Drug self-administration in head-restrained mice for simultaneous multiphoton imaging
Bibliographic record
Abstract
ABSTRACT Multiphoton microscopy is one of several new technologies providing unprecedented insight into the activity dynamics and function of neural circuits. Unfortunately, many of these technologies require experimentation in head-restrained animals, greatly limiting the behavioral repertoire that can be studied with each approach. This issue is especially evident in drug addiction research, as no laboratories have coupled multiphoton microscopy with simultaneous intravenous drug self-administration, the gold standard of behavioral paradigms for investigating the neural mechanisms of drug addiction. Such experiments would be transformative for addiction research as one could measure or perturb an array of behavior and drug-related adaptations in precisely defined neural circuit elements over time, including but not limited to dendritic spine plasticity, neurotransmitter release, and neuronal activity. Here, we describe a new experimental assay wherein mice self-administer drugs of abuse while head-restrained, allowing for simultaneous multiphoton imaging. We demonstrate that this approach enables longitudinal tracking of activity in single neurons from the onset of drug use to relapse. The assay can be easily replicated by interested labs for relatively little cost with readily available materials and can provide unprecedented insight into 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".