Association Between Day‐to‐Day Variation in Electroencephalographic (EEG) Sleep Features and Daytime Cognitive Function in Adults at Risk for Dementia
Bibliographic record
Abstract
BACKGROUND: Older adults experience considerable day-to-day variability in cognitive function. We aimed to test the hypothesis that this is in part related to sleep, and determine which EEG sleep features are most important in supporting day to day cognitive resilience. METHOD: We analyzed data from 149 adults at high risk for dementia participating in the Brain Health Pro (BHPro) study. At BHPro baseline, participants underwent up to 3 nights of overnight ambulatory EEG using the MUSE-S (Interaxon, Toronto, Canada) as well as multi-day app-based cognitive testing (MyCogHealth, Victoria, Canada). Of 350 participants, 149 had EEG and cognitive evaluation that overlapped by at least 1 day. We performed automated sleep staging and computed frontal NREM (N2 and N3) delta power and REM theta power. We used linear mixed effect models to relate each morning's composite global cognitive test results to the previous night's sleep measures. RESULT: 149 individuals had >=1 cognitive evaluation within 12 hours of an overnight EEG recording. Of these, 63 had 2 nights, and 37 had >=3 nights. Greater % REM sleep (+0.15 per 1SD greater REM sleep, SE 0.04 p = 0.0001) and relative REM theta power (+0.08 per 1SD greater relative REM theta power, SE 0.04, p = 0.02) the night before were associated with better cognitive performance the next morning, and there was a non-significant positive relationship (+0.06 per 1SD difference, SE 0.04, p = 0.11) between NREM delta power and cognitive performance the following morning. These effects were particularly strong in those with mild cognitive impairment (delta power interaction p = 0.055; theta power interaction p = 0.02) CONCLUSION: REM sleep theta power and NREM delta power may support day to day cognitive performance in older adults at high risk for dementia, particularly those with mild cognitive impairment, and may represent electrophysiologic therapeutic targets to support cognitive resilience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".