- Email: [email protected]
Neural effects of mental fatigue caused by continuous attention load: A magnetoencephalography study
brain research 1561 (2014) 60–66
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
Neural effects of mental fatigue caused by continuous attention load: 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
ar t ic l e in f o
abs tra ct
Article history:
Mental fatigue can be defined as a psychobiological state caused by prolonged periods of
Accepted 11 March 2014
demanding cognitive activity and manifests as a reduced efficiency in cognitive perfor-
Available online 16 March 2014
mance. Mental fatigue is one of the most significant causes of accidents in modern society.
Keywords:
Therefore, understanding the neural mechanisms of mental fatigue is important. However,
Attention
the neural mechanisms of mental fatigue are not fully understood. In this study, we
Beta-frequency band
investigated the neural activity that results from mental fatigue caused by a continuous
Magnetoencephalography (MEG)
attention load. We used magnetoencephalography (MEG) to evaluate the neural activities
Mental fatigue
during the attention task. Ten healthy male volunteers participated in this study. They
Prefrontal cortex
performed a continuous attention task lasting 10 min. Subjective ratings of mental fatigue, mental stress, boredom, and sleepiness were performed just after the task trial. MEG data were analyzed using narrow-band adaptive spatial filtering methods. An increase in the beta-frequency band (13–25 Hz) power in the right inferior and middle frontal gyri (Brodmann's areas 44 and 9 respectively) was caused by the mental fatigue. The increase in the beta-frequency band power in the right middle frontal gyrus was negatively associated with the self-reported level of mental stress and was positively associated with those of boredom and sleepiness. These results demonstrate that performing a continuous mental fatigue-inducing task causes changes in the activation of the prefrontal cortex, and manifests as an increased beta-frequency power in this brain area as well as sleepiness. Our results contribute to greater understanding of the neural mechanisms of mental fatigue. & 2014 Elsevier B.V. All rights reserved.
1.
Introduction
Mental fatigue is defined as a psychobiological state caused by prolonged periods of demanding cognitive activity (Boksem and Tops, 2008). Mental fatigue manifests as a reduced efficiency of mental workload (Chaudhuri and Behan, 2004) and has become n
Corresponding author. Fax: þ81 6 6645 3712. E-mail address: [email protected] (M. Tanaka).
http://dx.doi.org/10.1016/j.brainres.2014.03.009 0006-8993/& 2014 Elsevier B.V. All rights reserved.
one of the most significant causes of accidents in modern society (Dinges, 1995; Shen et al., 2008). Indeed, more than half of the general adult populations in Japan complain of fatigue (Watanabe, 2007), i.e., fatigue is a common symptom in our society. Therefore, it is important to understand the neural mechanisms of mental fatigue, which would contribute to the
61
brain research 1561 (2014) 60–66
future development of treatment strategies of mental fatigue. However, the neural mechanisms of mental fatigue are not fully understood. Changes in neural activities that are caused by performing mental fatigue-inducing task trials have been previously investigated (Shigihara et al., 2013a; Ishii et al., 2013). Because 2-back task trials, which require working memory (Braver et al., 1997), have been successfully applied to induce mental fatigue to examine their neural mechanisms (Mizuno and Watanabe, 2007; Tanaka et al., 2012; Shigihara et al., 2013b), we also adopted the 2-back task as a mental fatigue-inducing task. Just before and after the mental fatigue-inducing mental task trials, neural activities in an eyes-closed state were evaluated by using magnetoencephalography (MEG). Mental fatigue-inducing tasks led to the suppression of spontaneous MEG alpha-band (8–13 Hz) power in the cerebral cortex, suggesting an overactivation of the thalamo-cortical feedback loop (Shigihara et al., 2013a; Ishii et al., 2013). The thalamo-cortical feedback loop is related to complex cognitive functions such as attention, memory, and mental imagery (Burruss et al., 2000; Gevins and Schaffer, 1980; Klimesch et al., 2007; Tesche et al., 1995), and overactivation of this loop reflects heavy cognitive load. In the previous MEG studies, it was difficult to evaluate neural activity during 2-back task trials, because the muscle activity required to press the button caused electromagnetic noise. Thus, neural activity during task trials has not been assessed, although any changes in neural activity could help to clarify the neural mechanisms of mental fatigue. In order to evaluate neural activity during task trials with a minimum level of electromagnetic interference, it seemed to be the best choice was to conduct a continuous mental fatigue-inducing task without any button pressing and with participants' eyes closed. The aim of our present study was therefore to clarify the neural mechanisms of mental fatigue when participants perform a fatigue-inducing mental task. Our study consisted of a 10 min continuous attention task. Subjective evaluations were performed immediately after the task trial. In the evaluation session, we assessed subjective rating of mental fatigue, mental stress, boredom, and sleepiness using visual analog scales (VAS).
