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.

Novel use of a virtual driving assessment to classify driver skill at the time of licensure.

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary "Driving Classes"; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.

Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data.

BACKGROUND: A large Midwestern state commissioned a virtual driving test (VDT) to assess driving skills preparedness before the on-road examination (ORE). Since July 2017, a pilot deployment of the VDT in state licensing centers (VDT pilot) has collected both VDT and ORE data from new license applicants with the aim of creating a scoring algorithm that could predict those who were underprepared. OBJECTIVE: Leveraging data collected from the VDT pilot, this study aimed to develop and conduct an initial evaluation of a novel machine learning (ML)-based classifier using limited domain knowledge and minimal feature engineering to reliably predict applicant pass/fail on the ORE. Such methods, if proven useful, could be applicable to the classification of other time series data collected within medical and other settings. METHODS: We analyzed an initial dataset that comprised 4308 drivers who completed both the VDT and the ORE, in which 1096 (25.4%) drivers went on to fail the ORE. We studied 2 different approaches to constructing feature sets to use as input to ML algorithms: the standard method of reducing the time series data to a set of manually defined variables that summarize driving behavior and a novel approach using time series clustering. We then fed these representations into different ML algorithms to compare their ability to predict a driver's ORE outcome (pass/fail). RESULTS: The new method using time series clustering performed similarly compared with the standard method in terms of overall accuracy for predicting pass or fail outcome (76.1% vs 76.2%) and area under the curve (0.656 vs 0.682). However, the time series clustering slightly outperformed the standard method in differentially predicting failure on the ORE. The novel clustering method yielded a risk ratio for failure of 3.07 (95% CI 2.75-3.43), whereas the standard variables method yielded a risk ratio for failure of 2.68 (95% CI 2.41-2.99). In addition, the time series clustering method with logistic regression produced the lowest ratio of false alarms (those who were predicted to fail but went on to pass the ORE; 27.2%). CONCLUSIONS: Our results provide initial evidence that the clustering method is useful for feature construction in classification tasks involving time series data when resources are limited to create multiple, domain-relevant variables.

Adult-supervised practice in learner's permit phase has a significant but limited ability to improve safe driving skills.

OBJECTIVES: Most U.S. states require adult-supervised practice for adolescent learner permit holders intending to obtain a driver's license before 18. However, the effectiveness of adult-supervised practice in imparting safe driving skills had been inconclusive prior to the recent development of consistent measures of adolescent learners' driving performance. We examined the relationship between the number of adult-supervised practice hours and driving performance and skill deficits for 441 learner permit holders ages 16 and 17 in Pennsylvania. METHODS: Data came from a virtual driving assessment (VDA) deployed across Children's Hospital of Philadelphia's Primary Care Network and a self-reported survey at the time of the VDA. Based on a previous study, we defined two VDA Driving Classes that describe driving performance: Major Issues or Major Issues with Dangerous Behavior Class, and Minor Issues or No Issues Class. The response options for adult-supervised practice hours were presented to adolescents as categories (none, <15 h, 15-<65 h, 65 h, and >65 h). We grouped those with 65 h with those with >65 h given the low numbers of responses in these categories. The analysis used a Chi-square test and a binomial logit to investigate how adult-supervised practice hours correspond to adolescents' VDA Driving Classes. RESULTS: Chi-square test showed weak associations between VDA Driving Classes and adult-supervised practice hours. The binomial logit found that adolescents who reported zero adult-supervised practice hours were more likely to be classified into the Major Issues or Major Issues with Dangerous Behavior Class than those who reported adult-supervised practice hours. For those who reported adult-supervised practice, the differences in VDA Driving Class were not statistically significant across the three practice categories (i.e., <15 h, 15-<65 h, and ≥65 h). The model found no significant associations between VDA Driving Class and whether adolescent learners reported practicing with a formal instructor. CONCLUSIONS: Our findings suggest that under current adult-supervised practice routines, longer practice hours have limited associations with improvements in adolescents' VDA Driving Class. Some adolescents might not be ready to deal with the more dangerous settings the VDA presents even after completing the required hours of adult-supervised practice.

