Central Fatigue Assessed by Transcranial Magnetic Stimulation in Ultratrail Running
Bibliographic record
Abstract
PURPOSE: The well-established central deficit in ultraendurance running races is not understood. The use of transcranial magnetic stimulation (TMS) in parallel with peripheral nerve stimulation provides insight into the source of these central changes. The aims of this study were to determine the presence and magnitude of voluntary activation deficits, especially supraspinal deficits, after a mountain trail-running race and to determine whether this can be explained by simultaneous changes in corticospinal excitability and intracortical inhibition. METHODS: Neuromuscular function (TMS and femoral nerve electrical stimulation) of the knee extensors was evaluated before and after a 110-km ultratrail in 25 experienced ultraendurance trail runners during maximal and submaximal voluntary contractions and in relaxed muscle. RESULTS: Voluntary activation assessed by both femoral nerve electrical stimulation (-26%) and TMS (-16%) decreased and were correlated (P < 0.01). Decreases in potentiated twitch and doublet amplitudes were correlated with decreased voluntary activation assessed by TMS (P < 0.05). There was increased motor-evoked potential (MEP) amplitude (P < 0.05) without change in cortical silent period (CSP) elicited by TMS at optimal stimulus intensity. Conversely, CSP at suboptimal TMS intensity increased (P < 0.05) without concurrent change in MEP amplitude. CONCLUSIONS: The present results demonstrate the development of a large central activation deficit assessed by TMS, indicating that cortical motoneurons are severely impaired in their ability to fire at optimal frequency or be fully recruited after an ultraendurance running race. MEP and CSP responses suggest a shift in the sigmoidal MEP-stimulus intensity relationship toward larger MEP at higher TMS intensity without change in inflection point of the curve and a left shift in the CSP-stimulus intensity relationship.
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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".