Brain-behavior relationships of simulated naturalistic automobile driving under the influence of acute cannabis intoxication: A double-blind, placebo-controlled study

Background: Driving is a complex everyday activity that requires the use and integration of different cognitive and psychomotor functions, many of which are known to be affected when under the influence of cannabis (CNB). Given legal implications of drugged-driving and rapidly increasing use of CNB nationwide, there is an urgent need to better understand the effects of CNB on such functions in the context of driving. This longitudinal, double-blind placebo-controlled study investigated the effects of CNB on driving brain-behavior relationships in a controlled simulated environment using functional MRI (fMRI). Methods: N=26 frequent cannabis users were administered 0.5 grams of 13% THC or placebo flower cannabis via a Stortz+Bickel ‘Volcano’ vaporizer using paced inhalation, on separate days at least 1 week apart. On each study day, participants drove a virtual driving simulator (steering wheel, brake, gas pedal) inside an MRI scanner approximately 40 minutes post-dosing. Each fMRI driving session presented a naturalistic simulated environment that unobtrusively engaged drivers with scenarios that tested specific driving skills and response. There were three, approximately 10 min epochs where drivers engaged in task of lane keeping/weaving (LK), lead car following (CF), and safe overtaking (OT). fMRI data were prepared for analyses using the Human Connectome Project pipeline, then subjected to group independent component analysis (ICA) to isolate 50 spatially independent networks. 40 ICA networks were deemed valid and non-noisy. Network regions in these components were identified using 387 parcel locations, incorporating a cortical parcellation atlas (Glasser et al 2016) and detailed subcortical labels. A placebo minus high difference connectivity map was generated for each subject. A similar placebo minus high behavioral score was generated for each subject and then subjected to a principal component analysis (PCA) to reduce it to 8 orthogonal behavioral factors. Of the 8 driving behavior factors, two represented CF events (F1 and F5), three LK (F3, F4, and F8), and three OT (F2, F6, and F7). Driving behavior factors were evaluated for linear association with connectivity maps via FSL’s randomize (p