3.3.2. Contrast Results

Direct contrast between both conditions revealed a suprathreshold cluster only in the contrast fatigued > rested. The cluster was located in the right superior frontal gyrus (see Figure 4c). Interestingly, this region did not exceed the Z = 3.1 threshold in either condition, and it appeared in the fatigued > rested contrast because of the negative Z values in the rested condition (see Figure 4a showing the unthresholded Z values) and positive Z values in the fatigued condition (Figure 4b shows the unthresholded Z values). Thus, the difference of activation of this region in both conditions was caused by low response of this region in the rested condition and the moderately elevated response in the fatigued condition.

**Figure 4.** Unthresholded Z-statistics image of rested condition results (**a**), unthresholded Z-statistics image of the fatigued condition results (**b**). Note the larger extension of activations in the rested condition (**a**) as compared to the fatigued condition (**b**). The region of statistically significant differences; i.e., the contrast fatigued > rested conditions thresholded at Z > 3.1 (**c**). The crosshairs indicate the same voxel at the center of the suprathreshold cluster in the right superior frontal gyrus (MNI coordinates: x = 18, y = 26, y = 56, cluster volume = 136, Z-score (maximum—4.53)).

#### **4. Discussion**

In this study, we assessed the susceptibility of the networks involved in selective attention to the fatigue state induced by sleep restriction in professional truck drivers. In order to engage these networks, the visual feature detection cognitive task was used, in which the participants responded to the conjunctions of color and shape of visual stimuli. The difficulty level of the cognitive task was adjusted to ensure a commensurate performance in both arousal states.

Our manipulation of fatigue level via sleep deprivation proved successful. Both the CHICa scale and the Japanese Questionnaire revealed significant changes in self-reported arousal level due to the 10 h of overnight driving under normal working conditions. The results of the CHICa demonstrated that the reduced level of cognitive functioning in the fatigued condition was accompanied by the symptoms of impaired thermoregulation, disrupted appetite, and irritation. These results were consistent with the data obtained by the authors of this questionnaire themselves [15], according to which the symptoms of cognitive attenuation; i.e., problems with concentration, memory, logical thinking and understanding, lack of energy, and decreased accuracy at work, seemed to be the most characteristic and specific subjective manifestations of the sleep-deprivation state. It should also be noted, however, that while some physiological symptoms of sleep deprivation may be unpleasant for the driver (cold, hunger), they can be easily modified and fixed; whereas coping with emotional and cognitive problems is much more difficult, and consequently, they can endanger driving safety by influencing, for example, the driver's situational awareness in road traffic, as well as his sense of control behind the wheel [15].

The increase in response time in the fatigued condition was consistent with other studies [4,19,20]. The changes in response time can be interpreted as indicators of lowered alertness, accompanied by slowed processing of relevant stimuli without affecting the accuracy.

Regarding the whole-brain fMRI results in each condition, we observed a widespread activation spanning across the whole cortical mantle. The activated clusters in the rested condition were located in the regions consistently observed in the task involving selective attention (dorsal frontal areas, posterior parietal areas, medial frontal regions, and basal ganglia) [9–12]. Moreover, the additional suprathreshold clusters were observed in regions related to the visual ventral stream in the fusiform gyrus, as well as regions related to willed action in the anterior insula [21]. The fact that a similar pattern of activated regions involved in attentional selection was observed in both conditions suggests that in both situations, participants recruited basically the same brain networks. This observation was also supported by the lack of significant brain response differences in those networks as revealed by the direct contrast between the conditions.

However, at the same time we observed a notable decrease of the extent of activation in the fatigued condition, indicating a less-consistent brain response to the experimental task across participants. Interestingly, we observed a more lateralized pattern of activation in the fatigued condition, with the majority of clusters located within the right hemisphere. We surmised that this effect might be associated with lowered brain metabolism in the state of sleep deprivation [22]. Of course, this does not mean that truck driver's brain resembles a brain of a dolphin, yet it points to a lowered energy capacity of the fatigued brain, which can affect each hemisphere in a different manner [23]. The direct contrast between conditions revealed only one suprathreshold cluster, obtained in the fatigued > rested contrast. The reverse contrast did not reveal any significant differences. The cluster was located in the dorsal part of the prefrontal cortex. Interestingly, this region did not cross the significance threshold in any of the main effect contrasts for both the rested and fatigued conditions. However, a close inspection of the unthresholded images revealed that in the rested condition, this cluster was strongly deactivated, and in the fatigued condition, the response was positive, yet below the significance threshold. The inspection also revealed that the region discovered in the fatigued > rested contrast was a part of the larger cluster encompassing the medial frontal cortex—covering the SMA, pre-SMA, and parts of the

