Cortical thickness abnormalities in attention deficit hyperactivity disorder revealed by structural magnetic resonance imaging: Newborns to young adults
Bibliographic record
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition for which we have an incomplete understanding, and so brain imaging methods, such as magnetic resonance imaging (MRI), may be able to assist in characterising and understanding the presentation of the brain in an ADHD population. Statistical and computational methods were used to compare participants with ADHD and neurotypical controls at a variety of developmental stages to assess detectable abnormal neurodevelopment potentially associated with ADHD and to assess our ability to diagnose and characterise the condition from real-world clinical MRI examinations. T1-weighted structural MRI examinations (n = 993; 0-31 years old [YO]) were obtained from neurotypical controls, and 637 examinations were obtained from patients with ADHD (0-26 YO). Measures of average (mean) regional cortical thickness were acquired, alongside the first reporting of regional cortical thickness variability (as assessed with the standard deviation [SD]) in ADHD. A comparison between the inattentive and combined (inattentive and hyperactive) subtypes of ADHD is also provided. A preliminary independent validation was also performed on the publicly available ADHD200 dataset. Relative to controls, subjects with ADHD had, on average, lowered SD of cortical thicknesses and increased mean thicknesses across several key regions potentially linked with known symptoms of ADHD, including the precuneus and supramarginal gyrus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".