A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions

How does drivers' attention change when using a two-stage warning system? Effects of expectation and cognitive load

The advantages of two-stage warnings have been validated. This study investigated how drivers' expectations of automated driving system capabilities and cognitive load affect their attention allocation and takeover performance when using a two-stage warning system in a Level 3 automated driving system. Thirty-two drivers participated in a driving simulation study. The results showed that drivers under high cognitive load had longer and more frequent fixation on the road, which suggested a cautious attention strategy. The high-expectation group gazed less on the road and got greater lateral deviation and maximum acceleration. Attention allocation of the high-expectation group was similar between warning stages but was more susceptible to cognitive load within the same stage. The two-stage warnings need to be designed to direct drivers' attention effectively.

Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data

Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex-valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude-only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex-valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex-valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex-valued fMRI, this framework is generalized to work with magnitude-only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex-valued data from University of New Mexico and magnitude-only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude-only fMRI data in terms of retaining more BOLD-related activity and fewer unwanted voxels, compared with amplitude-based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.

A large-scale fMRI dataset for the visual processing of naturalistic scenes

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

Using EEG to investigate the influence of boredom on prospective memory in top-down and bottom-up processing mechanisms for intelligent interaction

We aimed to investigate the alpha (α) activity in operators experiencing boredom while performing prolonged monitoring and prospective memory tasks using different processing mechanisms. Fifty-four participants underwent electroencephalography (EEG) and were found to have poorer prospective memory performance under top-down conditions. Further, α power and synchronisation were higher during bottom-up than in top-down processes, revealing an inhibition effect of the former. Significant differences in brain regions and hemispheres were identified to distinguish different cognitive processes in both information-processing mechanisms. Thus, people are likely to cope with boredom differently in terms of top-down and bottom-up processes. Specifically, a higher attention level was reported during top-down processing, to mitigate the negative influences of boredom. Overall, this study provides EEG evidence which suggests that prospective memory can be enhanced in top-down processing during prolonged monitoring tasks by increasing the salience of cues.

An event-based magnetoencephalography study of simulated driving: Establishing a novel paradigm to probe the dynamic interplay of executive and motor function

Magnetoencephalography (MEG) is particularly well-suited to the study of human motor cortex oscillatory rhythms and motor control. However, the motor tasks studied to date are largely overly simplistic. This study describes a new approach: a novel event-based simulated drive made operational via MEG compatible driving simulator hardware, paired with differential beamformer methods to characterize the neural correlates of realistic, complex motor activity. We scanned 23 healthy individuals aged 16-23 years (mean age = 19.5, SD = 2.5; 18 males and 5 females, all right-handed) who completed a custom-built repeated trials driving scenario. MEG data were recorded with a 275-channel CTF, and a volumetric magnetic resonance imaging scan was used for MEG source localization. To validate this paradigm, we hypothesized that pedal-use would elicit expected modulation of primary motor responses beta-event-related desynchronization (B-ERD) and movement-related gamma synchrony (MRGS). To confirm the added utility of this paradigm, we hypothesized that the driving task could also probe frontal cognitive control responses (specifically, frontal midline theta [FMT]). Three of 23 participants were removed due to excess head motion (>1.5 cm/trial), confirming feasibility. Nonparametric group analysis revealed significant regions of pedal-use related B-ERD activity (at left precentral foot area, as well as bilateral superior parietal lobe: p < .01 corrected), MRGS (at medial precentral gyrus: p < .01 corrected), and FMT band activity sustained around planned braking (at bilateral superior frontal gyrus: p < .01 corrected). This paradigm overcomes the limits of previous efforts by allowing for characterization of the neural correlates of realistic, complex motor activity in terms of brain regions, frequency bands and their dynamic temporal interplay.

Emergency Braking Evoked Brain Activities during Distracted Driving

Electroencephalogram (EEG) was used to analyze the mechanisms and differences in brain neural activity of drivers in visual, auditory, and cognitive distracted vs. normal driving emergency braking conditions. A pedestrian intrusion emergency braking stimulus module and three distraction subtasks were designed in a simulated experiment, and 30 subjects participated in the study. The common activated brain regions during emergency braking in different distracted driving states included the inferior temporal gyrus, associated with visual information processing and attention; the left dorsolateral superior frontal gyrus, related to cognitive decision-making; and the postcentral gyrus, supplementary motor area, and paracentral lobule associated with motor control and coordination. When performing emergency braking under different driving distraction states, the brain regions were activated in accordance with the need to process the specific distraction task. Furthermore, the extent and degree of activation of cognitive function-related prefrontal regions increased accordingly with the increasing task complexity. All distractions caused a lag in emergency braking reaction time, with 107.22, 67.15, and 126.38 ms for visual, auditory, and cognitive distractions, respectively. Auditory distraction had the least effect and cognitive distraction the greatest effect on the lag.

EEG-EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09776-w.

Neural Correlates Predicting Lane-Keeping and Hazard Detection: An fMRI Study Featuring a Pedestrian-Rich Simulator Environment

Distracted attention is considered responsible for most car accidents, and many functional magnetic resonance imaging (fMRI) researchers have addressed its neural correlates using a car-driving simulator. Previous studies, however, have not directly addressed safe driving performance and did not place pedestrians in the simulator environment. In this fMRI study, we simulated a pedestrian-rich environment to explore the neural correlates of three types of safe driving performance: accurate lane-keeping during driving (driving accuracy), the braking response to a preceding car, and the braking response to a crossing pedestrian. Activation of the bilateral frontoparietal control network predicted high driving accuracy. On the other hand, activation of the left posterior and right anterior superior temporal sulci preceding a sudden pedestrian crossing predicted a slow braking response. The results suggest the involvement of different cognitive processes in different components of driving safety: the facilitatory effect of maintained attention on driving accuracy and the distracting effect of social–cognitive processes on the braking response to pedestrians.

Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop brain-based systems for assistive devices. Electroencephalography (EEG) is the cornerstone of these studies providing the highest temporal resolution to track those cerebral processes underlying overt behavior. Particularly when investigating real-world scenarios as driving, EEG is constrained by factors such as robustness, comfortability, and high data variability affecting the decoding performance. Hence, additional peripheral signals can be combined with EEG for increasing replicability and the overall performance of the brain-based action decoder. In this regard, hybrid systems have been proposed for the detection of braking and steering actions in driving scenarios to improve the predictive power of the single neurophysiological measurement. These recent results represent a proof of concept of the level of technological maturity. They may pave the way for increasing the predictive power of peripheral signals, such as electroculogram (EOG) and electromyography (EMG), collected in real-world scenarios when informed by EEG measurements, even if collected only offline in standard laboratory settings. The promising usability of such hybrid systems should be further investigated in other domains of neuroergonomics.

Cannabis and Driving

As more states in the U.S legalize recreational and medicinal cannabis, rates of driving under the influence of this drug are increasing significantly. Aspects of this emerging public health issue potentially pit science against public policy. The authors believe that the legal cart is currently significantly ahead of the scientific horse. Issues such as detection procedures for cannabis-impaired drivers, and use of blood THC levels to gauge impairment, should rely heavily on current scientific knowledge. However, there are many, often unacknowledged research gaps in these and related areas, that need to be addressed in order provide a more coherent basis for public policies. This review focuses especially on those areas. In this article we review in a focused manner, current information linking cannabis to motor vehicle accidents and examine patterns of cannabis-impairment of driving related behaviors, their time courses, relationship to cannabis dose and THC blood levels, and compare cannabis and alcohol-impaired driving patterns directly. This review also delves into questions of alcohol-cannabis combinations and addresses the basis for of per-se limits in cannabis driving convictions. Finally, we distinguish between areas where research has provided clear answers to the above questions, areas that remain unclear, and make recommendations to fill gaps in current knowledge.

Brain mitochondrial dysfunction and driving simulator performance in untreated obstructive sleep apnea

It is challenging to determine which patients with obstructive sleep apnea (OSA) have impaired driving ability. Vulnerability to this neurobehavioral impairment may be explained by lower brain metabolites levels involved in mitochondrial metabolism. This study compared markers of brain energy metabolism in OSA patients identified as vulnerable vs resistant to driving impairment following extended wakefulness. 44 patients with moderate-severe OSA underwent 28hr extended wakefulness with three 90min driving simulation assessments. Using a two-step cluster analysis, objective driving data (steering deviation and crashes) from the 2nd driving assessment (22.5 h awake) was used to categorise patients into vulnerable (poor driving, n = 21) or resistant groups (good driving, n = 23). ¹ H magnetic resonance spectra were acquired at baseline using two scan sequences (short echo PRESS and longer echo-time asymmetric PRESS), focusing on key metabolites, creatine, glutamate, N-acetylaspartate (NAA) in the hippocampus, anterior cingulate cortex and left orbito-frontal cortex. Based on cluster analysis, the vulnerable group had impaired driving performance compared with the resistant group and had lower levels of creatine (PRESS p = ns, APRESS p = 0.039), glutamate, (PRESS p < 0.01, APRESS p < 0.01), NAA (PRESS p = 0.038, APRESS p = 0.035) exclusively in the left orbito-frontal cortex. Adjusted analysis, higher glutamate was associated with a 21% (PRESS) and 36% (APRESS) reduced risk of vulnerable classification. Brain mitochondrial bioenergetics in the frontal brain regions are impaired in OSA patients who are vulnerable to driving impairment following sleep loss. These findings provide a potential way to identify at risk OSA phenotype when assessing fitness to drive, but this requires confirmation in larger future studies.

Driving With Distraction: Measuring Brain Activity and Oculomotor Behavior Using fMRI and Eye-Tracking

Introduction

Driving motor vehicles is a complex task that depends heavily on how visual stimuli are received and subsequently processed by the brain. The potential impact of distraction on driving performance is well known and poses a safety concern - especially for individuals with cognitive impairments who may be clinically unfit to drive. The present study is the first to combine functional magnetic resonance imaging (fMRI) and eye-tracking during simulated driving with distraction, providing oculomotor metrics to enhance scientific understanding of the brain activity that supports driving performance.

Materials and Methods

As initial work, twelve healthy young, right-handed participants performed turns ranging in complexity, including simple right and left turns without oncoming traffic, and left turns with oncoming traffic. Distraction was introduced as an auditory task during straight driving, and during left turns with oncoming traffic. Eye-tracking data were recorded during fMRI to characterize fixations, saccades, pupil diameter and blink rate.

Results

Brain activation maps for right turns, left turns without oncoming traffic, left turns with oncoming traffic, and the distraction conditions were largely consistent with previous literature reporting the neural correlates of simulated driving. When the effects of distraction were evaluated for left turns with oncoming traffic, increased activation was observed in areas involved in executive function (e.g., middle and inferior frontal gyri) as well as decreased activation in the posterior brain (e.g., middle and superior occipital gyri). Whereas driving performance remained mostly unchanged (e.g., turn speed, time to turn, collisions), the oculomotor measures showed that distraction resulted in more consistent gaze at oncoming traffic in a small area of the visual scene; less time spent gazing at off-road targets (e.g., speedometer, rear-view mirror); more time spent performing saccadic eye movements; and decreased blink rate.

