Neurophysiological evidence of motor imagery training in Parkinson’s disease: a case series study
Bibliographic record
Abstract
Abstract Background: Motor imagery (MI) has become an increasingly popular rehabilitation tool for individuals with motor impairments. However, it has been proposed that individuals with Parkinson’s Disease (PKD) may not benefit from MI due to impairments in motor learning. Objective: This case series study investigated the effects of a 4-week MI training protocol on MI ability in three male individuals with PKD, with an emphasis on examining changes in brain responses. Methods: Training was completed primarily at home, via audio recordings, and emphasized the imagination of functional tasks. MI ability was assessed pre and post-training using subjective and objective imagery questionnaires, alongside an electroencephalographic (EEG) recording of a functional MI task. EEG analysis focused on the mu rhythm, as it has been proposed that suppression in the mu rhythm may reflect MI success and motor learning. Previous research has indicated that mu suppression is impaired in individuals with PKD, and may contribute to the disease’s associated deficits in motor learning. Results: Following training, all three participants improved in MI accuracy, but reported no notable improvements in MI vividness. Greater suppression in the mu rhythm was also exhibited by all three participants post-training. Conclusion: These results suggest the participants learned from the training protocol and that individuals with PKD are responsive to MI training. Further research on a larger scale is needed to verify the findings and determine if this learning translates to improvements in motor function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".