Does Family History of Alcohol Use Disorder Relate to Differences in Regional Brain Volumes? A Descriptive Review with New Data
Bibliographic record
Abstract
BACKGROUND: Differences in regional brain volumes as a function of family history (FH) of alcohol use disorder (AUD) have been reported, and it has been suggested that these differences might index genetic risk for AUD. However, results have been inconsistent. The aims of the current study were (i) to provide an updated descriptive review of the existing literature and (ii) to examine the association of FH with indices of subcortical volumes and cortical thickness in a sample of youth recruited based on FH status. METHODS: To address aim 1, a literature search located 15 published studies comprising 1,735 participants. Studies were characterized according to population, analytic methods, regions of interest, and primary findings. To address the second aim, we examined volumetric and cortical thickness in a sample of 69 youth (mean age = 19.71 years, SD = 0.79) recruited based on FH status and matched on drinking variables. Associations of sex and alcohol use with volumetric outcomes were also examined. RESULTS: Our descriptive review revealed an inconsistent pattern of results with respect to the presence, direction, and regional specificity of volumetric differences across FH groups. The most consistent finding, significantly smaller amygdala volumes in FH+ participants, was not replicated in all studies. In the current sample of youth, measures of subcortical volumes and cortical thickness did not significantly differ as a function of FH, sex, or their interaction. CONCLUSIONS: Evidence for FH group differences in regional brain volumes is inconsistent, and the current study failed to detect any group differences. Further research is needed to confirm the reproducibility of FH group differences and implications for AUD risk.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".