Sleep spindles and rapid eye movement sleep as predictors of next morning cognitive performance in healthy middle‐aged and older participants
Bibliographic record
Abstract
Spindles and slow waves are hallmarks of non-rapid eye movement sleep. Both these oscillations are markers of neuronal plasticity, and play a role in memory and cognition. Normal ageing is associated with spindle and slow wave decline and cognitive changes. The present study aimed to assess whether spindle and slow wave characteristics during a baseline night predict cognitive performance in healthy older adults the next morning. Specifically, we examined performance on tasks measuring selective and sustained visual attention, declarative verbal memory, working memory and verbal fluency. Fifty-eight healthy middle-aged and older adults (aged 50-91 years) without sleep disorders underwent baseline polysomnographic sleep recording followed by neuropsychological assessment the next morning. Spindles and slow waves were detected automatically on artefact-free non-rapid eye movement sleep electroencephalogram. All-night stage N2 spindle density (no./min) and mean frequency (Hz) and all-night non-rapid eye movement sleep slow wave density (no./min) and mean slope (μV/s) were analysed. Pearson's correlations were performed between spindles, slow waves, polysomnography and cognitive performance. Higher spindle density predicted better performance on verbal learning, visual attention and verbal fluency, whereas spindle frequency and slow wave density or slope predicted fewer cognitive performance variables. In addition, rapid eye movement sleep duration was associated with better verbal learning potential. These results suggest that spindle density is a marker of cognitive functioning in older adults and may reflect neuroanatomic integrity. Rapid eye movement sleep may be a marker of age-related changes in acetylcholine transmission, which plays a role in new information encoding.
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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 | 0.001 |
| 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".