephalography (MEG) can measure brain activity using timefrequency 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
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altered after the mental fatigue-inducing mental trials (Fig. 1A). Similarly, the level of subjective fatigue of the left hand during the MEG recording was not altered after the mental fatigue-inducing mental trials (Fig. 1B). To assess changes in subjective levels of general fatigue after the fatigue-inducing mental task session, comparisons of fatigue scores between the control and mental fatigue sessions were performed. The level of subjective general fatigue during the MEG recording was significantly increased after the mental fatigue-inducing mental trials (Fig. 2). To identify the brain region affected by mental fatigue during physical fatigue, the increased and decreased oscillatory powers, that is, event-related synchronization (ERS) and event-related desynchronization (ERD), respectively, for the alpha-frequency band in the mental fatigue session relative to the control session within the time window of 0–1000 ms (every 100 ms) were evaluated. Results are shown in Table 1 and Fig. 3. Among the entire brain regions within the time windows of 0–1000 ms (every 100 ms), only the right anterior cingulate cortex (Brodmann's area 23) within the time window of 500–600 ms showed a significant ERS (Po0.05, corrected for multiple comparisons). No brain regions showed a significant ERD within the time window assessed.
100 80 VAS for fatigue
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, alterations of alpha-frequency band (8–13 Hz) power were reported to be associated with fatigue in the central nervous system (Shigihara et al., 2013a; Ishii et al., 2013; Tanaka et al., in press). The alterations of the MEG alpha power densities in some brain regions induced by mental fatigue when performing physical trials under the condition of physical fatigue may provide valuable clues to identifying the neural effect of mental fatigue on physical fatigue. However, because physical trials cause a lot of electromagnetic noise, it is difficult to evaluate neural activities when participants perform physical tasks. 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 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 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 using MEG. The level of physical fatigue sensation caused by listening to the metronome sounds on the second day was higher relative to the first day and the MEG recordings and evaluations were successfully performed. These findings suggest that classical conditioning of physical fatigue took place, and the neural activities related to the physical trials under the condition of physical fatigue could be evaluated using the MEG and classical conditioning techniques. The aim of the present study was to identify the neural effect of mental fatigue on physical fatigue using these techniques.
*
60 40 20
2.
Results
0 Control session
100
100
80
80
60 40 20
Mental fatigue session
Fig. 2 – Visual analog scale (VAS) values for general fatigue in the control (open columns) and mental fatigue (closed columns) sessions. Data are presented as mean and SD. n Po0.05, significant difference (paired t-test).
VAS for fatigue
VAS for fatigue
To assess changes in subjective levels of right- and left-hand fatigue after a fatigue-inducing mental task session, comparisons of fatigue scores between the control and mental fatigue sessions were performed. The level of subjective fatigue of the right hand during the MEG recording was not
60 40 20
0
0 Control session
Mental fatigue session
Control session
Mental fatigue session
Fig. 1 – Visual analog scale (VAS) values of right (A) and left (B) hands for fatigue in the control (open columns) and mental fatigue (closed columns) sessions. Data are presented as mean and SD.
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brain research 1542 (2014) 49–55
3.
Discussion
The present study identified the alpha-band ERS of the mental fatigue session relative to the control session within the time window of 500 to 600 ms after the onset of handgrip cue sounds in the right anterior cingulate cortex (Brodmann's area 23). Oscillatory brain activity in the alpha-band power is generated in the process of thalamo-cortical interactions (Lopes da Silva, 1991). Although the precise mechanism of the alpha rhythm generation is still a matter of debate, it has been suggested that inhibitory activity induced by GABAergic neurons generates alpha rhythms (Klimesch et al., 2007).
