Introduction: Current methods to detect recent delta-9-tetrahydrocannabinol (THC) use cannot objectively quantify its psychoactive effects (PE). The Cognalyzer, an electroencephalography (EEG)-based method, detects and quantifies the strength of THC-induced PE on a scale from 0 to 100%. This study assesses the relationship between the magnitude of Cognalyzer PE predictions and reported subjective drug effects for 4-h post-cannabis inhalation.

Methods: Seventy-five participants were enrolled in the study. Prior to ad libitum cannabis inhalation, an EEG recording episode was completed. Immediately after inhalation, the Drug Effects Questionnaire (DEQ) was administered and another EEG recording performed. For 25 participants, the study ended. For 50 participants, assessments were repeated at 30-min intervals for 4 h. EEG files were blinded and analyzed using two versions of the Cognalyzer algorithm. The relationship between the Cognalyzer PE level results and the DEQ was assessed using generalized linear models and multiple regression.

Results: There were significant PE increases from pre-cannabis for up to 3.5 h. Mean reports of feeling drug effects were > 0 at all post-inhalation time points (p ≤ 0.024). Furthermore, there were significant relationships between the Cognalyzer PE and self-reported perception of drug effects (p ≤ 0.001). Subgroup analysis showed that Cognalyzer PE levels were impacted by cannabis use history, subjective ratings of drug effects, oral fluid THC concentration and the cannabis product inhaled.

Conclusion: The findings show that the Cognalyzer can be used to objectively determine the strength of cannabis psychoactive effects that cannabis products create on consumers and how it changes depending on their experience with cannabis. The Cognalyzer can be used to conduct scientific consumer research to generate trustworthy informational material about the psychoactive experience of cannabis products. For clinical research, the Cognalyzer can be used to study the pharmacodynamics of cannabinoids or delivery systems, such as nano-emulsifications.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857346PMC
http://dx.doi.org/10.1007/s40120-021-00293-wDOI Listing

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