Persistent white matter vulnerability in a mouse model of mild traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Following one mild traumatic brain injury (mTBI), there is a window of vulnerability during which subsequent mTBIs can cause substantially exacerbated impairments. Currently, there are no known methods to monitor, shorten or mitigate this window. METHODS: To characterize a preclinical model of this window of vulnerability, we first gave male and female mice one or two high-depth or low-depth mTBIs separated by 1, 7, or 14 days. We assessed brain white matter integrity using silver staining within the corpus callosum and optic tracts, as well as behavioural performance on the Y-maze test and visual cliff test. RESULTS: The injuries resulted in windows of white matter vulnerability longer than 2 weeks but produced no behavioural impairments. Notably, this window duration is substantially longer than those reported in any previous preclinical vulnerability study, despite our injury model likely being milder than the ones used in those studies. We also found that sex and impact depth differentially influenced white matter integrity in different white matter regions. CONCLUSIONS: These results suggest that the experimental window of vulnerability following mTBI may be longer than previously reported. Additionally, this work highlights the value of including white matter damage, sex, and replicable injury models for the study of post-mTBI vulnerability and establishes important groundwork for the investigation of potential vulnerability mechanisms, biomarkers, and therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".