Developmental trajectories of sensitivity to threat in children and adolescents predict larger medial frontal theta differentiation during response inhibition
Bibliographic record
Abstract
Sensitivity to threat (ST) is thought to be a hallmark of the onset and maintenance of anxiety, which often manifests behaviorally as withdrawal, increased arousal and hypervigilant monitoring of performance. The current study investigated whether longitudinal trajectories of ST were linked to medial frontal (MF) theta power dynamics, a robust marker of performance monitoring. Youth (N = 432, Mage = 11.96 years) completed self-report measures of threat sensitivity annually for 3 years. A latent class growth curve analysis was used to identify distinct profiles of threat sensitivity over time. Participants also completed a GO/NOGO task while electroencephalography was recorded. We identified three threat sensitivity profiles: (i) high (n = 83), (ii) moderate (n = 273) and (iii) low ( n= 76). Participants in the high threat sensitivity class had greater levels of MF theta power differentiation (NOGO-GO) compared to participants in the low threat sensitivity class, indicating that consistently high threat sensitivity is associated with neural indicators of performance monitoring. Of concern, both hypervigilant performance monitoring and threat sensitivity have been associated with anxiety; thus, youth with high threat sensitivity may be at risk for the development of anxiety.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".