Association of Sleep Spindle Activity With Cognitive Decline in Early Clinical Stages of Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sleep architecture, including spindles and slow oscillations, is disrupted in Alzheimer disease (AD). How changes in these sleep elements relate to cognitive decline is less clear. Our objectives were to examine changes in sleep macroarchitecture and microarchitecture in early clinical stages of AD compared with older adult controls (CTLs) and to investigate their associations with longitudinal cognitive change. METHODS: This was both a cross-sectional and longitudinal study performed at Mass General Brigham Hospitals, where participants with early AD or CTLs underwent overnight ambulatory scalp EEG and longitudinal cognitive testing. We compared sleep microarchitectural features extracted from the EEG, including spindle activity, across the brain topography and between groups. We then performed longitudinal analyses using mixed-effects models to test the association of these sleep features with changes in cognition on the Montreal Cognitive Assessment (MoCA), collected annually for up to 7 years. RESULTS: = 0.005) were each associated with a faster rate of cognitive decline. DISCUSSION: Temporal lobe sleep spindle activity is reduced in early clinical stages of AD and is associated with a faster rate of cognitive decline. Our results underscore the importance of including temporal lobe measurements when assessing sleep neurophysiology in AD, which is not standard in polysomnography. Future work examining the relationship between AD biomarkers and reduced spindle activity is needed to elucidate the potential mechanisms underlying these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".