The Bivalent Rewarding and Aversive properties of Δ9-tetrahydrocannabinol are Mediated Through Dissociable Opioid Receptor Substrates and Neuronal Modulation Mechanisms in Distinct Striatal Sub-Regions
Bibliographic record
Abstract
-tetrahydrocannabinol (THC), is capable of producing bivalent rewarding and aversive affective states through interactions with the mesolimbic system. However, the precise mechanisms underlying the dissociable effects of THC are not currently understood. In the present study, we identify anatomically dissociable effects of THC within the rat nucleus accumbens (NAc), using an integrative combination of behavioral pharmacology and in vivo neuronal electrophysiology. We report that the rewarding vs. aversive stimulus properties of THC are both anatomically and pharmacologically dissociable within distinct anterior vs. posterior sub-regions of the NAc. While the rewarding effects of THC were dependent upon local μ-opioid receptor signaling, the aversive effects of THC were processed via a κ-opioid receptor substrate. Behaviorally, THC in the posterior NASh induced deficits in social reward and cognition whereas THC in the anterior NAc, potentiated opioid-related reward salience. In vivo neuronal recordings demonstrated that THC decreased medium spiny neuron (MSN) activity in the anterior NAc and increased the power of gamma (γ) oscillations. In contrast, THC increased MSN activity states in the posterior NASh and decreased γ-oscillation power. These findings reveal critical new insights into the bi-directional neuronal and pharmacological mechanisms controlling the dissociable effects of THC in mesolimbic-mediated affective processing.
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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) | 0.001 | 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".