Differential behavioral and molecular alterations upon protracted abstinence from cocaine versus morphine, nicotine, THC and alcohol
Bibliographic record
Abstract
Unified theories of addiction are challenged by differing drug-seeking behaviors and neurobiological adaptations across drug classes, particularly for narcotics and psychostimulants. We previously showed that protracted abstinence to opiates leads to despair behavior and social withdrawal in mice, and we identified a transcriptional signature in the extended amygdala that was also present in animals abstinent from nicotine, Δ9-tetrahydrocannabinol (THC) and alcohol. Here we examined whether protracted abstinence to these four drugs would also share common behavioral features, and eventually differ from abstinence to the prototypic psychostimulant cocaine. We found similar reduced social recognition, increased motor stereotypies and increased anxiety with relevant c-fos response alterations in morphine, nicotine, THC and alcohol abstinent mice. Protracted abstinence to cocaine, however, led to strikingly distinct, mostly opposing adaptations at all levels, including behavioral responses, neuronal activation and gene expression. Together, these data further document the existence of common hallmarks for protracted abstinence to opiates, nicotine, THC and alcohol that develop within motivation/emotion brain circuits. In our model, however, these do not apply to cocaine, supporting the notion of unique mechanisms in psychostimulant abuse.
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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".