Integrating Multimodal Neuroimaging of Error Monitoring to Estimate Future Anxiety in Adolescents
Bibliographic record
Abstract
Importance: Anxiety disorders are highly prevalent and associated with heightened error monitoring, the detection of one's mistakes. However, error monitoring, anxiety, and their associations change throughout adolescence, limiting the ability to estimate future anxiety trajectories during this period. Objective: To ascertain whether measures of error monitoring obtained via the integration of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) improve estimations of future anxiety compared with EEG or fMRI alone, in adolescents with or without a history of behaviorally inhibited temperament. Design, Setting, and Participants: This longitudinal cohort study was conducted at a university research laboratory and government research hospital. Study assessments took place between January 2014 and July 2019, and data analyses were completed in January 2025. A community sample of infants completed a laboratory screening at age 4 months. A subset of these infants was oversampled to maximize variability of early temperament and followed up throughout adolescence. At ages 13 and 15 years, participants completed a flanker task during an EEG session and separate fMRI session. Brain activity at age 13 years and its change from 13 to 15 years of age were evaluated as potential risk factors for anxiety. Main Outcomes and Measures: Change in anxiety from age 13 to 15 years as measured using clinical interviews and questionnaires. Hypotheses were formulated after data collection. Results: Analyses included 176 adolescents with neuroimaging data (92 females at birth [52.3%]; 5 Asian individuals [2.8%], 21 Black or African American individuals [11.9%], 11 Hispanic or Latino individuals [6.3%], 133 White individuals [75.6%], and 6 individuals of other [3.4%] race and ethnicity). Among neural variables (EEG-only, fMRI-only, and EEG-fMRI fusion), only the EEG-fMRI fusion scores explained additional variance in anxiety change scores (change in R2 = 0.25; P = .001) beyond demographics (sex, racial and ethnic minority status) and anxiety at age 13 years. Planned follow-up analyses revealed that early temperament interacted with dorsal anterior cingulate activity at age 13 years (β = 0.40; B = 8.77; 95% CI, 0.74-16.79; P = .03) and with changes in posterior cingulate activity (β = -0.42; B = -16.89; 95% CI, -28.21 to -5.57; P = .003). Conclusions and Relevance: This cohort study found that integrating multimodal neuroimaging measures of error monitoring was associated with improved estimations of future anxiety in youths over and above each modality separately. Early temperament interacted with error monitoring to further differentiate anxiety trajectories; yet, these interactions differed across brain regions, highlighting the value of incorporating the complementary temporal and spatial information of EEG and fMRI.
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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 | 0.002 |
| 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 | 0.000 | 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 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".