Driving Skills at Licensure and Time to First Crash.

BACKGROUND AND OBJECTIVES: Young drivers are overrepresented in crashes, and newly licensed drivers are at high risk, particularly in the months immediately post-licensure. Using a virtual driving assessment (VDA) implemented in the licensing workflow in Ohio, this study examined how driving skills measured at the time of licensure contribute to crash risk post-licensure in newly licensed young drivers. METHODS: This study examined 16 914 young drivers (<25 years of age) in Ohio who completed the VDA at the time of licensure and their subsequent police-reported crash records. By using the outcome of time to first crash, a Cox proportional hazard model was used to estimate the risk of a crash during the follow-up period as a function of VDA Driving Class (and Skill Cluster) membership. RESULTS: The best performing No Issues Driving Class had a crash risk 10% lower than average (95% confidence interval [CI] 13% to 6%), whereas the Major Issues with Dangerous Behavior Class had a crash risk 11% higher than average (95% CI 1% to 22%). These results withstood adjusting for covariates (age, sex, and tract-level socioeconomic status indicators). At the same time, drivers licensed at age 18 had a crash risk 16% higher than average (95% CI 6% to 27%). CONCLUSIONS: This population-level study reveals that driving skills measured at the time of licensure are a predictor of crashes early in licensure, paving the way for better prediction models and targeted, personalized interventions. The authors of future studies should explore time- and exposure-varying risks.

Financial status and travel time to driving schools as barriers to obtaining a young driver license in a state with comprehensive young driver licensing policy.

The highest lifetime risk for a motor vehicle crash is immediately after the point of licensure, with teen drivers most at risk. Comprehensive teen driver licensing policies that require completion of driver education and behind-the-wheel training along with Graduated Driver Licensing (GDL) are associated with lower young driver crash rates early in licensure. We hypothesize that lack of financial resources and travel time to driving schools reduce the likelihood for teens to complete driver training and gain a young driver's license before age 18. We utilize licensing data from the Ohio Bureau of Motor Vehicles on over 35,000 applicants between 15.5 and 25 years old collected between 2017 and 2019. This dataset of driving schools is maintained by the Ohio Department of Public Safety and is linked with Census tract-level socioeconomic data from the U.S. Census. Using logit models, we estimate the completion of driver training and license obtainment among young drivers in the Columbus, Ohio metro area. We find that young drivers in lower-income Census tracts have a lower likelihood to complete driver training and get licensed before age 18. As travel time to driving schools increases, teens in wealthier Census tracts are more likely to forgo driver training and licensure than teens in lower-income Census tracts. For jurisdictions aspiring to improve safe driving for young drivers, our findings help shape recommendations on policies to enhance access to driver training and licensure especially among teens living in lower-income Census tracts.

Licensing Examination and Crash Outcomes Postlicensure in Young Drivers.