2.
Results
2.1.
Changes in oscillatory brain activity
To identify the brain regions affected by mental fatigue, the increased and decreased oscillatory powers, that is, event-related
synchronization (ERS) and event-related desynchronization (ERD), respectively, of the alpha- (8–13 Hz), beta- (13–25 Hz), and gamma- (25–50 Hz) frequency bands in ‘fatigued condition’ relative to ‘non-fatigued condition’ within the time window of 0–1000 ms were evaluated. Results are shown in Table 1 and Fig. 1. Across all brain regions for those time-frequency bands, only the ERSs of the beta-frequency band in the right inferior and middle frontal gyri (Brodmann's areas 44 and 9, respectively) were identified (Po0.05, corrected for multiple comparisons). No brain regions showed significant ERDs for all the time-frequency bands assessed.
2.2. Relationships between the MEG responses and the subjective scores To evaluate the relationships between the ERS levels of the beta-frequency band in the right inferior and middle frontal gyri and the VAS scores of mental fatigue, mental stress, boredom, and sleepiness during a mental fatigue-inducing task, correlation analyses were performed. The ERS level in the inferior frontal gyrus was not significantly associated with the VAS scores of mental fatigue (Fig. 2A), mental stress (Fig. 2B), boredom, (Fig. 2C), or sleepiness (Fig. 2D). However, although the ERS level in the middle frontal gyrus was not significantly associated with the VAS score of mental fatigue (Fig. 3A), the ERS level was negatively associated with that of mental stress (Fig. 3B; R¼ 0.651, P ¼0.041) and positively associated with those of boredom (Fig. 3C; R¼ 0.661, P¼ 0.037) and sleepiness (Fig. 3D; R ¼0.801, P¼ 0.005).
3.
Discussion
In this study, we evaluated the changes in neural activity caused by performing a mental fatigue-inducing continuous attention task. We showed that increases in the betafrequency band powers in the right inferior and middle frontal gyri (Brodmann's areas 44 and 9, respectively) were caused by mental fatigue. In addition, the increase in the beta-frequency band power in the right middle frontal gyrus was negatively associated with self-reported levels of mental stress and was positively associated with levels of boredom and sleepiness. These results demonstrate that performing a continuous mental fatigue-inducing task causes changes of the activation of the prefrontal cortex, and manifests as an increased beta-frequency power in this brain area as well as sleepiness.
Table 1 – Brain regions that showed event-related synchronization of the beta-frequency band in the ‘fatigued condition’ relative to the ‘non-fatigued condition’. Location
Inferior frontal gyrus Middle frontal gyrus
Side
Right Right
Brodmann's area
44 9
Coordinate (mm)
Z-value
x
y
z
57 57
33 18
5 40
x, y, z: Stereotaxic coordinate of peak of activated cluster. Random-effects analysis of 10 participants (Po0.05, corrected for multiple comparisons).
3.96 3.77
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brain research 1561 (2014) 60–66
Fig. 1 – Statistical parametric maps of event-related synchronization of beta-frequency band (‘fatigued condition’ relative to ‘non-fatigued condition’; random-effects analysis of 10 participants; Po0.05, corrected for multiple comparisons). Statistical parametric maps are superimposed on high-resolution magnetic resonance imaging. Sagittal (upper left), coronal (upper right), and axial (lower left) sections passing through the right inferior frontal gyrus are shown. The color bar indicates T-values. R, right side.
