A Novel Assay Allowing Drug Self-Administration, Extinction, and Reinstatement Testing in Head-Restrained Mice
Bibliographic record
Abstract
Multiphoton microscopy is one of several new technologies providing unprecedented insight into the activity dynamics and function of neural circuits. Unfortunately, some of these technologies require experimentation in head-restrained animals, limiting the behavioral repertoire that can be integrated and studied. This issue is especially evident in drug addiction research, as no laboratories have coupled multiphoton microscopy with simultaneous intravenous drug self-administration, a behavioral paradigm that has predictive validity for treatment outcomes and abuse liability. Here, we describe a new experimental assay wherein head-restrained mice will press an active lever, but not inactive lever, for intravenous delivery of heroin or cocaine. Similar to freely moving animals, we find that lever pressing is suppressed through daily extinction training and subsequently reinstated through the presentation of relapse-provoking triggers (drug-associative cues, the drug itself, and stressors). Finally, we show that head-restrained mice will show similar patterns of behavior for oral delivery of a sucrose reward, a common control used for drug self-administration experiments. Overall, these data demonstrate the feasibility of combining drug self-administration experiments with technologies that require head-restraint, such as multiphoton imaging. The assay described could be replicated by interested labs with readily available materials to aid in identifying the neural underpinnings of substance use disorder.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".