Conclusion

Oculomotor behavior modulated with driving task complexity and distraction in a manner consistent with the brain activation features revealed by fMRI. The results suggest that eye-tracking technology should be included in future fMRI studies of simulated driving behavior in targeted populations, such as the elderly and individuals with cognitive complaints - ultimately toward developing better technology to assess and enhance fitness to drive.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

Evaluation of smartphone interactions on drivers' brain function and vehicle control in an immersive simulated environment

Smartphones and other modern technologies have introduced multiple new forms of distraction that color the modern driving experience. While many smartphone functions aim to improve driving by providing the driver with real-time navigation and traffic updates, others, such as texting, are not compatible with driving and are often the cause of accidents. Because both functions elicit driver attention, an outstanding question is the degree to which drivers' naturalistic interactions with navigation and texting applications differ in regard to brain and behavioral indices of distracted driving. Here, we employed functional near-infrared spectroscopy to examine the cortical activity that occurs under parametrically increasing levels of smartphone distraction during naturalistic driving. Our results highlight a significant increase in bilateral prefrontal and parietal cortical activity that occurs in response to increasingly greater levels of smartphone distraction that, in turn, predicts changes in common indices of vehicle control.

Influence of Increasing Task Complexity and Use of Informational Assistance Systems on Mental Workload

(1) Background: Cognitive aspects and complexity in modern manual mixed model assembly are increasing. To reduce mental workload (MWL), informational assistance systems are introduced. The influence of complexity and used assistance system on MWL should be investigated to further improve the implementation of such assistance systems. (2) Methods: Using a simulated close to real-life assembly task a 2 × 3 design was chosen, with two levels of assembly complexity (within subjects) and three different assistance systems (paper, Augmented Reality (AR)-glasses, tablet-between subjects). MWL was measured using either physiological response (electrocardiogram (ECG) and eye-tracking) or performance indicators. (3) Results: An influence of task complexity on MWL can be shown. Additionally, usability based differences between the used assistance systems become more evident with reference to the results of area of interest analysis. (4) Conclusions: Using a multi-modal measurement approach, it is possible to detect complexity-based differences in MWL. Additional research on validity and alignment is needed to further use these for (neuro-) ergonomic considerations and recommendations.

Assessing Fitness-To-Drive among Older Drivers: A Comparative Analysis of Potential Alternatives to on-Road Driving Test

To enable older drivers to maintain mobility without endangering public safety, it is necessary to develop more effective means of assessing their fitness-to-drive as alternatives to an on-road driving test. In this study, a functional ability test, simulated driving test, and on-road driving test were carried out for 136 older drivers. Influencing factors related to fitness-to-drive were selected based on the correlation between the outcome measure of each test and the pass/fail outcome of the on-road driving test. Four potential alternatives combining different tests were considered and three modeling techniques were compared when constructing the fitness-to-drive assessment model for the elderly. As a result, 92 participants completed all of the tests, of which 61 passed the on-road driving test and the remaining 31 failed. A total of seven influencing factors from all types of tests were selected. The best model was trained by the technique of gradient boosted machine using all of the seven factors, generating the highest accuracy of 92.8%, with sensitivity of 0.94 and specificity of 0.90. The proposed fitness-to-drive assessment method is considered an effective alternative to the on-road driving test, and the results offer a valuable reference for those unfit-to-drive older drivers to either adjust their driving behavior or cease driving.

Neurophysiological correlates of age differences in driving behavior during concurrent subtask performance

Driving is a complex cognitive-motor task that requires the continuous integration of multisensory information, cognitive processes, and motor actions. With higher age, driving becomes increasingly challenging as a result of naturally declining neurophysiological resources. Performing additional subtasks, such as conversations with passengers or interactions with in-vehicle devices (e.g., adjusting the radio), may further challenge neurocognitive resources that are required to maintain driving performance. Based on declining brain physiological resources and inferior neurocognitive functioning, older adults (OA) may show higher brain activation and larger performance decrements than younger adults (YA) when engaging in additional subtasks during driving. Age differences, however, may further vary for different neurocognitive task demands, such that driving performance of OA might be particularly affected by certain subtasks. In this study, we hence investigated the brain functional correlates of age differences in driving behavior during concurrent subtask performance in YA and OA. Our final sample consisted of thirty younger (21.80 ± 1.73y, 15 female) and thirty older (69.43 ± 3.30y, 12 female) regular drivers that drove along a typical rural road (25 - 30 min) in a driving simulator and performed three different concurrent subtasks that were presented auditorily or visually: typing a 3-digit number (TYPE), comparing traffic news and gas station prices (working memory, WM), and stating arguments (ARG). We measured variability in lateral car position, velocity, and following distance to a frontal lead car as the standard deviation from 0 to 15 s after subtask onset. Brain activity was continuously recorded using functional near-infrared spectroscopy over the dorsolateral prefrontal cortex. Both YA and OA particularly varied in their lateral position during TYPE with a more pronounced effect in OA. For YA, in contrast, ARG led to higher variability in velocity compared to TYPE and WM, whereas OA showed no task-specific differences. Substantiating our behavioral findings, OA revealed the largest brain functional response to TYPE, while YA demonstrated a very distinct activation during ARG and smaller hemodynamic responses to TYPE and WM. Brain activity in the DLPFC was, overall, not significantly, but small to moderately related to certain behavioral performance parameters (mainly lateral position). We conclude that both OA and YA are vulnerable to distractive subtasks while driving. Age differences, however, seem to largely depend on neurocognitive task demands. OA may be at higher risk for accidents when performing visuo-motor subtasks (e.g., interacting with navigational systems) during driving while YA may be more (cognitively) distracted when talking to passengers.

