Anodal Transcranial Direct Current Stimulation Over Prefrontal Cortex Slows Sequence Learning in Older Adults
Bibliographic record
Abstract
Aging is associated with declines in sensorimotor function. Several studies have demonstrated that transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, can be combined with training to mitigate age-related cognitive and motor declines. However, in some cases, the application of tDCS disrupts performance and learning. Here, we applied anodal tDCS either over the left prefrontal cortex (PFC), right PFC, supplementary motor complex (SMC), the left M1, or in a sham condition while older adults (n = 63) practiced a Discrete Sequence Production (DSP), an explicit motor sequence, task across 3 days. We hypothesized that stimulation to either the right or left PFC would enhance motor learning for older adults, based on the extensive literature showing increased prefrontal cortical activity during motor task performance in older adults. Contrary to our predictions, stimulation to the right and left PFC resulted in slowed motor learning, as evidenced by a slower reduction rate of reduction of reaction time and the number of sequence chunks across trials relative to sham in session one and session two, respectively. These findings suggest an integral role of the right PFC early in sequence learning and a role of the left PFC in chunking in older adults, and contribute to mounting evidence of the difficultly of using tDCS in an aging population.
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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 | 0.000 |
| 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".