**1. Introduction**

Driving a car is a complex behavior that requires diverse abilities, including perceptual, attentional, decision-making, and motor skills [1]. All-night driving leads the buildup of fatigue. Driver's fatigue is related to about 10% of the total number of accidents, and about 25% of single-vehicle accidents [1]. The impact of fatigued driving in the EU is significant: social costs (including healthcare) of all road accidents leading to injuries and fatalities are at least EUR 100 billion per year [2].

Fatigue is a cumulative process. It is related to a sustained activity and often results in impaired performance. The main symptoms are: difficulty in maintaining alertness and focus of selective attention, vigilance, and staying awake [3]. Understanding the mechanisms that modulate selective attention in fatigued subjects may provide implications for accident prevention, as this information may help with management of human errors and minimization of number of accidents and injuries [4]. For this reason, non-invasive measurements of brain activity can play a role in understanding the neural basis of driving ability [5].

**Citation:** Gazdzinski, S.P.; Binder, M.; Bortkiewicz, A.; Baran, P.; Dziuda, Ł. Effects of All-Night Driving on Selective Attention in Professional Truck Drivers: A Preliminary Functional Magnetic Resonance Study. *Energies* **2021**, *14*, 5409. https://doi.org/10.3390/en14175409

Academic Editor: Hugo Morais

Received: 22 July 2021 Accepted: 24 August 2021 Published: 31 August 2021

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The ability to attend to one's surroundings is of utmost importance to safety in an environment with competing stimuli; failure to perceive relevant stimuli due to decreased attentional control can result in accident and injury [4]. Alertness and selective attention are closely intertwined but separable dimensions of attention. Both play an important role in ensuring driving safety. According to the salience effort expectancy value (SEEV) attention allocation model, 90% of attention used to operate a vehicle has visual character [6]. Visual selective attention plays a special role in driving behavior/control, since each driver is confronted with a plethora of competing stimuli that must be recognized and processed quickly to ensure the coordinated responsiveness to all the environmental events occurring while driving a car. Inadequate allocation of attention was identified as one of major factors leading to road accidents [7].

According to feature integration theory [8], visual selective attention is based on two linked levels of representation. On the first level, visual features such as color or shape are represented in separate feature maps. The second level of representation, the master map of locations, encodes the current site of the attentional focus. The attentional selection works on the second level by binding visual features present in the site of attentional focus. Thus, the attentional selection of visual objects requires not only the correct registration of their features, but also their proper integration [9]. Earlier fMRI studies identified the nodes of the underlying neural network using feature and conjunction search tasks [10]. An object defined by multiple features is simultaneously processed by functionally specialized systems in the brain. Cognitive processes of visual short-term memory (color, shape, and their conjunctions) were shown to activate (in a load-dependent manner) the bilateral superior parietal lobule (Brodmann area 7), close to intraparietal sulcus [11]. The posterior parietal cortex was sensitive to visual short-term memory load for color and shape [11]; in particular, it was sensitive to feature and visual working memory load manipulations. Other studies found greater activity in the right parietal cortex at the junction of the intraparietal cortex and transverse occipital sulcus during a conjunction search, as compared to a single-feature search [9,12]. On the other hand, the prefrontal regions were sensitive to visual working memory load manipulation, but relatively insensitive to feature differences [13].

Acknowledging the complexity of the factors contributing to driver's fatigue and ensuing decrease in driving performance, in this study we focused on the modulatory influence of the fatigue on the attentional system of the brain. While the brain networks underlying alertness have been shown to be sensitive to fatigue, less is known about its influence on selective attention. We used a feature conjunction detection task in which the participants were required to respond to a specific combination of color and shape. Such a task specifically addressed the domain of attentional control, since feature integration in target detection tasks involves this type of top-down attentional processing. In our study, all participants performed the task in the fMRI scanner twice—in a fatigued condition, following substantial sleep deprivation during the preceding 24 h period, and in a rested condition, after several hours of ceaseless sleep. We predicted decreased neuronal activity in the nodes of the attentional control network associated with the fatigued condition, irrespective of the task performance.

#### **2. Materials and Methods**

This study was part of a larger project to detect early signs of fatigue to improve the safety of driving. Qualification for participation in the project was preceded by general medical, neurological, and ophthalmological examinations. Seventeen male, right-handed [4], professional drivers with a current medical examination to qualify for professional driving, aged 32.9 ± 4.4 years, with work experience in the profession of 12.6 ± 5.6 years, without self-reported chronic conditions that impaired sensory and cognitive functions and could lead to sleepiness during driving [2,3], took part in the randomized cross-over functional MRI examination. The sex of the participants reflected the fact that this profession is rarely chosen by females. Their body mass index (BMI = 28.7 ± 3.3) was above the recommended values [14]; only three participants had a proper body weight, eight were overweight, and

six were obese. Nine participants had a higher education, five completed secondary school, two had vocational training, and one had junior high school). Their average number of working hours per month was 220 ± 36.

Each participant underwent fMRI scanning session twice; i.e., according to randomized, control trial methodology: (1) after a normal night of rest for the driver, and (2) after a 10 h, overnight period of driving under normal working conditions. The median interval between both sessions was seven days (range from one day to 98 days). In order to minimize the impact of the so-called learning effect (interfering variable) on the results, seven of the drivers were first tested at rest, while for the remaining 10, the first fMRI was after the 10 h driving period. All the tests were performed during morning hours to eliminate the influence of circadian rhythms on physiological functions.

All the procedures were approved by the Bioethics Committee of the Military Institute of Aviation Medicine, Warszawa, Poland (Decision No. 11/2015) and were performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Prior to the study, all the participants gave written informed consent to all procedures and personal data processing for scientific purposes. All data was defaced and anonymized before analyses.
