Repeated unilateral handgrip contractions alter functional connectivity and improve contralateral limb response times
Bibliographic record
Abstract
In humans, motor learning is underpinned by changes in sensorimotor network functional connectivity (FC). Unilateral contractions increase FC in the ipsilateral primary motor cortex (M1) and supplementary motor area (SMA); areas involved in motor planning and execution of the contralateral hand. Therefore, unilateral contractions are a promising approach to augment motor performance in the contralateral hand. In a within-participant, randomized, cross-over design, 15 right-handed adults had two magnetic resonance imaging (MRI) sessions, where functional-MRI and MR-Spectroscopic Imaging were acquired before and after repeated right-hand contractions at either 5% or 50% maximum voluntary contraction (MVC). Before and after scanning, response times (RTs) were determined in both hands. Nine minutes of 50% MVC contractions resulted in decreased handgrip force in the contracting hand, and decreased RTs and increased handgrip force in the contralateral hand. This improved motor performance in the contralateral hand was supported by significant neural changes: increased FC between SMA-SMA and increased FC between right M1 and right Orbitofrontal Cortex. At a neurochemical level, the degree of GABA decline in left M1, left and right SMA correlated with subsequent behavioural improvements in the left-hand. These results support the use of repeated handgrip contractions as a potential modality for improving motor performance in the contralateral hand.
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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) | 0.001 | 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".