Using Virtual Reality to Study Alcohol Intoxication Effects on the Neural Correlates of Simulated Driving

Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN

Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.

Stimulant-like subjective effects of alcohol are not related to resting-state connectivity in healthy men

Individual differences in subjective, stimulant-like effects of alcohol are associated with the risk of developing alcohol use disorder. Specifically, individuals who experience more pronounced stimulant-like effects from alcohol are more likely to continue and escalate their usage. The neural basis for these individual differences in subjective response is not yet known. Using a within-subject design, 27 healthy male social drinkers completed three fMRI scans after ingesting a placebo, 0.4 and 0.8 g/kg alcohol, in a randomized order under double-blind conditions. Subjective stimulant effects of alcohol were assessed at regular intervals during each session. Seed-based and regional homogeneity analyses were conducted to evaluate changes in resting-state functional connectivity in relation to the stimulant effect of alcohol. Results indicated that 0.4 g/kg alcohol increased the connectivity to thalamus, and 0.8 g/kg alcohol decreased the connectivity to ventral anterior insula, primarily from the superior parietal lobule. Both doses reduced regional homogeneity in the superior parietal lobule but without an exact overlap with clusters showing connectivity changes in the seed-based analyses. The self-reported stimulant effect of alcohol was not significantly related to changes in seed-based connectivity or regional homogeneity. These findings suggest that alcohol-induced stimulation effects are not related to these indices of neural activity.

In search of a naturalistic neuroimaging approach: Exploration of general feasibility through the case of VR-fMRI and application in the domain of episodic memory

Virtual Reality (VR) is an increasingly widespread tool for research as it allows the creation of experiments taking place in multimodal and daily-life-like environments, while keeping a strong experimental control. Adding neuroimaging to VR leads to a better understanding of the underlying brain networks activated during a naturalistic task, whether for research purposes or rehabilitation. The present paper focuses on the specific use of concurrent VR and fMRI and its technical challenges and feasibility, with a brief examination of the general existing solutions. Following the PRISMA guidelines, the review investigates the particular case of how VR-fMRI has explored episodic memory so far, with a comparison of object- and place-based episodic memory. This review confirms the involvement of cerebral regions well-known to be implicated in episodic memory and unravels other regions devoted to bodily and narrative aspects of the self, promoting new avenues of research in the domain of naturalistic episodic memory. Future studies should develop more immersive and interactive virtual neuroimaging features to increase ecological and embodied neurocognition aspects.

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.

Risk of 30-day mortality and its association with alcohol concentration level among driver victims of motor vehicle crashes: comparison of population- and hospital-based designs

BACKGROUND

Although blood alcohol concentration (BAC) is undoubtedly associated with increased risk of injury among driver victims involved in motor vehicle crashes (MVCs), some studies noted that high BAC was associated with reduced risk of mortality after injury. In addition, most of the previous studies included only injured patients admitted, which may lead to potential selection bias arising from exclusion of those with minor injury and those who died at the accident scene of MVC.

METHOD

The population-based design included 2586 driver victims with BAC equivalent >0 and 10 307 matched controls (BAC equivalent =0) selected from the Police-reported Traffic Accident Registry from 1 July to 31 December 2016 in Taiwan. The hospital-based design comprised a subset sample, which included 517 driver victims with BAC equivalent >0 and 662 with BAC equivalent =0 hospitalised on the same day the MVCs occurred. Conditional logistic regression models with adjustment for potential confounders were used to estimate the ORs and 95% CIs of 30-day mortality associated with BAC equivalent level.

RESULTS

In the population-based design, a positive dose-gradient relationship was observed between BAC equivalent level and 30-day mortality, with a covariate-adjusted OR of 3.77 (95% CI 1.84 to 7.72), 6.19 (95% CI 3.13 to 12.26) and 7.75 (95% CI 4.51 to 13.32) for low, moderate and high BAC equivalent levels, respectively. By contrast, the hospital-based design revealed no significant association between 30-day mortality and alcohol concentration regardless of the BAC equivalent level.

