Muscarinic control of rostromedial tegmental nucleus<scp>GABA</scp>neurons and morphine‐induced locomotion
Bibliographic record
Abstract
Opioids induce rewarding and locomotor effects by inhibiting rostromedial tegmental GABA neurons that express μ-opioid and nociceptin receptors. These GABA neurons then strongly inhibit dopamine neurons. Opioid-induced reward, locomotion and dopamine release also depend on pedunculopontine and laterodorsal tegmental cholinergic and glutamate neurons, many of which project to and activate ventral tegmental area dopamine neurons. Here we show that laterodorsal tegmental and pedunculopontine cholinergic neurons project to both rostromedial tegmental nucleus and ventral tegmental area, and that M4 muscarinic receptors are co-localized with μ-opioid receptors associated with rostromedial tegmental GABA neurons. To inhibit or excite rostromedial tegmental GABA neurons, we utilized adeno-associated viral vectors and DREADDs to express designed muscarinic receptors (M4D or M3D respectively) in GAD2::Cre mice. In M4D-expressing mice, clozapine-N-oxide increased morphine-induced, but not vehicle-induced, locomotion. In M3D-expressing mice, clozapine-N-oxide blocked morphine-induced, but not vehicle-induced, locomotion. We propose that cholinergic inhibition of rostromedial tegmental GABA neurons via M4 muscarinic receptors facilitates opioid inhibition of the same neurons. This model explains how mesopontine cholinergic systems and muscarinic receptors in the rostromedial tegmental nucleus and ventral tegmental area are important for dopamine-dependent and dopamine-independent opioid-induced rewards and locomotion.
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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 | 0.001 |
| 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".