Subcortical volumetric alterations as potential predictors of methylphenidate treatment response in youth with attention-deficit/hyperactivity disorder
Bibliographic record
Abstract
BACKGROUND: Patients with attention-deficit/hyperactivity disorder (ADHD) show structural alterations in the subcortical and dopaminergic regions of the brain. Methylphenidate is a first-line treatment for ADHD, and it is known to affect the subcortical and dopaminergic systems. The degree of pretreatment structural alterations in patients with ADHD may be an important factor in predicting methylphenidate treatment outcomes. The present study examined whether pretreatment volumetric alterations in the subcortical and dopaminergic regions predicted treatment response in youth with ADHD. METHODS: -weighted structural MRI scans and underwent clinical assessments before and after methylphenidate treatment. The healthy controls also completed baseline structural MRI scans. We assessed volumetric alterations using relative volumes (volume of each region of interest/intracranial volume). RESULTS: Among 67 youth with ADHD, 44 were treatment responders and 23 were nonresponders based on post-treatment scores on the Clinical Global Impression Scale-Improvement. Nonresponders had larger volumes in the bilateral amygdala and right thalamus than responders. Nonresponders also had larger volumes in amygdalar subregions (i.e., the bilateral lateral nucleus and right basal nucleus) and hippocampal subregions (i.e., the right hippocampal head and right molecular layer) relative to responders. LIMITATIONS: -weighted images, so volumetric changes related to methylphenidate treatment in youth with ADHD were undetermined. CONCLUSION: These findings suggest that pretreatment volumetric alterations in subcortical regions may serve as biomarkers for predicting methylphenidate treatment response in youth 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".