Ali Shahidi Zandi1, Felix J E Comeau1, Robert E Mann2,3,4, Patricia Di Ciano2,4,5, Eliyas P Arslan2,4,5, Thomas Murphy2,4,5, Bernard Le Foll4,5,6,7,8,9,10, Christine M Wickens2,3,4,5,11. 1. Research & Development Department, Alcohol Countermeasure Systems (ACS), Toronto, Canada. 2. Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Canada. 3. Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. 4. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada. 5. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada. 6. Translational Addiction Research Laboratory, Centre for Addiction and Mental Health, and Centre for Addiction and Mental Health, Toronto, Canada. 7. Acute Care Program, Centre for Addiction and Mental Health, Toronto, Canada. 8. Department of Family and Community Medicine, Management and Evaluation, University of Toronto, Toronto, Canada. 9. Division of Brain and Therapeutics, Department of Psychiatry, Management and Evaluation, University of Toronto, Toronto, Canada. 10. Institute of Medical Sciences, and Management and Evaluation, University of Toronto, Toronto, Canada. 11. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
Abstract
Background: Cannabis is one of the drugs most often found in drivers involved in serious motor vehicle collisions. Validity and reliability of roadside cannabis detection strategies are questioned. This pilot study aimed to investigate the relationship between eye characteristics and cannabis effects in simulated driving to inform potential development of an alternative detection strategy. Materials and Methods: Multimodal data, including blood samples, eye-tracking recordings, and driving performance data, were acquired from 10 participants during a prolonged single-session driving simulator experiment. The study session included a baseline driving trial before cannabis exposure and seven trials at various times over ∼5 h after exposure. The multidimensional eye-tracking recording from each driving trial for each participant was segmented into nonoverlapping epochs (time windows); 34 features were extracted from each epoch. Blood Δ-9-tetrahydrocannabinol (THC) concentration, standard deviation of lateral position (SDLP), and mean vehicle speed were target variables. The cross-correlation between the temporal profile of each eye-tracking feature and target variable was assessed and a nonlinear regression analysis evaluated temporal trend of features following cannabis exposure. Results: Mean pupil diameter (r=0.81-0.86) and gaze pitch angle standard deviation (r=0.79-0.87) were significantly correlated with blood THC concentration (p<0.01) for all epoch lengths. For driving performance variables, saccade-related features were among those showing the most significant correlation (r=0.61-0.83, p<0.05). Epoch length significantly affected correlations between eye-tracking features and speed (p<0.05), but not SDLP or blood THC concentration (p>0.1). Temporal trend analysis of eye-tracking features after cannabis also showed a significant increasing trend (p<0.01) in saccade-related features, including velocity, scanpath, and duration, as the influence of cannabis decreased by time. A decreasing trend was observed for fixation percentage and mean pupil diameter. Due to the lack of placebo control in this study, these results are considered preliminary. Conclusion: Specific eye characteristics could potentially be used as nonintrusive markers of THC presence and driving-related effects of cannabis. clinicaltrials.gov (NCT03813602).
Background: Cannabis is one of the drugs most often found in drivers involved in serious motor vehicle collisions. Validity and reliability of roadside cannabis detection strategies are questioned. This pilot study aimed to investigate the relationship between eye characteristics and cannabis effects in simulated driving to inform potential development of an alternative detection strategy. Materials and Methods: Multimodal data, including blood samples, eye-tracking recordings, and driving performance data, were acquired from 10 participants during a prolonged single-session driving simulator experiment. The study session included a baseline driving trial before cannabis exposure and seven trials at various times over ∼5 h after exposure. The multidimensional eye-tracking recording from each driving trial for each participant was segmented into nonoverlapping epochs (time windows); 34 features were extracted from each epoch. Blood Δ-9-tetrahydrocannabinol (THC) concentration, standard deviation of lateral position (SDLP), and mean vehicle speed were target variables. The cross-correlation between the temporal profile of each eye-tracking feature and target variable was assessed and a nonlinear regression analysis evaluated temporal trend of features following cannabis exposure. Results: Mean pupil diameter (r=0.81-0.86) and gaze pitch angle standard deviation (r=0.79-0.87) were significantly correlated with blood THC concentration (p<0.01) for all epoch lengths. For driving performance variables, saccade-related features were among those showing the most significant correlation (r=0.61-0.83, p<0.05). Epoch length significantly affected correlations between eye-tracking features and speed (p<0.05), but not SDLP or blood THC concentration (p>0.1). Temporal trend analysis of eye-tracking features after cannabis also showed a significant increasing trend (p<0.01) in saccade-related features, including velocity, scanpath, and duration, as the influence of cannabis decreased by time. A decreasing trend was observed for fixation percentage and mean pupil diameter. Due to the lack of placebo control in this study, these results are considered preliminary. Conclusion: Specific eye characteristics could potentially be used as nonintrusive markers of THC presence and driving-related effects of cannabis. clinicaltrials.gov (NCT03813602).
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