Sleep spindles and slow waves are physiological markers for age-related changes in gray matter in brain regions supporting problem-solving skills
Bibliographic record
Abstract
As we age, the added benefit of sleep for memory consolidation is lost. One of the hallmark age-related changes in sleep is the reduction of sleep spindles and slow waves. Gray matter neurodegeneration is related to both age-related changes in sleep and age-related changes in memory, including memory for problem-solving skills. Here, we investigated whether spindles and slow waves might serve as biological markers for neurodegeneration of gray matter and for the related memory consolidation deficits in older adults. Forty healthy young adults (20-35 yr) and 30 healthy older adults (60-85 yr) were assigned to either nap or wake conditions. Participants were trained on the Tower of Hanoi in the morning, followed by either a 90-min nap opportunity or period of wakefulness, and were retested afterward. We found that age-related changes in sleep spindles and slow waves were differentially related to gray matter intensity in young and older adults in brain regions that support sleep-dependent memory consolidation for problem-solving skills. Specifically, we found that spindles were related to gray matter in neocortical areas (e.g., somatosensory and parietal cortex), and slow waves were related to gray matter in the anterior cingulate, hippocampus, and caudate, all areas known to support problem-solving skills. These results suggest that both sleep spindles and slow waves may serve as biological markers of age-related neurodegeneration of gray matter and the associated reduced benefit of sleep for memory consolidation in older adults.
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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 | 0.001 | 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) | 0.002 | 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".