Ventral Striatal Cholinergic Interneurons Regulate Decision-Making or Motor Impulsivity Differentially across Learning and Biological Sex
Bibliographic record
Abstract
Dopaminergic transmission within the ventral striatum is broadly implicated in risk/reward decision-making and impulse control, and the rat gambling task (rGT) measures both behaviors concurrently. While the resulting indices of risky choice and impulsivity correlate at the population level, dopaminergic manipulations rarely impact both behaviors uniformly, with changes in choice more likely when dopaminergic transmission is altered during task acquisition. Although the task structure of the rGT remains constant, the relative importance of ventral striatal dopamine (DA) signals relevant for reward prediction versus impulse control may vary as learning progresses; the former should dominate while rats learn the probabilistic contingencies of the task, whereas suppression of premature responses becomes more valuable once a decision-making strategy is established and exploited. Striatal cholinergic interneurons (CINs) critically influence reinforcement learning by modulating dopamine (DA) release and gating periods of DA-facilitated neuroplasticity. We therefore hypothesized that ventral striatal CINs (vsCINs) could influence reward learning or impulse control during task acquisition or stable performance, respectively. Using chemogenetics in Sprague Dawley rats ( Rattus norvegicus ), we found support for this hypothesis: activation and inhibition of vsCINs once behavior was stable increased and decreased motor impulsivity in both sexes but had no effect on choice patterns. In contrast, activating and inhibiting vsCINs during task acquisition did not alter motor impulsivity but instead decreased and increased risky choice, respectively. Notably, the former effect was only observed in males and the latter in females. We conclude by proposing testable predictions regarding acetylcholine–DA interactions that may explain sex differences.
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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".