CONCLUSION

The association between BAC equivalent level and short-term mortality could have been overlooked in hospital-based studies that excluded MVC-related deaths outside hospital settings.

The Shape of a Vehicle Windshield Affects Reaction Time and Brain Activity During a Target Detection Task

Background: Achieving clear visibility through a windshield is one of the crucial factors in manufacturing a safe and comfortable vehicle. The optic flow (OF) through the windshield has been reported to divert attention and could impair visibility. Although a growing number of behavioral and neuroimaging studies have assessed drivers’ attention in various driving scenarios, there is still little evidence of a relationship between OF, windshield shape, and driver’s attentional efficacy. The purpose of this research was to examine this relationship.

Methods: First, we quantified the OF across the windshield in a simulated driving scenario with either of two types of the windshield (a tilted or vertical pillar) at different speeds (60 km/h or 160 km/h) and found more upward OF along the tilted pillar than along the vertical pillar. Therefore, we hypothesized that the predominance of upward OF around the windshield along a tilted pillar could distract a driver and that we could observe the corresponding neural activity. Magnetic resonance scans were then obtained while the subjects performed a visual detection task while watching the driving scene used in the OF analysis. The subjects were required to press a button as rapidly as possible when a target appeared at one of five positions (leftmost, left, center, right, and rightmost).

Results: We found that the reaction time (RT) on exposure to a tilted pillar was longer than that on exposure to a vertical pillar in the leftmost and rightmost conditions. Furthermore, there was more brain activity in the precuneus when the pillar was tilted than when it was vertical in the rightmost condition near the pillar. In a separate analysis, activation in the precuneus was found to reflect relative changes in the amount of upward OF when the target was at the rightmost position.

Conclusions: Overall, these observations suggest that activation in the precuneus may reflect extraneous cognitive load driven by upward OF along the pillar and could distract visual attention. The findings of this study highlight the value of a cognitive neuroscientific approach to research and development in the motor vehicle manufacturing industry.

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.

Brain-based limitations in attention and secondary task engagement during high-fidelity driving simulation among young adults

Distracted driving remains a leading factor in fatal motor vehicle crashes, particularly in young drivers. Due to ongoing neuromaturation, attention capabilities are changing and improving throughout young adulthood. Here, we sought to bridge neuroscience with driving simulation by evaluating the effects of driving on attention processing through a selective auditory attention task. Participants (18-25 years old) engaged in an auditory attention task during LOAD (driving a high-fidelity simulator) and No-LOAD conditions (sitting in the parked simulator). For the auditory task, participants responded to a target auditory tone in a target ear. The event-related potential components, collected from frontal and posterior regions, P2 and P3, were used to evaluate attention processing across LOAD and No-LOAD conditions for attended and unattended stimuli. Data from 24 participants were evaluated in repeated measures ANOVAs, considering interactions between load and attention conditions for the P2 and P3 components of the cortical region. We observed a significant difference between response to attended and unattended stimuli for posterior P2 and P3 responses at the frontal and posterior midline sites. Comparing LOAD and No-LOAD conditions, there was a significant difference for P2 response at the posterior site and P3 response at the frontal site. A significant interaction between load and attended stimuli was found for P3 response at the posterior site. These data document differences in neural processing of auditory stimuli during high-fidelity simulated driving versus sitting parked in the simulator. Findings suggest the cognitive load of driving affects auditory attention.

Causal Inference in Generalizable Environments: Systematic Representative Design

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - Systematic Representative Design (SRD) - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine-dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine-dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs). Eight ICs were selected manually as relevant brain networks, which were used to classify healthy versus cocaine-dependent participants. FC and DFC measures of the chosen IC pairs were used as features for the classification algorithm. Support Vector Machines were used for both feature selection/reduction and participant classification. Based on DFC with only seven IC pairs, participants were successfully classified with 95% accuracy (and with 90% accuracy with three IC pairs), whereas static FC yielded only 81% accuracy. Visual, sensorimotor, default mode, and executive control networks, amygdala, and insula played the most significant role in the DFC-based classification. These findings support the use of DFC-based classification of fMRI data as a potential biomarker for the identification of cocaine dependence.

