The effects of transcranial direct current stimulation on corticospinal excitability: A systematic review of nonsignificant findings
Bibliographic record
Abstract
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that can modulate brain activity through the application of low-intensity electrical currents. Based on its reported effects on corticospinal excitability (CSE), tDCS has been used to study cognition in healthy individuals and reduce symptoms in a variety of clinical conditions. Despite its increasing popularity as a research and clinical tool, high interindividual variability has been reported in the response to protocols using transcranial magnetic stimulation (TMS) to assess tDCS-induced changes in CSE leading to several nonsignificant findings. In this systematic review, studies that reported no significant modulation of CSE following tDCS were identified from PubMed and Embase (Ovid) databases. Forty-three articles were identified where demographic, TMS and tDCS parameters were extracted. Overall, stimulation parameters, CSE measurements and participant characteristics were similar to those described in studies reporting positive results and were likewise heterogeneous between studies. Small sample sizes and inadequate blinding were notable features of the reviewed studies. This systematic review suggests that studies reporting nonsignificant findings do not markedly differ from those reporting significant modulation of CSE.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".