Genetic associations of externalising and internalising symptoms with brain imaging and cell types among autistic individuals and the general population
Bibliographic record
Abstract
ABSTRACT Externalising and internalising symptoms span multiple psychiatric diagnoses. Although similar measures assess these traits in autistic and non-autistic populations, it remains unclear whether their polygenic influences and biological mechanisms align. This study compared genetic contributions to these symptoms in autistic individuals (SPARK, N=3,486) and the general population (ABCD, N=4,637; external datasets: Neff=523,150 externalising; Neff=132,260 internalising). Regression models tested associations between polygenic scores, demographics, and symptom outcomes. Genetic correlations were computed with 12 global and 2,159 regional brain phenotypes, and with 461 cell types across 31 superclusters. In both cohorts, higher symptoms correlated with lower maternal education, lower household income and polygenic scores for depression. The strongest associations were observed for externalising symptoms in the general population, showing negative correlations with cortical expansion and enrichment in hypothalamic and histaminergic neurons. These findings suggest shared genetic architectures but different neurobiological correlates of externalising and internalising symptoms across autism and the general population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".