Importance: Despite US graduated driver licensing laws, young novice driver crash rates remain high. Study findings suggest comprehensive license policy that mandates driver education including behind-the-wheel (BTW) training may reduce crashes postlicensure. However, only 15 states mandate BTW training. Objective: To identify differences in licensing and crash outcomes for drivers younger than 18 years who are subject to comprehensive licensing requirements (graduated driver licensing, driver education, and BTW training) vs those aged 18 to 24 years who are exempt from these requirements. Design, Setting, and Participants: This prospective, population-based cohort study used Ohio licensing data to define a cohort of 2018 license applicants (age 16-24 years, n = 136 643) and tracked licensed driver (n = 129 897) crash outcomes up to 12 months postlicensure. The study was conducted from January 1, 2018, to December 31, 2019, and data analysis was performed from October 7, 2019, to February 11, 2022. Main Outcomes and Measures: Licensing examination performance and population-based, police-reported crash rates in the first 2 months and 12 months postlicensure across age groups, sex, and census tract-level sociodemographic variables were measured. Poisson regression models compared newly licensed driver crash rates, with reference to individuals licensed at 18 years, while controlling for census tract-level sociodemographic factors, time spent in the learner permit period, and licensing examination performance measures. Results: Of 136 643 novice drivers, 69 488 (50.9%) were male and 67 152 (49.1%) were female. Mean (SD) age at enrollment (age at first on-road examination) was 17.7 (2.1) years. License applicants aged 16 and 17 years performed best on license examinations (15 466 [21.6%] and 5112 [30.9%] failing vs 7981 [37.5%] of applicants aged 18 years). Drivers licensed at 18 years had the highest crash rates of all those younger than 25 years. Compared with drivers licensed at 18 years, crash rates were 27% lower in individuals aged 16 years and 14% lower in those aged 17 years during the first 2 months postlicensure when controlling for socioeconomic status, time spent in learner permit status, and license examination performance measures (adjusted relative risk [aRR] at age 16 years: 0.73; 95% CI, 0.67-0.80; age 17 years: aRR, 0.86; 95% CI, 0.77-0.96). At 12 months postlicensure, crash rates were 19% lower for individuals licensed at age 16 years (aRR, 0.81; 95%, CI, 0.77-0.85) and 6% lower at age 17 years (aRR, 0.94; 95% CI, 0.89-0.99) compared with individuals aged 18 years. Conclusions and Relevance: In Ohio, drivers younger than 18 years who are subject to graduated driver licensing and driver education, including BTW training requirements, had lower crash rates in the first year postlicensure compared with those aged 18 years, with controls applied. These findings suggest that it may be fruitful for future work to reconsider the value of mandated driver license policies, including BTW training, and to examine reasons for delayed licensure and barriers to accessing training.

Examining virtual driving test performance and its relationship to individuals with HIV-associated neurocognitive disorders.

Significance: Existing screening tools for HIV-associated neurocognitive disorders (HAND) are often clinically impractical for detecting milder forms of impairment. The formal diagnosis of HAND requires an assessment of both cognition and impairment in activities of daily living (ADL). To address the critical need for identifying patients who may have disability associated with HAND, we implemented a low-cost screening tool, the Virtual Driving Test (VDT) platform, in a vulnerable cohort of people with HIV (PWH). The VDT presents an opportunity to cost-effectively screen for milder forms of impairment while providing practical guidance for a cognitively demanding ADL. Objectives: We aimed to: (1) evaluate whether VDT performance variables were associated with a HAND diagnosis and if so; (2) systematically identify a manageable subset of variables for use in a future screening model for HAND. As a secondary objective, we examined the relative associations of identified variables with impairment within the individual domains used to diagnose HAND. Methods: In a cross-sectional design, 62 PWH were recruited from an established HIV cohort and completed a comprehensive neuropsychological assessment (CNPA), followed by a self-directed VDT. Dichotomized diagnoses of HAND-specific impairment and impairment within each of the seven CNPA domains were ascertained. A systematic variable selection process was used to reduce the large amount of VDT data generated, to a smaller subset of VDT variables, estimated to be associated with HAND. In addition, we examined associations between the identified variables and impairment within each of the CNPA domains. Results: More than half of the participants (N = 35) had a confirmed presence of HAND. A subset of twenty VDT performance variables was isolated and then ranked by the strength of its estimated associations with HAND. In addition, several variables within the final subset had statistically significant associations with impairment in motor function, executive function, and attention and working memory, consistent with previous research. Conclusion: We identified a subset of VDT performance variables that are associated with HAND and assess relevant functional abilities among individuals with HAND. Additional research is required to develop and validate a predictive HAND screening model incorporating this subset.

Rethinking Cell Phone Use While Driving: Isolated Risk Behavior or a Pattern of Risk-Taking Associated with Impulsivity in Young Drivers?

