LEAP-InovAND a multiscale resource to explore genetics, brain imaging and clinical data in autism
Bibliographic record
Abstract
Most current autism research focuses on categorical comparisons (e.g., autistic vs. neurotypical people) and usually examines only one biological domain (e.g., cognition, genetics, or brain imaging). Here, we present a comprehensive resource integrating quantitative phenotypic data, whole genome sequencing, brain magnetic resonance imaging, and electroencephalography. A total of 5,549 people were recruited in Europe through LEAP and InovAND, including 2,061 autistic people, 62 people with intellectual developmental disability who do not meet diagnostic criteria for autism, 2,551 undiagnosed relatives and 875 neurotypical people. Among these people, 2,531 have both clinical and genetic data, and 875 people additionally have neuroimaging data (EEG and/or MRI). We stratified people based on autistic traits and cognitive skills, revealing clusters with distinct genetic and brain signatures. Differences were observed in both rare and common variants, particularly in synaptic and chromatin remodeling genes pathways, and suggesting distinct trajectories of cortical maturation at early stages of development. This resource is available to support research into the complex links between genes, brain structures/functions, and 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.016 |
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".