Modulating Brain Excitability with Cardiovascular Exercise in Chronic Stroke: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Corticospinal excitability (CSE) is a surrogate measure of neuroplasticity within the corticospinal tract measured with transcranial magnetic stimulation (TMS). A single bout of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) cardiovascular exercise (CE) have been both demonstrated to transiently augment CSE in people with stroke. However, the effect of multiple sessions of CE and exercise intensity is unknown. Objectives: We conducted a randomized controlled trial (NCT03614585) to examine the effect of a HIIT vs. MICT CE program on CSE measures obtained using TMS applied on the ipsilesional (ILH) and contralesional (CLH) hemispheres. Methods: Fifty-six individuals with cortical and/or subcortical stroke lesions in the chronic phase of stroke recovery (>6 months) were randomly assigned to a 12-week HIIT (n = 28) or MICT (n = 28) program. CSE measures were obtained at baseline and post-intervention. Linear mixed model analyses were conducted to compare changes in CSE measures and their respective interhemispheric ratios. Results: CSE changes were not significantly different between HIIT and MICT but exploratory analyses showed that, when analyzed together, both groups increased resting motor evoked potential (MEP) amplitude ( P = .003), decreased resting motor threshold (rMT) ( P = .030), and reduced intracortical facilitation (ICF) ( P = .049) in the ILH. No CSE changes in the CLH were observed. HIIT and MICT rebalanced interhemispheric rMT ( P = .020) and ICF ratios ( P = .040), and increased resting MEP amplitude ratio ( P = .020). Conclusions: Chronic CE increases excitatory ILH CSE measures and reduces interhemispheric imbalances but intensity does not have a moderating effect. More studies are needed to determine the functional relevance of exercise-induced changes in CSE in post-stroke recovery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".