Both Global Gray Matter Volume and Regional Gray Matter Volume Negatively Correlate with Lifetime Alcohol Intake in Non–Alcohol‐Dependent Japanese Men: A Volumetric Analysis and a Voxel‐Based Morphometry
Bibliographic record
Abstract
BACKGROUND: Non-alcohol-dependent heavy drinkers, as well as alcohol-dependent individuals, show brain atrophy. The purpose of this study was to evaluate whether there are correlations between global and regional gray matter volumes and the lifetime alcohol intake using volumetric analysis and voxel-based morphometry (VBM) among Japanese non-alcohol-dependent male individuals. METHODS: High-resolution three-dimensional magnetic resonance images were acquired from 405 Japanese non-alcohol-dependent male individuals. The collected images were normalized, segmented for volumetric analysis, and additionally smoothed for VBM. For volumetric analysis, the partial correlation coefficient was estimated between the gray matter ratio, which represents the percentage of gray matter volume in the intracranial volume, and the lifetime alcohol intake adjusted for the age of each subject. Multiple regression analysis was performed among regional gray matter volume, lifetime alcohol intake, and age using VBM. RESULTS: Volumetric analysis revealed that gray matter ratio showed a negative correlation with the lifetime alcohol intake adjusted for age (p=0.059, partial correlation coefficient=-0.091). The VBM revealed that the gray matter volumes of the bilateral middle frontal gyri showed a significant negative correlation with the lifetime alcohol intake adjusted for age and systolic blood pressure (left side, p=0.006, Z=4.77; right side, p=0.023, Z=4.45, and p=0.046, Z=4.27). CONCLUSIONS: Our study suggests that non-alcohol-dependent Japanese male individuals show that both global gray matter volume and regional gray matter volume have negative correlations with the lifetime alcohol intake. Our study may contribute to clarifying the mechanism underlying the brain structural changes because of alcohol influence in healthy non-alcohol-dependent individuals.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".