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.

Why cognitive event-related potentials (ERPs) should have a role in the management of alcohol disorders

Alcohol dependence is currently one of the most serious public health problems. Indeed, 3-8% of all deaths worldwide are attributable to effects of alcohol consumption. Although the first step in alcohol dependence treatment is straightforward, the main problem for clinicians lies with the prevention of relapse, as 40-70% of patients who only undergo psychosocial therapy resume alcohol use within a year following treatment. This review of the literature regarding event-related potentials (ERPs) is focused on two major neurocognitive factors that partially account for the inability of many alcoholics to remain abstinent: attentional biases towards alcohol-related stimuli that increase the urge to drink, and impaired response inhibition towards these cues that makes it more difficult for alcoholics to resist the temptation to drink. On this basis, we propose new research avenues to better implement ERPs in the management of alcohol disorders, according to four main directions that relate to (1) the development of ERP serial recordings; (2) the promotion of a multi-component ERP approach; (3) the definition of multi-site guidelines; and (4) the use of more representative laboratory situations through the use of more compelling environments.

Spiritual Care Therapy on Quality of Life in Cancer Patients and Their Caregivers: A Prospective Non-randomized Single-Cohort Study

Spiritual care is still in infancy stage all over the globe including India. The present study was an original study evaluating the role of spiritual care in cancer patients and their primary caregivers regarding their spiritual and general well-being. The study was a prospective, non-randomized single-group study involving cancer patients undergoing surgery and their primary caregivers. Functional assessment of cancer therapy-general and functional assessment of chronic illness therapy-spiritual care was evaluated during the admission and at the time of discharge, two, four  and 6 months following discharge from the hospital. Descriptive statistics was used for demographic details and repeated measure ANOVA with Dunn's test was used for analysis of changes in the scores. A total of 107 (63 males and 44 females) patients with a mean (SD) of age 51 (13) years were recruited in the study. Similarly, for each patient one of their primary caregivers was recruited with their mean (SD) age of 39.4 (12.7) years. A total of 11/107 (10.3%) patients died and nine out of 107 (8.4%) were lost to follow-up eventually during the study period. There was a statistically significant (P < 0.0001) increase in the scores at all the follow-up periods in both the patient and their relative groups. To conclude, we found out that spiritual care on the basis of MATCH guideline improved the level of not only spiritual well-being but general well-being also in both the patients and their primary caregivers. Control group could have improved scientific validity of study in accessing effect of spiritual care. Authors believe that more robust comparative study on each principle against all five MATCH principles in future will add scientific validity and clear the various ambiguities in spiritual care.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

Background

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations.

Results

The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance.

Conclusions

The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants’ SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1218-z) contains supplementary material, which is available to authorized users.

The sensitivity of laboratory tests assessing driving related skills to dose-related impairment of alcohol: A literature review

Laboratory tests assessing driving related skills can be useful as initial screening tools to assess potential drug induced impairment as part of a standardized behavioural assessment. Unfortunately, consensus about which laboratory tests should be included to reliably assess drug induced impairment has not yet been reached. The aim of the present review was to evaluate the sensitivity of laboratory tests to the dose dependent effects of alcohol, as a benchmark, on performance parameters. In total, 179 experimental studies were included. Results show that a cued go/no-go task and a divided attention test with primary tracking and secondary visual search were consistently sensitive to the impairing effects at medium and high blood alcohol concentrations. Driving performance assessed in a simulator was less sensitive to the effects of alcohol as compared to naturalistic, on-the-road driving. In conclusion, replicating results of several potentially useful tests and their predictive validity of actual driving impairment should deserve further research. In addition, driving simulators should be validated and compared head to head to naturalistic driving in order to increase construct validity.

