Neurons expressing mu opioid receptors of the habenula promote negative affect in a projection-specific manner
Bibliographic record
Abstract
ABSTRACT BACKGROUND The mu opioid receptor (MOR) is central to hedonic balance, and produces euphoria by engaging reward circuits. MOR signaling may also influence aversion centers, and notably the medial habenula (MHb) where the receptor is highly dense, however this was not investigated. Our prior data suggest that the inhibitory activity of MOR in the MHb limits aversive states. Here we therefore tested the hypothesis that neurons expressing MOR in the MHb (MHb-MOR neurons) promote negative affective states. METHODS Using Oprm1 -Cre knock-in mice, we combined tracing and optogenetics with behavioral testing to investigate consequences of MHb-MOR neuron stimulation in approach/avoidance (real-time place preference), anxiety-related responses (open field, elevated plus maze and marble burying) and despair-like behavior (tail suspension). RESULTS Opto-stimulation of MHb-MOR neurons elicited avoidance behavior, demonstrating that these neurons promote aversive states. Anterograde tracing showed that, in addition to the interpeduncular nucleus (IPN), MHb-MOR neurons project to the dorsal raphe nucleus (DRN), uncovering a yet unreported connection of MHb to a main mood center. Opto-stimulation of MHb-MOR/IPN neurons triggered avoidance and despair-like responses with no anxiety-related effect, whereas light-activation of MHb-MOR/DRN neurons increased levels of anxiety with no effect on other behaviors, revealing two dissociable pathways controlling negative affect. CONCLUSIONS This study demonstrates aversive activity of MHb neurons that respond to MOR opioids. We propose that inhibition of these neurons by endogenous or exogenous opioids relieves negative affect via two distinct MHb microcircuits, contributing to despair-like behavior (MHb-MOR/IPN) and anxiety (MHb-MOR/DRN). This mechanism has implications for hedonic homeostasis and addiction.
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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) | 0.002 | 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".