Electroencephalographic Functional Connectivity Patterns in Children With Tourette Syndrome and Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
BACKGROUND: Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD) often co-occur and are linked to emotional and behavioral difficulties. However, their shared and distinct neural underpinnings, particularly in terms of functional connectivity, remain unclear. Here, we assessed how functional connectivity differs across TS and ADHD as well as its association with emotional and behavioral difficulties. METHODS: Resting-state electroencephalography (EEG) was recorded from 137 children with TS (n = 51), ADHD (n = 24), or TS + ADHD (n = 29) or from typically developing control subjects (n = 33). Functional connectivity was computed from source-reconstructed EEG data in five frequency bands (delta, theta, alpha, beta, and gamma). Behavioral and emotional problems were assessed with the Child Behavior Checklist. RESULTS: Both TS and ADHD were independently associated with reduced functional connectivity across different brain regions, with no interaction effect. However, externalizing problems showed a TS by ADHD interaction across three frequency bands, such that distinct patterns of functional connectivity were associated with externalizing problems in children with TS + ADHD, relative to those with either TS or ADHD. CONCLUSIONS: Although TS and ADHD are associated with decreased functional connectivity in different networks, their effects appear additive rather than interactive at the neural level. However, interactions emerged when examining behavioral problems, suggesting that although TS and ADHD contribute independently to brain connectivity disruptions, their combined impact may uniquely influence emotional and behavioral functioning. This fact highlights the need to consider both shared and disorder-specific mechanisms when studying TS and 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 | 0.001 |
| 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".