Pediatric Moderate-Severe Traumatic Brain Injury and Gray Matter Structural Covariance Networks: A Preliminary Longitudinal Investigation
Bibliographic record
Abstract
Pediatric traumatic brain injury (TBI) is prevalent and can disrupt ongoing brain maturation. However, the long-term consequences of pediatric TBI on the brain's network architecture are poorly understood. Structural covariance networks (SCN), based on anatomical correlations between brain regions, may provide important insights into brain topology following TBI. Changes in global SCN (default-mode network [DMN], central executive network [CEN], and salience network [SN]) were compared sub-acutely (<90 days) and in the long-term (approximately 12-24 months) after pediatric moderate-severe TBI (n = 16), and compared to typically developing children assessed concurrently (n = 15). Gray matter (GM) volumes from selected seeds (DMN: right angular gyrus [rAG], CEN: right dorsolateral prefrontal cortex [rDLPFC], SN: right anterior insula) were extracted from T1-weighted images at both timepoints. No group differences were found sub-acutely; at the second timepoint, the TBI group showed significantly reduced structural covariance within the DMN seeded from the rAG and the (1) right middle frontal gyrus, (2) left superior frontal gyrus, and (3) left fusiform gyrus. Reduced structural covariance was also found within the CEN, that is, between the rDLPFC and the (1) calcarine sulcus, and (2) right occipital gyrus. In addition, injury severity was positively associated with GM volumes in the identified CEN regions. Over time, there were no significant changes in SCN in either group. The findings, albeit preliminary, suggest for the first time a long-term effect of pediatric TBI on SCN. SCN may be a complementary approach to characterize the global effect of TBI on the developing brain. Future work needs to further examine how disruptions of these networks relate to behavioral and cognitive difficulties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".