Improved cognitive-motor processing speed and decreased functional connectivity after high intensity aerobic exercise in individuals with chronic stroke
Bibliographic record
Abstract
Abstract After stroke, impaired motor performance is linked to an increased demand for cognitive resources. Aerobic exercise improves cognitive function in healthy populations and may be effective in altering cognitive function post-stroke. We sought to determine if high intensity aerobic exercise paired with motor training in individuals with chronic stroke alters cognitive-motor function and functional connectivity between the dorsolateral prefrontal cortex (DLPFC), a key region for cognitive-motor processes, and the sensorimotor network. Twenty-five participants with chronic stroke were randomly assigned to exercise (n = 14; 66 ± 11 years; 4 females), or control (n = 11; 68 ± 8 years; 2 females) groups. Both groups performed five-days of paretic upper limb motor training after either high intensity aerobic exercise (3 intervals of 3 minutes each, total exercise duration of 23-minutes) or watching a documentary (control). Resting-state fMRI, and TMT-A and B were recorded pre- and post-intervention. Both groups showed implicit motor sequence learning ( p < .001), but there was no added benefit of exercise ( p = .738). Regardless of group, the changes in task score ( p = .025), and dwell time ( p = .043) were correlated with a decrease in DLPFC-sensorimotor network functional connectivity ( p = .024), which is thought to reflect a reduction in the cognitive demand and increased automaticity. The exercise group experienced greater overall cognitive-motor improvements measured with the trail making test part A (TMT-A: task score: p = .012; dwell time: p = .024; movement time: p = .567). Aerobic exercise may improve cognitive-motor processing speed post-stroke. Significance statement After stroke, impaired motor performance is linked to an increased demand for cognitive resources. In our work we show that high intensity aerobic exercise paired with an implicit motor learning task improves cognitive-motor processing speed and reduces resting-state functional connectivity between the dorsolateral prefrontal cortex and the sensorimotor network in individuals living with chronic stroke. These data likely reflect a reduction in cognitive resource dependence during a cognitive-motor task after stroke and a shift towards cognitive-motor automaticity.
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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) | 0.002 | 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".