Older adults with lower working memory capacity benefit from transcranial direct current stimulation when combined with working memory training: A preliminary study
Bibliographic record
Abstract
Aging is a very diverse process: successful agers retain most cognitive functioning, while others experience mild to severe cognitive decline. This decline may eventually negatively impact one's everyday activities. Therefore, scientists must develop approaches to counteract or, at least, slow down the negative change in cognitive performance of aging individuals. Combining cognitive training and transcranial direct current stimulation (tDCS) is a promising approach that capitalizes on the plasticity of brain networks. However, the efficacy of combined methods depends on individual characteristics, such as the cognitive and emotional state of the individual entering the training program. In this report, we explored the effectiveness of working memory training, combined with tDCS to the right dorsolateral prefrontal cortex (DLPFC), to manipulate working memory performance in older individuals. We hypothesized that individuals with lower working memory capacity would benefit the most from the combined regimen. Thirty older adults took part in a 5-day combined regimen. Before and after the training, we evaluated participants' working memory performance with five working memory tasks. We found that individual characteristics influenced the outcome of combined cognitive training and tDCS regimens, with the intervention selectively benefiting old-old adults with lower working memory capacity. Future work should consider developing individualized treatments by considering individual differences in cognitive profiles.
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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) | 0.001 | 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".