Neural Rhythms of Change: Long-Term Improvement after Successful Treatment in Children with Disruptive Behavior Problems
Bibliographic record
Abstract
Neural changes were investigated for children with disruptive behavior problems one year after a treatment program ended. Thirty-nine children and their parents visited the research lab before, after, and a year after treatment ended. During those lab visits, electroencephalography (EEG) was recorded during a challenging Go/No-go task. Treatment consisted of intensive 14-week combined cognitive behavioral therapy and parent management training sessions. For the analysis, participants were divided into long-term improvers (IMPs) and long-term nonimprovers (NIMPs) based on changes in their externalizing problem scores. The results showed early no-go theta power (4-8 Hz, 100-250 ms) decreased for long-term IMPs compared to NIMPs. When participants were divided based on changes in their comorbid internalizing symptoms, effects were stronger and reductions in theta power were found for early as well as later phases (250-650 ms). We provided preliminary evidence that theta power is a useful neural measure to trace behavioral change linked to improved self-regulation even up to a year after treatment ended. Results may have implications for the characterization of children with disruptive behavior problems and may lead to the development of novel markers of treatment success.
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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 | 0.000 | 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".