Detection of Δ9-Tetrahydrocannabinol Impairment Using Resting-State Functional Near-Infrared Spectroscopy: A...

Area of Science:

• Neuroscience
• Pharmacology
• Forensic Science

Background:

• Delta-9-tetrahydrocannabinol (THC) is the primary psychoactive compound in cannabis, causing intoxication and functional impairment.
• Traditional impairment detection methods like field sobriety tests (FSTs) are prone to bias and may not accurately reflect THC-induced impairment.

Purpose of the Study:

• To investigate the efficacy of resting-state functional near-infrared spectroscopy (fNIRS) in detecting THC-induced impairment.
• To compare the accuracy and false-positive rates of fNIRS with traditional FSTs for identifying impairment.

Main Methods:

• A double-blind, randomized, crossover trial involving adults (18-55 years) who used cannabis.
• Participants received either synthetic THC or a placebo, with fNIRS scans acquired before and after administration.
• Machine learning models analyzed fNIRS data to classify THC-induced impairment, comparing performance against FSTs.

Main Results:

• Resting-state fNIRS data yielded a classifier with high accuracy (0.90) and a low false-positive rate (0.05) for impairment detection.
• fNIRS demonstrated superior performance compared to FSTs across multiple metrics, including precision, accuracy, false-positive rate, and area under the receiver operating curve (ROC-AUC).
• fNIRS achieved an ROC-AUC of 0.87, significantly outperforming the FST's ROC-AUC of 0.75.

Conclusions:

• THC intoxication alters prefrontal cortex activation patterns detectable by resting-state fNIRS.
• fNIRS provides a more accurate and reliable method for identifying THC-induced impairment at an individual level compared to FSTs.
• These findings support the potential of fNIRS as an objective neuroimaging tool for impairment detection.