Lateralized effects of post-learning transcranial direct current stimulation on motor memory consolidation in older adults: An fMRI investigation
Bibliographic record
Abstract
Previous research has consistently demonstrated that older adults have difficulties transforming recently learned movements into robust, long-lasting memories (i.e., motor memory consolidation). One potential avenue to enhance consolidation in older individuals is the administration of transcranial direct current stimulation (tDCS) to task-relevant brain regions after initial learning. Although this approach has shown promise, the underlying cerebral correlates have yet to be revealed. Moreover, it is unknown whether the effects of tDCS are lateralized, an open question with implications for rehabilitative approaches following predominantly unilateral neurological injuries. In this research, healthy older adults completed a sequential motor task before and 6 h after receiving anodal or sham stimulation to right or left primary motor cortex (M1) while functional magnetic resonance images were acquired. Unexpectedly, anodal stimulation to right M1 following left-hand sequence learning significantly hindered consolidation as compared to a sham control, whereas no differences were observed with left M1 stimulation following right-hand learning. Impaired performance following right M1 stimulation was paralleled by sustained engagement of regions known to be critical for early learning stages, including the caudate nucleus and the premotor and parietal cortices. Thus, post-learning tDCS in older adults not only exerts heterogenous effects across the two hemispheres but can also disrupt ongoing memory processing.
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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".