Sex-specific associations between subcortical morphometry in childhood and adult alcohol consumption: A 17-year follow-up study
Bibliographic record
Abstract
Men and women tend to differ in the age of first alcohol consumption, transition into disordered drinking, and the prevalence of alcohol use disorder. Here, we use a unique longitudinal dataset to test for potentially predispositonal sex-biases in brain organization prior to initial alcohol exposure. Our study combines measures of subcortical morphometry gathered in alcohol naive individuals during childhood (mean age: 9.43 years, SD = 2.06) with self-report measures of alcohol use in the same individuals an average of 17 years later (N = 81, 46 males, 35 females). We observe that pediatric amygdala and hippocampus volume both show sex-biased relationships with adult drinking. Specifically, females show a stronger association between subcortical volumetric reductions in childhood and peak drinking in adulthood as compared to males. Detailed analysis of subcortical shape localizes these effects to the rostro-medial hippocampus and basolateral amygdala subnuclei. In contrast, we did not observe sex-specific associations between striatal anatomy and peak alcohol consumption. These results are consistent with a model in which organization of the amygdala and hippocampus in childhood is more relevant for subsequent patterns of peak alcohol use in females as compared to males. Differential neuroanatomical precursors of alcohol use in males and females could provide a potential developmental basis for well recognized sex-differences in alcohol use behaviors.. Thus, our findings not only indicate that brain correlates of human alcohol consumption are manifest long before alcohol initiation, but that some of these correlates are not equivalent between males and females.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".