Premature white matter microstructure in female children with a history of concussion
Bibliographic record
Abstract
Abstract As maturation of the brain continues throughout development, there is a risk of interference from concussions which are common in childhood. A concussion can cause widespread disruption to axons and inflammation in the brain and may influence emerging cognitive abilities. Females are more likely to experience persistent problems after a concussion, yet the sex-specific impact of concussions on brain microstructure in childhood is not well understood. In children from a large population sample, this study (1) investigated differences in white matter and cortical microstructure between children with and without a history of concussion, and (2) examined relationships between altered brain microstructure and cognitive performance. Neurite density measures from diffusion weighted magnetic resonance imaging were examined in 9-to 10-year-old children in the Adolescent Brain Cognitive Development Study with ( n = 336) and without ( n = 7368) a history of concussion. (1) Multivariate regression models were used to investigate the relationships between concussion history, sex, and age in the deep white matter, superficial white matter, subcortical structures, and cortex. (2) Principal component analysis was performed on neurite density, and components were examined in relation to performance on the Flanker Inhibitory Control and Attention Task and the Pattern Comparison Processing Speed Task to investigate the relationship between altered neurite density and cognitive performance. Neurite density in all tissue types demonstrated robust positive relationships with age reflecting maturation of brain microstructure. (1) Comparisons between children with and without a history of concussion revealed higher neurite density in deep and superficial white matter in females with concussion. No group differences were observed in subcortical or cortical neurite density. (2) Higher neurite density in superficial white matter beneath the frontal and temporal cortices was associated with lower scores on the processing speed test in females with concussion, and higher scores on the processing speed test in males with concussion. These findings suggest that concussion in childhood leads to premature white matter maturation in females and that this may be associated with slower processing speed. These sex-specific effects on the developing brain may contribute to the enhanced vulnerability to persistent symptoms after concussion in females.
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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 | 0.001 | 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) | 0.002 | 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".