Covert effects of "one drink" of alcohol on brain processes related to car driving: an event-related potential study

The effects of a low dose of alcohol on car driving remain controversial. To address this issue, event-related potentials were recorded while subjects performed a simple car-following task in a driving simulator before and after consuming either "one drink" of beer (representing one standard alcoholic beverage containing 14 g of alcohol) or mineral water (control condition). Subjects who had consumed the determined amount of alcohol demonstrated no detectable outward behavioral signs of intoxication while performing the driving task, an observation in agreement with previous findings. However, the parietal P3 elicited by the brake lights of the preceding car was significantly reduced in amplitude, approximately 50% that observed under the control condition, likely indicating alteration of the neural processing of visual information critical for safe driving. The finding suggests that alcohol begins to affect neural processes for driving even at quantities too low to modify behavior.

An exploratory fNIRS study with immersive virtual reality: a new method for technical implementation

For over two decades Virtual Reality (VR) has been used as a useful tool in several fields, from medical and psychological treatments, to industrial and military applications. Only in recent years researchers have begun to study the neural correlates that subtend VR experiences. Even if the functional Magnetic Resonance Imaging (fMRI) is the most common and used technique, it suffers several limitations and problems. Here we present a methodology that involves the use of a new and growing brain imaging technique, functional Near-infrared Spectroscopy (fNIRS), while participants experience immersive VR. In order to allow a proper fNIRS probe application, a custom-made VR helmet was created. To test the adapted helmet, a virtual version of the line bisection task was used. Participants could bisect the lines in a virtual peripersonal or extrapersonal space, through the manipulation of a Nintendo Wiimote ® controller in order for the participants to move a virtual laser pointer. Although no neural correlates of the dissociation between peripersonal and extrapersonal space were found, a significant hemodynamic activity with respect to the baseline was present in the right parietal and occipital areas. Both advantages and disadvantages of the presented methodology are discussed.

Virtual reality cues for binge drinking in college students

We investigated the ability of virtual reality (VR) cue exposure to trigger a desire for alcohol among binge-drinking students. Fifteen binge-drinking college students and eight students who were nonbingers were immersed into a neutral-cue environment or room (underwater scenes), followed by four alcohol-cue rooms (bar, party, kitchen, argument), followed by a repeat of the neutral room. The virtual rooms were computer generated via head-mounted visual displays with associated auditory and olfactory stimuli. In each room, participants reported their subjective cravings for alcohol, the amount of attention given to the sight and smell of alcohol, and how much they were thinking of drinking. A 2 x 6 (type of drinker by VR room) repeated measures ANOVA was conducted on the responses to each question. After alcohol exposure, binge drinkers reported significantly higher cravings for and thoughts of alcohol than nonbinge drinkers, whereas differences between the groups following the neutral rooms were not significant.

Disruptions in functional network connectivity during alcohol intoxicated driving

BACKGROUND

Driving while under the influence of alcohol is a major public health problem whose neural basis is not well understood. In a recently published functional magnetic resonance imaging (fMRI) study (Meda et al., 2009), our group identified 5, independent critical driving-associated brain circuits whose inter-regional connectivity was disrupted by alcohol intoxication. However, the functional connectivity between these circuits has not yet been explored in order to determine how these networks communicate with each other during sober and alcohol-intoxicated states.

METHODS

In the current study, we explored such differences in connections between the above brain circuits and driving behavior, under the influence of alcohol versus placebo. Forty social drinkers who drove regularly underwent fMRI scans during virtual reality driving simulations following 2 alcohol doses, placebo and an individualized dose producing blood alcohol concentrations (BACs) of 0.10%.

RESULTS

At the active dose, we found specific disruptions of functional network connectivity between the frontal-temporal-basal ganglia and the cerebellar circuits. The temporal connectivity between these 2 circuits was found to be less correlated (p < 0.05) when driving under the influence of alcohol. This disconnection was also associated with an abnormal driving behavior (unstable motor vehicle steering).

