Anodal transcranial direct current stimulation enhances the effects of motor imagery training in a finger tapping task
Bibliographic record
Abstract
Motor imagery (MI) training and anodal transcranial direct current stimulation (tDCS) applied over the primary motor cortex can independently improve hand motor function. The main objective of this double-blind, sham-controlled study was to examine whether anodal tDCS over the primary motor cortex could enhance the effects of MI training on the learning of a finger tapping sequence. Thirty-six right-handed young human adults were assigned to one of three groups: (i) who performed MI training combined with anodal tDCS applied over the primary motor cortex; (ii) who performed MI training combined with sham tDCS; and (iii) who received tDCS while reading a book. The MI training consisted of mentally rehearsing an eight-item complex finger sequence for 13 min. Before (Pre-test), immediately after (Post-test 1), and at 90 min after (Post-test 2) MI training, the participants physically repeated the sequence as fast and as accurately as possible. An anova showed that the number of sequences correctly performed significantly increased between Pre-test and Post-test 1 and remained stable at Post-test 2 in the three groups (P < 0.001). Furthermore, the percentage increase in performance between Pre-test and Post-test 1 and Post-test 2 was significantly greater in the group that performed MI training combined with anodal tDCS compared with the other two groups (P < 0.05). As a potential physiological explanation, the synaptic strength within the primary motor cortex could have been reinforced by the association of MI training and tDCS compared with MI training alone and tDCS alone.
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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 | 0.000 |
| 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 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".