Effects of transcranial direct current stimulation of primary somatosensory cortex on vibrotactile detection and discrimination
Bibliographic record
Abstract
Anodal transcranial direct current stimulation (a-tDCS) of primary somatosensory cortex (S1) has been shown to enhance tactile spatial acuity, but there is little information as to the underlying neuronal mechanisms. We examined vibrotactile perception on the distal phalanx of the middle finger before, during, and after contralateral S1 tDCS [a-, cathodal (c)-, and sham (s)-tDCS]. The experiments tested our shift-gain hypothesis, which predicted that a-tDCS would decrease vibrotactile detection and discrimination thresholds (leftward shift of the stimulus-response function with increased gain/slope) relative to s-tDCS, whereas c-tDCS would have the opposite effects (relative to s-tDCS). The results showed that weak a-tDCS (1 mA, 20 min) led to a reduction in both vibrotactile detection and discrimination thresholds to 73-76% of baseline during the application of the stimulation in subjects categorized as responders. These effects persisted after the end of a-tDCS but were absent 30 min later. Most, but not all, subjects showed a decrease in threshold (8/12 for detection; 9/12 for discrimination). Intersubject variability was explained by a ceiling effect in the discrimination task. c-tDCS had no significant effect on either detection or discrimination threshold. Taken together, our results supported our shift-gain hypothesis for a-tDCS but not c-tDCS.
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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 | 0.001 |
| 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".