Fig. 2 – Relationships between the event-related synchronization (ERS) levels of beta-frequency band in the right inferior frontal gyrus and visual analog scale (VAS) scores of mental fatigue (A), mental stress (B), boredom (C), and sleepiness (D). Linear regression lines, Pearson's correlation coefficients, and P values are shown. Previous studies using functional magnetic resonance imaging (fMRI) have shown involvement of the prefrontal area in attention control (Corbetta and Shulman, 2002) and
during an attention task, and a power decrease in the betafrequency band was shown using MEG recordings (Siegel et al., 2008; van Ede et al., 2010, 2011). In addition, the studies
brain research 1561 (2014) 60–66
63
Fig. 3 – Relationships between the event-related synchronization (ERS) levels of beta-frequency band in the right middle frontal gyrus and visual analog scale (VAS) scores of mental fatigue (A), mental stress (B), boredom (C), and sleepiness (D). Linear regression lines, Pearson's correlation coefficients, and P values are shown.
using MEG have shown that orienting attention to an upcoming sensory event modulates the beta-frequency band oscillation (van Ede et al., 2010, 2011; Jones et al., 2010; Anderson and Ding, 2011), and also that the MEG beta-frequency band oscillation was observed in the cue-target interval which requires the control of attention to stimulus in the prefrontal and sensori-motor areas (Kida and Kakigi, 2013), indicating that global hubs exist in the these areas involved in the control of attention to stimulus and action. These results suggest a close relationship between attention control and beta-frequency band oscillation in the prefrontal brain area. In our study, mental fatigue caused the increase in the beta-frequency band powers in the prefrontal brain area. Since increased beta-frequency band power has been associated with decreased alertness and arousal (Okogbaa et al., 1994), these alterations in the power band induced by mental fatigue may be related to declines in brain arousal levels. In fact, we also showed that the increase of the beta-frequency band power in the prefrontal area was positively associated with the self-reported levels of sleepiness. Since sleepiness is a type of request for rest in order to recover from fatigue (Kumar, 2008), it can be considered that the continuous mental fatigue-inducing attention task trial activated a mental inhibitory system and/or deactivated a mental facilitation system in the central nervous system. These mental facilitation and inhibition systems are involved in the neural mechanisms of mental fatigue, and modulate the activities of the task-related brain regions to regulate cognitive performance (Tanaka et al., 2013). We recently proposed a conceptual model of fatigue (Tanaka and Watanabe, 2010, 2012; Tanaka et al., 2013): workload activates a facilitation system to maintain performance against fatigue. An increase in the motivational input to this facilitation system increases the activation of this
system. On the other hand, workload activates the inhibition system to impair the performance. Acute workload activates the facilitation and inhibition systems to cause acute fatigue. The activated facilitation system maintains or improves performance, while the activated inhibition system impairs performance. Depending on the balance between the level of the activation of the facilitation and inhibition systems, performance may be impaired, maintained, or even improved. Performance may be therefore regulated by these two systems, i.e., a dual regulation system. In a situation in which workload is high enough to cause further impaired performance, increased motivational input against fatigue further activates the facilitation system to maintain performance, while the inhibition system is further activated to avoid upsetting homeostasis, request for rest, and recovery from fatigue. Repeated and prolonged workload causes dysfunction of the facilitation system due to impaired energy metabolism and/or oxidative damage as well as the overactivation of the inhibition system through the central sensitization and/or classical conditioning. These alterations in the facilitation and inhibition systems result in severely reduced cognitive performance, i.e., chronic fatigue. The present study has two limitations. First, the number of participants was relatively small. To generalize the results of our studies, studies involving a large number of participants are essential. Second, we did not examine neural activity when participants perform continuous task trials, because the muscle activity required to perform continuous task trials causes electromagnetic noise. Thus, we could not evaluate the change in neural activity caused by mental fatigue related to impaired cognitive performance. We instead focused on the neural activity caused by mental fatigue related to the subjective alterations when the participants perform the continuous task in an eyes-closed state.
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brain research 1561 (2014) 60–66
In conclusion, we demonstrated that performing the continuous mental fatigue-inducing task causes changes of the activation of the prefrontal cortex, and manifests as an increased beta-frequency power in this brain area as well as sleepiness. We believe that our findings are of great value for greater understanding of the neural mechanisms of mental fatigue as well for developing efficient strategies to overcome mental fatigue.
4.
Experimental procedures
4.1.
