Exploratory study of associations between monetary reward anticipation brain responses and mu-opioid signalling in alcohol dependence, gambling disorder and healthy controls
Bibliographic record
Abstract
Alcohol dependence (AD) and gambling disorder (GD) are common addiction disorders with significant physical and mental health consequences. AD and GD are associated with dysregulated responses to reward which could be due to a common mechanism of dysregulated endogenous opioid signalling. We explored associations between reward anticipation responses, using the Monetary Incentive Delay (MID) functional magnetic resonance imaging (fMRI) task, and mu-opioid receptor (MOR) availability and endogenous opioid release capacity using [ 11 C]carfentanil positron emission tomography (PET), in 13 AD, 15 GD and 14 heathy control (HC) participants. We also examined differences in MID task reward anticipation responses between AD, GD and HC participants. These were secondary exploratory analysis of data collected to examine differences in MOR PET in addiction. We did not find significant differences in MID win > neutral anticipation BOLD responses compared between participant groups in a priori ROIs (ventral striatum, putamen, caudate) or whole brain analyses. We found no significant correlations between MID win > neutral anticipation BOLD responses and [ 11 C]carfentanil PET measures, except for limited negative correlations between putamen MOR availability and MID win > neutral anticipation BOLD response in AD participants. Previous research has suggested a limited role of endogenous opioid signalling on MID task reward anticipation responses in AD and HCs as these responses are not modulated by opioid receptor blockade and this may explain our lack of significant correlations in HC and AD or GD participants. Our results, particularly the lack of differences in MID win > neutral anticipation BOLD responses across participants groups, may be limited due to only including AD or GD participants who are abstinent or in active treatment.
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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 | 0.001 |
| 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 | 0.000 |
| 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".