Opioidergic modulation of monetary incentive delay fMRI responses
Bibliographic record
Abstract
RATIONALE: It is hypothesised that modulation of striatal dopaminergic signalling plays a key role in the rewarding effects of opioids. The monetary incentive delay (MID) task is a functional magnetic resonance imaging (fMRI) paradigm used to investigate striatal responses, which may reflect striatal dopamine release, during the anticipation of a financial reward. OBJECTIVES: We hypothesised that fentanyl would modulate striatal MID task Blood Oxygenation Level Dependent (BOLD) responses, reflecting opioidergic modulation of striatal dopaminergic signalling. METHODS: data were collected to control for respiratory depression. RESULTS: We demonstrated fentanyl induced increases in MID task reward and loss anticipation BOLD compared with placebo and naloxone in both region of interest (ROI) and whole brain analyses. These results were in cortical regions including the lingual gyrus, precuneus, posterior cingulate and frontal pole rather than the striatum. CONCLUSIONS: Our results show the primary effects of fentanyl on MID anticipation BOLD in regions associated with the preparation of a motor response to a salient visual cue, rather than in regions typically associated with reward processing such as the striatum. This suggests that opioid agonists do not affect striatal activation during the MID task. Tasks using naturalistic rewards, for example feeding, sex or social contact which induce endogenous opioid signalling, may be more appropriate to probe the effects of fentanyl on reward processing. These results are from male participants' data and therefore may not be generalisable to female participants.
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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".