Structural brain correlates of externalizing traits and symptoms in the IMAGEN sample.
Bibliographic record
Abstract
The evidence supporting the presence of individual brain structure correlates of the externalizing spectrum (EXT) is sparse and mixed. To date, large-sample studies of brain-EXT relations have found mainly null to very small effects by focusing exclusively on either EXT-related personality traits (e.g., Hyatt et al., 2022) or EXT-related disorders/symptoms (e.g., Mewton et al., 2022). In this preregistered study using IMAGEN data (N = 1,370), we investigated the structural brain correlates of EXT factors that comprise both personality (e.g., antagonism) and psychopathology constructs (e.g., conduct disorder) across levels of morphometric specificity. Brain morphometry was operationalized in terms of omnibus measures (e.g., total brain volume), subcortical volume, and Desikan atlas regions (N = 161 sMRI metrics). We operationalized our integrated personality-psychopathology EXT through exploratory factor analyses of EXT-related measures, which identified two dimensions — Non-Substance Use and Substance Use— and one overarching EXT domain. Results were consistent with previous large-sample neuroscientific investigations of EXT: The vast majority of relations were null, and all effect sizes were very small (largest marginal R2 < .02). Preregistered supplementary analyses indicated that all significant relations found were driven by total intracranial volume and sex of the participant and became nonsignificant following the inclusion of these covariates. We conclude with suggestions regarding the importance of relevant covariates and large samples in clinical neuroscientific investigations.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".