Does Right-Hemispheric Anodal tDCS Enhance the Impact of Script Training in Chronic Aphasia? A Single-Subject Experimental Study
Bibliographic record
Abstract
Background: Script training is an aphasia treatment approach that has been demonstrated to have a positive effect on communication of individuals with aphasia; however, it is time intensive as a therapeutic modality. To augment therapy-induced neuroplasticity, transcranial direct current stimulation (tDCS) may be implemented. tDCS has been paired with other speech-language treatments, however, has not been investigated with script training. Aims: The purpose of this study was to determine if tDCS improves communication proficiency when paired with script training, compared to script training alone. Methods and Procedures: A single-subject experimental design was implemented with a participant with non-fluent aphasia, using two scripts across treatment conditions: script training with sham-tDCS, and script training with anodal-tDCS. Treatment sessions were 75 min long, administered three times weekly. Anodal tDCS was implemented for 20 min with a current of 1.5 mA over the right inferior frontal gyrus. Results: Large effect sizes were obtained on script mastery for both stimulation conditions (anodal d2 = 9.94; sham d2 = 11.93). tDCS did not improve script accuracy, however, there was a significant improvement in the rate of change of script pace relative to baseline (3.99 seconds/day, p < 0.001) in the anodal tDCS condition. Conclusion: Despite a null tDCS result on accuracy, the script training protocol increased script performance to a near-fluent level of communication. There is preliminary evidence to suggest that tDCS may alter the rate of script acquisition, however, further research to corroborate this finding is required. Implications for future studies are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".