The medial prefrontal cortex is required for responding to alcohol-predictive cues but only in the absence of alcohol delivery
Bibliographic record
Abstract
BACKGROUND: The prelimbic medial prefrontal cortex is implicated in promoting drug-seeking in relapse tests. However, drug-seeking behaviour is typically extinguished before a test and tests normally occur without drug delivery. AIMS: We investigated the involvement of the prelimbic and the infralimbic cortex in responding elicited by a non-extinguished cue for alcohol that was presented without alcohol in an alcohol-associated context or a neutral context, and in responding to the same cue when it was paired with alcohol. METHODS: Male, Long-Evans rats (220-240 g on arrival) were acclimated to 15% ethanol (v/v; 'alcohol') and then trained to associate a conditioned stimulus (10 s white noise; 15 trials/session) with alcohol delivery into a fluid port (0.2 mL/conditioned stimulus, 3 mL per session) for oral intake. Conditioning sessions occurred in a specific 'alcohol context' and were alternated daily with exposure to a second 'neutral' context that contained neither the conditioned stimulus nor alcohol. RESULTS: At test, functional prelimbic cortex inactivation using baclofen/muscimol reduced fluid port entries elicited by a non-extinguished conditioned stimulus that was presented without alcohol, but had no subsequent impact on port entries when the conditioned stimulus was paired with alcohol. Similar results were obtained following infralimbic cortex inactivation; however, infralimbic cortex inactivation also non-specifically reduced port entries in the absence of alcohol. CONCLUSIONS: These data indicate that the prelimbic and infralimbic cortex are involved in responding to cues for alcohol when alcohol delivery is omitted, but suggest that other brain regions are engaged in responding to such cues in the presence of alcohol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".