Parental Age Effects on Cortical Morphology in Offspring
Bibliographic record
Abstract
The age at which a parent has a child impacts the child's cognition and risk for mental illness. It appears that this risk is curvilinear, with both age extremes associated with lower intelligence and increased prevalence of some neuropsychiatric disorders. Little is known of the neural mechanisms underpinning this phenomenon. We extracted lobar volumes, surface areas, and cortical thickness from 489 neuroanatomic magnetic resonance images acquired on 171 youth. Using linear mixed model regression, we determined the association between parental age and offspring's neuroanatomy, adjusting for offspring's age, sex, intelligence, and parental socioeconomic class. For gray matter volumes, quadratic paternal and maternal age terms contributed significantly (maternal quadratic age effect: t = -2.2, P = 0.03; paternal quadratic age effect: t = -2.4, P = 0.02) delineating an inverted "U" relationship between parental age and gray matter volume. Cortical volume increased with both advancing paternal and maternal age until around the early 30s after which it fell. Paternal age effects were more pronounced on cortical surface area, whereas maternal age impacted more on cortical thickness. There were no significant effects of parental age on white matter volumes. These parental age effects on cerebral morphology may form part of the link between parental age extremes and suboptimal neurocognitive outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".