Females versus males exhibit greater brain activation in sensorimotor regions during motor imagery after stroke
Bibliographic record
Abstract
Motor imagery (MI; the mental rehearsal of movement) activates sensorimotor regions, providing the basis for its effectiveness as an intervention for motor recovery after stroke. Yet, the impact of biological sex on MI-related brain activation after stroke is unexplored. Here, we investigated sex-related differences in MI-related brain activation after stroke and explored associations between questionnaire-based MI ability and MI-related brain activation. Thirty-four individuals with chronic stroke performed MI of a complex, upper-limb movement with their paretic arm/hand. Brain activation was captured using functional magnetic resonance imaging (fMRI). Individual differences in MI ability were also assessed prior to the scan using an established MI questionnaire. MI-related brain activation was observed across sensorimotor regions. Yet, group-level contrasts revealed greater activation in sensorimotor regions for females vs. males, while greater activation in the cerebellar and parietal regions was noted in males vs. females. Females also recruited greater ipsilesional regions vs. males. Questionnaire-based MI ability was associated with brain activation localized to occipital regions. Our findings suggest that females and males may respond differently to MI-based tasks. Further, we did not find a relationship between questionnaire-based data and activity in sensorimotor regions. This finding suggests that multiple measures of MI may be needed to assess its impact. Overall, this work informs the use of MI after 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 | 0.000 |
| 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) | 0.008 | 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".