Increased functional coupling of the mu opioid receptor in the anterior insula of depressed individuals
Bibliographic record
Abstract
Abstract The mu opioid receptor (MOR) is a G protein-coupled receptor that plays an essential role in reward and hedonic processes, and that has been implicated in disorders such as depression and addiction. Over the last decade, several brain imaging studies in depressed patients have consistently found that dysregulation of MOR function occurs in particular in the anterior insular cortex, an important brain site for the perception of internal states and emotional regulation. To investigate molecular mechanisms that may underlie these effects, here we assessed genetic polymorphisms, expression, and functional G-protein coupling of MOR in a large post-mortem cohort (N=95) composed of depressed individuals who died by suicide, and healthy controls. Results indicated that depression, but not comorbid substance use disorder or acute opiate consumption, was associated with increased MOR activity. This effect was partly explained by a specific increase in expression of the inhibitory alpha G-protein subunit GNAI2. Consistent with previous neuroimaging studies, our findings support the notion that enhanced endogenous opioidergic tone in the anterior insula may buffer negative affective states in depressed individuals, a mechanism that could potentially contribute to the antidepressant efficacy of emerging opioid-based medications.
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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 | 0.000 | 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) | 0.002 | 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".