Neural correlates of increased alcohol demand following alcohol cue exposure in adult heavy drinkers
Bibliographic record
Abstract
Alcohol use disorder is associated with overvaluation of alcohol relative to other rewards, in part due to dynamic increases in value in response to alcohol-related cues. In a neuroeconomic framework, alcohol cues increase behavioral economic demand for alcohol, but the neural correlates these cue effects are unknown. This functional magnetic resonance imaging study combined a neuroeconomic alcohol purchase task with an alcohol cue exposure in 72 heavy drinkers with established sensitivity to alcohol cues (51 % female; mean age=33.74). Participants reported how many drinks they would consume from $0-$80/drink following exposure to neutral and alcohol images in a fixed order. Participants purchased significantly more drinks in the alcohol compared to the neutral condition, which was also evident for demand indices (i.e., intensity, breakpoint, Omax, elasticity; ps<0.001; ds=0.46-0.92). Alcohol purchase decisions were associated with activation in rostral middle and medial frontal gyri, anterior insula, posterior parietal cortex, and dorsal striatum, among other regions. Activation was lower across regions in the alcohol relative to neutral cue condition, potentially due to greater automaticity of choices in the presence of alcohol cues or attenuation of responses due to fixed cue order. These results contribute to growing literature using neuroeconomics to understand alcohol misuse and provide a foundation for future research investigating decision-making effects of environmental alcohol triggers.
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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 | 0.000 | 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".