Diffusion Tensor Imaging in Contact and Non-Contact University-Level Sport Athletes
Bibliographic record
Abstract
Subconcussive hits to the head and physical fitness both have been associated with alterations in white matter (WM) microstructure in partly overlapping areas of the brain. The aim of the present study was to determine whether WM damage associated with repeated exposure to subconcussive hits to the head in university level contact sports athletes is modulated by high levels of fitness. To this end, 72 students were recruited: 24 athletes practicing a varsity contact sport (A-CS), 24 athletes practicing a varsity non-contact sport (A-NCS), and 24 healthy non-athletes (NA). Participants underwent a magnetic resonance imaging session that included diffusion-weighted imaging. Between-groups, statistical analyses were performed with diffusion tensor imaging measures extracted by tractometry of sections of the corpus callosum and the corticospinal tract. Most significant effects were found in A-NCS who exhibited higher fractional anisotropy (FA) values than A-CS in almost all segments of the corpus callosum and in the corticospinal tract. The A-NCS also showed higher FA compared with NA in the anterior regions of the corpus callosum and the corticospinal tracts. No group difference was found between the A-CS and the NA groups. These data suggest that repeated subconcussive hits to the head lead to anisotropic changes in the WM that may counteract the beneficial effects associated with high levels of fitness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".