Two types of mental fatigue affect spontaneous oscillatory brain activities in different ways

Ten male healthy volunteers [30.8 ± 9.4 years of age (mean ± SD)] were enrolled in this study. They had normal or corrected-to-normal visual acuity, no history of medical illness, and were right-handed according to the Edinburgh handedness inventory [16]. The study protocol was approved by the Ethics Committee of Osaka City University, and all the participants gave written informed consent for participation in this study.

This study was composed of two experiments in order to compare two types of mental fatigue. After enrollment, the participants were randomly assigned to two groups in a single-blinded, crossover fashion to perform two types of fatigue-inducing mental tasks (the same participants performed both 0- and 2-back tasks and that order of task was counterbalanced, i.e., five participants began with the 0-back test and the others with the 2-back test). An experiment was composed of three sessions: one fatigue-inducing mental task session and two evaluation sessions (Figure 1). During the fatigue-inducing mental task session, they performed 0-back or 2-back test trials for 30 min [10]. Just before and after the fatigue-inducing mental task session, MEG recordings were performed in the evaluation sessions with the subject’s eyes closed for 1 min in relaxed wakefulness, and subjective scaling using visual analogue scales (VAS’s) was performed before the first MEG recording and after the second recording. This study was conducted in a magnetically shielded room at Osaka City University Hospital. For 1 day before each visit, they refrained from intense mental and physical activities and caffeinated beverages, consumed a normal diet, and maintained normal sleeping hours. The healthy volunteers had not taken any form of medication during the last week. The time interval between the two experiments was 24 hr.

Participants performed a 0-back or 2-back test for 30 min on a bed of a MEG scanner lying in supine position during the fatigue-inducing mental task session. They watched a video projection screen located 30 cm straight ahead of their eyes. Any of four types of letters were continually presented on the video screen every 3 sec. The letter was presented on the screen for 500 ms and not presented for 2500 ms thereafter. During the 0-back trials, they were asked to press the right button with their right middle finger if the target letter (shown beside the video screen) was presented at the center of the video screen. If any other letters appeared, they were to press the left button with their right index finger. During the 2-back trials, they had to judge whether the target letter presented at the center of the video screen was the same as the one that had appeared 2 presentations before. If it was, they were to press the right button with their right middle finger, while if it was not they were to press the left button with their right index finger. They were instructed to perform the task trials as quickly and as correctly as possible. The result of each 0-back or 2-back trial - correct response or error - was continually presented on the display of the video screen. Performance on these tests was evaluated through the percentage correct and mean reaction time. To evaluate the time course of task performance during the fatigue-inducing mental task session, the performance measures were calculated every 5 min.

Subjective scaling was performed to evaluate participants’ subjective levels of fatigue and sleepiness. The participants were verbally asked to rate their subjective levels of fatigue (in Japanese, tsukareteiru) and sleepiness (in Japanese, nemui) on a VAS from 0 (minimum) to 100 (maximum) using a paper-and pencil questionnaire [17].

MEG recordings were performed using a 160-channel whole-head type MEG system (MEG vision; Yokogawa Electric Corporation, Tokyo, Japan) with a magnetic field resolution of 4 fT/Hz^1/2 in the white-noise region. The sensor and reference coils were gradiometers 15.5 mm in diameter and 50 mm in baseline, and each pair of sensor coils was separated at a distance of 23 mm. The sampling rate was 1,000 Hz with a 1 Hz high-pass filter.

MEG signal data were analyzed offline after analogue-to-digital conversion. Epochs of the raw MEG data including artifacts such as running trains of subway were excluded from the analysis by careful visual detection of the artifacts before averaging. After averaging, the power was determined in two frequency bands, alpha (8–13 Hz) and beta (13–25 Hz) by a fast Fourier transformation using Frequency Trend (Yokogawa Electric Corporation) for each participant and the contrast images of (after the n-back test trials)/(before the n-back test trials) was constructed for each frequency band.

Anatomical magnetic resonance imaging (MRI) was performed using a Philips Achieva 3.0TX (Royal Philips Electronics, Eindhoven, Netherland) to permit registration of magnetic source locations with their respective anatomical locations. Before MRI scanning, five adhesive markers (Medtronic Surgical Navigation Technologies Inc., Broomfield, CO) were attached to the skin of the participant’s head (the first and second ones were located at 10 mm in front of the left tragus and right tragus, the third at 35 mm above the nasion, and the fourth and fifth at 40 mm to the right and left of the third one). The MEG data were superimposed on MR images using information obtained from these markers and the MEG localization coils. Localization and intensity of the time-frequency power of the cortical activity were estimated using the software Brain Rhythmic Analysis for MEG (BRAM; Yokogawa Electric Corporation) [18], which used narrow-band adaptive spatial filtering technique. These data were then analyzed using Matlab (Mathworks, Sherbon, MA), implemented in statistical parametric mapping (SPM8, Wellcome Department of Cognitive Neurology, London, UK). The MEG anatomical/spatial parameters used to warp the volumetric data were transformed into the Montreal Neurological Institute (MNI) template of T1-weighed images [19] and applied to the MEG data. The anatomically normalized MEG data were filtered with a Gaussian kernel of 15 mm (full-width at half-maximum) in the x, y, and z axes. The oscillatory power for each frequency band after the n-back test trials relative to that before the n-back test trials was measured on a region-of-interest basis. The resulting set of voxel values for each comparison constituted a SPM of the t statistics (SPM{t}). The SPM{t} was transformed to the unit of normal distribution (SPM{Z}). The threshold for the SPM{Z} of individual analyses was set at P < 0.001 (uncorrected 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 [20]. So that inferences could be made at a population level, individual data were summarized and incorporated into a random-effect model [20]. 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 [20]. The threshold for the SPM{Z} of group analyses was set at P < 0.001 (uncorrected for multiple comparisons). Anatomical localizations of significant voxels within clusters were done using the Talairach Demon software [21] with the nearest gray matter option enabled. We captured the fiducial coils and tracked the head position before and after the MEG recording. The criterion for head movement exclusion was the movement more than one voxel size (5 × 5 × 5 mm).

While the results of the present study are suggestive of the mechanisms of mental fatigue, only a limited number of participants and limited time-frequency bands were tested. To generalize our results, studies involving a larger number of participants and a variety of time-frequency bands will be needed. In addition, we did not measure the objective markers of mental fatigue in urine and serum [46] and did not investigate the relationship between the MEG data and these markers. Furthermore, although the participants did not complain of physical fatigue, we could not exclude the possibility that the MEG data was affected by physical fatigue. Finally, the use of the task with more burdens, such as 3-back or more, longer duration of tasks could effectively extract the fatigue as the difference of these two conditions. We used 30-min 2-back test as a fatigue-inducing task based on the results of our previous study [7]: To evaluate mental fatigue, participants completed advanced trail-making test (ATMT [11]) for 30 min, and after the 2-back test session for 30 min, ATMT performance was impaired, and this session was shown to be mental fatigue-inducing tasks. In the ATMT, circles numbered from 1 to 25 were randomly located on the display of a personal computer, and the participants were required to use a computer mouse to click the center of the circles in sequence, starting with circle number 1. The task performance was evaluated by number of errors. In addition, by using the 0-back and 2-back tasks, we could extract the fatigue as the difference related to the MEG responses to visual stimuli of these two conditions [47, 48]. Therefore, we adopted 30-min 2-back test as a fatigue-inducing task.