The effects of µ-opioid receptor activation on GABAergic synaptic transmission within the orbital frontal cortex
Bibliographic record
Abstract
The orbital frontal cortex (OFC) plays a critical role in evaluating outcomes in a changing environment. Several studies have demonstrated that administering opioids can alter reward valuation and action selection. More specifically, µ-opioid activation within the OFC has been shown to enhance both consumption of food rewards and the hedonic reaction to them. Mechanistically, there is ample evidence confirming that µ-opioid agonists act pre-synaptically to disinhibit the output of other cortical regions; however, the precise cellular mechanism of µ-opioid signalling across the OFC remains unknown. Thus, we investigated the cellular actions of µ-opioids within the medial and lateral OFC. Using in-vitro patch clamp electrophysiology in brain slices containing the OFC, I found a dose-dependant effect of µ-opioid receptor (MOR) activation on GABAergic synaptic transmission within the medial, but not lateral, OFC. Furthermore, this effect occurred via decreased pre-synaptic release probability of GABA onto pyramidal neurons, consistent with actions of µ-opioids in other cortical regions. Preliminary data also suggest µ-opioid agonists are acting on parvalbumin-positive subpopulations of interneurons. The findings of this study further elucidate the effects of MOR activity on synaptic transmission within the OFC, which remains largely understudied. Importantly, understanding the interaction between the OFC and the opioid system may reveal new mechanisms of action in disorders of aberrantly motivated behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".