Genomic structural equation modeling of impulsivity and risk-taking traits reveals three latent factors distinctly associated with brain structure and development
Bibliographic record
Abstract
Background: Impulsivity is a multifaceted transdiagnostic trait that emerges in childhood. Research has identified genetic loci and brain systems associated with different facets of impulsivity and risk-taking. However, how these genetic underpinnings overlap across different facets, and how they are associated with brain development during childhood remain unknown. Methods: Using genomic structural equation modeling on 17 impulsivity and risk-taking traits, we identified latent factors capturing overlapping genetic architecture. We then calculated polygenic scores for these factors using Adolescent Brain Cognitive Development Study data (N = 4,142) and examined their associations with brain structure, development, and behavior in children aged 9-14 years. Finally, we tested whether socioeconomic status modulated the associations between latent polygenic scores and brain structures. Results: We identified three distinct genetic latent factors, which we label lack of self-control, reward drive, and sensation seeking. In children, polygenic scores for the three factors showed associations with distinct brain patterns: lack of self-control associated with reduced prefrontal cortical thickness, reward drive with increased subcortical cellular density, and sensation seeking with increased cortical surface area and white matter integrity. Longitudinally, lack of self-control predicted slower white matter development. The association between polygenetic score for lack of self-control and white matter mean diffusivity was modulated by socioeconomic status. Conclusions: We identified three genetically distinct dimensions of impulsivity and risk-taking with separable neurodevelopmental origins. These genetic predispositions manifested as distinct brain patterns as early as ages 9-10. Environmental experience modulated some of the genetic effects on brain development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".