Social cognition in context: A naturalistic imaging approach

Social processing occurs within dynamic, complex, and multimodal contexts, but the study of social cognition typically involves static, artificial stimuli. Naturalistic approaches (e.g., movie viewing) can recapture the richness and complexity of real-world interactions. Novel analytic approaches allow for the investigation of functional brain organization in response to contextually embedded and extended events with a complex temporal structure during movie viewing or narrative processing. In addition to these within-brain measures, movies afford between-brain analyses such as inter-subject correlation, which allows for identification of stimulus-specific brain response through the correlation of brain activity between participants' brains. Research using these approaches offers both practical and theoretical advantages in understanding how we navigate our social world. Practically, movies are engaging stimuli that allow for more rapid presentation of multiple event types and improve compliance even in very young populations. Theoretically, studies have validated the use of these measures by demonstrating functional selectivity to contextually embedded stimuli. Naturalistic approaches also allow for novel insights. For example, regions associated with social cognition have longer temporal receptive windows, making them well suited to social-cognitive processes that require integration of information over longer timescales. Furthermore, the similarity in the temporal and spatial brain response between individuals during naturalistic viewing is related to age, predictive of friendships, and reduced in autism spectrum disorder. These findings offer first glimpses into the power of using these naturalistic, dynamic approaches to understand how we perceive, reason about, and interact with others.

Electrophysiological correlates of attentional monitoring during a complex driving simulation task

Starting from the evidence that complex tasks (e.g., driving) require lots of cognitive resources, this research aims at assessing the change of attentional electrophysiological correlates during an oddball task performed while driving a simulator. Twenty-four participants drove along six courses on a moped simulator, preceded by a baseline condition (i.e., watching a video clip of one driving course). Throughout the task, an auditory passive multi-feature oddball with both traffic-related and unrelated stimuli was presented, and the EEG activity was recorded along with driving performance indexes. The main results point out that, as participants learn to drive safely, more attentional resources are available to process the deviant oddball stimuli, as shown by the increase in the amplitude of mismatch negativity (deviant pure tones) and P3a (traffic-related sounds) in the second block of driving. We interpreted these effects as dependent on stimuli complexity and salience.

The Drives for Driving Simulation: A Scientometric Analysis and a Selective Review of Reviews on Simulated Driving Research

Driving behaviors and fitness to drive have been assessed over time using different tools: standardized neuropsychological, on-road and driving simulation testing. Nowadays, the great variability of topics related to driving simulation has elicited a high number of reviews. The present work aims to perform a scientometric analysis on driving simulation reviews and to propose a selective review of reviews focusing on relevant aspects related to validity and fidelity. A scientometric analysis of driving simulation reviews published from 1988 to 2019 was conducted. Bibliographic data from 298 reviews were extracted from Scopus and WoS. Performance analysis was conducted to investigate most prolific Countries, Journals, Institutes and Authors. A cluster analysis on authors' keywords was performed to identify relevant associations between different research topics. Based on the reviews extracted from cluster analysis, a selective review of reviews was conducted to answer questions regarding validity, fidelity and critical issues. United States and Germany are the first two Countries for number of driving simulation reviews. United States is the leading Country with 5 Institutes in the top-ten. Top Authors wrote from 3 to 7 reviews each and belong to Institutes located in North America and Europe. Cluster analysis identified three clusters and eight keywords. The selective review of reviews showed a substantial agreement for supporting validity of driving simulation with respect to neuropsychological and on-road testing, while for fidelity with respect to real-world driving experience a blurred representation emerged. The most relevant critical issues were the a) lack of a common set of standards, b) phenomenon of simulation sickness, c) need for psychometric properties, lack of studies investigating d) predictive validity with respect to collision rates and e) ecological validity. Driving simulation represents a cross-cutting topic in scientific literature on driving, and there are several evidences for considering it as a valid alternative to neuropsychological and on-road testing. Further research efforts could be aimed at establishing a consensus statement for protocols assessing fitness to drive, in order to (a) use standardized systems, (b) compare systematically driving simulators with regard to their validity and fidelity, and (c) employ shared criteria for conducting studies in a given sub-topic.

Functional Imaging of Visuospatial Attention in Complex and Naturalistic Conditions

One of the ultimate goals of cognitive neuroscience is to understand how the brain works in the real world. Functional imaging with naturalistic stimuli provides us with the opportunity to study the brain in situations similar to the everyday life. This includes the processing of complex stimuli that can trigger many types of signals related both to the physical characteristics of the external input and to the internal knowledge that we have about natural objects and environments. In this chapter, I will first outline different types of stimuli that have been used in naturalistic imaging studies. These include static pictures, short video clips, full-length movies, and virtual reality, each comprising specific advantages and disadvantages. Next, I will turn to the main issue of visual-spatial orienting in naturalistic conditions and its neural substrates. I will discuss different classes of internal signals, related to objects, scene structure, and long-term memory. All of these, together with external signals about stimulus salience, have been found to modulate the activity and the connectivity of the frontoparietal attention networks. I will conclude by pointing out some promising future directions for functional imaging with naturalistic stimuli. Despite this field of research is still in its early days, I consider that it will play a major role in bridging the gap between standard laboratory paradigms and mechanisms of brain functioning in the real world.