We therefore interpret the increased alpha-band power in the anterior cortex in the present study as follows: mental fatigue suppressed activities in the right anterior cingulate cortex during physical fatigue. Chronic fatigue syndrome is an illness characterized by a profound, disabling, and unexplained sensation of fatigue lasting at least 6 months, which severely impairs daily functioning (Fukuda et al., 1994). Metabolic impairments in the anterior cingulate cortex have been reported in these patients (Kuratsune et al., 2002; Siessmeier et al., 2003; Yamamoto et al., 2004; Tanaka and Watanabe, 2010, 2012a, b,c). This suggests that impairments in this brain region are involved in the pathophysiology of this fatigue-related
Table 1 – Brain region that showed event-related synchronization of the alpha frequency band in the mental fatigue session relative to the control session. Location
Anterior cingulate cortex
Side
Right
Brodmann's area
23
Coordinate (mm)
Z-value
x
y
z
12
28
20
3.99
x, y, z: Stereotaxic coordinate of peak of activated cluster.Random-effect analysis of 11 participants (Po0.05, corrected for multiple comparisons).
Fig. 3 – Statistical parametric maps of event-related synchronization of alpha-frequency band (the mental fatigue session relative to the control session; random-effect analysis of 11 participants, Po0.05, corrected for multiple comparisons). Statistical parametric maps are superimposed on surface-rendered high-resolution MRIs. Sagittal (upper left), coronal (upper right), and axial (lower left) sections passing through the anterior cingulate cortex are shown. The color bar indicates T-values. R, right side.
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human syndrome. In addition to CFS, mental fatigue in healthy persons may also be associated with alterations in the anterior cingulate cortex: During the time course of prolonged mental task trials, the amplitude of an eventrelated potential component of error-related negativity, indexing performance monitoring by the anterior cingulate cortex, was significantly attenuated (Lorist et al., 2005; Boksem and Tops, 2008). These findings suggest that impairments in the anterior cingulate cortex are a characteristic feature of mental fatigue. As active muscle fibers become fatigued, we progressively increase voluntary efforts to increase motor output from the primary motor cortex (M1) to compensate for physical fatigue until the task requires a maximal effort, and a facilitation system increases the motor output from the M1 to overcome physical fatigue (Gandevia et al., 1996; Taylor et al., 1996; Taylor and Gandevia, 2008). A re-entrant neural circuit that interconnects the limbic system, basal ganglia, thalamus, orbitofrontal cortex, prefrontal cortex, anterior cingulate cortex, premotor area, supplementary motor area, and M1, constitutes the facilitation system (Dettmers et al., 1996; Chaudhuri and Behan, 2000; Johnston et al., 2001; Liu et al., 2003; Chaudhuri and Behan, 2004; Korotkov et al., 2005; Liu et al., 2007; Post et al., 2009; Tanaka and Watanabe, 2010, 2012a,b,c). A motivational input to this facilitation system enhances the supplementary motor area and then the M1 to increase the motor output to the peripheral system (Tanaka and Watanabe, 2010, 2012a,b,c). Interestingly, a moderate physical task was reported to improve cognitive function, and enhanced functional near-infrared spectroscopy response in the frontal area coincided with improved cognitive function (Yanagisawa et al., 2010). This finding implies that the physical facilitation system shares common neural substrates with the mental facilitation system; that is, activation of the physical facilitation system may cause enhancement of the mental facilitation system through activation of common neural networks. In fact, the neural circuit or re-entrant loop that interconnects the limbic system, basal, thalamus, orbitofrontal cortex, prefrontal cortex, and anterior cingulate cortex, is considered to constitute the mental facilitation system (Boksem and Tops, 2008; Tanaka and Watanabe, 2010, 2012a, b,c). Hence, mental fatigue may cause not only suppression of the mental facilitation system but also that of the physical facilitation system through the deactivation of common neural networks during physical fatigue, manifested as a decreased level of alpha-band ERS in the anterior cingulate cortex. There are two limitations to our study. First, we had a limited number of participants. To generalize our results, studies involving a larger number of participants are essential. Second, we could not identify the neural mechanisms of the suppression of the activities in the right anterior cingulate cortex. 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 mental fatigue suppresses activities in the right anterior cingulate cortex (Brodmann's area 23) during physical fatigue. Our result may benefit not only endurance athletes, but also people suffering from impaired physical performance. Our findings may help
clarify the neural mechanisms of fatigue as well as aid in the development of evaluation and treatment methods for people suffering from physical fatigue.
4.
Experimental procedures
4.1.
