The effects of tDCS on speech fluency in people who stutter: A Narrative Review
Bibliographic record
Abstract
To improve the outcomes of current treatments for stuttering, researchers are looking at additional tools such as transcranial Direct Current Stimulation (tDCS). The purpose of this paper is to review the literature investigating the effectiveness of tDCS for people who stutter (PWS) and offer some directions for future research. To achieve the objective of this study, we conducted a narrative review of the existing literature. Articles were selected using these inclusion criteria: participants, tDCS protocol, control group, and primary outcomes. Our literature search was limited to studies published in English with no limits on the publication year. We only extracted data from studies that investigated the effects of tDCS on PWS. Systematic reviews, meta-analyses, and other forms of reviews regarding tDCS were not included. 101 studies were identified during the exploratory phase. Ultimately, 7 studies met our inclusion criteria. Of the included papers, five studies reported mixed effects, one study showed no effect, and one study showed positive effects of tDCS. We summarized the studies in terms of several methodological features and the observed effects from tDCS. We also used the SimNIBS software to compare the effects of different stimulation parameters on brain activity and outcomes. The studies reviewed in this study have reported a variable effectiveness of tDCS for enhancing speech fluency in PWS. As a result, it remains unclear whether tDCS is an effective tool for stuttering intervention, and the optimal stimulation parameters are not yet established. Several suggestions for future research are offered.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".