Participants
Ten healthy male volunteers [23.572.5 years of age (mean7SD)] were enrolled in this study. All the participants were righthanded according to the Edinburgh handedness inventory (Oldfield, 1971). Current smokers, participants with a history of mental or brain disorders, or 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
After enrollment, the participants performed a 10 min mental fatigue-inducing task (Fig. 4). During the task, they lay on a bed quietly with their eyes closed. Just after the task, they were asked to rate their subjective levels of mental fatigue, mental stress, boredom, and sleepiness on VASs from 0 (minimum) to 100 (maximum) (Lee et al., 1991). This study was conducted in a quiet, temperature-, and humidity-controlled, magnetically shielded room at Osaka City University Hospital. On the day before the study, all the participants refrained from intense mental and physical activities and caffeinated beverages, consumed a normal diet, and maintained normal sleeping hours.
4.3.
The participants performed a continuous attention task for 10 min. They were requested to press a button with their right index finger as soon as possible when a cue sound was heard. The cue sound consisted of white noise that lasted 33 ms, and this sound was produced by Windows Media Player (Microsoft Corporation, Redmond, WA) and was 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. They waited for a cue sound that was in reality not presented at all. Before the mental fatigue-inducing attention task, they performed another continuous attention task. They were requested to press a button with their right index finger as soon as possible when they felt mental fatigue and requested rest. The reaction time to press the button was 210.17123.9 s (mean7SD), and the minimum and maximum reaction times were 64 and 304 s, respectively. Thus, we determined that 0–64 s after the start of the mental fatigue-inducing attention task was a ‘non-fatigued period’ and that 304–600 s after the start of the attention task was a ‘fatigued period’. For the MEG comparison between ‘non-fatigued condition’ and ‘fatigued condition’, ‘non-fatigued period’ and ‘fatigued period’ should be the same time duration. Therefore, we analyzed the MEG data after the start of 0–60 s and 540–600 s as ‘non-fatigued condition’ and ‘fatigued condition’, respectively (Fig. 4), and we compared these conditions in order to obtain the neural activation pattern evoked by mental fatigue.
4.4.
MEG recording
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 1 Hz high-pass filter and a 500 Hz low-pass filter.
4.5.
Fig. 4 – Experimental design. Participants performed a continuous attention task for 10 min. They lay on a bed quietly with their eyes closed. They were requested to press a button with their right index finger as soon as possible when a cue sound was heard. ‘Non-fatigued condition’ and ‘fatigued condition’ were determined as the conditions 0–60 s and 540–600 s after the start of the continuous attention task, respectively.
Mental fatigue-inducing task
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 in length using a software-trigger. The data were band-pass filtered at 8–13 Hz, 13–25 Hz, and 25–50 Hz by a fast Fourier transform using Frequency Trend (Yokogawa Electric Corporation) to obtain alpha, beta, and gamma signals, respectively, using the 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
brain research 1561 (2014) 60–66
used narrow-band adaptive spatial filtering methods as an algorithm (Dalal et al., 2008; Sekihara and Nagarajan, 2008). The oscillatory power in each voxel was assessed, and the ERS level was calculated as 10 log10[(oscillatory power in the ‘fatigued condition’)/(oscillatory power in the ‘non-fatigued condition’)]. 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 5.0 5.0 mm3). The increased and decreased oscillatory powers, that is, ERS and ERD, respectively, for alpha-, beta-, and gamma-frequency bands within the time window of 0–1000 ms in the ‘fatigued condition’ relative to those in the ‘non-fatigued condition’ were measured on a region-of-interest basis to obtain the neural activation pattern caused by mental 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 units of a 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-effects 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-by-voxel basis (Friston et al., 1999). The threshold for the SPM{Z} for group analyses was set at Po0.05 (corrected for multiple comparisons). The extent threshold in terms of the number of voxels was more than 10 voxels. Anatomical localization of significant voxels within each cluster was done using Talairach Demon software (Lancaster et al., 2000).
4.6.
Magnetic resonance imaging overlay
Anatomic MRI was performed using a Philips Achieva 3.0TX (Royal Philips Electronics, Eindhoven, The Netherlands) for all the 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 to the right and left of the third marker). MEG data were superimposed on MRI scans using information obtained from these markers and MEG localization coils.
4.7.
Statistical analyses
Data are presented as mean7SD, unless otherwise stated. Pearson's correlation analyses were conducted to evaluate
65
the relationships between the MEG responses and the subjective scores. All P values were two-tailed, and values less than 0.05 were considered to be 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 by the Senryakutekikenkyu (Hoga Kenkyu) of Osaka City University and by the Health Labour Sciences Research Grant of Japan.
