Microstructural neuroimaging of white matter tracts in persistent post-concussion syndrome: A prospective controlled cohort study
Bibliographic record
Abstract
INTRODUCTION: Children with mild traumatic brain injury (mTBI) typically recover quickly, however approximately 15% experience persistent post-concussive symptoms (PPCS) past 3 months. The microstructural pathology associated with underlying persistent symptoms is poorly understood but is suggested to involve axonal injury to white matter tracts. Diffusion tensor imaging (DTI) can be used to visualize and characterize damage to white matter microstructure of the brain. OBJECTIVE: We aimed to investigate white matter microstructure in children with persistent concussive symptoms as compared to typically developing controls, alongside evaluating differences in white matter changes over time and how this relates to symptom recovery. METHODS: The current study is a prospective, longitudinal, controlled cohort study of children with mTBI. 104 children aged 8 to 18 years with a mTBI (72 symptomatic; 32 asymptomatic) were recruited from the Alberta Children's Hospital and compared to 20 healthy controls. Microstructural evidence of white matter injury was evaluated using DTI one month post injury and repeated 4 to 6 weeks later. Primary outcomes included fractional anisotropy and mean diffusivity of the corticospinal tracts, uncinate fasciculi, and motor fibers of the corpus callosum. Post-concussive symptoms were also measured using the Post-Concussion Symptom Inventory (PCSI) taken at both time points. RESULTS: Fractional anisotropy of the left uncinate fasciculi was lower in symptomatic children compared to controls (F(2,119) = 3.582, p = 0.031). No other significant differences were observed. CONCLUSIONS: Our findings provide evidence of microstructural injury following mTBI in children with ongoing post-concussive symptoms one month post injury. The changes were persistent 4-6 weeks later. Further longitudinal studies of white matter microstructure in PPCS will be helpful to clarify whether these white matter alterations resolve over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".