Cerebral Hemodynamics and Microvasculature Changes in Relation to White Matter Microstructure After Pediatric Mild Traumatic Brain Injury: An A-CAP Pilot Study
Bibliographic record
Abstract
Advanced neuroimaging techniques show promise as a biomarker for mild traumatic brain injury (mTBI). However, little research has evaluated cerebral hemodynamics or its relation to white matter microstructure post-mTBI in children. This novel pilot study examined differences in cerebral hemodynamics, as measured using functional near-infrared spectroscopy (fNIRS), and its association with diffusion tensor imaging (DTI) metrics in children with mTBI or mild orthopedic injury (OI) to address these gaps. Children 8.00–16.99 years of age with mTBI ( n = 9) or OI ( n = 6) were recruited in a pediatric emergency department, where acute injury characteristics were assessed. Participants completed DTI twice, post-acutely (2–33 days) and chronically (3 or 6 months), and fNIRS ∼1 month post-injury. Automated deterministic tractography was used to compute DTI metrics. There was reduced absolute phase globally and coherence in the dorsolateral pre-frontal cortex (DLPFC) after mTBI compared to the OI group. Coherence in the DLPFC and absolute phase globally showed distinct associations with fractional anisotropy in interhemispheric white matter pathways. Two fNIRS metrics (coherence and absolute phase) differentiated mTBI from OI in children. Variability in cerebral hemodynamics related to white matter microstructure. The results provide initial evidence that fNIRS may have utility as a clinical biomarker of pediatric mTBI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".