Measuring Alcohol‐Induced Striatal Dopamine Release in Healthy Humans With [<sup>11</sup>C]‐Raclopride: A Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Alcohol consumption is known to affect dopamine (DA) release in the brain, with significant implications for understanding addiction and its neurobiological underpinnings. This meta‐analysis examined the effects of acute alcohol administration on striatal DA release in healthy humans as measured with [11C]‐raclopride positron emission tomography (PET). Oral alcohol administration was associated with a significant reduction in [11C]‐raclopride binding potential (BPND) in the ventral striatum (Cohen's d = –0.76), indicative of increased DA release, particularly at lower blood alcohol concentration (BAC) levels (0.08 gm%; Z = 2.34, p = 0.02). That oral alcohol may increase DA release in the ventral striatum at lower doses, and decrease DA release at higher doses, warrants further investigation but appears consistent with other known biphasic, hermetic dose–response effects of alcohol. Additionally, larger effect‐sizes in the ventral striatum were observed among those studies which sampled more males than females (Z = –2.08, p = 0.04). While oral alcohol administration was associated with reduced [11C]‐raclopride BPND in the caudate (Cohen's d = –0.39) and putamen (Cohen's d = –0.37), these findings in the dorsal striatum were more variable and less robust. Our analyses suggests that study design (i.e., counterbalanced versus fixed order) may moderate effect sizes observed in the putamen across studies (Z = –2.27, p = 0.02). By identifying gaps in the current literature and proposing directions for future research, this study hopes to inform the design of future PET studies aimed at quantifying alcohol‐induced dopamine release in the striatum of humans.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.027 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".