Neurophysiology of Downhill Mountain Bike Athletes—Benchmark Assessments of Event-Related Potentials
Bibliographic record
Abstract
Background: Downhill mountain biking (MTB) is a high-velocity sport where riders are potentially exposed to impacts and concussion. Rapidly obtained Event-Related Potentials (ERPs) are a promising use of portable electroencephalography for clinical assessment of concussion-related neurological impairment at the point of care. However, the baseline neuroelectric profile of this specific sporting population is not fully understood. This study investigated the amplitude and latency of the N100, P300 and N400 ERPs at baseline among MTB athletes by age, sex and caffeine consumption. Methods: MTB athletes, aged 13–45, competing in the 2024 Whistler Crankworx MTB competition were recruited for this cross-sectional analysis. Self-reported sex, age and caffeine consumption were captured and pre-event baseline neurological evaluations of the N100, P300 and N400 ERPs (n = 92) were completed using a portable electroencephalograph (NeuroCatch). Results: Females had a 1.12 µV higher N100 amplitude (95%CI; 0.15, 2.08, p = 0.024) and 2.11 µV higher P300 amplitude (95%CI; 0.69, 3.52, p = 0.004) than males. Further, the N400 amplitude was 0.05 µV lower per year that age increases (95%CI; −0.08, −0.01, p = 0.011). Greater than normal caffeine consumption was associated with a 1.39 µV lower (95%CI; −2.68, −0.09, p = 0.036) and 18.96 ms (−28.29, −9.64, p < 0.001) faster N100 response. Conclusions: Age, sex and caffeine consumption must be considered to inform the clinical implement of ERPs for monitoring concussion in extreme sport athletes.
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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 | 0.000 | 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".