Correlation Among Behavior, Personality, and Electroencephalography Revealed by a Simulated Driving Experiment

Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.

Neuroergonomics of car driving: A critical meta-analysis of neuroimaging data on the human brain behind the wheel

Car driving, an everyday life activity, has been under the scope of investigation for long. Neurosciences and psychology have contributed to better understand the human processes engaged while driving, to such an extent that a meta-analysis of all available fMRI data is now possible to extract the most relevant information. Using the Activation Likelihood Estimation method, we therefore conducted such a meta-analysis on 9 studies, representing 27 neuroimaging contrasts and 131 participants. We identified a network composed of brain areas underlying the cognitive abilities required for driving: sensorimotor coordination, sensory and attentional processing, high-level cognitive control and allocation of attentional resources. We complemented this meta-analysis with a neuroergonomics approach combining driving control knowledge, distinguishing the strategical, tactical and operational levels, with neuroscientific knowledge and models on cognitive control operated by the prefrontal cortex. The results exposed the distinct neural circuits engaged behind the wheel depending on the task performed. Based on the combination of neuroscientific and ergonomic knowledge, a hybrid car driving framework is also proposed.

Effects of acute alcohol and driving complexity in older and younger adults

RATIONALE

Our previous work demonstrated differential neurobehavioral effects of low-dose alcohol consumption on older and younger adults in a driving simulator. However, the ability to enhance or suppress a response in such context has yet to be examined.

OBJECTIVES

The current study contrasted older and younger drivers' responses to specific stimuli (i.e., relevant, irrelevant) in scenarios of differing complexity following low-dose acute alcohol administration.

METHODS

Healthy older (55-70) and younger (25-35) adults completed two driving scenarios (i.e., country and metropolis) both before and after consuming beverages targeted to reach peak BrACs of 0.00, 0.04, or 0.065%. Throughout the simulation, participants encountered relevant stimuli (e.g., pedestrians walking into the street) and irrelevant stimuli (e.g., pedestrians walking parallel). Peak deceleration, range of steering, and distance until brake application were assessed within a 450-ft window preceding each stimulus.

RESULTS

Following low-dose alcohol consumption, older adults shifted from a strategy using both deceleration and steering to relying solely on deceleration in responding to relevant stimuli in the country. Older adults under both low and moderate alcohol conditions displayed an inability to withhold responses to irrelevant stimuli in the metropolis.

CONCLUSION

These findings are consistent with our prior work showing differential effects of low-dose alcohol on older, relative to younger, adults. The interactive effects of age and alcohol, however, depend on stimulus type and environmental complexity. Continued investigation of neurobehavioral mechanisms in ecologically valid paradigms is necessary for understanding the implications of the combined impairing effects of alcohol and older age.

Theta and Alpha Oscillations in Attentional Interaction during Distracted Driving

Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely.

The coordinate-based meta-analysis of neuroimaging data

Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies is included. We discuss some still-open problems in the field to highlight the need for future research.

Functional connectivity analysis of distracted drivers based on the wavelet phase coherence of functional near-infrared spectroscopy signals

The present study aimed to evaluate the functional connectivity (FC) in relevant cortex areas during simulated driving with distraction based on functional near-infrared spectroscopy (fNIRS) method. Twelve subjects were recruited to perform three types of driving tasks, namely, straight driving, straight driving with secondary auditory task, and straight driving with secondary visual vigilance task, on a driving simulator. The wavelet amplitude (WA) and wavelet phase coherence (WPCO) of the fNIRS signals were calculated in six frequency intervals: I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; and V, 0.0095-0.021 Hz, VI, 0.005-0.0095Hz. Results showed that secondary tasks during driving led to worse driving performance, brain activity changes, and dynamic configuration of the connectivity. The significantly lower WA value in the right motor cortex in interval IV, and higher WPCO values in intervals II, V, and VI were found with additional auditory task. Significant standard deviation of speed and lower WA values in the left prefrontal cortex and right prefrontal cortex in interval VI, and lower WPCO values in intervals I, IV, V, and VI were found under the additional visual vigilance task. The results suggest that the changed FC levels in intervals IV, V, and VI were more likely to reflect the driver's distraction condition. The present study provides new insights into the relationship between distracted driving behavior and brain activity. The method may be used for the evaluation of drivers' attention level.

Effective Connectivity Analysis of the Brain Network in Drivers during Actual Driving Using Near-Infrared Spectroscopy

Driving a vehicle is a complex activity that requires high-level brain functions. This study aimed to assess the change in effective connectivity (EC) between the prefrontal cortex (PFC), motor-related areas (MA) and vision-related areas (VA) in the brain network among the resting, simple-driving and car-following states. Twelve young male right-handed adults were recruited to participate in an actual driving experiment. The brain delta [HbO₂] signals were continuously recorded using functional near infrared spectroscopy (fNIRS) instruments. The conditional Granger causality (GC) analysis, which is a data-driven method that can explore the causal interactions among different brain areas, was performed to evaluate the EC. The results demonstrated that the hemodynamic activity level of the brain increased with an increase in the cognitive workload. The connection strength among PFC, MA and VA increased from the resting state to the simple-driving state, whereas the connection strength relatively decreased during the car-following task. The PFC in EC appeared as the causal target, while the MA and VA appeared as the causal sources. However, l-MA turned into causal targets with the subtask of car-following. These findings indicate that the hemodynamic activity level of the cerebral cortex increases linearly with increasing cognitive workload. The EC of the brain network can be strengthened by a cognitive workload, but also can be weakened by a superfluous cognitive workload such as driving with subtasks.

