A multi-modal application of magnetic resonance imaging (MRI) techniques to identify and quantify brain abnormalities in retired professional football players
Bibliographic record
Abstract
High contact sports put athletes at a higher risk of sustaining a concussion. This work focused on assessing regional brain health in aging, retired Canadian Football League (rCFL) players years to decades after retirement. Advanced, quantitative magnetic resonance imaging (MRI) techniques were implemented to identify and quantify microstructural brain white matter damage, cognitive functional signal characteristics (fractal dimension (FD) and amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF)), and cerebral blood flow (CBF) dysregulation. Due to the high reproducibility of diffusion tensor imaging (DTI) and resting state functional MRI (rsfMRI), a Z-scoring approach exploring outliers relative to a large normative dataset was implemented to examine each rCFL subject individually. However, arterial spin labelling (ASL) data is more sensitive to scanner inconsistencies, therefore a group-wise analysis was performed with the CBF and ASL spatial coefficient of variance (ASL sCoV) data. Minimal microstructural damage was detected in the rCFL subjects, but a substantial amount of functional and CBF abnormalities were present. The FD was significantly reduced in 48 of 91 regions-of-interest (ROIs) examined, and the four rCFL subjects with the highest number of abnormal ROIs all exhibited worse motor speed, social functioning and general health scores than the other rCFL subjects. Furthermore, the ALFF analysis identified the cerebellum, parietal lobe ROIs, and central sub-cortical ROIs to be consistently abnormal. Finally, the temporal occipital fusiform cortex, superior parietal gyrus, caudate nucleus, and the cerebellum were significantly abnormal bilaterally based on CBF and ASL sCoV values, which also correlated with worse physical functioning and elevated daily chronic pain. This work adds to the growing literature that brain changes are present later in life that may be related to concussions and repetitive sub-concussive head impacts sustained years earlier. Several consistently damaged ROIs also correlated with adverse clinical presentations to indicate areas of future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".