Cortical differences across psychiatric disorders and associated common and rare genetic variants
Bibliographic record
Abstract
Genetic studies have identified common and rare variants increasing the risk for neurodevelopmental and psychiatric disorders (NPDs). These risk variants have also been shown to influence the structure of the cerebral cortex. However, it is unknown whether cortical differences associated with genetic variants are linked to the risk they confer for NPDs. To answer this question, we analyzed cortical thickness (CT) and surface area (SA) for common and rare variants associated with NPDs, in ~33000 individuals from the general population and clinical cohorts, as well as ENIGMA summary statistics for 8 NPDs. Rare and common genetic variants increasing risk for NPDs were preferentially associated with total SA, while NPDs were preferentially associated with mean CT. Larger effects on mean CT, but not total SA, were observed in NPD medicated subgroups. At the regional level, genetic variants were preferentially associated with effects in sensorimotor areas, while NPDs showed higher effects in association areas. We show that schizophrenia- and bipolar-disorder-associated SNPs show positive and negative effect sizes on SA suggesting that their aggregated effects cancel out in additive polygenic models. Overall, CT and SA differences associated with NPDs do not relate to those observed across individual genetic variants and may be linked with critical non-genetic factors, such as medication and the lived experience of the disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".