Concussion in motor sport: A medical literature review and engineering perspective

‘WARNING: motor sport can be dangerous’. The spectrum of head injuries in motor sport has shifted dramatically in recent decades, fuelled by advances in medicine and engineering. Despite these successes, there are growing public and professional concerns regarding concussion in motor sport. This review appraises the published literature concerning concussion in motor sport, with particular focus on the current medical and technical challenges in the field. The incidence and assessment of concussion in motor sport is discussed, in addition to modifiable risk factors within and outside the automobile environment. Lastly, areas for further research and development are outlined.

Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving

Noninvasive and accurate assessment of driving fatigue in relation to brain activity during long-term driving can contribute to traffic safety and accident prevention. This study evaluated functional connectivity (FC) in relevant brain regions. Synergistic mechanisms in different brain regions were detected by a novel simulator, which combined semi-immersive virtual reality technology and functional near-infrared spectroscopy. Each subject was instructed to complete driving tasks coupled with a mental calculation task. Wavelet coherence (WCO) and wavelet phase coherence (WPCO) were calculated and assessed in frequency intervals (I) 0.6-2 and (II) 0.145-0.6Hz as global connectivity measures; (III) 0.052-0.145, (IV) 0.021-0.052, (V) 0.0095-0.021 and (VI) 0.005-0.0095Hz as FC. WCO and WPCO revealed the strength and synchronization of cerebral connectivity, respectively. Significantly low WCO levels were found in intervals I and III in prefrontal cortex (PFC) and IV in motor cortex (MC) at the end of the driving task. Furthermore, significantly low WPCO were found in intervals I, and III in PFC and interval IV in MC. Experimental findings suggested that progressive mental fatigue adversely influences the cognitive function in the PFC and the cooperative mechanism between the PFC and MC.

Data-driven approaches in the investigation of social perception

The complexity of social perception poses a challenge to traditional approaches to understand its psychological and neurobiological underpinnings. Data-driven methods are particularly well suited to tackling the often high-dimensional nature of stimulus spaces and of neural representations that characterize social perception. Such methods are more exploratory, capitalize on rich and large datasets, and attempt to discover patterns often without strict hypothesis testing. We present four case studies here: behavioural studies on face judgements, two neuroimaging studies of movies, and eyetracking studies in autism. We conclude with suggestions for particular topics that seem ripe for data-driven approaches, as well as caveats and limitations.

Virtual Reality for Enhanced Ecological Validity and Experimental Control in the Clinical, Affective and Social Neurosciences

An essential tension can be found between researchers interested in ecological validity and those concerned with maintaining experimental control. Research in the human neurosciences often involves the use of simple and static stimuli lacking many of the potentially important aspects of real world activities and interactions. While this research is valuable, there is a growing interest in the human neurosciences to use cues about target states in the real world via multimodal scenarios that involve visual, semantic, and prosodic information. These scenarios should include dynamic stimuli presented concurrently or serially in a manner that allows researchers to assess the integrative processes carried out by perceivers over time. Furthermore, there is growing interest in contextually embedded stimuli that can constrain participant interpretations of cues about a target’s internal states. Virtual reality environments proffer assessment paradigms that combine the experimental control of laboratory measures with emotionally engaging background narratives to enhance affective experience and social interactions. The present review highlights the potential of virtual reality environments for enhanced ecological validity in the clinical, affective, and social neurosciences.

The potential of function-led virtual environments for ecologically valid measures of executive function in experimental and clinical neuropsychology

The assessment of executive functions is an integral task of neuropsychological assessment. Traditional measures of executive function are often based on hypothetical constructs that may have little relevance to real-world behaviours. In fact, some traditional tests utilised today were not originally developed for clinical use. Recently, researchers have been arguing for a new generation of "function-led" neuropsychological assessments that are developed from directly observable everyday behaviours. Although virtual environments (VEs) have been presented as potential aides in enhancing ecological validity, many were modelled on construct-driven approaches found in traditional assessments. In the current paper, we review construct-driven and function-led VE-based neuropsychological assessments of executive functions. Overall, function-led VEs best represent the sorts of tasks needed for enhanced ecological validity and prediction of real-world functioning.

Time-resolved detection of stimulus/task-related networks, via clustering of transient intersubject synchronization

Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time-series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time-series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task-dependent changes of connectivity). We present a fully data-driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time-windows was used to identify if/when any area showed stimulus/task-related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time-series (stimulus/task-related networks). Finally, for each network, a second clustering step grouped together all the time-windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task-related activity at specific time-points during the fMRI time-series. We label these configurations: "brain modes" (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli.

Greater Activity in the Frontal Cortex on Left Curves: A Vector-Based fNIRS Study of Left and Right Curve Driving

OBJECTIVES

In the brain, the mechanisms of attention to the left and the right are known to be different. It is possible that brain activity when driving also differs with different horizontal road alignments (left or right curves), but little is known about this. We found driver brain activity to be different when driving on left and right curves, in an experiment using a large-scale driving simulator and functional near-infrared spectroscopy (fNIRS).

