Cross disorder homogeneity: An examination of neurodevelopmental disorders through behavioural correlates and functional connectivity
Bibliographic record
Abstract
Over 300,000 children in Ontario are diagnosed with neurodevelopmental disorders, which are defined as mental disorders with an onset in the developmental period. Autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD) are three neurodevelopmental disorders with symptom overlap including difficulties with social skills, inattention, and behavioural flexibility. The National Institute of Mental Health (NIMH) proposed the Research Domain Criteria (RDoC) to address these overlaps by examining symptoms at a biological, as well as observable, level. This study investigated how children with diagnoses of ASD (n=90), ADHD (n=47), and OCD (n=32) group together based on their symptom scores on the Social Communication Questionnaire (SCQ), the inattention subscales of the Child Behaviour Checklist (CBCL), and the behaviour flexibility subscales of the Repetitive-Behaviour Scale-Revised (RBS-R). Correlations between cluster groupings and functional connectivity were then evaluated. Children were clustered into 3 groups: (1) a group characterized by high inattention; (2) a group characterized by moderate impairment across social skills, inattention, and behavioural flexibility; and (3) a group characterized by high impairment in all measures. Functional connectivity between the anterior cingulate cortex and intraparietal sulcus was positively correlated with symptom scores on behavioural flexibility in group 1. Connectivity between the right amygdala and both the left superior temporal gyrus and the lateral parietal region were negatively correlated with symptom scores on behavioural flexibility in group 3. This study was the first to collapse across diagnostic groups of neurodevelopmental disorders, and examine the correlation between symptom severity and functional connectivity. Findings support the use of the RDoC framework.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".