Oxycodone-Mediated Activation of the Mu Opioid Receptor Reduces Whole Brain Functional Connectivity in Mice
Bibliographic record
Abstract
Oxycodone is a potent medicinal opioid analgesic to treat pain. It is also addictive and a main cause for the current opioid crisis. At present, the impact of oxycodone on coordinated brain network activities, and contribution of the mu opioid receptor (MOR) to these effects, is unknown. We used pharmacological magnetic resonance imaging in mice to characterize MOR-mediated oxycodone effects on whole-brain functional connectivity (FC). Control (CTL) and MOR knockout (KO) animals were imaged under dexmedetomidine in a 7Tesla scanner. Acquisition was performed continuously before and after 2 mg/kg oxycodone administration (analgesic in CTL mice). Independent component analysis (data-driven) produced a correlation matrix, showing widespread oxycodone-induced reduction of FC across 71 components. Isocortex, nucleus accumbens (NAc), pontine reticular nucleus, and periacqueducal gray (PAG) components showed the highest number of significant changes. Seed-to-voxel FC analysis (hypothesis-driven) was then focused on PAG and NAc considered key pain and reward centers. The two seeds showed reduced FC with 8 and 22 Allen Brain Atlas-based regions, respectively, in CTL but not KO mice. Further seed-to-seed quantification showed highest FC modifications of both PAG and NAc seeds with hypothalamic and amygdalar areas, as well as between them, revealing the strongest impact across reward and aversion/pain centers of the brain. In conclusion, we demonstrate that oxycodone reduces brain communication in a MOR-dependent manner, and establish a preliminary whole-brain FC signature of oxycodone. This proof-of-principle study provides a unique platform and reference data set to test other MOR opioid agonists and perhaps discover new mechanisms and FC biomarkers predicting safer analgesics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".