2026/01/30 by Moshe Berchansky, A. Eden Evins, Bryn Evohr +6 · 1 voice
Medicine · #Cannabis and Cannabinoid Research #Opioid Use Disorder Treatment #Optical Imaging and Spectroscopy Techniques
paper · pdf · doi:10.1001/jamanetworkopen.2025.56647
openalex publication_date 2026/01/30 · openalex created_date 2026/02/01 · openalex updated_date 2026/08/01
Importance: The primary psychoactive compound in cannabis, ∆9-tetrahydrocannabinol (THC) induces intoxication and functional impairment, raising safety concerns in driving. The traditional impairment detection method, behavioral field sobriety tests (FSTs), are subject to bias. Objective: To determine whether resting-state functional near-infrared spectroscopy (fNIRS) can detect THC-related impairment with greater accuracy and a lower rate of false positives than FSTs. Design, Setting, and Participants: This double-blind, randomized, crossover trial was conducted from January 2017 to January 2021 at a single site. Eligible participants were adults aged 18 to 55 years who used cannabis. Analyses were performed from November 2024 to November 2025. Interventions: Participants received a single oral dose of synthetic THC (range, 5-80 mg) intended to induce intoxication or placebo in separate visits. Main Outcome and Measures: fNIRS scans were acquired before and approximately 100 and 200 minutes after study drug administration to assess prefrontal cortex responses at rest and during a working memory task. Machine learning models trained on fNIRS data were then used to identify clinically determined THC-induced impairment. The primary outcome of this study was accuracy of THC-induced impairment classification using fNIRS data as compared with an FST. Model performance was quantified using false-positive rate, precision, recall, F1 score, and area under the receiver operating curve (ROC-AUC). Results: A total of 183 participants (mean [SD] age, 25.3 [6.3] years; 90 [49.2%] female) who used cannabis for a median (IQR) of 6.5 (4-7) days per week completed at least 1 study visit. fNIRS data collected during rest produced a classifier for impairment, with an ROC-AUC of 0.87 (95% CI, 0.83 to 0.91), accuracy of 0.90 (95% CI, 0.88 to 0.92), and false-positive rate of 0.05 (95% CI, 0.04 to 0.07), using clinical impairment assessment as ground truth. The FST showed an ROC-AUC of 0.75 (95% CI, 0.74 to 0.76), accuracy of 0.69 (95% CI, 0.67 to 0.71), and a false-positive rate of 0.34 (95% CI, 0.32 to 0.36). fNIRS performed significantly better than the FST in precision (difference = 0.23; 95% CI, 0.14 to 0.33; P < .001), accuracy (difference = 0.15; 95% CI, 0.10 to 0.19; P < .001), false-positive rate (difference = -0.25, 95% CI, -0.31 to -0.20; P < .001), and ROC-AUC (difference = 0.08; 95% CI, 0.01 to 0.14; P = .005). Conclusions and Relevance: In this crossover trial of THC vs placebo, THC intoxication produced prefrontal cortex activation patterns detectable with resting state fNIRS neuroimaging, producing a neural signature of THC-induced impairment that was superior to FSTs for individual-level impairment identification. These findings lay the groundwork for further exploration of fNIRS as a tool for detecting impairment. Trial Registration: ClinicalTrials.gov Identifier: NCT03655717.