Disturbances in Brain Physiology Due to Season Play: A Multi-Sport Study of Male and Female University Athletes
Bibliographic record
Abstract
High-performance university athletes experience frequent exertion, resulting in disrupted biological homeostasis, but it is unclear to what extent brain physiology is affected. We examined whether athletes without overtraining symptoms show signs of increased neurophysiological stress over the course of a single athletic season, and whether the effects are modified by demographic factors of age, sex and concussion history, and sport-related factors of contact exposure and season length. Fifty-three university-level athletes were recruited from multiple sports at a single institution and followed longitudinally from beginning of season (BOS) to end of season (EOS) and 1 month afterwards, with a subset followed up at the subsequent beginning of season. MRI was used to comprehensively assess white matter (WM) diffusivity, cerebral blood flow (CBF), and brain activity, while overtraining symptoms were assessed with Hooper's Index (HI). Although athletes did not report increased HI scores, they showed significantly increased white matter diffusivity and decreased CBF at EOS and 1 month afterwards, with recovery at follow-up. Global brain activity was not significantly altered though, highlighting the ability of the brain to adapt to exercise-related stressors. Male athletes had greater white matter diffusivity at EOS, but female athletes had greater declines in CBF at 1 month afterwards. Post-season changes in MRI measures were not related to change in HI score, age, concussion history, contact exposure, or length of athletic season. Hence, the brain shows substantial but reversible neurophysiological changes due to season play in the absence of overtraining symptoms, with effects that are sex-dependent but otherwise insensitive to demographic variations. These findings provide new insights into the effects of training and competitive play on brain health.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".