RESEARCH DESIGN AND METHODS

The participants were fifteen healthy adults. We created a course simulating an expressway, comprising straight line driving and gentle left and right curves, and monitored the participants under driving conditions, in which they drove at a constant speed of 100 km/h, and under non-driving conditions, in which they simply watched the screen (visual task). Changes in hemoglobin concentrations were monitored at 48 channels including the prefrontal cortex, the premotor cortex, the primary motor cortex and the parietal cortex. From orthogonal vectors of changes in deoxyhemoglobin and changes in oxyhemoglobin, we calculated changes in cerebral oxygen exchange, reflecting neural activity, and statistically compared the resulting values from the right and left curve sections.

RESULTS

Under driving conditions, there were no sites where cerebral oxygen exchange increased significantly more during right curves than during left curves (p > 0.05), but cerebral oxygen exchange increased significantly more during left curves (p < 0.05) in the right premotor cortex, the right frontal eye field and the bilateral prefrontal cortex. Under non-driving conditions, increases were significantly greater during left curves (p < 0.05) only in the right frontal eye field.

CONCLUSIONS

Left curve driving was thus found to require more brain activity at multiple sites, suggesting that left curve driving may require more visual attention than right curve driving. The right frontal eye field was activated under both driving and non-driving conditions.

Weekly and daily cycle of alcohol use among the U.S. general population

BACKGROUND

Studies such on alcohol and injuries have defined alcohol-related injury as an injury with a positive self-report of alcohol consumption in the 6h prior to the event. However, there is very limited data on the pattern of alcohol use over time of day and day of week among the general population. The aim of this study is to estimate the rate of alcohol use by time of day, and day of week for the U.S. general adult (≥ 18 years) population.

METHODS

This study employed the design of a retrospective cohort study using data collected from three waves (2005-06, 2007-08, 2009-10) of the National Health and Nutrition Examination Survey (NHANES). Incidence rates of overall drinking (≥ 10 g of alcohol) and incidence rates of heavy drinking (≥ 40 g of alcohol) were estimated for day of week, and time of day (in hours). Multivariable Poisson regression models were used to investigate the difference between weekend nights and weekday nights.

RESULTS

The incidence rates (95% confidence interval) of all drinking episodes were 30.5 (29.2-32.0) per 100 person-days and 24.4 (22.8-26.2) per 100 person-days for weekend and the rest of the week, respectively. The incidence rates of heavy drinking episodes were 11.0 (10.2-11.9) and 7.7 (6.8-8.7) for weekend and the rest of the week. Multivariable analysis indicated that risks of overall drinking and heavy drinking were significantly higher (18% and 34%, respectively) during the weekend nights when compared to weekday nights. It was also observed young adults (18-29 years old) were more likely to increase their alcohol use during weekend nights compared to older age groups.

CONCLUSIONS

The general US population, especially young adults are exposed to alcohol and its acute effects at a much higher level during the night, and this in-turn increases the risk of alcohol-related injuries during that time.

Mapping brain activity during loss of situation awareness: an EEG investigation of a basis for top-down influence on perception

OBJECTIVE

The objective was to map brain activity during early intervals in loss of situation awareness (SA) to examine any co-activity in visual and high-order regions, reflecting grounds for top-down influences on Level I SA.

BACKGROUND

Behavioral and neuroscience evidence indicates that high-order brain areas can engage before perception is complete. Inappropriate top-down messages may distort perception during loss of SA. Evidence of co-activity of perceptual and high-order regions would not confirm such influence but may reflect a basis for it.

METHOD

SA and bias were measured using Quantitative Analysis of Situation Awareness and brain activity recorded with 128-channel EEG (electroencephalography) during loss of SA. One task (15 participants) required identification of a target pattern, and another task (10 participants) identification of "threat" in urban scenes. In both, the target was changed without warning, enforcing loss of SA. Key regions of brain activity were identified using source localization with standardized low-resolution electrical tomography (sLORETA) 150 to 160 ms post-stimulus onset in both tasks and also 100 to 110 ms in the second task.

RESULTS

In both tasks, there was significant loss of SA and bias shift (p < .02), associated at both 150- and 100-ms intervals with co-activity of visual regions and prefrontal, anterior cingulate and parietal regions linked to cognition under uncertainty.

CONCLUSION

There was early co-activity in high- order and visual perception regions that may provide a basis for top-down influence on perception.

APPLICATION

Co-activity in high- and low-order brain regions may explain either beneficial or disruptive top-down influence on perception affecting Level I SA in real-world operations.

It's not all in your car: functional and structural correlates of exceptional driving skills in professional racers

Driving is a complex behavior that requires the integration of multiple cognitive functions. While many studies have investigated brain activity related to driving simulation under distinct conditions, little is known about the brain morphological and functional architecture in professional competitive driving, which requires exceptional motor and navigational skills. Here, 11 professional racing-car drivers and 11 “naïve” volunteers underwent both structural and functional brain magnetic resonance imaging (MRI) scans. Subjects were presented with short movies depicting a Formula One car racing in four different official circuits. Brain activity was assessed in terms of regional response, using an Inter-Subject Correlation (ISC) approach, and regional interactions by mean of functional connectivity. In addition, voxel-based morphometry (VBM) was used to identify specific structural differences between the two groups and potential interactions with functional differences detected by the ISC analysis. Relative to non-experienced drivers, professional drivers showed a more consistent recruitment of motor control and spatial navigation devoted areas, including premotor/motor cortex, striatum, anterior, and posterior cingulate cortex and retrosplenial cortex, precuneus, middle temporal cortex, and parahippocampus. Moreover, some of these brain regions, including the retrosplenial cortex, also had an increased gray matter density in professional car drivers. Furthermore, the retrosplenial cortex, which has been previously associated with the storage of observer-independent spatial maps, revealed a specific correlation with the individual driver's success in official competitions. These findings indicate that the brain functional and structural organization in highly trained racing-car drivers differs from that of subjects with an ordinary driving experience, suggesting that specific anatomo-functional changes may subtend the attainment of exceptional driving performance.