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Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
Neural effects of mental fatigue caused by continuous attention load: 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
ar t ic l e in f o
abs tra ct
Article history:
Mental fatigue can be defined as a psychobiological state caused by prolonged periods of
Accepted 11 March 2014
demanding cognitive activity and manifests as a reduced efficiency in cognitive perfor-
Available online 16 March 2014
mance. Mental fatigue is one of the most significant causes of accidents in modern society.
Keywords:
Therefore, understanding the neural mechanisms of mental fatigue is important. However,
Attention
the neural mechanisms of mental fatigue are not fully understood. In this study, we
Beta-frequency band
investigated the neural activity that results from mental fatigue caused by a continuous
Magnetoencephalography (MEG)
attention load. We used magnetoencephalography (MEG) to evaluate the neural activities
Mental fatigue
during the attention task. Ten healthy male volunteers participated in this study. They
Prefrontal cortex
performed a continuous attention task lasting 10 min. Subjective ratings of mental fatigue, mental stress, boredom, and sleepiness were performed just after the task trial. MEG data were analyzed using narrow-band adaptive spatial filtering methods. An increase in the beta-frequency band (13–25 Hz) power in the right inferior and middle frontal gyri (Brodmann's areas 44 and 9 respectively) was caused by the mental fatigue. The increase in the beta-frequency band power in the right middle frontal gyrus was negatively associated with the self-reported level of mental stress and was positively associated with those of boredom and sleepiness. These results demonstrate that performing a continuous mental fatigue-inducing task causes changes in the activation of the prefrontal cortex, and manifests as an increased beta-frequency power in this brain area as well as sleepiness. Our results contribute to greater understanding of the neural mechanisms of mental fatigue. & 2014 Elsevier B.V. All rights reserved.
1.
Introduction
Mental fatigue is defined as a psychobiological state caused by prolonged periods of demanding cognitive activity (Boksem and Tops, 2008). Mental fatigue manifests as a reduced efficiency of mental workload (Chaudhuri and Behan, 2004) and has become n
Corresponding author. Fax: þ81 6 6645 3712. E-mail address: [email protected] (M. Tanaka).
http://dx.doi.org/10.1016/j.brainres.2014.03.009 0006-8993/& 2014 Elsevier B.V. All rights reserved.
one of the most significant causes of accidents in modern society (Dinges, 1995; Shen et al., 2008). Indeed, more than half of the general adult populations in Japan complain of fatigue (Watanabe, 2007), i.e., fatigue is a common symptom in our society. Therefore, it is important to understand the neural mechanisms of mental fatigue, which would contribute to the
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future development of treatment strategies of mental fatigue. However, the neural mechanisms of mental fatigue are not fully understood. Changes in neural activities that are caused by performing mental fatigue-inducing task trials have been previously investigated (Shigihara et al., 2013a; Ishii et al., 2013). Because 2-back task trials, which require working memory (Braver et al., 1997), have been successfully applied to induce mental fatigue to examine their neural mechanisms (Mizuno and Watanabe, 2007; Tanaka et al., 2012; Shigihara et al., 2013b), we also adopted the 2-back task as a mental fatigue-inducing task. Just before and after the mental fatigue-inducing mental task trials, neural activities in an eyes-closed state were evaluated by using magnetoencephalography (MEG). Mental fatigue-inducing tasks led to the suppression of spontaneous MEG alpha-band (8–13 Hz) power in the cerebral cortex, suggesting an overactivation of the thalamo-cortical feedback loop (Shigihara et al., 2013a; Ishii et al., 2013). The thalamo-cortical feedback loop is related to complex cognitive functions such as attention, memory, and mental imagery (Burruss et al., 2000; Gevins and Schaffer, 1980; Klimesch et al., 2007; Tesche et al., 1995), and overactivation of this loop reflects heavy cognitive load. In the previous MEG studies, it was difficult to evaluate neural activity during 2-back task trials, because the muscle activity required to press the button caused electromagnetic noise. Thus, neural activity during task trials has not been assessed, although any changes in neural activity could help to clarify the neural mechanisms of mental fatigue. In order to evaluate neural activity during task trials with a minimum level of electromagnetic interference, it seemed to be the best choice was to conduct a continuous mental fatigue-inducing task without any button pressing and with participants' eyes closed. The aim of our present study was therefore to clarify the neural mechanisms of mental fatigue when participants perform a fatigue-inducing mental task. Our study consisted of a 10 min continuous attention task. Subjective evaluations were performed immediately after the task trial. In the evaluation session, we assessed subjective rating of mental fatigue, mental stress, boredom, and sleepiness using visual analog scales (VAS).
