Transcranial Direct Current Stimulation to Augment Motor Imagery Training: A Systematic Review
Bibliographic record
Abstract
Motor imagery training (MIT) is a widely used technique for motor learning and recovery. To optimize training outcomes, researchers have explored the integration of MIT with complementary approaches. One such approach is transcranial direct current stimulation (tDCS), which also shows promise as a method to enhance motor performance and neuroplasticity. This systematic review aimed to synthesize the current evidence on the synergistic effects of MIT combined with tDCS, with a specific focus on behavioral outcomes. Heterogeneous methods across 16 studies with 432 participants in total, including both healthy and clinical populations, yielded mixed results. Nonetheless, the potential of anodal tDCS applied over the primary motor cortex to augment the beneficial effects of MIT for motor performance in healthy participants is suggested by the current literature. The benefits of combining tDCS with MIT in brain-computer interface (BCI) protocols with stroke patients were less clear, which may relate to population differences, timing of stimulation, or the similarity between outcome measures and trained tasks. Overall, small samples and heterogeneous methods limit interpretation of the findings of combined intervention studies, and further research should aim to measure both behavioral and neurophysiological outcomes in larger samples as well as examining longer-term synergistic effects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".