Anodal tDCS over the primary motor cortex improves motor imagery benefits on postural control: A pilot study
Bibliographic record
Abstract
Performing everyday actions requires fine postural control, which is a major focus of functional rehabilitation programs. Among the various range of training methods likely to improve balance and postural stability, motor imagery practice (MIP) yielded promising results. Transcranial direct current stimulation (tDCS) applied over the primary motor cortex was also found to potentiate the benefits of MIP on upper-limb motor tasks. Yet, combining both techniques has not been tested for tasks requiring fine postural control. To determine the impact of MIP and the additional effects of tDCS, 14 participants performed a postural control task before and after two experimental (MIP + anodal or sham tDCS over the primary motor cortex) and one control (control task + sham tDCS) conditions, in a double blind randomized study. Data revealed a significant decrease of the time required to perform the postural task. Greater performance gains were recorded when MIP was paired with anodal tDCS and when the task involved the most complex postural adjustments. Altogether, findings highlight short-term effects of MIP on postural control and suggest that combining MIP with tDCS might also be effective in rehabilitation programs for regaining postural skills in easily fatigable persons and neurologic populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".