Diffusion Tensor Imaging Indicators of White Matter Injury Are Correlated with a Multimodal Electroencephalography-Based Biomarker in Slow Recovering, Concussed Collegiate Athletes
Bibliographic record
Abstract
There are no validated, objective diagnostic or prognostic biomarkers for sports-related concussion (SRC), which hinders evidence-based treatment for concussed athletes. While quantitative electrophysiology (EEG) and diffusion tensor imaging (DTI) are promising technologies for providing objective biomarkers for concussion, the degree to which they are related has not been systematically investigated in concussed athletes. This study examined whether diffusion metrics differentiated concussed athletes with prolonged recovery ( n = 18) from non-conccused athletes ( n = 13) and whether observed diffusion alterations related to EEG. Collegiate athletes ( N = 31) completed EEG, neurocognitive, and magnetic resonance imaging. White matter diffusivity differed between the groups in multiple white matter tracts, including the corpus callosum, cingulum bundle, thalamic radiations, and inferior fronto-occipital, inferior longitudinal, and uncinate fasciculi, but not after correction for multiple comparisons. The enhanced Brain Function Index (eBFI), a measure that combines EEG and neurocognitive data, significantly correlated with altered diffusion in the concussed athletes. These preliminary findings suggest that the absolute deviation of diffusion metrics in concussed versus non-concussed athletes may have clinically utility. Results also suggested that the eBFI may be sensitive to early changes from sports-related concussion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".