Cerebral Activation During Initial Motor Learning Forecasts Subsequent Sleep-Facilitated Memory Consolidation in Older Adults
Bibliographic record
Abstract
Older adults exhibit deficits in motor memory consolidation; however, little is known about the cerebral correlates of this impairment. We thus employed fMRI to investigate the neural substrates underlying motor sequence memory consolidation, and the modulatory influence of post-learning sleep, in healthy older adults. Participants were trained on a motor sequence and retested following an 8-h interval including wake or diurnal sleep as well as a 22-h interval including a night of sleep. Results demonstrated that a post-learning nap improved offline consolidation across same- and next-day retests. This enhanced consolidation was reflected by increased activity in the putamen and the medial temporal lobe, including the hippocampus, regions that have previously been implicated in sleep-dependent neural plasticity in young adults. Moreover, for the first time in older adults, the neural substrates subserving initial motor learning, including the putamen, cerebellum, and parietal cortex, were shown to forecast subsequent consolidation depending on whether a post-learning nap was afforded. Specifically, sufficient activation in a motor-related network appears to be necessary to trigger sleep-facilitated consolidation in older adults. Our findings not only demonstrate that post-learning sleep can enhance motor memory consolidation in older adults, but also provide the system-level neural correlates of this beneficial effect.
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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".