2.
Results
2.1.
Changes in oscillatory brain activity
To identify the brain regions affected by mental fatigue, the increased and decreased oscillatory powers, that is, event-related
synchronization (ERS) and event-related desynchronization (ERD), respectively, of the alpha- (8–13 Hz), beta- (13–25 Hz), and gamma- (25–50 Hz) frequency bands in ‘fatigued condition’ relative to ‘non-fatigued condition’ within the time window of 0–1000 ms were evaluated. Results are shown in Table 1 and Fig. 1. Across all brain regions for those time-frequency bands, only the ERSs of the beta-frequency band in the right inferior and middle frontal gyri (Brodmann's areas 44 and 9, respectively) were identified (Po0.05, corrected for multiple comparisons). No brain regions showed significant ERDs for all the time-frequency bands assessed.
2.2. Relationships between the MEG responses and the subjective scores To evaluate the relationships between the ERS levels of the beta-frequency band in the right inferior and middle frontal gyri and the VAS scores of mental fatigue, mental stress, boredom, and sleepiness during a mental fatigue-inducing task, correlation analyses were performed. The ERS level in the inferior frontal gyrus was not significantly associated with the VAS scores of mental fatigue (Fig. 2A), mental stress (Fig. 2B), boredom, (Fig. 2C), or sleepiness (Fig. 2D). However, although the ERS level in the middle frontal gyrus was not significantly associated with the VAS score of mental fatigue (Fig. 3A), the ERS level was negatively associated with that of mental stress (Fig. 3B; R¼ 0.651, P ¼0.041) and positively associated with those of boredom (Fig. 3C; R¼ 0.661, P¼ 0.037) and sleepiness (Fig. 3D; R ¼0.801, P¼ 0.005).
3.
Discussion
In this study, we evaluated the changes in neural activity caused by performing a mental fatigue-inducing continuous attention task. We showed that increases in the betafrequency band powers in the right inferior and middle frontal gyri (Brodmann's areas 44 and 9, respectively) were caused by mental fatigue. In addition, the increase in the beta-frequency band power in the right middle frontal gyrus was negatively associated with self-reported levels of mental stress and was positively associated with levels of boredom and sleepiness. These results demonstrate that performing a continuous mental fatigue-inducing task causes changes of the activation of the prefrontal cortex, and manifests as an increased beta-frequency power in this brain area as well as sleepiness.
Table 1 – Brain regions that showed event-related synchronization of the beta-frequency band in the ‘fatigued condition’ relative to the ‘non-fatigued condition’. Location
Inferior frontal gyrus Middle frontal gyrus
Side
Right Right
Brodmann's area
44 9
Coordinate (mm)
Z-value
x
y
z
57 57
33 18
5 40
x, y, z: Stereotaxic coordinate of peak of activated cluster. Random-effects analysis of 10 participants (Po0.05, corrected for multiple comparisons).
3.96 3.77
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Fig. 1 – Statistical parametric maps of event-related synchronization of beta-frequency band (‘fatigued condition’ relative to ‘non-fatigued condition’; random-effects analysis of 10 participants; Po0.05, corrected for multiple comparisons). Statistical parametric maps are superimposed on high-resolution magnetic resonance imaging. Sagittal (upper left), coronal (upper right), and axial (lower left) sections passing through the right inferior frontal gyrus are shown. The color bar indicates T-values. R, right side.
