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17 March 2023 Optical noninvasive resting-state identification of ∆9-tetrahydrocannabinol (THC)
Nisan Ozana, Michael Pascale, Kevin Potter, Brian Kendzior, Gladys Pachas, Eden Evins, Jodi Gilman
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Proceedings Volume PC12365, Neural Imaging and Sensing 2023; PC1236508 (2023) https://doi.org/10.1117/12.2650575
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
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 Δ9-tetrahydrocannabinol (THC) impairment. Comparing resting state connectivity of post-dose THC and post-dose placebo in impaired participants, we identified decreased connectivity after THC. Furthermore, using standard machine learning algorithms, we were able to predict impairment with >70% accuracy.
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Nisan Ozana, Michael Pascale, Kevin Potter, Brian Kendzior, Gladys Pachas, Eden Evins, and Jodi Gilman "Optical noninvasive resting-state identification of ∆9-tetrahydrocannabinol (THC)", Proc. SPIE PC12365, Neural Imaging and Sensing 2023, PC1236508 (17 March 2023); https://doi.org/10.1117/12.2650575
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KEYWORDS
Machine learning
Prefrontal cortex
Safety
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