Rhythmic activity in resting-state EEG predicts trait anxiety
Bibliographic record
Abstract
Trait anxiety is characterized by heightened apprehension and worry about future events, with individuals experiencing it at varying levels. Identifying physiological markers of these differences could enhance our understanding of anxiety-related processes. We tested the hypothesis that resting-state rhythmic electroencephalography (EEG) activity (oscillations during alternating 1-minute eyes-open/eyes-closed rest conditions) predicts trait anxiety, as measured by the Behavioral Inhibition Scale (BIS). Multiple regression revealed that during eyes-open rest, higher frontal midline theta and lower frontal midline alpha activity significantly predicted BIS scores. A more stringent analysis confirmed that frontal midline theta and alpha activity met stricter criteria for rhythmicity. Interestingly, only parietal midline alpha power during eyes-open rest, but not oscillations, was predictive of BIS, suggesting that rhythmicity may differentiate the functional roles of frontal and posterior alpha in relation to anxiety. We also found that alpha asymmetry (power or oscillations) did not predict BIS scores. Our results suggest that heightened frontal midline theta during eyes-open rest may reflect increased baseline vigilance in individuals with high trait anxiety. Although speculative, lower frontal midline alpha may indicate greater cortical activation in regions involved in inhibition and cognitive control. Moreover, by differentiating power and oscillation effects, our findings help resolve ambiguities in related research.
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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 | 0.000 | 0.000 |
| 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".