A single bout of high-intensity aerobic exercise facilitates response to paired associative stimulation and promotes sequence-specific implicit motor learning
Bibliographic record
Abstract
The objectives of the present study were to evaluate the impact of a single bout of high-intensity aerobic exercise on 1) long-term potentiation (LTP)-like neuroplasticity via response to paired associative stimulation (PAS) and 2) the temporal and spatial components of sequence-specific implicit motor learning. Additionally, relationships between exercise-induced increases in systemic brain-derived neurotrophic factor (BDNF) and response to PAS and motor learning were evaluated. Sixteen young healthy participants completed six experimental sessions, including the following: 1) rest followed by PAS; 2) aerobic exercise followed by PAS; 3) rest followed by practice of a continuous tracking (CT) task and 4) a no-exercise 24-h retention test; and 5) aerobic exercise followed by CT task practice and 6) a no-exercise 24-h retention test. The CT task included an embedded repeated sequence allowing for evaluation of sequence-specific implicit learning. Slope of motor-evoked potential recruitment curves generated with transcranial magnetic stimulation showed larger increases when PAS was preceded by aerobic exercise (59.8% increase) compared with rest (14.2% increase, P = 0.02). Time lag of CT task performance on the repeated sequence improved under the aerobic exercise condition from early (-100.8 ms) to late practice (-75.2 ms, P < 0.001) and was maintained at retention (-79.2 ms, P = 0.004) but did not change under the rest condition (P > 0.16). Systemic BDNF increased on average by 3.4-fold following aerobic exercise (P = 0.003), but the changes did not relate to neurophysiological or behavioral measures (P > 0.42). These results indicate that a single bout of high-intensity aerobic exercise can prime LTP-like neuroplasticity and promote sequence-specific implicit motor learning.
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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 | 0.000 |
| 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".