Choice RT and corticospinal excitability differences following bi-hemispheric TDCS
Bibliographic record
Abstract
Transcranial direct current stimulation (tDCS) uses a weak electrical current applied to the scalp to alter neural excitability, whereby anodal stimulation increases and cathodal stimulation decreases the excitability of underlying structures. Excitability changes to the motor cortex have also been linked to changes in motor performance (e.g., reaction time; RT). A previous study used tDCS with electrodes placed over both motor cortices in a choice RT task involving left or right wrist extension. They predicted that RT would be faster contralateral to the anode and slower contralateral to the cathode. However, RT was decreased for both limbs, irrespective of electrode placement. This unexpected finding may have been due to a reversal in the effect of cathodal stimulation resulting in increased excitability under both electrodes. To investigate this possibility, neural excitability was monitored using transcranial magnetic stimulation (TMS) over left motor cortex during a choice RT task following bi-hemispheric tDCS. In seven participants, three tDCS protocols were applied (anode left-cathode right, anode right-cathode left, and sham; counterbalanced), with excitability and choice RT measured at 6-minute intervals for 36 minutes. Preliminary analyses indicate that choice RT was marginally reduced (p=.25) in each hand following only bi-hemispheric tDCS with anode-left. Although non-significant (p=.16), excitability was also largest following tDCS in the anode-left protocol compared to sham. While further data collection is required to confirm these results, they suggest that bi-hemispheric tDCS with the anode over left motor cortex can reduce RT bilaterally through changes in cortical excitability.Acknowledgments: Supported by NSERC and the Ontario Ministry of Research and Innovation and Science
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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 | 0.002 |
| 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) | 0.002 | 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".