The segregation of task-based EEG networks into functionally specialized systems is reduced in those with hyperactive traits
Bibliographic record
Abstract
Although attention-deficit/hyperactivity disorder (ADHD) is associated with the atypical development of large-scale brain networks, network organization in those with different ADHD-traits (inattention/hyperactivity) is not well understood. Here, we examine the relationship between ADHD traits and EEG functional networks during an attentional control task. To do so, 128-channel EEG was recorded while 62 non-clinical participants (ages 18-24) underwent a Go/No-Go task. Networks were created using EEG sensors as nodes and across-trial phase-lag index values as edges (10% threshold, binarized). Using cross-validated LASSO regression, we examined whether dynamic graph-theory metrics of integration, segregation and modularity predict inattention and/or hyperactivity. In the gamma-band (30-90Hz), and no other frequency bands, a three feature model accounted for a substantial amount of variance in hyperactivity (R2 = .28). This showed that throughout processing (0-500ms, 2ms intervals), networks of those with low hyperactivity: (1) have nodes which tend to remain in the same module (r = -.44, p = .0004), (2) consistently share connections with the same neighbours (r = -.34, p = .007), and (3) take more time to transfer information globally (r = .36, p = .004; all powers > .85). Thus, the well-reported developmental process whereby task-specific systems of brain regions become increasingly specialized might be protracted in the ADHD Hyperactive-Impulsive subtype. Understanding which developmental processes might be altered in ADHD has implications for diagnosis and intervention.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".