White matter during concussion recovery: Comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it