Behavioral and Neurophysiological Signatures of Benzodiazepine-Related Driving Impairments - Stone, Bradly T.; Correa, Kelly A.; Brown, Timothy L.; Spurgin, Andrew L.; Stikic, Maja; Johnson, Robin R.; Berka, Chris (Frontiers in Psychology, 2015)
Impaired driving due to drug use is a growing problem, worldwide; estimates show that 18-23.5% of fatal accidents, and up to 34% of injury accidents may be caused by drivers under the influence of drugs (Drummer et al., 2003; NHTSA, 2010; Walsh et al., 2004). Furthermore, at any given time, up to 16% of drivers may be using drugs that can impair one’s driving abilities (NHTSA, 2009). Currently, drug recognition experts (law enforcement officers with specialized training to identify drugged driving), have the most difficult time with identifying drivers potentially impaired on central nervous system (CNS) depressants (Smith, Hayes, Yolton, Rutledge, & Citek, 2002). The fact that the use of benzodiazepines, a type of CNS depressant, is also associated with the greatest likelihood of causing accidents (Dassanayake, Michie, Carter, & Jones, 2011), further emphasizes the need to improve research tools in this area which can facilitate the refinement of, or additions to, current assessments of impaired driving. Our laboratories collaborated to evaluate both the behavioral and neurophysiological effects of a benzodiazepine, alprazolam, in a driving simulation (miniSim™). This drive was combined with a neurocognitive assessment utilizing time synched neurophysiology (EEG, ECG). While the behavioral effects of benzodiazepines are well characterized (Rapoport et al., 2009), we hypothesized that, with the addition of real-time neurophysiology and the utilization of simulation and neurocognitive assessment, we could find objective assessments of drug impairment that could improve the detection capabilities of drug recognition experts. Our analyses revealed that 1) specific driving conditions were significantly more difficult for benzodiazepine impaired drivers and; 2) the neurocognitive tasks’ metrics were able to classify "impaired" vs. "unimpaired" with up to 80% accuracy based on lane position deviation and lane departures. While this work requires replication in larger studies, our results not only identified criteria that could potentially improve the identification of benzodiazepine intoxication by drug recognition experts, but also demonstrated the promise for future studies using this approach to improve upon current, real-world assessments of impaired driving.