CONCLUSIONS

Connections between frontal-temporal-basal ganglia and cerebellum have recently been explored; these may be responsible in part for maintaining normal motor behavior by integrating their overlapping motor control functions. These connections appear to be disrupted by alcohol intoxication, in turn associated with an explicit type of impaired driving behavior.

Alcohol dose effects on brain circuits during simulated driving: an fMRI study

Driving while intoxicated remains a major public health hazard. Driving is a complex task involving simultaneous recruitment of multiple cognitive functions. The investigators studied the neural substrates of driving and their response to different blood alcohol concentrations (BACs), using functional magnetic resonance imaging (fMRI) and a virtual reality driving simulator. We used independent component analysis (ICA) to isolate spatially independent and temporally correlated driving-related brain circuits in 40 healthy, adult moderate social drinkers. Each subject received three individualized, separate single-blind doses of beverage alcohol to produce BACs of 0.05% (moderate), 0.10% (high), or 0% (placebo). 3 T fMRI scanning and continuous behavioral measurement occurred during simulated driving. Brain function was assessed and compared using both ICA and a conventional general linear model (GLM) analysis. ICA results replicated and significantly extended our previous 1.5T study (Calhoun et al. [2004a]: Neuropsychopharmacology 29:2097-2017). GLM analysis revealed significant dose-related functional differences, complementing ICA data. Driving behaviors including opposite white line crossings and mean speed independently demonstrated significant dose-dependent changes. Behavior-based factors also predicted a frontal-basal-temporal circuit to be functionally impaired with alcohol dosage across baseline scaled, good versus poorly performing drivers. We report neural correlates of driving behavior and found dose-related spatio-temporal disruptions in critical driving-associated regions including the superior, middle and orbito frontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia, and cerebellum. Overall, results suggest that alcohol (especially at high doses) causes significant impairment of both driving behavior and brain functionality related to motor planning and control, goal directedness, error monitoring, and memory.

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

Brain activation during fast driving in a driving simulator: the role of the lateral prefrontal cortex

Little is currently known about the neural underpinnings of the cognitive control of driving behavior in realistic situations and of the driver's speeding behavior in particular. In this study, participants drove in realistic scenarios presented in a high-end driving simulator. Scalp-recorded EEG oscillations in the alpha-band (8-13 Hz) with a 30-electrode montage were recorded while the participants drove under different conditions: (i) excessively fast (Fast), (ii) in a controlled manner at a safe speed (Correct), and (iii) impatiently in the context of testing traffic conditions (Impatient). Intracerebral sources of alpha-band activation were estimated using low resolution electrical tomography. Given that previous studies have shown a strong negative correlation between the Bold response in the frontal cortex and the alpha-band power, we used alpha-band-related activity as an estimation of frontal activation. Statistical analysis revealed more alpha-band-related activity (i.e. less neuronal activation) in the right lateral prefrontal cortex, including the dorsolateral prefrontal cortex, during fast driving. Those participants who speeded most and exhibited greater risk-taking behavior demonstrated stronger alpha-related activity (i.e. less neuronal activation) in the left anterior lateral prefrontal cortex. These findings are discussed in the context of current theories about the role of the lateral prefrontal cortex in controlling risk-taking behavior, task switching, and multitasking.

Neural correlates of "absence" in interactive simulator protocols

While much has been written about the importance of the experience of presence in simulation protocols, the role of "absence" deserves closer attention. The role of subjective experience and neurophysiologic processes involved in fluctuating states of consciousness is a key issue in developing assessment and treatment tools using interactive immersive simulator tasks. This paper proposes that when engaging in an interactive simulator task, there are fluctuations of consciousness that determine both motivational engagement and measured performance. Rather than expecting a continuous experience, both in terms of perceptual and motor output flow, factors such as circadian fluctuations, fatigue, and actual intrusion of sleep into waking consciousness are relevant in assessments and treatments using virtual environment-based tasks. These factors are particularly relevant in treatment populations with neurological and psychiatric disorders, where such disturbances are more common. To illustrate this construct, a series of experiments examining these phenomena in relation to a standardized driving-simulation protocol to detect psychomotor impairment developed for clinical and diagnostic testing in a sleep laboratory setting are reviewed.

