Preliminary Eye-Tracking Data as a Nonintrusive Marker for Blood Δ-9-Tetrahydrocannabinol Concentration and Drugged Driving

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).

Keywords: THC; cannabis; correlation; driving performance; eye-tracking; lateral position; nonlinear regression; temporal trend; vehicle speed.

Conflict of interest statement

Dr. Shahidi Zandi is an ACS employee and has no competing financial interests to report. Mr. Felix Comeau is the ACS President/CEO and has no competing financial interests to report. Dr. Mann has no competing financial interests to report. Dr. Di Ciano, Mr. Arslan, and Mr. Murphy have no conflicts to report. Dr. Le Foll has obtained funding from Pfizer (GRAND Awards, including salary support) for investigator-initiated projects. Dr. Le Foll has/will received some in-kind donation of cannabis product from Canopy and Aurora and medication donation from Pfizer and Bioprojet and was provided a coil for TMS study from Brainsway. Dr. Le Foll has performed research with industry funding obtained from Canopy (through research grants handled by CAMH or University of Toronto), Bioprojet, ACS and Alkermes. Dr. Wickens has no confict of interest to report.

Figures

FIG. 1.

The overall blood THC concentration. The x-axis represents the relative time with respect to the time of cannabis exposure (time: 0; the reference time). The mean and SD calculated over all participants are shown. SD, standard deviation; THC, Δ-9-tetrahydrocannabinol.

FIG. 2.

Correlation between each target variable and eye-tracking features. (a, c, e) Number of features individually showing a significant correlation (r≥0.5, p<0.05) with blood THC concentration, SDLP, and speed, respectively, for each epoch length. (b, d, f) The distribution of the correlations for every epoch length (with blood THC concentration, SDLP, and speed, respectively). SDLP, standard deviation of lateral position.

FIG. 3.

Temporal trend analysis for eye-tracking features (following cannabis exposure) at different epoch lengths: (a) number of features showing a significant increasing or decreasing trend (adjusted R2 ≥ 0.7, p<0.01), (b) the mean (±SD) of adjusted R2 for features with significant trend, and (c) the distribution of adjusted R2 over all features.