Cortical Thinning Explains Changes in Sleep Slow Waves during Adulthood
Bibliographic record
Abstract
Sleep slow waves (SWs) change considerably throughout normal aging. In humans, SWs are generated and propagate on a structural backbone of highly interconnected cortical regions that form most of the default mode network, such as the insula, cingulate cortices, temporal lobe, parietal lobe, and medial frontal lobe. Regions in this network undergo cortical thinning and breakdown in structural and functional connectivity over the course of normal aging. In this study, we investigated how changes in cortical thickness (CT), a measure of gray matter integrity, are involved in modifications of sleep SWs during adulthood in humans. Thirty young (mean age = 23.49 years; SD = 2.79) and 33 older (mean age = 60.35 years; SD = 5.71) healthy subjects underwent a nocturnal polysomnography and T1 MRI. We show that, when controlling for age, higher SW density (nb/min of nonrapid eye movement sleep) was associated with higher CT in cortical regions involved in SW generation surrounding the lateral fissure (insula, superior temporal, parietal, middle frontal), whereas higher SW amplitude was associated with higher CT in middle frontal, medial prefrontal, and medial posterior regions. Mediation analyses demonstrated that thinning in a network of cortical regions involved in SW generation and propagation, but also in cognitive functions, explained the age-related decrease in SW density and amplitude. Altogether, our results suggest that microstructural degradation of specific cortical regions compromise SW generation and propagation in older subjects, critically contributing to age-related changes in SW oscillations.
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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) | 0.001 | 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".