Neurophysiological mechanisms of emotion regulation for subtypes of externalizing children
Bibliographic record
Abstract
Children referred for externalizing behavior problems may not represent a homogeneous population. Our objective was to assess neural mechanisms of emotion regulation that might distinguish subtypes of externalizing children from each other and from their normal age mates. Children with pure externalizing (EXT) problems were compared with children comorbid for externalizing and internalizing (MIXED) problems and with age-matched controls. Only boys were included in the analysis because so few girls were referred for treatment. We used a go/no-go task with a negative emotion induction, and we examined dense-array EEG data together with behavioral measures of performance. We investigated two event-related potential (ERP) components tapping inhibitory control or self-monitoring—the inhibitory N2 and error-related negativity (ERN)—and we constructed source models estimating their cortical generators. The MIXED children's N2s increased in response to the emotion induction, resulting in greater amplitudes than EXT children in the following trial block. ERN amplitudes were greatest for control children and smallest for EXT children with MIXED children in between, but only prior to the emotion induction. These results were paralleled by behavioral differences in response time and performance monitoring. ERP activity was localized to cortical sources suggestive of the dorsal anterior cingulate for control children, posterior cingulate areas for the EXT children, and both posterior cingulate and ventral cingulate/prefrontal regions for the MIXED children. These findings highlight different mechanisms of self-regulation underlying externalizing subtypes and point toward distinct developmental pathways and treatment strategies.We gratefully acknowledge the financial support provided by Grant 1 R21 MH67357-01 from the Developmental Psychopathology and Prevention Research branch of the National Institute of Mental Health (NIMH), as well as support from the Canadian Institutes for Health Research (CIHR). We are also grateful for support provided (to P.D.Z.) by the Canadian Foundation for Innovation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".