Guided Motor Imagery in Healthy Adults and Stroke: Does Strategy Matter?
Bibliographic record
Abstract
BACKGROUND: Motor imagery (MI) enhances physical performance and skill acquisition in healthy and neurorehabilitation populations, yet little is known about the use of strategies to guide MI. OBJECTIVES: To examine the relative effectiveness of visual, auditory, and combined (visual + auditory) cueing of an imagined finger abduction task on corticomotor excitability. METHODS: A total of 15 young (20-35 years) and 15 older people (over 55 years) and 10 people with chronic stroke, who could make voluntary movements of selected muscles, participated. Motor evoked potentials (MEPs, primary outcome) were measured following transcranial magnetic stimulation applied while participants imagined abducting their index finger under guidance of cueing strategies. Amplitudes of the MEPs from the first dorsal interosseous (FDI), abductor pollicis brevis (APB), and abductor digiti minimi (ADM) muscles were compared with rest, contrasted with MEPs elicited during active task performance, and expressed relative to rest to reflect facilitation. RESULTS: Cued MI enhanced MEPs in all groups, preferentially to the target FDI muscle. In stroke, APB was also facilitated. ADM was least affected by cueing. Analyses of simple effects of condition on FDI MEPs in each group revealed that visual cueing was most effective in young people, whereas auditory cueing was most effective in healthy older people and when directed at the nonparetic side in stroke ( P < .04). On the paretic side, strategies were equally effective. In all cases, MEPs were largest during physical performance. CONCLUSIONS: Cued MI augments corticomotor excitability associated with healthy and paretic muscles related to the imagined task. Age should be considered when selecting a cueing strategy for maximum effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".