High-intensity Interval Training Enhances Corticospinal Excitability And Motor Learning
Bibliographic record
Abstract
High intensity interval training (HIIT) is effective in modulating brain excitability. This effect on neuroplasticity is thought to promote the learning and retention of motor skills. However, it is unclear whether the timing of HIIT with respect to the training of the motor skill can aid in its acquisition and retention. PURPOSE: To examine the effect of HIIT on motor learning when performed before vs after multiple sessions of motor task practice, and to examine the effect of HIIT on corticospinal excitability (CSE) and intracortical networks. METHODS: 28 participants (16 F) performed 4 sessions of HIIT (3 sets of 3 min at 90% maximal power output (POmax) separated by 2 min at 50% POmax). During session 1 participants performed a maximal graded exercise test to determine their POmax, which was used to determine workload for the HIIT sessions. HIIT-mediated changes in CSE and intracortical networks were assessed by transcranial magnetic stimulation, a form of non-invasive brain stimulation, during session 2. During sessions 3-5, either before or after HIIT, participants trained on a complex motor skill requiring them to reproduce trajectories on a touchscreen, where learning was confirmed via a decrease in performance error across training sessions. RESULTS: Using Bayesian linear mixed effects modeling a credible difference in performance was observed between the HIIT groups by the third session, where the group who performed HIIT after task practice showed improved learning when compared to the group performing HIIT before (probability distribution (pd) = 95.4%, d = 0.22; Fig 1). Further, HIIT was shown to drive an increase in CSE both immediately after (pd = 98.44%, d = 0.30) and 30 minutes after the exercise session (pd = 99.25%, d = 0.30; Fig 1). CONCLUSION: This study supports the use of HIIT for motor learning through consolidation of a motor skill, and that HIIT-mediated changes in CSE and intracortical networks are a likely mechanism underlying the effect.
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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".