Corticomotor Excitability in Parkinson's Disease During Observation, Imagery and Imitation of Action: Effects of Rehabilitation Using Wii Fit and Comparison to Healthy Controls
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is responsible for a progressive motor impairment. Wii Fit game represents an innovative treatment modality using external visual cueing, which can be used to help patients with PD in executing motor tasks and improving function. OBJECTIVES: The objectives of this study were (1) to compare lower limb corticomotor activation in subjects with and without PD during action observation (OBS), motor imagery (IMAG), and imitation (IMIT), and (2) to evaluate the effects of a 6-week training program using Wii Fit on corticomotor excitability during these 3 conditions. METHODS: Using transcranial magnetic stimulation, we assessed motor evoked potentials (MEPs) in Quadriceps Femoris (QF) and Soleus (SOL) muscles in 8 subjects with moderate PD (PD group) and 8 healthy subjects (HS group) before and after 6 weeks of training using Wii Fit during OBS, IMAG and IMIT of a mini-squat action. RESULTS: At baseline, PD showed significantly less MEPs in QF during OBS, IMAG and IMIT compared with HS. In SOL, MEPs were reduced in PD only during IMIT. Following training, PD increased QF MEPs during OBS, which was opposite to the decrease in QF MEPs noted in HS. During IMAG, increased MEPs were found only in PD for SOL. During IMIT, both groups significantly reduced MEPs in both QF and SOL. CONCLUSION: Subjects with PD may have improved their dual-task ability considering changes observed during OBS and IMIT. Visual cueing training using Wii Fit may provide an interesting rehabilitation method for PD.
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 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) | 0.001 | 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".