Intermittent Cocaine Use Patterns, Not Total Intake, Predict Cue-induced Drug Seeking in Rats
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cocaine-associated cues trigger relapse to drug use in humans and animal models. In rats, long daily cocaine access (4-6 vs. 1-2 h) increases drug self-administration and cue-induced cocaine seeking, suggesting that greater intake promotes relapse. However, prior studies used continuous drug access paradigms, whereas human cocaine use is typically intermittent. Prior studies also used conditioned stimuli (CS), whereas discriminative stimuli (DS) are more effective in triggering drug seeking. Here, we used intermittent access (IntA) self-administration to examine how session length influences CS- and DS-induced cocaine seeking. EXPERIMENTAL APPROACH: Female rats self-administered cocaine intermittently during daily 2- or 4-h IntA sessions (Short-IntA and Long-IntA), with alternating DS+ (cocaine available; 5 min) and DS- (no cocaine; 25 min) periods. Lever pressing during DS+ produced cocaine and a CS+; lever pressing during DS- produced only a CS-. After 4 weeks of abstinence, rats received a test where all cues were presented response-independently, and lever presses-which had no consequence-measured cocaine seeking. KEY RESULTS: Long-IntA rats took twice more cocaine than did Short-IntA rats. In both groups, DS+ but not CS+ later triggered significant increases in cocaine seeking, with no group differences. Thus, total intake did not predict cue-induced relapse intensity. However, individual cocaine intake patterns did, including hourly consumption, episodes of burst-like self-administration and latency to self-administer. CONCLUSIONS AND IMPLICATIONS: Under intermittent-access conditions, individual cocaine-use patterns, not cumulative intake, predict vulnerability to cue-induced relapse. This highlights the importance of individual drug-taking profiles in relapse risk assessment.
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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 | 0.001 | 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".