Effects of Docosahexaenoic Acid Supplementation on Cortical Network Integrity in Medication-Free Children with Attention-Deficit/Hyperactivity Disorder: A Preliminary Multimodal Neuroimaging Trial
Bibliographic record
Abstract
Children with attention deficit/hyperactivity disorder (ADHD) exhibit blood docosahexaenoic acid (DHA) deficits and cortical network pathology. This neuroimaging study investigated the effects of DHA supplementation on cortical attention network integrity in medication-free children with ADHD. Children (mean age 9.6 years, n=30) with ADHD were randomized to DHA (1,200 mg/d) or placebo for 10 weeks. Blood DHA levels and ADHD symptom severity ratings were obtained from all participants (n=30). Cortical network integrity was evaluated in a subset of patients (n=20) using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Erythrocyte DHA levels increased significantly in patients receiving DHA (+60%, p≤0.0001) but not placebo (-4%, p=0.77). There were no group differences in baseline-endpoint change in ADHD symptom severity scores, sustained attention performance, or voxelwise cortical activation patterns during performance of a sustained attention task. In the region-of-interest (ROI) analysis, patients treated with DHA but not placebo exhibited significant endpoint reductions in left amygdala activation. At study endpoint, but not at baseline, DHA-treated patients exhibited significantly greater event-related functional connectivity between the pregenual and subgenual anterior cingulate cortex and regions within the cortical attention network including the inferior parietal lobe and dorsolateral prefrontal cortex compared with placebo. Trends with large effect sizes for reductions in medial and radial diffusivity in the left corpus callosum were observed in DHA-treated patients. These preliminary findings suggest that DHA supplementation may be associated with subtle changes in cortical attention networks of medication-free children with ADHD which warrant additional investigation in a larger patient sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".