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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".