To drink or not to drink: Harmful drinking is associated with hyperactivation of reward areas rather than hypoactivation of control areas in men
Bibliographic record
Abstract
BACKGROUND: The maintenance of harmful alcohol use can be considered a reiterated decision in favour of alcohol in concrete drinking occasions. These decisions are often made despite an intention to quit or reduce alcohol consumption. We tested if a hyperactive reward system and/or an impaired cognitive control system contribute to such unfavourable decision-making. METHODS: In this fMRI study, men with modest to harmful drinking behaviour, which was measured using the Alcohol Use Disorders Identification Test (AUDIT), repeatedly made decisions between alcoholic and nonalcoholic drinks. Based on prior individual ratings, decision pairs were created with an alcoholic decision option considered more desirable but less beneficial by the participant. By correlating AUDIT scores with brain activation during decision-making, we determined areas explicitly related to pro-alcohol decisions in men with greater drinking severity. RESULTS: Thirty-eight men participated in our study. Behaviourally, we found a positive correlation between AUDIT scores and the number of decisions for desired alcoholic drinks compared with beneficial nonalcoholic drinks. The fMRI results show that AUDIT scores were positively associated with activation in areas associated with reward and motivation processing (i.e., ventral striatum, amygdala, medial prefrontal cortex) during decisions favouring a desired, nonbeneficial alcoholic drink. Conversely, we did not find hypoactivation in areas associated with self-control (dorsolateral prefrontal cortex). These effects were not present when participants chose a desired, nonbenefical, nonalcoholic drink. LIMITATIONS: The men participating in our study had to be abstinent and would potentially consume an alcoholic drink at the end of the experiment. Hence, we did not define manifest alcohol dependence as an inclusion criterion and instead focused on less severely affected individuals. CONCLUSION: Our results indicate that with growing drinking severity, decisions for alcoholic drinks are associated with increasing activity in reward-associated neural systems, rather than decreasing activity in self-control-associated systems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".