Insulin in the ventral tegmental area reduces cocaine‐evoked dopamine in the nucleus accumbens <i>in vivo</i>
Bibliographic record
Abstract
Mesolimbic dopamine circuits, implicated in incentive motivation, are sensitive to changes in metabolic state such as weight loss and diet-induced obesity. These neurons are important targets for metabolic hormones such as leptin, glucagon-like peptide-1, ghrelin and insulin. Insulin receptors are located on dopamine neurons in the ventral tegmental area (VTA) and we have previously demonstrated that insulin induces long-term depression of excitatory synapses onto VTA dopamine neurons. While insulin can decrease dopamine concentration in somatodendritic regions, it can increase dopamine in striatal slices. Whether insulin directly targets the VTA to alter dopamine release in projection areas, such as the nucleus accumbens (NAc), remains unknown. The main goal of the present experiments was to examine NAc dopamine concentration following VTA administration of insulin. Using in vivo FSCV to detect rapid fluctuations in dopamine concentration, we showed that intra-VTA insulin via action at insulin receptors reduced pedunculopontine nucleus-evoked dopamine release in the NAc. Furthermore, intra-VTA insulin reduced cocaine-potentiated NAc dopamine. Finally, intra-VTA or intranasal insulin decreased locomotor responses to cocaine, an effect blocked by an intra-VTA administered insulin receptor antagonist. Together, these data demonstrate that mesolimbic dopaminergic projections are important targets of the metabolic hormone, insulin.
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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".