Functional, but minimal microstructural brain changes present in aging Canadian football league players years after retirement
Bibliographic record
Abstract
This brain imaging study examined subjects with a history of repetitive concussive and sub-concussive impacts sustained over the course of their careers in the Canadian Football League (CFL). We hypothesized that microstructural and functional abnormalities, assessed using diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rsfMRI) respectively, would be present in these retired athletes, that are not present in matched controls. Seventeen aging, retired CFL players (aged 58.5±6.2y, ranged 45–66) completed three neuropsychological tests, and had anatomical, diffusion and functional MRI scans performed. Healthy age- and sex-matched control data (n = 2117) were used to develop a subject-specific and region-wise Z-scoring approach. Regional DTI fractional anisotropy (FA) and rsfMRI signal complexity (fractal dimension; FD) Z-score data was further analyzed as a subject-specific total, left, and right injury burden (IB) value for each MRI metric. Microstructural abnormality was detected in 6 of 17 subjects based on DTI FA. The rsfMRI data showed 4 subjects with higher total FDIB, and several regions had Z-score outliers detected in multiple subjects. The right pre-motor cortex, right hippocampus dentate gyrus, and right visual cortex were the most abnormally functioning grey matter brain regions. Total FAIB was negatively correlated with career length, social functioning, and significantly with emotional well-being, and positively correlated with physical health. Total FDIB was negatively correlated with energy and fatigue and general health, and positively correlated with age, career length, and education. This study provides evidence of brain changes years after professional athletes have retired.
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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 | 0.001 | 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) | 0.001 | 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".