Effects of a sedative antihistamine, D-chlorpheniramine, on regional cerebral perfusion and performance during simulated car driving

OBJECTIVES

The sedative side effects of antihistamines have been recognized to be potentially dangerous in car driving, but the mechanism underlying these effects has not yet been elucidated to date. The aim of the present study is to examine regional cerebral blood flow (rCBF) responses during a simulated car-driving task following oral administration of D-chlorpheniramine using positron emission tomography (PET) and [15O]H2O, based on a single-blind cross-over study-design.

METHODS

Right-handed, healthy male volunteers (n = 14) drove a car in a simulated environment following oral administration of D-chlorpheniramine repetab 6 mg or placebo. Their rCBF was measured using PET with [15O]H2O in the following three conditions: (1) resting, (2) active driving, and (3) passive driving. All 'in-car' views during the simulated driving were videotaped and used for rating driving performance.

RESULTS

Performance evaluation revealed that the number of lane deviations significantly increased in the D-chlorpheniramine condition compared with the placebo condition (p < 0.01). Subjective sleepiness was not significantly different between the two drug conditions. The regions of diminished brain responses following D-chlorpheniramine treatment were detected in the parietal, temporal and visual cortices, and in the cerebellum. The regions of augmented rCBF responses were found in the orbitofrontal cortex and cerebellar vermis.

CONCLUSION

These results suggest that D-chlorpheniramine tends to suppress visuo-spatial cognition and visuo-motor coordinating functions rather than attention and motor functions during car driving.

Structural and functional magnetic resonance imaging in psychiatric disorders

OBJECTIVE

To report on recent advances in both structural and functional brain imaging studies in psychiatry and to highlight their importance for the field.

METHOD

We reviewed recently published articles dealing with such advances and abstracted them into a selective review of the field.

RESULTS

Some of the more important trends include integration of genetic information into research studies, use of novel quantitative image measurement techniques, studies of new subject populations, the use of pharmacologic probes in functional magnetic resonance imaging (fMRI) studies, the incorporation of elements of virtual reality into fMRI task stimuli, and the methodological innovation of hyperscanning.

CONCLUSIONS

A whole series of new approaches and techniques are resulting in rapid advances in neuroimaging in psychiatry. Several of these show the potential for clinical translation.

Functional brain mapping of actual car-driving using [18F]FDG-PET

AIMS

This study aims at identifying the brain activation during actual car-driving on the road, and at comparing the results to those of previous studies on simulated car-driving.

METHODS

Thirty normal volunteers, aged 20 to 56 years, were divided into three subgroups, active driving, passive driving and control groups, for examination by positron emission tomography (PET) and [18F]2-deoxy-2-fluoro-D-glucose (FDG). The active driving subjects (n = 10) drove for 30 minutes on quiet normal roads with a few traffic signals. The passive driving subjects (n = 10) participated as passengers on the front seat. The control subjects (n = 10) remained seated in a lit room with their eyes open. Voxel-based t-statistics were applied using SPM2 to search brain activation among the subgroups mentioned above.

RESULTS

Significant brain activation was detected during active driving in the primary and secondary visual cortices, primary sensorimotor areas, premotor area, parietal association area, cingulate gyrus, the parahippocampal gyrus as well as in thalamus and cerebellum. The passive driving manifested a similar-looking activation pattern, lacking activations in the premotor area, cingulate and parahippocampal gyri and thalamus. Direct comparison of the active and passive driving conditions revealed activation in the cerebellum.

CONCLUSION

The result of actual driving looked similar to that of simulated driving, suggesting that visual perception and visuomotor coordination were the main brain functions while driving. In terms of attention and autonomic arousal, however, it seems there was a significant difference between simulated and actual driving possibly due to risk of accidents. Autonomic and emotional aspects of driving should be studied using an actual driving study-design.