The Relationship Between Frontal Cortical Thickness and Externalizing Psychopathology is Associated with Treatment Outcomes in Children with Externalizing Problems: A Preliminary Pilot Study: La relation entre l’épaisseur du cortex frontal et les troubles extériorisés est associée aux résultats thérapeutiques chez les enfants ayant des problèmes extériorisés : une étude pilote préliminaire
Bibliographic record
Abstract
Objectives Children with externalizing disorders commonly show emotion dysregulation and callous–unemotional (CU) traits. However, it is unclear whether emotion dysregulation and CU traits share underlying neurobiology that can be predictive of psychosocial treatment outcomes. In this preliminary study, we examined neural correlates of externalizing psychopathology dimensions and their prediction of treatment outcomes. Methods We analyzed a pilot sample of 17 children with an externalizing disorder (9–12 years; 10.45 ± 1.02) who underwent structural magnetic resonance imaging (MRI) before participating in a 15-week psychosocial group intervention targeting conduct problems. We examined cross-sectional associations between emotion dysregulation or CU traits and cortical thickness (anterior cingulate cortex [ACC] and insula) and amygdala volume at baseline. We then examined whether the pre-treatment brain–behaviour relationships were linked to reduction in conduct problems post-treatment. Results Lower ACC and insula thickness as well as amygdala volume was associated with greater levels of emotion dysregulation and CU traits at baseline (pre-treatment, r = |0.36–0.61|). There was a significant three-way interaction between emotion dysregulation/CU traits, left insula/right rostral ACC, and treatment (pre/post; β = −1.01 to 3.6). Overall, greater baseline insular and rostral ACC thickness was related to reductions in conduct problems following group-based psychosocial intervention regardless of baseline emotion dysregulation and CU trait levels. Conclusions The results provide preliminary evidence of shared neural signatures underlying both emotion dysregulation and CU traits. Additionally, alterations in frontolimbic brain structure may be useful predictors of pre-treatment associations with externalizing psychopathology dimensions and post-treatment behavioural outcomes. Plain Language Summary Title Frontal cortical thickness and externalizing psychopathology are associated with treatment outcomes in children with externalizing problems
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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) | 0.000 | 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".