Right Caudate Volume and Executive Functions in Children with Attention-Deficit/Hyperactivity Disorder (ADHD)
Bibliographic record
Abstract
Abstract Background The caudate and putamen have previously been implicated in Attention-Deficit/Hyperactivity Disorder (ADHD). However, previous studies have not investigated the relationship between the caudate and putamen with executive function (EF). The current study investigated the clinical relevance of the caudate and putamen with respect to EF. Method We studied 49 children (24 ADHD/25 typically developing children (TDC)). All participants in the ADHD group had to undergo a 48-hour stimulant medication washout period. Participants completed cognitive tasks related to working memory/inhibition and underwent a T1-weighted MRI sequence. All parents completed behaviour rating scales using the Behavior Rating Inventory of Executive Function, Second Edition (BRIEF-2). Data were analyzed using multivariate analysis of covariance, Pearson correlations, and linear regressions. Results Children with ADHD demonstrated a higher frequency of perseverative errors compared to TDC ( p <. 05), and their parents reported significantly more EF challenges (p <.001). No difference was observed in the working memory tasks. No significant volumetric differences were seen in the caudate or the putamen. A linear regression model suggested that the right caudate volume accounted for 10.3% of the variance in emotion regulation as reported by parents on the BRIEF-2 in the overall sample. Discussion We observed significant EF challenges without volumetric differences. However, the right caudate was correlated to parent ratings of emotional regulation, highlighting the need to consider emotional regulation difficulties in 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 | 0.002 |
| 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 | 0.001 | 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".