Linked MRI signatures of the brain's acute and persistent response to concussion in female varsity rugby players
Bibliographic record
Abstract
Acute brain changes are expected after concussion, yet there is growing evidence of persistent abnormalities well beyond clinical recovery and clearance to return to play. Multiparametric MRI is a powerful approach to non-invasively study structure-function relationships in the brain, however it remains challenging to interpret the complex and heterogeneous cascade of brain changes that manifest after concussion. Emerging conjunctive, data-driven analysis approaches like linked independent component analysis can integrate structural and functional imaging data to produce linked components that describe the shared inter-subject variance across images. These linked components not only offer the potential of a more comprehensive understanding of the underlying neurobiology of concussion, but can also provide reliable information at the level of an individual athlete. In this study, we analyzed resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) within a cohort of female varsity rugby players (n = 52) through the in- and off-season, including concussed athletes (n = 21) who were studied longitudinally at three days, three months and six months after a diagnosed concussion. Linked components representing co-varying white matter microstructure and functional network connectivity characterized (a) the brain's acute response to concussion and (b) persistent alterations beyond clinical recovery. Furthermore, we demonstrate that these long-term brain changes related to specific aspects of a concussion history and allowed us to monitor individual athletes before and longitudinally after a diagnosed concussion.
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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".