From mother to child: orbitofrontal cortex gyrification and changes of drinking behaviour during adolescence
Bibliographic record
Abstract
Adolescence is a common time for initiation of alcohol use and alcohol use disorders. Importantly, the neuro-anatomical foundation for later alcohol-related problems may already manifest pre-natally, particularly due to smoking and alcohol consumption during pregnancy. In this context, cortical gyrification is an interesting marker of neuronal development but has not been investigated as a risk factor for adolescent alcohol use. On magnetic resonance imaging scans of 595 14-year-old adolescents from the IMAGEN sample, we computed whole-brain mean curvature indices to predict change in alcohol-related problems over the following 2 years. Change of alcohol use-related problems was significantly predicted from mean curvature in left orbitofrontal cortex (OFC). Less gyrification of OFC was associated with an increase in alcohol use-related problems over the next 2 years. Moreover, lower gyrification in left OFC was related to pre-natal alcohol exposure, whereas maternal smoking during pregnancy had no effect. Current alcohol use-related problems of the biological mother had no effect on offsprings' OFC gyrification or drinking behaviour. The data support the idea that alcohol consumption during pregnancy mediates the development of neuro-anatomical phenotypes, which in turn constitute a risk factor for increasing problems due to alcohol consumption in a vulnerable stage of life. Maternal smoking during pregnancy or current maternal alcohol/nicotine consumption had no significant effect. The OFC mediates behaviours known to be disturbed in addiction, namely impulse control and reward processing. The results stress the importance of pre-natal alcohol exposure for later increases in alcohol use-related problems, mediated by structural brain characteristics.
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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) | 0.003 | 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".