Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences
Bibliographic record
Abstract
Social communication differences are seen in autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), but the brain mechanisms contributing to these differences remain largely unknown. To address this gap, we used a data-driven and diagnosis-agnostic approach to discover brain correlates of social communication differences in ASD, ADHD, and OCD, and subgroups of individuals who share similar patterns of brain-behavior associations. A machine learning pipeline (regression clustering) was used to discover the pattern of association between structural brain measures (volume, surface area, and cortical thickness) and social communication abilities. Participants (n = 416) included children with a diagnosis of ASD (n = 192, age = 12.0[5.6], 19% female), ADHD (n = 109, age = 11.1[4.1], 18% female), or OCD (n = 50, age = 12.3[4.2], 42% female), and typically developing controls (n = 65, age = 11.6[7.1], 48% female). The analyses revealed (1) associations with social communication abilities in distributed cortical and subcortical networks implicated in social behaviors, language, attention, memory, and executive functions, and (2) three data-driven, diagnosis-agnostic subgroups based on the patterns of association in the above networks. Our results suggest that different brain networks may contribute to social communication differences in subgroups that are not diagnosis-specific.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".