Error-related negativity predicts increases in anxiety in a sample of clinically anxious female children and adolescents over 2 years
Bibliographic record
Abstract
BACKGROUND: An increased neural response to making errors has emerged as a biomarker of anxiety. Error negativity (Ne) or errorrelated negativity (ERN) is an event-related potential generated when people commit errors; the Ne/ERN is greater among people with anxiety and predicts increases in anxiety. However, no previous study has examined whether the Ne/ERN can be used as a prognostic indicator among people with current anxiety. The present study addressed this gap by examining whether the Ne/ERN prospectively predicts increases in anxiety symptoms in clinically anxious children and adolescents. METHODS: The sample included 34 female participants between the ages of 8 and 14 years who met the criteria for a clinical anxiety disorder based on clinical interview. The Ne/ERN was measured using a flanker task. RESULTS: Increased Ne/ERN at baseline predicted increases in total anxiety symptoms 2 years later, even when accounting for baseline symptoms. The Ne/ERN predicted increases in the symptom domains of generalized anxiety, social anxiety and harm avoidance/perfectionism, but not panic, separation anxiety, school avoidance or physical symptoms. LIMITATIONS: The sample size was small, which may have inflated the false discovery rate. To mitigate this possibility, we used multiple self-report measures, and the results for the 2 measures (as well as their symptom domains) converged. CONCLUSION: These data suggest that the Ne/ERN can delineate specific risk trajectories, even among those who already meet the criteria for a clinical anxiety disorder. Considering the need for prognostic markers among people with clinical anxiety, the current findings are an important and novel extension of previous work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".