Maturity of brain structures and white matter connectomes, and their relationship with psychiatric symptoms in youth
Bibliographic record
Abstract
Abstract Background Brain neuromaturation can be indexed using brain predicted age difference (BrainPAD), a metric derived by the application of machine learning (ML) algorithms to neuroimaging. Previous studies in youth have been limited to a single type of imaging data, single ML approach, or specific psychiatric condition. Here, we use multimodal neuroimaging and an ensemble ML algorithm to estimate BrainPAD and examine its relationship with broad measures of symptoms and functioning in youth. Methods We used neuroimaging from eligible participants in the Healthy Brain Network (HBN, N = 498). Participants with a Child Behavior Checklist Total Problem T-Score < 60 were split into training (N=215) and test sets (N=48). Morphometry estimates (from structural MRI), white matter connectomes (from diffusion MRI), or both were fed to an automated ML pipeline to develop BrainPAD models. The most accurate model was applied to a held-out evaluation set (N=249), and the association with several psychometrics was estimated. Results Models using morphometry and connectomes together had a mean absolute error of 1.16 years, outperforming unimodal models. After dividing participants into positive, normal, and negative BrainPAD groups, negative BrainPAD values were associated with more symptoms on the Child Behavior Checklist (negative=71.6, normal 59.0, p=0.011) and lower functioning on the Children’s Global Assessment Scale (negative=49.3, normal=58.3, p=0.002). Higher scores were associated with better performance on the Flanker task (positive=62.4, normal=52.5, p=0.006). Conclusion These findings suggest that a multimodal approach, in combination with an ensemble method, yields a robust biomarker correlated with clinically relevant measures in youth.
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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 | 0.002 | 0.008 |
| 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) | 0.001 | 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".