White matter during concussion recovery: Comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI)
Bibliographic record
Abstract
Concussion pathophysiology in humans remains incompletely understood. Diffusion tensor imaging (DTI) has identified microstructural abnormalities in otherwise normal appearing brain tissue, using measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). The results of prior DTI studies suggest that acute alterations in microstructure persist beyond medical clearance to return to play (RTP), but these measures lack specificity. To better understand the observed effects, this study combined DTI with neurite orientation dispersion and density imaging (NODDI), which employs a more sophisticated description of water diffusion in the brain. A total of 66 athletes were recruited, including 33 concussed athletes, scanned within 7 days after concussion and at RTP, along with 33 matched controls. Both univariate and multivariate methods identified DTI and NODDI parameters showing effects of concussion on white matter. Spatially extensive decreases in FA and increases in AD and RD were associated with reduced intra-neurite water volume, at both the symptomatic phase of injury and RTP, indicating that effects persist beyond medical clearance. Subsequent analyses also demonstrated that concussed athletes with higher symptom burden and a longer recovery time had greater reductions in FA and increased AD, RD, along with increased neurite dispersion. This study provides the first longitudinal evaluation of concussion from acute injury to RTP using combined DTI and NODDI, significantly enhancing our understanding of the effects of concussion on white matter microstructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".