Reduced Regional NREM Sleep Slow-Wave Activity Is Associated With Cognitive Impairment in Parkinson Disease
Bibliographic record
Abstract
Growing evidence implicates a distinct role of disturbed slow-wave sleep in neurodegenerative diseases. Reduced non-rapid eye movement (NREM) sleep slow-wave activity (SWA), a marker of slow-wave sleep intensity, has been linked with age-related cognitive impairment and Alzheimer disease pathology. However, it remains debated if SWA is associated with cognition in Parkinson disease (PD). Here, we investigated the relationship of regional SWA with cognitive performance in PD. In the present study, 140 non-demented PD patients underwent polysomnography and were administered the Montréal Cognitive Assessment (MoCA) to screen for cognitive impairment. We performed spectral analysis of frontal, central, and occipital sleep electroencephalography (EEG) derivations to measure SWA, and spectral power in other frequency bands, which we compared to cognition using linear mixed models. We found that worse MoCA performance was associated with reduced 1–4 Hz SWA in a region-dependent manner ( F 2, 687 =11.67, p < 0.001). This effect was driven by reduced regional SWA in the lower delta frequencies, with a strong association of worse MoCA performance with reduced 1–2 Hz SWA ( F 2, 687 =18.0, p < 0.001). The association of MoCA with 1–2 Hz SWA (and 1–4 Hz SWA) followed an antero-posterior gradient, with strongest, weaker, and absent associations over frontal (rho = 0.33, p < 0.001), central (rho = 0.28, p < 0.001), and occipital derivations, respectively. Our study shows that cognitive impairment in PD is associated with reduced NREM sleep SWA, predominantly in lower delta frequencies (1–2 Hz) and over frontal regions. This finding suggests a potential role of reduced frontal slow-wave sleep intensity in cognitive impairment in PD.
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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".