Moderate-intensity exercise-induced changes in lactate do not predict changes in corticospinal excitability
Bibliographic record
Abstract
Aerobic exercise increases corticospinal excitability (CSE), creating an optimal environment for learning to occur. It has been hypothesized that this may be a result of lactate accumulation in the blood during exercise. However, while prior literature has linked moderate-intensity exercise with enhanced CSE, previous lactate-based work has mainly used maximal or fatiguing exercise. To date, the effect of lactate concentration on CSE during steady-state moderate-intensity exercise has not been investigated. Twenty-four participants (13F, mean age 23.7 ± 2.7) attended two separate study sessions. Session 1: Participants completed a maximal graded exercise test on a cycle ergometer. Session 2: Participants completed a moderate-intensity exercise session (20 min of cycling at 60% peak power output). Measures of CSE obtained from the first dorsal interosseous muscle were assessed via transcranial magnetic stimulation before, immediately after, and 10 min following the exercise session. Lactate was obtained via blood sample at these same timepoints. Linear regression demonstrated that lactate concentration did not predict an exercise-induced change in CSE immediately following exercise ( R 2 = 0.02184, F(1, 22) = 0.4913, p = 0.4907) or 10 min post-exercise ( R 2 = 0.02121, F(1, 22) = 0.4767, p = 0.4971). These results suggest that lactate is not the primary mechanism behind alterations in CSE driven by moderate-intensity exercise.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".