Characterizing the Learning-to-Drive Period for Teens with Attention Deficits

Executive Function Brain Network Activation Predicts Driving Hazard Detection in ADHD

Drivers with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are at increased risk of experiencing driving difficulties. An important aspect of driving safety and skill involves hazard detection. This functional magnetic resonance imaging study examined the neural responses associated with driving hazard detection in drivers with ASD, ADHD, and typically developing (TD) drivers. Forty participants (12 ASD, 15 ADHD, 13 TD) ages 16-30 years completed a driving simulator task in which they encountered social and nonsocial hazards; reaction time (RT) for responding to hazards was measured. Participants then completed a similar hazard detection task in the MRI scanner so that neural response to hazards could be measured. Activation of regions of interest considered part of the executive function (EF) and theory of mind (ToM) networks were examined and related to driving simulator behavior. Results showed that stronger activation of the EF network during social hazard processing, including the bilateral dorsolateral prefrontal cortex and posterior parietal cortex, was associated with faster RT to social hazards among drivers with ADHD, but not among drivers with ASD. This provides the first evidence of a relationship between EF network brain activation and driving skills in ADHD and suggests that alterations in this network may underlie driving behavior. In comparison, the current study did not observe a relationship between ToM network activation and RT to social hazards in any group. This study lays the groundwork for relating neural activation to driving behavior among individuals with NDDs.

Neuropsychological predictors of driving hazard detection in autism spectrum disorder and ADHD

Driving is a neuropsychologically complex task; this can present challenges for individuals with neurodevelopmental disorders (NDDs) such asautism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Deficits in theory of mind (ToM) and executive function (EF) are common features of ASD and ADHD, respectively, and may influence driving processes such as hazard perception. No studies have directly examined the neuropsychological contributions to hazard detection among drivers with ASD compared to ADHD.In the current study, 48 participants ages 16-30 years (13 ASD, 17 ADHD, 18 typically developing (TD)) completed a driving simulator task in which they encountered hazards in the driving environment. Hazards varied in whether they were social (contained a human component) or nonsocial (were physical objects) to examine the contribution of ToM and social processing to hazard response. Additionally, participants completed a neuropsychological battery targeting ToM and EF/attention skills (cognitive tasks and self-report measures).Within the ASD group, participants responded relatively slower to social compared to nonsocial hazards; no effect of hazard type was observed in the ADHD or TD groups. Additionally, measures of ToM and EF were correlated with driving performanceamong ASD participants; within the ADHD group, only self-reported behavior regulation was associated with driving performance. Broadly, this suggests that cognitive factors such as ToM and EF impact driving hazard performance in ASD and ADHD. The results of the study have implications for developing driving intervention programs for individuals with NDDs.

Advancing our understanding of cognitive development and motor vehicle crash risk: A multiverse representation analysis

Neurobiological and cognitive maturational models are the dominant theoretical account of adolescents' risk-taking behavior. Both the protracted development of working memory (WM) through adolescence, as well as individual differences in WM capacity have been theorized to be related to risk-taking behavior, including reckless driving. In a cohort study of 84 adolescent drivers Walshe et al. (2019) found adolescents who crashed had an attenuated trajectory of WM growth compared to adolescent drivers who never reported being in a crash, but observed no difference in WM capacity at baseline. The objectives of this report were to attempt to replicate these associations and to evaluate their robustness using a hybrid multiverse - specification curve analysis approach, henceforth called multiverse representation analysis (MRA). The authors of the original report provided their data: 84 adolescent drivers with annual evaluations of WM and other risk factors from 2005 to 2013, and of driving experiences in 2015. The original analysis was implemented as described in the original report. An MRA approach was used to evaluate the robustness of the association between developmental trajectories of WM and adolescents' risk-taking (indexed by motor vehicle crash involvement) to different reasonable methodological choices. We enumerated 6 reasonable choice points in data processing-analysis configurations: (1) model type: latent growth or multi-level regression, (2) treatment of WM data; (3) which waves are included; (4) covariate treatment; (5) how time is coded; and (6) link function/estimation method: weighted least squares means and variance estimation (WLSMV) with a linear link versus logistic regression with maximum likelihood estimation. This multiverse consists of 96 latent growth models and 18 multi-level regression models.