This study examines whether cell phone use stands apart from a general pattern of risky driving practices associated with crashes and impulsivity-related personality traits in young drivers. A retrospective online survey study recruited 384 young drivers from across the United States using Amazon's Mechanical Turk to complete a survey measuring risky driving practices (including cell phone use), history of crashes, and impulsivity-related personality traits. Almost half (44.5%) of the drivers reported being involved in at least one crash, and the majority engaged in cell phone use while driving (up to 73%). Factor analysis and structural equation modeling found that cell phone use loaded highly on a latent factor with other risky driving practices that were associated with prior crashes (b = 0.15, [95% CI: 0.01, 0.29]). There was also an indirect relationship between one form of impulsivity and crashes through risky driving (b = 0.127, [95% CI: 0.01, 0.30]). Additional analyses did not find an independent contribution to crashes for frequent cell phone use. These results suggest a pattern of risky driving practices associated with impulsivity in young drivers, indicating the benefit of exploring a more comprehensive safe driving strategy that includes the avoidance of cell phone use as well as other risky practices, particularly for young drivers with greater impulsive tendencies.

A Novel Health-Transportation Partnership Paves The Road For Young Driver Safety Through Virtual Assessment.

Motor vehicle crashes remain the leading cause of adolescent mortality and injury in the United States. For young drivers, crash risk peaks immediately after licensure and declines during the next two years, making the point of licensure an important safety intervention opportunity. Legislation in Ohio established a unique health-transportation partnership among the State of Ohio, Children's Hospital of Philadelphia, and Diagnostic Driving, Inc., to identify underprepared driver license applicants through a virtual driving assessment system. The system, a computer-based virtual driving test, exposes drivers to common serious crash scenarios to identify critical skill deficits and is delivered in testing centers immediately before the on-road examination. A pilot study of license applicants who completed it showed that the virtual driving assessment system accurately predicted which drivers would fail the on-road examination and provided automated feedback that informed drivers on their skill deficits. At this time, the partnership's work is informing policy changes around integrating the virtual driving assessment system into licensing and driver training with the aim of reducing crashes in the first months of independent driving. The system can be developed to identify deficits in safety-critical skills that lead to crashes in new drivers and to address challenges that the coronavirus disease 2019 pandemic has introduced to driver testing and training.

Variation in Young Driver Training Requirements by State.

This cross-sectional study evaluates graduated driver licensing laws and requirements for behind-the-wheel training or adult-supervised practice for drivers younger than 18 years.

Comparable walking gait performance during executive and non-executive cognitive dual-tasks in chronic stroke: A pilot study.

BACKGROUND: Falls are a serious problem among stroke survivors due to subsequent injuries, recovery setbacks, dependence, and mortality. A growing body of dual-task (DT) studies suggests a role of executive functions in gait control and falls, particularly in subacute stroke. However, few studies have compared distinct executive and non-executive tasks, nor their effects on chronic stroke gait. RESEARCH QUESTION: The purpose of this cross-sectional study was to compare the effects of distinct working memory (2-back) and inhibition (Stroop) tasks on walking gait performance in chronic stroke survivors. METHODS: A pilot sample of chronic stroke survivors (n = 11, 8 males, mean age = 70.91, 6-12months post-stroke event) and age-matched healthy controls (n = 13, 4 male; mean age = 68.46) were tested. Gait performance (speed, stride time, stride time variability, stride length and stride length variability) was measured using 2 wireless inertial measurement sensors under 4 walking conditions: 1) preferred walking (single-task: ST), 2) walking with a 2-back DT, 3) walking with a Stroop DT, and 4) walking with a non-executive motor response DT. The secondary tasks were also carried out in both ST (seated) and DT conditions, to examine bidirectional effects. RESULTS: While the stroke survivor sample had a slower gait speed across conditions and tasks, there were no significant differences between the groups [F(1, 22) = 1.13, p =.299, η2p = .049] on the spatial or temporal gait characteristics recorded: gait performance was maintained during executive and non-executive DTs. In addition, we did not find a significant effect of group on cognitive task performance (all p > .052). However, we observed a cost in accuracy on the 2-back DT for both groups, suggesting resource overlap and greater cognitive load (all t > 19.72, all p < .001). SIGNIFICANCE: Our gait data contradict previous studies evidencing impaired gait post-stroke, suggesting functional recovery in this chronic stroke sample.

Working Memory Development and Motor Vehicle Crashes in Young Drivers.

