Frontal brain delta‐beta correlation, salivary cortisol, and social anxiety in children
Bibliographic record
Abstract
BACKGROUND: Correlated activity of slow-wave (e.g. delta) and fast-wave (e.g. beta) frontal brain oscillations is thought to be an electrophysiological correlate of individual differences in neuroendocrine activity and anxiety in adult samples. We know, however, relatively little about the physiological and functional correlates of delta-beta coupling in children. METHOD: = 7.59 years, SD = 1.70). At Time 1 (T1), resting baseline electroencephalogram (EEG) recordings were collected from the children and delta and beta power was measured, and at both T1 and Time 2 (T2), basal salivary cortisol was measured, and parents reported on children's symptoms of social anxiety. RESULTS: Using latent class growth curve analysis, we found that children's salivary cortisol across visits was characterized by a high, stable class (53%), and a low, unstable class (47%), and children's social anxiety was characterized by a high, stable class (50%) and a low, stable class (50%). Using Fisher's r-to-z transformation, we found that frontal EEG delta-beta correlation was significantly stronger among children with high, stable salivary cortisol levels (compared to the low, unstable class; z = 2.11, p = .02), and among children with high, stable social anxiety levels (compared to the low, stable class; z = 1.72, p = .04). CONCLUSIONS: These findings demonstrate that longitudinal patterns of neuroendocrine stress activity and social anxiety may be associated with the correlation of EEG power in slow and fast frontal brain oscillations as early as childhood.
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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 | 0.001 | 0.003 |
| 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".