Slowing of Motor Imagery after a Right Hemispheric Stroke
Bibliographic record
Abstract
The temporal congruence between real and imagined movements is not always preserved after stroke. We investigated the dependence of temporal incongruence on the side of the hemispheric lesion and its link with working memory deficits. Thirty-seven persons with a chronic stroke after a right or left hemispheric lesion (RHL : n = 19; LHL : n = 18) and 32 age-matched healthy persons (CTL) were administered a motor imagery questionnaire, mental chronometry and working memory tests. In contrast to persons in the CTL group and LHL subgroup, persons with a RHL had longer movement times during the imagination than the physical execution of stepping movements on both sides, indicating a reduced ability to predict movement duration (temporal incongruence). While motor imagery vividness was good in both subgroups, the RHL group had greater visuospatial working memory deficits. The bilateral slowing of stepping movements in the RHL group indicates that temporal congruence during motor imagery is impaired after a right hemispheric stroke and is also associated with greater visuospatial working memory deficits. Findings emphasize the need to use mental chronometry to control for movement representation during motor imagery training and may indicate that mental practice through motor imagery will have limitations in patients with a right hemispheric stroke.
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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".