Kinesthesia in a sustained-attention driving task

This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments.

Epilepsy and driving: potential impact of transient impaired consciousness

Driving is an important part of everyday life for most adults, and restrictions on driving can place a significant burden on individuals diagnosed with epilepsy. Although sensorimotor deficits during seizures may impair driving, decreased level of consciousness often has a more global effect on patients' ability to respond appropriately to the environment. Better understanding of the mechanisms underlying alteration of consciousness in epilepsy is important for decision-making by people with epilepsy, their physicians, and regulators in regard to the question of fitness to drive. Retrospective cohort and cross-sectional studies based on surveys or crash records can provide valuable information about driving in epilepsy. However, prospective objective testing of ictal driving ability during different types of seizures is needed to more fully understand the role of impaired consciousness and other deficits in disrupting driving. Driving simulators adapted for use in the epilepsy video-EEG monitoring unit may be well suited to provide both ictal and interictal data in patients with epilepsy. Objective information about impaired driving in specific types of epilepsy and seizures can provide better informed recommendations regarding fitness to drive, potentially improving the quality of life of people living with epilepsy.

Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway

The prefrontal cortex is considered to have a significant effect on driving behavior, but little is known about prefrontal cortex function in actual road driving. Driving simulation experiments are not the same, because the subject is in a stationary state, and the results may be different. Functional near-infrared spectroscopy (fNIRS) is advantageous in that it can measure cerebral hemodynamic responses in a person driving an actual vehicle. We mounted fNIRS equipment in a vehicle to evaluate brain functions related to various actual driving operations while the subjects drove on a section of an expressway that was not yet open to the public. Measurements were recorded while parked, and during acceleration, constant velocity driving (CVD), deceleration, and U-turns, in the daytime and at night. Changes in cerebral oxygen exchange (ΔCOE) and cerebral blood volume were calculated and imaged for each part of the task. Responses from the prefrontal cortex and the parietal cortex were highly reproducible in the daytime and nighttime experiments. Significant increases in ΔCOE were observed in the frontal eye field (FEF), which has not been mentioned much in previous simulation experiments. In particular, significant activation was detected during acceleration in the right FEF, and during deceleration in the left FEF. Weaker responses during CVD suggest that FEF function was increased during changes in vehicle speed. As the FEF contributes to control of eye movement in three-dimensional space, FEF activation may be important in actual road driving. fNIRS is a powerful technique for investigating brain activation outdoors, and it proved to be sufficiently robust for use in an actual highway driving experiment in the field of intelligent transport systems (ITS).

Lower hippocampal volume predicts decrements in lane control among drivers with amnestic mild cognitive impairment

OBJECTIVES

There are few methods to discern driving risks in patients with early dementia and mild cognitive impairment (MCI). We aimed to determine whether structural magnetic resonance imaging (MRI) of the hippocampus-a biomarker of probable Alzheimer pathology and a measure of disease severity in those affected--is linked to objective ratings of on-road driving performance in older adults with and without amnestic MCI.

METHODS

In all, 49 consensus-diagnosed participants from an Alzheimer's Disease Research Center (15 diagnosed with amnestic MCI and 34 demographically similar controls) underwent structural MRI and on-road driving assessments.

RESULTS

Mild atrophy of the left hippocampus was associated with less-than-optimal ratings in lane control but not with other discrete driving skills. Decrements in left hippocampal volume conferred higher risk for less-than-optimal lane control ratings in the patients with MCI (B = -1.63, standard error [SE] = .74, Wald = 4.85, P = .028), but not in controls (B = 0.13, SE = .415, Wald = 0.10, P = .752). The odds ratio and 95% confidence interval for below-optimal lane control in the MCI group was 4.41 (1.18-16.36), which was attenuated to 3.46 (0.88-13.60) after accounting for the contribution of left hippocampal volume.

CONCLUSION

These findings suggest that there may be a link between hippocampal atrophy and difficulties with lane control in persons with amnestic MCI. Further study appears warranted to better discern patterns of brain atrophy in MCI and Alzheimer disease and whether these could be early markers of clinically meaningful driving risk.

Using fMRI virtual-reality technology to predict driving ability after brain damage: a preliminary report

The cerebellum, which is important for movement control and planning, is often affected by many neurological conditions. Until now there has been limited information regarding how the function of the cerebellum impacts driving ability. This study used fMRI with an integrated virtual reality driving simulator to determine which aspects of driving performance are related to the cerebellum in healthy drivers (Experiment 1). It also investigated drivers with focal cerebellar lesions to identify how damage to this brain region impairs driving abilities. The results showed that cerebellar functioning is responsible for motor-speed coordination and complex temporal-motor integration necessary to execute driving behaviours. As predicted, drivers with cerebellar damage, showed significantly compromised speed control during basic driving conditions, whereas their ability to perform during interactive driving situations was preserved. New insights into neural mechanisms and brain plasticity regarding driving behaviour are discussed. Strategies in assessing and rehabilitating drivers with related neurological conditions are provided.