Can electroencephalography (EEG) identify ADHD subtypes? A systematic review
Bibliographic record
Abstract
Abstract Attention Deficit/Hyperactivity Disorder (ADHD) has been associated with atypical patterns of neural activity measured by electroencephalography (EEG). However, the identification of EEG diagnostic biomarkers has been complicated by the disorder’s heterogeneity. The objective of this review was to synthesize the literature investigating EEG variation in patients diagnosed with ADHD, addressing the following questions: 1) Are the diagnostic ADHD subtypes associated with different EEG characteristics? 2) Are EEG measures correlated with ADHD traits and/or symptom severity? and 3) Do classification techniques using EEG measures reveal different clinical presentations of ADHD? Outcomes highlight the potential for electrophysiological measures to provide meaningful insights into the heterogeneity of ADHD, although direct translation of EEG biomarkers for diagnostic purposes is not yet supported. Key measures that show promise for the discrimination of existing ADHD subtypes and symptomatology include: resting state and task-related modulation of alpha, beta and theta power, and the event-related N2 and P3 components. Prescriptions are discussed for future studies that may help to bridge the gap between research and clinical application.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".