Abstract
The primary cannabinoid in cannabis, Δ9-tetrahydrocannabinol (THC), causes intoxication and impaired function, with implications
for traffic, workplace, and other situational safety risks. There are currently no evidence-based methods to detect cannabis-impaired
driving, and current field sobriety tests with gold-standard, drug recognition evaluations are resource-intensive and may be prone
to bias. This study evaluated the capability of a simple, portable imaging method to accurately detect individuals with THC
impairment. In this double-blind, randomized, cross-over study, 169 cannabis users, aged 18–55 years, underwent functional nearinfrared spectroscopy (fNIRS) before and after receiving oral THC and placebo, at study visits one week apart. Impairment was
defined by convergent classification by consensus clinical ratings and an algorithm based on post-dose tachycardia and self-rated
“high.” Our primary outcome, prefrontal cortex (PFC) oxygenated hemoglobin concentration (HbO), was increased after THC only in
participants operationalized as impaired, independent of THC dose. ML models using fNIRS time course features and connectivity
matrices identified impairment with 76.4% accuracy, 69.8% positive predictive value (PPV), and 10% false-positive rate using
convergent classification as ground truth, which exceeded Drug Recognition Evaluator-conducted expanded field sobriety
examination (67.8% accuracy, 35.4% PPV, and 35.4% false-positive rate). These findings demonstrate that PFC response activation
patterns and connectivity produce a neural signature of impairment, and that PFC signal, measured with fNIRS, can be used as a
sole input to ML models to objectively determine impairment from THC intoxication at the individual level. Future work is
warranted to determine the specificity of this classifier to acute THC impairment