Participants
Eleven healthy male volunteers (age, 23.873.8 years [mean7SD]) were enrolled. All participants were right-handed according to the Edinburgh handedness inventory (Oldfield, 1971). 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, a conditioning session, and a fatigue-inducing mental task session (Fig. 4). On the first day, 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 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 physical fatigue (Tanaka et al., 2013). The participants were not informed about the metronome
Metronome (5 min)
First day
Conditioning session (10 min)
Metronome (10 min)
Second day
Control session
Metronome (10 min)
Fatigue-inducing mental task session (30 min)
Mental fatigue session
Time
Fig. 4 – Experimental design. On the first day, participants performed fatigue-inducing maximum handgrip trials for 10 min (conditioning session); metronome sounds were started 5 min after the beginning of the handgrip trials. We used metronome sounds as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause physical fatigue. On the second day, magnetoencephalography recordings during the imagery of maximum grips of the right hand guided by the metronome sounds were performed for 10 min just before (control session) and after (mental fatigue session) a 30-min fatigue-inducing mental task session.
brain research 1542 (2014) 49–55
sounds before the task trials. On the next day, MEG recordings during the imagery of maximum grips of the right hand guided by the metronome sounds (same as the conditioning session) were performed for 10 min just before (control session) and after (mental fatigue session) a 30-min fatigueinducing mental task session. During the conditioning session, the 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. During the fatigue-inducing mental task session, the participants performed 2-back test trials (Braver et al., 1997) for 30 min. The reliability and validity of these tests to cause mental fatigue have been confirmed (Tanaka et al., 2009, 2012a,b; Shigihara et al., 2013b). One of four types of white letters was continually presented on a black background in the display of a laptop computer every 3 s. The letter size was 30 mm 30 mm. During the trials, they had to judge whether the target letter presented at the center of the screen was the same as the one that had appeared two presentations earlier. If it was, they were to press the right button with their right middle finger, and if it was not, they were to press the left button. They were instructed to perform the task trials as quickly and as correctly as possible. The result of each 2-back trial, that is, a correct response or error, was continually presented on the display of the laptop computer. 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 desktop 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. Just after the control and mental fatigue sessions, the participants were asked to subjectively rate their fatigued levels (right hand fatigue, left hand fatigue, and general fatigue) on a visual analog scale (VAS) from 0 (minimum) to 100 (maximum) (Lee et al., 1991). This study was conducted in a quiet, temperature-, and humidity-controlled, magnetically shielded room. During the experiment, the 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
53
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 transformer 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 using BRAM software, which used narrow-band adaptive spatial filtering methods as an algorithm (Dalal et al., 2008). 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 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 halfmaximum) in the x, y, and z axes (voxel dimension was 5.0 5.0 5.0 mm3). The increased and decreased oscillatory powers, that is, ERS and ERD, respectively, for alpha bands within the time window of 0–1000 ms (every 100 ms) in the mental fatigue session relative to the control session were measured on a region-of-interest basis to obtain the change of the neural activation pattern caused by mental fatigue during physical fatigue. The resulting set of voxel values for each comparison constituted a Statistical Parametric Mapping (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 Po0.05 (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
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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 Po0.05 (corrected for multiple comparisons). Anatomical localization of significant voxels within each cluster was 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 markers (Medtronic Surgical Navigation Technologies Inc., Broomfield, CO) were attached to the skin of each participant's head (the first and second markers were located 10 mm anterior the left tragus and right tragus, the third at 35 mm superior 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.
Statistical analysis
A paired t-test was used to evaluate significant differences between the two conditions. All P values were two-tailed, and values less than 0.05 were considered significant. Statistical analyses were performed using IBM SPSS 20.0 (IBM, Armonk, NY).
Acknowledgments We thank the Forte Science Communications for editorial help with the manuscript and Manryoukai Imaging Clinic for MRI scans. This work was supported by the Grant-in-Aid for Scientific Research B (KAKENHI: 23300241) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, the Senryakutekikenkyu (Hoga kenkyu) of Osaka City University, and by the Health Labor Sciences Research Grant of Japan.
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