Assessing Functional and Structural Connectivity in Former Professional Athletes
Bibliographic record
Abstract
Recently there has been considerable attention directed towards the increased risk for head injuries that athletes face while participating in high impact sports. Furthermore, there is also heightened interest in the asymptomatic sport related sub-concussive blows, commonly experienced during play, that possibly lead to long term neurological deficits. Purpose: The goal of this study was to investigate retired professional athletes of the Canadian Football League with a history of sport-related concussions, using several advanced MRI methods. The ultimate goal being the identification of any potential synergistic effects between a history of sport-related concussions, and exacerbated cognitive decline later on in life. Materials and Methods: Twenty former professional athletes of the Canadian Football League were scanned using a GE Discovery MR750 3T MRI with a 32-channel RF-coil. Axial FSPGR-3D images were used to define rs-BOLD and DTI scans. Seed based network analysis of the DMN was performed on rs-BOLD data. Voxel-wise tensor fitting of DTI data provided the means for estimating several tensor metrics. Results were normalized through comparison with a database of healthy controls. Potential associations between functional connectivity, white matter integrity, and cortical thickness measures were correlated with retired athlete position and years of professional play. Results: We found widespread cortical thinning in retired CFL subjects, alongside significant increases in axial and mean diffusivity in the corona radiata and splenium and genu of the corpus callosum compared to controls. Seed based correlation analysis of the DMN network revealed interrupted connectivity in retired athletes. Athlete age, po- sition, and number of years played appear to be factors in overall core white matter microstructural integrity. Conclusions: When compared to an age and sex matched control population, differences were observed both in functional and structural con- nectivity, suggesting that even years after retiring the brains of these former athletes still exhibit signs of damage.
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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) | 0.002 | 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".