Repeated Pediatric Concussions Evoke Long-Term Oligodendrocyte and White Matter Microstructural Dysregulation Distant from the Injury
Bibliographic record
Abstract
Concussion or mild traumatic brain injury (mTBI) is often accompanied by long-term behavioral and neuropsychological deficits. Emerging data suggest that these deficits can be exacerbated following repeated injuries. However, despite the overwhelming prevalence of mTBI in children due to falls and sports-related activities, the effects of mTBI on white matter (WM) structure and its development in children have not been extensively examined. Moreover, the effect of repeated mTBI (rmTBI) on developing WM has not yet been studied, despite the possibility of exacerbated outcomes with repeat injuries. To address this knowledge gap, we investigated the long-term effects of single (s)mTBI and rmTBI on the WM in the pediatric brain, focusing on the anterior commissure (AC), a WM structure distant to the injury site, using diffusion tensor imaging (DTI) and immunohistochemistry (IHC). We hypothesized that smTBI and rmTBI to the developing mouse brain would lead to abnormalities in microstructural integrity and impaired oligodendrocyte (OL) development. We used a postnatal day 14 Ascl1-CreER: ccGFP mouse closed head injury (CHI) model with a bilateral repeated injury. We demonstrate that smTBI and rmTBI differentially lead to myelin-related diffusion changes in the WM and to abnormal OL development in the AC, which are accompanied by behavioral deficits 2 months after the initial injury. Our results suggest that mTBIs elicit long-term behavioral alterations and OL-associated WM dysregulation in the developing brain. These findings warrant additional research into the development of WM and OL as key components of pediatric TBI pathology and potential therapeutic targets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".