Δ9-Tetrahydrocannabinol does not upregulate an aversive dopamine receptor mechanism in adolescent brain unlike in adults
Bibliographic record
Abstract
Earlier age of cannabis usage poses higher risk of Cannabis Use Disorder and adverse consequences, such as addiction, anxiety, dysphoria, psychosis, largely attributed to its principal psychoactive component, Δ9-tetrahydrocannabinol (THC) and altered dopaminergic function. As dopamine D1-D2 receptor heteromer activation causes anxiety and anhedonia, this signaling complex was postulated to contribute to THC-induced affective symptoms. To investigate this, we administered THC repeatedly to adolescent monkeys and adolescent or adult rats. Drug-naïve adolescent rat had lower striatal densities of D1-D2 heteromer compared to adult rat. Repeated administration of THC to adolescent rat or adolescent monkey did not alter D1-D2 heteromer expression in nucleus accumbens or dorsal striatum but upregulated it in adult rat. Behaviourally, THC-treated adult, but not adolescent rat manifested anxiety and anhedonia-like behaviour, with elevated composite negative emotionality scores that correlated with striatal D1-D2 density. THC modified downstream markers of D1-D2 activation in adult, but not adolescent striatum. THC administered with cannabidiol did not alter D1-D2 expression. In adult rat, co-administration of CB1 receptor (CB1R) inverse agonist with THC attenuated D1-D2 upregulation, implicating cannabinoids in the regulation of striatal D1-D2 heteromer expression. THC exposure revealed an adaptable age-specific, anxiogenic, anti-reward mechanism operant in adult striatum but deficient in adolescent rat and monkey striatum that may confer increased sensitivity to THC reward in adolescence while limiting its negative effects, thus promoting continued use and increasing vulnerability to long-term adverse cannabis effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".