Neuroanatomy of childhood disruptive behavior disorders
Bibliographic record
Abstract
Our aims were to (1) examine possible neuroanatomical abnormalities associated with the Disruptive Behavior Disorders (DBDs) as a group and (2) assess neuroanatomical anomalies specific to each DBD (i.e., conduct disorder [CD] and oppositional defiant disorder). Cortical thickness analysis and voxel-based morphometry were analyzed in 47 8-year-old boys (22 DBDs with and without CD and/or ODD and 25 healthy controls) from Magnetic Resonance Imaging brain scans. DBD symptoms were assessed using the Dominic-R. In DBD subjects relative to controls, we found (1) a decreased overall mean cortical thickness; (2) thinning of the cingulate, prefrontal and insular cortices; and (3) decreased gray matter density (GMd) in the same brain regions. We also found that scores on the Dominic-R were negatively correlated with GMd in the prefrontal and precuneus/superior temporal regions. There was a subdiagnostic main effect for CD, related to thinning of the middle/medial frontal, and for ODD in the left rectal/orbitofrontal. Findings suggest that thinning and decreased GMd of the insula disorganizes prefrontal circuits, diminishing the inhibitory influence of the prefrontal cortex on anger, aggression, cruelty, and impulsivity, and increasing a person's likelihood of aggressive behavior. These findings have implications for pathophysiologic models of the DBDs, their diagnostic classification system, and for designing more effective intervention programs.
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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 | 0.002 | 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) | 0.001 | 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".