Functional magnetic resonance imaging of facial information processing in children with autistic disorder, attention deficit hyperactivity disorder and typically developing controls
Bibliographic record
Abstract
The present study used functional magnetic resonance imaging (fMRI) to compare the neural activation patterns of children diagnosed with autistic disorder (AD), attention deficit hyperactivity disorder (ADHD), and typically developing controls (TCs) in response to a task involving evaluation of facial expressions. Substantially greater functional activity was noted in TCs compared to both subjects diagnosed with AD and ADHD. Consistent with previous studies, differences in functional activation of the amygdala, fusiform gyrus, cerebellum, mesolimbic, and temporal lobe cortical regions of the brain during a task evaluating facial expressions were noted in AD compared to TCs. Differences in the neural activity in these brain regions were also observed in children diagnosed with AD compared to those diagnosed with ADHD. Overall decreased neural activity was observed during the faces task performance in the AD group compared to the other two groups, a finding consistent with studies using adults. Both TC and ADHD control groups showed increased inferior frontal cortex activity compared to the AD group. Significant activity was present in both TC and ADHD control groups in the insula which was absent in the AD group; this is consistent with other studies showing dysfunction of the mesolimbic system in children with AD. Although frontostriatal and mesolimbic systems appear to be affected in AD, these deficits were not in the same attentional networks which are dysfunctional in children diagnosed 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".