The link between autism and sex-specific neuroanatomy, and associated cognition and gene expression
Bibliographic record
Abstract
ABSTRACT Objectives The male preponderance in autism spectrum conditions (ASC) prevalence is among the most pronounced sex ratios across different neurodevelopmental conditions. Here, we aimed to elucidate the relationship between autism and typical sex-differential neuroanatomy, cognition, and related gene expression. Methods Using a novel deep learning framework trained to predict biological sex, we compared sex prediction model performance across neurotypical and autistic males and females. Multiple large-scale datasets were employed at different stages of the analysis pipeline: a) Pre-training: the UK Biobank sample (>10.000 individuals); b) Transfer learning and validation: the ABIDE datasets (1,412 individuals, 5-56 years of age); c) Test and discovery: the EU-AIMS/AIMS-2-TRIALS LEAP dataset (681 individuals, 6-30 years of age) and d) Specificity: the Neuroimage and ADHD200 datasets (887 individuals, 7-26 years of age). Results Across both ABIDE and LEAP we showed that features positively predictive of neurotypical males were on average more predictive of autistic males ( P =1.1e-23). Features positively predictive of neurotypical females were on average less predictive of autistic females ( P =1.2e-22). These accuracy differences in autism were not observed in individuals with ADHD. In autistic females the male-shifted neurophenotype was further associated with poorer social sensitivity and emotional face processing while also with associated gene expression patterns of midgestational cell types. Conclusions Our results demonstrate a shift in both autistic male and female individuals’ neuroanatomy towards male-characteristic patterns associated with typically sex-differential, social cognitive features and related gene expression patterns. Findings hold promise for future research aimed at refining the quest for biological mechanisms underpinning the etiology of autism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".