Fig. 2 – Relationships between the event-related synchronization (ERS) levels of beta-frequency band in the right inferior frontal gyrus and visual analog scale (VAS) scores of mental fatigue (A), mental stress (B), boredom (C), and sleepiness (D). Linear regression lines, Pearson's correlation coefficients, and P values are shown. Previous studies using functional magnetic resonance imaging (fMRI) have shown involvement of the prefrontal area in attention control (Corbetta and Shulman, 2002) and
during an attention task, and a power decrease in the betafrequency band was shown using MEG recordings (Siegel et al., 2008; van Ede et al., 2010, 2011). In addition, the studies
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Fig. 3 – Relationships between the event-related synchronization (ERS) levels of beta-frequency band in the right middle frontal gyrus and visual analog scale (VAS) scores of mental fatigue (A), mental stress (B), boredom (C), and sleepiness (D). Linear regression lines, Pearson's correlation coefficients, and P values are shown.
using MEG have shown that orienting attention to an upcoming sensory event modulates the beta-frequency band oscillation (van Ede et al., 2010, 2011; Jones et al., 2010; Anderson and Ding, 2011), and also that the MEG beta-frequency band oscillation was observed in the cue-target interval which requires the control of attention to stimulus in the prefrontal and sensori-motor areas (Kida and Kakigi, 2013), indicating that global hubs exist in the these areas involved in the control of attention to stimulus and action. These results suggest a close relationship between attention control and beta-frequency band oscillation in the prefrontal brain area. In our study, mental fatigue caused the increase in the beta-frequency band powers in the prefrontal brain area. Since increased beta-frequency band power has been associated with decreased alertness and arousal (Okogbaa et al., 1994), these alterations in the power band induced by mental fatigue may be related to declines in brain arousal levels. In fact, we also showed that the increase of the beta-frequency band power in the prefrontal area was positively associated with the self-reported levels of sleepiness. Since sleepiness is a type of request for rest in order to recover from fatigue (Kumar, 2008), it can be considered that the continuous mental fatigue-inducing attention task trial activated a mental inhibitory system and/or deactivated a mental facilitation system in the central nervous system. These mental facilitation and inhibition systems are involved in the neural mechanisms of mental fatigue, and modulate the activities of the task-related brain regions to regulate cognitive performance (Tanaka et al., 2013). We recently proposed a conceptual model of fatigue (Tanaka and Watanabe, 2010, 2012; Tanaka et al., 2013): workload activates a facilitation system to maintain performance against fatigue. An increase in the motivational input to this facilitation system increases the activation of this
system. On the other hand, workload activates the inhibition system to impair the performance. Acute workload activates the facilitation and inhibition systems to cause acute fatigue. The activated facilitation system maintains or improves performance, while the activated inhibition system impairs performance. Depending on the balance between the level of the activation of the facilitation and inhibition systems, performance may be impaired, maintained, or even improved. Performance may be therefore regulated by these two systems, i.e., a dual regulation system. In a situation in which workload is high enough to cause further impaired performance, increased motivational input against fatigue further activates the facilitation system to maintain performance, while the inhibition system is further activated to avoid upsetting homeostasis, request for rest, and recovery from fatigue. Repeated and prolonged workload causes dysfunction of the facilitation system due to impaired energy metabolism and/or oxidative damage as well as the overactivation of the inhibition system through the central sensitization and/or classical conditioning. These alterations in the facilitation and inhibition systems result in severely reduced cognitive performance, i.e., chronic fatigue. The present study has two limitations. First, the number of participants was relatively small. To generalize the results of our studies, studies involving a large number of participants are essential. Second, we did not examine neural activity when participants perform continuous task trials, because the muscle activity required to perform continuous task trials causes electromagnetic noise. Thus, we could not evaluate the change in neural activity caused by mental fatigue related to impaired cognitive performance. We instead focused on the neural activity caused by mental fatigue related to the subjective alterations when the participants perform the continuous task in an eyes-closed state.
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In conclusion, we demonstrated that performing the continuous mental fatigue-inducing task causes changes of the activation of the prefrontal cortex, and manifests as an increased beta-frequency power in this brain area as well as sleepiness. We believe that our findings are of great value for greater understanding of the neural mechanisms of mental fatigue as well for developing efficient strategies to overcome mental fatigue.
4.
Experimental procedures
4.1.
