Consumption of palatable food primes food approach behavior by rapidly increasing synaptic density in the VTA
Bibliographic record
Abstract
In an environment with easy access to highly palatable and energy-dense food, food-related cues drive food-seeking regardless of satiety, an effect that can lead to obesity. The ventral tegmental area (VTA) and its mesolimbic projections are critical structures involved in the learning of environmental cues used to predict motivationally relevant outcomes. Priming effects of food-related advertising and consumption of palatable food can drive food intake. However, the mechanism by which this effect occurs, and whether these priming effects last days after consumption, is unknown. Here, we demonstrate that short-term consumption of palatable food can prime future food approach behaviors and food intake. This effect is mediated by the strengthening of excitatory synaptic transmission onto dopamine neurons that is initially offset by a transient increase in endocannabinoid tone, but lasts days after an initial 24-h exposure to sweetened high-fat food (SHF). This enhanced synaptic strength is mediated by a long-lasting increase in excitatory synaptic density onto VTA dopamine neurons. Administration of insulin into the VTA, which suppresses excitatory synaptic transmission onto dopamine neurons, can abolish food approach behaviors and food intake observed days after 24-h access to SHF. These results suggest that even a short-term exposure to palatable foods can drive future feeding behavior by "rewiring" mesolimbic dopamine neurons.
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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".