superior frontal gyrus (see Figure 4). Previous research on the effects of cognitive working memory and attentional training research suggested that this region is a part of the cognitive control network, which is engaged in executive control over ongoing cognitive activity, and it is known that its responsiveness becomes significantly decreased over a training period [24,25]. Thus, in our case, the heightened activity of this region might suggest that a relatively simple task we used, involving simple visual feature conjunction detection, in the fatigued condition with lowered metabolic capacity due to sleep deprivation became a task that required more cognitive control resources in order to maintain the performance level. Thus, our results suggested that the influence in the arousal state of drivers on the cognitive level manifested as an increased demand for controlled attentional processing.

Although visually apparent, there were no statistically significant differences in activation of Brodmann area 7. An earlier study by Muto and colleagues [26] demonstrated that in young, healthy participants, there were no effects of one night of total sleep deprivation on attention; however, orienting and conflict resolutions were associated with significantly larger thalamic responses during sleep deprivation than during rested wakefulness. They concluded that sleep deprivation influenced different components of human attention nonselectively by affecting the structures maintaining vigilance or ubiquitously perturbing neuronal function. They further concluded that compensatory responses can counter these effects transiently by recruiting thalamic responses via that supporting thalamocortical function.

According to Johns [27], there is a continuous inhibitory interaction between a wake drive and a sleep drive, each of which involves integrated action of several different neuronal centers in the brain. At any time, the state of sleep or wakefulness depends on the comparative strengths of the total wake drive and the total sleep drive. Under the circumstances of fatigued driving, the driver can stay awake only by maintaining or increasing the secondary wake drive. Long-haul truck drivers are known to have developed methods to increase their wake drive, such as chewing gum, singing, or making frequent changes to their sitting position [27]. In our task, no behavioral influences on the wake drive were possible, as they would result in deterioration of data quality, which was not the case in our study. Therefore, the differences in brain activation between the rested and fatigued conditions reflected the adaptation mechanism involving increased cognitive control over task execution—in this case, voluntary focusing of attention on the incoming stimuli and the response selection.

Our research had certain limitations. The study was performed while the subjects were lying on a scanner table—a posture (position) that is known to increase subjective perception of sleepiness [27]. Therefore, some interactive effects of this position could not be excluded. Moreover, the small sample might have obscured smaller differences between experimental conditions. Furthermore, caffeine is known to have short-term effects on basal cerebral blood flow, and thus the activation strength of the brain [28]. Similarly, being sleepy was associated with lower cerebral perfusion than during normal functioning [23,29]. Therefore, future studies should evaluate cerebral perfusion to properly account for this phenomenon.

#### **5. Conclusions**

In conclusion, our research showed that the state of decreased arousal associated with sleep deprivation substantially changed the activation patterns during a cognitive task involving selective attention. When comparing the rested and fatigued conditions, these changes were twofold. On the global level, we observed a general decrease of activation strength in the fatigued condition that was more pronounced in the left hemisphere. On the local level, we observed an extended activation of the medial prefrontal regions in the fatigued condition that reflected an increased contribution of cognitive control mechanisms compensating for the diminished efficiency of mechanisms involved in selective attention and response selection.

**Author Contributions:** Conceptualization, M.B. and A.B.; methodology, M.B.; software, M.B.; validation, M.B.; formal analysis, M.B. and S.P.G.; investigation, P.B.; data curation, S.P.G.; writing—original draft preparation, S.P.G. and M.B.; writing—review and editing, A.B.; project administration, Ł.D.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Polish National Centre For Research and Development, grant number PBS3/B9/29/2015 entitled: "Detector of early signs of fatigue as a part of improving the safety driving (Det)," project manager: Prof. Alicja Bortkiewicz.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Bioethics Committee of the Military Institute of Aviation Medicine, Warszawa, Poland (Decision No. 11/2015, Date of approval: 17 June 2015).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data are available upon request from the authors.

**Acknowledgments:** We thank Andrzej Ga ´zdzi ´nski for assistance in data processing and the radiology technicians for MRI data collection. We wish to extend our appreciation to all study participants who made this research possible.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

