Associations between drinking and cortical thickness in young adult drinkers: Findings from the Human Connectome Project
Bibliographic record
Abstract
Background. Previous neuroimaging studies examining relations between alcohol misuse and cortical thickness have revealed that increased drinking quantity and alcohol use disorder severity are associated with thinner cortex. Although conflicting regional effects are often observed, associations are generally localized to frontal regions (e.g., dorsolateral prefrontal cortex (DLPFC), inferior frontal gyrus (IFG), and anterior cingulate cortex), with parietal and temporal cortex also implicated in some studies. Inconsistent findings may be attributed to methodological differences, modest sample sizes, and limited consideration of sex differences. Method. This study examined neuroanatomical correlates of drinking quantity and heavy episodic drinking in a large sample of young adults (N=706; M age = 28.8; 51% female) using magnetic resonance imaging data from the Human Connectome Project (HCP). Results. Hierarchical linear regression models (controlling for age, sex, education, income, smoking, drug use, twin status, and intracranial volume) revealed significant inverse associations between drinks/week and frequency of heavy drinking and cortical thickness in a majority of regions examined. The largest effect sizes were found for frontal regions (i.e., DLPFC, IFG, and the precentral gyrus). Follow-up regression models revealed that the left DLPFC was uniquely associated with both drinking variables. Sex differences were also observed, with significant effects largely specific to men. Conclusions. This study adds to the understanding of brain correlates of alcohol use in a large, gender-balanced sample of young adults who exhibit a range of drinking levels. Although the cross-sectional methodology used in the present study preclude causal inferences, these findings provide a foundation for rigorous hypothesis testing in future longitudinal investigations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".