Functional Connectivity based Brain Signatures of Behavioral Regulation in Children with ADHD, DCD and ADHD-DCD
Bibliographic record
Abstract
ABSTRACT Background Children with neurodevelopmental disorders such as Attention Deficit Hyperactivity Disorder (ADHD) often struggle with behavioral self-regulation (BR), which is associated with daily-life challenges. ADHD sometimes presents with Developmental Coordination Disorder (DCD), but little is known about BR in DCD. BR is thought to involve limbic, prefrontal, parietal and temporal brain areas. Given the risk for negative outcomes, gaining a better understanding of the brain mechanisms underlying BR in children with ADHD and/or DCD is imperative. Methods Resting-state fMRI data collected from 115 children (31 typically developing (TD), 35 ADHD, 21 DCD, 28 ADHD-DCD) aged 7-17 years were preprocessed and motion was mitigated using ICA-AROMA. Emotion control, inhibition, and shifting were assessed as subdomains of BR. Functional connectivity (FC) maps were computed for ten limbic, prefrontal, parietal and temporal regions of interest and were investigated for associations with BR subdomains across all participants as well as for significant group differences. Results Multiple FC patterns were associated with BR across all participants. Some FC patterns were associated with multiple BR subdomains, while others were associated with only one. Differences in BR were found only between children with ADHD (i.e. ADHD and ADHD-DCD) and those without ADHD (i.e. TD and DCD). FC differences were also found between children with and without ADHD. Conclusions Our results show dimensional associations between BR subdomain scores and whole-brain FC and highlight the potential of these associative patterns as brain-based signatures of BR in children with and without ADHD.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".