Neural substrates underpinning intra-individual variability in children with ADHD: A voxel-based morphometry study
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Increased intra-individual variability (IIV) in reaction time (RT) is a key feature of attention-deficit/hyperactivity disorder (ADHD). However, little is known about neurobiology underpinnings of IIV in ADHD. METHODS: We assessed 55 youths with ADHD, and 55 individually-matched typically developing control (TDC) with the MRI and Conners' Continuous Performance Test. The ex-Gaussian distribution of RT was estimated to capture IIV with the parameters σ (sigma) and τ (tau). The regional brain volumes, analyzed by voxel-based morphometry, were correlated with IIV parameters. RESULTS: We found both distinct and shared correlations among ADHD and TDC. For grey matter, there were significant σ-by-group interactions in the cingulate cortex and thalamus and also a τ-by-group interaction in the right inferior frontal gyrus. There was also shared negative associations between σ and regional volumes of the right posterior cerebellum and a positive association between τ and the right anterior insula. For white matter, there was a significant σ-by-group interaction in the genu of the corpus callosum and significant τ-by-group interactions in the right anterior corona radiata, the left splenium of the corpus callosum, and bilateral posterior cerebellum. There were also shared patterns that increased τ was associated with increased regional volumes of the right anterior corona radiata and decreased regional volumes of the right posterior limb of the internal capsule. CONCLUSION: This study highlights that brain regions responsible for the motor, salience processing and multimodal information integration are associated with increased IIV in youths with 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".