Nicotine regulates activity of lateral habenula neurons via presynaptic and postsynaptic mechanisms
Bibliographic record
Abstract
There is much interest in brain regions that drive nicotine intake in smokers. Interestingly, both the rewarding and aversive effects of nicotine are probably critical for sustaining nicotine addiction. The medial and lateral habenular (LHb) nuclei play important roles in processing aversion, and recent work has focused on the critical involvement of the LHb in encoding and responding to aversive stimuli. Several neurotransmitter systems are implicated in nicotine's actions, but very little is known about how nicotinic acetylcholine receptors (nAChRs) regulate LHb activity. Here we report in brain slices that activation of nAChRs depolarizes LHb cells and robustly increases firing, and also potentiates glutamate release in LHb. These effects were blocked by selective antagonists of α6-containing (α6*) nAChRs, and were absent in α6*-nAChR knockout mice. In addition, nicotine activates GABAergic inputs to LHb via α4β2-nAChRs, at lower concentrations but with more rapid desensitization relative to α6*-nAChRs. These results demonstrate the existence of diverse functional nAChR subtypes at presynaptic and postsynaptic sites in LHb, through which nicotine could facilitate or inhibit LHb neuronal activity and thus contribute to nicotine aversion or reward.
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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".