Accumbal Cholinergic Interneurons Differentially Influence Motivation Related to Satiety Signaling
Bibliographic record
Abstract
Satiety, rather than all or none, can instead be viewed as a cumulative decrease in the drive to eat that develops over the course of a meal. The nucleus accumbens (NAc) is known to play a critical role in this type of value reappraisal, but the underlying circuits that influence such processes are unclear. Although NAc cholinergic interneurons (CINs) comprise only a small proportion of NAc neurons, their local impact on reward-based processes provides a candidate cell population for investigating the neural underpinnings of satiety. The present research therefore aimed to determine the role of NAc-CINs in motivation for food reinforcers in relation to satiety signaling. Through bidirectional control of CIN activity in mice, we show that when motivated by food restriction, increasing CIN activity led to a reduction in palatable food consumption while reducing CIN excitability enhanced food intake. These activity-dependent changes developed only late in the session and were unlikely to be driven by the innate reinforcer strength, suggesting that CIN modulation was instead impacting the cumulative change in motivation underlying satiety signaling. We propose that on a circuit level, an overall increase in inhibitory tone onto NAc output neurons played a role in the behavioral results, as activating NAc-CINs led to an inhibition of medium spiny neurons that was dependent on nicotinic receptor activation. Our results reveal an important role for NAc-CINs in controlling motivation for food intake and additionally provide a circuit-level framework for investigating the endogenous cholinergic circuits that signal satiety.
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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".