Transcranial electrical stimulation improves cognitive training effects in healthy elderly adults with low cognitive performance
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy of transcranial direct (tDCS) or alternating current stimulation (tACS) in boosting cognitive training efficiency in healthy older adults. We further explored whether such improvements depend on general cognitive performance or age. METHODS: In this randomized, sham-controlled study, 59 healthy elderly participants (mean age 71.7) were assigned to receive computer-based cognitive training (10 sessions, 50 min, twice weekly) combined with tDCS (2 mA), tACS (5 Hz), or sham stimulation over the left dorsolateral prefrontal cortex (20 minutes). Cognitive performance was assessed with the Montreal Cognitive Assessment (MoCA), and a cognitive composite score derived from a broad neuropsychological test battery before and immediately after the intervention as well as at 6 and 12 months follow-ups. RESULTS: Performance in the cognitive composite score improved significantly in all groups but was not further modulated by neurostimulation. Additional analyses revealed that participants with a low initial MoCA score (<1SD) improved significantly more in the tDCS than in the sham group. CONCLUSION: TDCS increased the efficacy of cognitive training, but only in participants with initially low general cognitive performance. SIGNIFICANCE: Cognitive interventions including tDCS should address baseline performance as modulating factor of cognitive outcomes.
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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".