Transcranial Direct Current Stimulation Over Motor Areas Improves Reaction Time in Parkinson's Disease
Bibliographic record
Abstract
Background: Transcranial direct current stimulation (tDCS) has been shown to modulate cortical motor excitability and improve bradykinesia symptoms in Parkinson's disease. It is unclear how targeting different cortical motor areas with tDCS may differentially influence upper limb function for individuals diagnosed with PD. Objective: This study investigated whether anodal tDCS applied separately to the primary motor cortex and the supplementary motor area would improve upper limb function for individuals with Parkinson's disease. In addition, a startling acoustic stimulus was used to differentiate between the effect of stimulation on motor preparatory and initiation processes associated with upper limb movements. Methods: Eleven participants with idiopathic Parkinson's disease performed two upper limb simple reaction time tasks, involving elbow extension or a button press before and after either anodal tDCS or sham tDCS was applied over the primary motor cortex or supplementary motor area. A loud, startling stimulus was presented on a selection of trials to involuntarily trigger the prepared action. Results: Anodal tDCS led to improved premotor reaction time in both tasks, but this was moderated by reaction time in pre-tDCS testing, such that individuals with slower pre-tDCS reaction time showed the greatest reaction time improvements. Startle-trial reaction time was not modified following tDCS, suggesting that the stimulation primarily modulated response initiation processes. Conclusion: Anodal tDCS improved response initiation speed, but only in slower reacting individuals with PD. However, no differences attributable to tDCS were observed in clinical measures of bradykinesia or kinematic variables, suggesting that reaction time may represent a more sensitive measure of some components of bradykinesia.
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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".