{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:01:16Z","timestamp":1777496476048,"version":"3.51.4"},"reference-count":34,"publisher":"American Medical Association (AMA)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n The primary psychoactive compound in cannabis, \u22069-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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Interventions<\/jats:title>\n Participants received a single oral dose of synthetic THC (range, 5-80 mg) intended to induce intoxication or placebo in separate visits.<\/jats:p>\n <\/jats:sec>\n \n Main Outcome and Measures<\/jats:title>\n 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).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n 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\u2009=\u20090.23; 95% CI, 0.14 to 0.33;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001), accuracy (difference\u2009=\u20090.15; 95% CI, 0.10 to 0.19;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001), false-positive rate (difference\u2009=\u2009\u22120.25, 95% CI, \u22120.31 to \u22120.20;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001), and ROC-AUC (difference\u2009=\u20090.08; 95% CI, 0.01 to 0.14;\n P<\/jats:italic>\n \u2009=\u2009.005).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n \n ClinicalTrials.gov Identifier:\n NCT03655717<\/jats:ext-link>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.56647","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T16:30:18Z","timestamp":1769790618000},"page":"e2556647","source":"Crossref","is-referenced-by-count":1,"title":["Detection of \u03949-Tetrahydrocannabinol Impairment Using Resting-State Functional Near-Infrared Spectroscopy"],"prefix":"10.1001","volume":"9","author":[{"given":"Moshe","family":"Berchansky","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Institute of Nanotechnology and Advanced Materials, The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. 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