ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2026
Identifying behavioral and biological predictors of cannabis vapor self-administration in rats.
Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The recent wave of recreational cannabis legalization in the US has underscored the importance of identifying predictors of individual variability in cannabis use. While a subset of recreational cannabis users will go on to meet diagnostic criteria for cannabis use disorder, many do not, making it critical to characterize traits that confer both vulnerability and resilience. However, progress in identifying relevant predictors has been hindered by limited mechanistic insight and a lack of translationally relevant animal models of cannabis use. To address this, we employed a rat model of cannabis vapor self-administration that uses whole-plant cannabis extract and mimics the intrapulmonary route of intake typically used in humans. Using this model, we sought to identify behavioral and biological predictors of motivation to self-administer vaporized cannabis. Male and female Long-Evans rats (N = 48) underwent a battery of assays indexing behavioral domains aligned with the NIMH Research Domain Criteria (RDoC) prior to self-administration training. After four weeks of cannabis vapor self-administration (1 h sessions daily), motivation for cannabis vapor was assessed via a 3-h fixed ratio escalation (FRE) procedure. A series of linear regressions revealed that Social Processes, Arousal/Regulatory Systems, Cognition, and Positive Valence domains significantly predicted the number of cannabis vapor deliveries earned during the FRE test. Specifically, higher basal corticosterone (CORT), lower morning anandamide, impaired set-shifting performance, superior visual cue discrimination, and more social grooming during adolescence each predicted responding for cannabis. The Negative Valence domain was not a significant predictor. A multivariate machine learning approach combining principal component analysis and permutation importance further identified basal CORT and set-shifting performance as the strongest predictors of responding for vaporized cannabis. These findings highlight individual differences in stress regulation and cognitive flexibility as possible risk factors for cannabis use propensity and demonstrate the utility of leveraging RDoC framework for identifying relevant phenotypes in rodents that may extend across human psychiatric conditions.
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