Participants
Ten healthy male volunteers [23.572.5 years of age (mean7SD)] were enrolled in this study. All the participants were righthanded according to the Edinburgh handedness inventory (Oldfield, 1971). Current smokers, participants with a history of mental or brain disorders, or 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
After enrollment, the participants performed a 10 min mental fatigue-inducing task (Fig. 4). During the task, they lay on a bed quietly with their eyes closed. Just after the task, they were asked to rate their subjective levels of mental fatigue, mental stress, boredom, and sleepiness on VASs from 0 (minimum) to 100 (maximum) (Lee et al., 1991). This study was conducted in a quiet, temperature-, and humidity-controlled, magnetically shielded room at Osaka City University Hospital. On the day before the study, all the participants refrained from intense mental and physical activities and caffeinated beverages, consumed a normal diet, and maintained normal sleeping hours.
4.3.
The participants performed a continuous attention task for 10 min. They were requested to press a button with their right index finger as soon as possible when a cue sound was heard. The cue sound consisted of white noise that lasted 33 ms, and this sound was produced by Windows Media Player (Microsoft Corporation, Redmond, WA) and was 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. They waited for a cue sound that was in reality not presented at all. Before the mental fatigue-inducing attention task, they performed another continuous attention task. They were requested to press a button with their right index finger as soon as possible when they felt mental fatigue and requested rest. The reaction time to press the button was 210.17123.9 s (mean7SD), and the minimum and maximum reaction times were 64 and 304 s, respectively. Thus, we determined that 0–64 s after the start of the mental fatigue-inducing attention task was a ‘non-fatigued period’ and that 304–600 s after the start of the attention task was a ‘fatigued period’. For the MEG comparison between ‘non-fatigued condition’ and ‘fatigued condition’, ‘non-fatigued period’ and ‘fatigued period’ should be the same time duration. Therefore, we analyzed the MEG data after the start of 0–60 s and 540–600 s as ‘non-fatigued condition’ and ‘fatigued condition’, respectively (Fig. 4), and we compared these conditions in order to obtain the neural activation pattern evoked by mental fatigue.
4.4.
MEG recording
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 1 Hz high-pass filter and a 500 Hz low-pass filter.
4.5.
Fig. 4 – Experimental design. Participants performed a continuous attention task for 10 min. They lay on a bed quietly with their eyes closed. They were requested to press a button with their right index finger as soon as possible when a cue sound was heard. ‘Non-fatigued condition’ and ‘fatigued condition’ were determined as the conditions 0–60 s and 540–600 s after the start of the continuous attention task, respectively.
Mental fatigue-inducing task
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 in length using a software-trigger. The data were band-pass filtered at 8–13 Hz, 13–25 Hz, and 25–50 Hz by a fast Fourier transform using Frequency Trend (Yokogawa Electric Corporation) to obtain alpha, beta, and gamma signals, respectively, using the 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
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used narrow-band adaptive spatial filtering methods as an algorithm (Dalal et al., 2008; Sekihara and Nagarajan, 2008). The oscillatory power in each voxel was assessed, and the ERS level was calculated as 10 log10[(oscillatory power in the ‘fatigued condition’)/(oscillatory power in the ‘non-fatigued condition’)]. 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 5.0 5.0 mm3). The increased and decreased oscillatory powers, that is, ERS and ERD, respectively, for alpha-, beta-, and gamma-frequency bands within the time window of 0–1000 ms in the ‘fatigued condition’ relative to those in the ‘non-fatigued condition’ were measured on a region-of-interest basis to obtain the neural activation pattern caused by mental 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 units of a 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-effects 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-by-voxel basis (Friston et al., 1999). The threshold for the SPM{Z} for group analyses was set at Po0.05 (corrected for multiple comparisons). The extent threshold in terms of the number of voxels was more than 10 voxels. Anatomical localization of significant voxels within each cluster was done using Talairach Demon software (Lancaster et al., 2000).
4.6.
Magnetic resonance imaging overlay
Anatomic MRI was performed using a Philips Achieva 3.0TX (Royal Philips Electronics, Eindhoven, The Netherlands) for all the 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 to the right and left of the third marker). MEG data were superimposed on MRI scans using information obtained from these markers and MEG localization coils.
4.7.
Statistical analyses
Data are presented as mean7SD, unless otherwise stated. Pearson's correlation analyses were conducted to evaluate
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the relationships between the MEG responses and the subjective scores. All P values were two-tailed, and values less than 0.05 were considered to be 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 by the Senryakutekikenkyu (Hoga Kenkyu) of Osaka City University and by the Health Labour Sciences Research Grant of Japan.
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