Importance: Adolescent drivers have the highest rate of motor vehicle crashes, and among equally novice drivers, crash risk is inversely age graded. Working memory (WM), crucial to driving hazard awareness, is also age graded, with ongoing development into late adolescence. Variability in WM capacity and growth trajectory positions WM as a candidate crash risk factor for study, clinical screening, and possible preventative intervention. Objective: To test the association between crashes and differential WM development. Design, Setting, and Participants: This prospective cohort study used data from a longitudinal cohort of 118 community youth in Philadelphia, Pennsylvania. Working memory and other risk factors were measured annually from age 11 to 13 years (prelicensure, in 2005) to 14 to 16 years (in 2009), and again at 18 to 20 years (in 2013). In 2015, a follow-up survey of driving experience identified 84 participants who had started driving. Latent growth curve modeling was used to examine the association between variability in the baseline (intercept) and developmental trajectory (slope) of WM and the crash outcome. Main Outcomes and Measures: Self-reported crashes were the primary outcome. Variability in the relative growth of WM development along with traits and behaviors associated with risky driving were assessed. Results: Of 84 participants (39 [46%] male; mean [SD] age, 20.46 [1.09] years), 25 (29.8%) reported they had been involved in at least 1 crash. Controlling for other crash risk factors, the model indicated that variation in the linear slope of WM growth was inversely associated with reporting a crash (b = -6.41; SE = 2.64; P = .02). Crashes were also associated with reckless driving behavior (b = 0.40; SE = 0.18; P = .03). Variation in the intercept of WM was not associated with crashes (b = -0.245; SE = 0.67; P = .72). Conclusions and Relevance: The results suggest that a relatively slower WM growth trajectory is associated with young driver crashes. Routine assessment of WM across adolescence may help to identify at-risk teen drivers and opportunities for providing adaptive interventions (eg, driving aids or training) that can address limitations in WM-related skills that are critical for safe driving.

Executive Function Capacities, Negative Driving Behavior and Crashes in Young Drivers.

Motor vehicle crashes remain a leading cause of injury and death in adolescents, with teen drivers three times more likely to be in a fatal crash when compared to adults. One potential contributing risk factor is the ongoing development of executive functioning with maturation of the frontal lobe through adolescence and into early adulthood. Atypical development resulting in poor or impaired executive functioning (as in Attention-Deficit/Hyperactivity Disorder) has been associated with risky driving and crash outcomes. However, executive function broadly encompasses a number of capacities and domains (e.g., working memory, inhibition, set-shifting). In this review, we examine the role of various executive function sub-processes in adolescent driver behavior and crash rates. We summarize the state of methods for measuring executive control and driving outcomes and highlight the great heterogeneity in tools with seemingly contradictory findings. Lastly, we offer some suggestions for improved methods and practical ways to compensate for the effects of poor executive function (such as in-vehicle assisted driving devices). Given the key role that executive function plays in safe driving, this review points to an urgent need for systematic research to inform development of more effective training and interventions for safe driving among adolescents.

Dual-task and electrophysiological markers of executive cognitive processing in older adult gait and fall-risk.

The role of cognition is becoming increasingly central to our understanding of the complexity of walking gait. In particular, higher-level executive functions are suggested to play a key role in gait and fall-risk, but the specific underlying neurocognitive processes remain unclear. Here, we report two experiments which investigated the cognitive and neural processes underlying older adult gait and falls. Experiment 1 employed a dual-task (DT) paradigm in young and older adults, to assess the relative effects of higher-level executive function tasks (n-Back, Serial Subtraction and visuo-spatial Clock task) in comparison to non-executive distracter tasks (motor response task and alphabet recitation) on gait. All DTs elicited changes in gait for both young and older adults, relative to baseline walking. Significantly greater DT costs were observed for the executive tasks in the older adult group. Experiment 2 compared normal walking gait, seated cognitive performances and concurrent event-related brain potentials (ERPs) in healthy young and older adults, to older adult fallers. No significant differences in cognitive performances were found between fallers and non-fallers. However, an initial late-positivity, considered a potential early P3a, was evident on the Stroop task for older non-fallers, which was notably absent in older fallers. We argue that executive control functions play a prominent role in walking and gait, but the use of neurocognitive processes as a predictor of fall-risk needs further investigation.