Uncovering the hidden effects of repetitive subconcussive head impact exposure: A mega‐analytic approach characterizing seasonal brain microstructural changes in contact and collision sports athletes
Bibliographic record
Abstract
Repetitive subconcussive head impacts (RSHI) are believed to induce sub-clinical brain injuries, potentially resulting in cumulative, long-term brain alterations. This study explores patterns of longitudinal brain white matter changes across sports with RSHI-exposure. A systematic literature search identified 22 datasets with longitudinal diffusion magnetic resonance imaging data. Four datasets were centrally pooled to perform uniform quality control and data preprocessing. A total of 131 non-concussed active athletes (American football, rugby, ice hockey; mean age: 20.06 ± 2.06 years) with baseline and post-season data were included. Nonparametric permutation inference (one-sample t tests, one-sided) was applied to analyze the difference maps of multiple diffusion parameters. The analyses revealed widespread lateralized patterns of sports-season-related increases and decreases in mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) across spatially distinct white matter regions. Increases were shown across one MD-cluster (3195 voxels; mean change: 2.34%), one AD-cluster (5740 voxels; mean change: 1.75%), and three RD-clusters (817 total voxels; mean change: 3.11 to 4.70%). Decreases were shown across two MD-clusters (1637 total voxels; mean change: -1.43 to -1.48%), two RD-clusters (1240 total voxels; mean change: -1.92 to -1.93%), and one AD-cluster (724 voxels; mean change: -1.28%). The resulting pattern implies the presence of strain-induced injuries in central and brainstem regions, with comparatively milder physical exercise-induced effects across frontal and superior regions of the left hemisphere, which need further investigation. This article highlights key considerations that need to be addressed in future work to enhance our understanding of the nature of observed white matter changes, improve the comparability of findings across studies, and promote data pooling initiatives to allow more detailed investigations (e.g., exploring sex- and sport-specific effects).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".