MétaCan
Menu
Back to cohort
Record W1978411851 · doi:10.1155/2012/297217

Slowing of Motor Imagery after a Right Hemispheric Stroke

2012· article· en· W1978411851 on OpenAlexafffund
Francine Malouin, Carol L. Richards, Anne Durand

Bibliographic record

VenueStroke Research and Treatment · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersCanadian Institutes of Health Research
KeywordsMedicineMotor imageryPhysical medicine and rehabilitationStroke (engine)Cognitive psychologyElectroencephalographyPsychiatryPsychologyBrain–computer interface

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.326
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueStroke Research and TreatmentSame topicMotor Control and AdaptationFrench-language works237,207