Sleep spindle density and temporal clustering are associated with sleep-dependent memory consolidation in Parkinson’s disease
Bibliographic record
Abstract
Abstract Sleep is required for successful memory consolidation. Sleep spindles, bursts of oscillatory activity occurring during non-REM sleep, are known to be crucial for this process and, recently, it has been proposed that the temporal organization of spindles into clusters might additionally play a role in memory consolidation. In Parkinson’s disease, spindle activity is reduced, and this reduction has been found to be predictive of cognitive decline. However, it remains unknown whether alterations in sleep spindles in Parkinson’s disease are predictive of sleep-dependent cognitive processes like memory consolidation, leaving open questions about the possible mechanisms linking sleep and more general cognitive state in Parkinson’s patients. The current study sought to fill this gap by recording overnight polysomnography and measuring overnight declarative memory consolidation in a sample of thirty-five Parkinson’s patients. Memory consolidation was measured using a verbal paired-associates task administered before and after the night of recorded sleep. We found that lower sleep spindle density at frontal leads during non-REM stage 3 was associated with worse overnight declarative memory consolidation. We also found that patients who showed less temporal clustering of spindles exhibited worse declarative memory consolidation. These results suggest alterations to sleep spindles, which are known to be a consequence of Parkinson’s disease, might represent a mechanism by which poor sleep leads to worse cognitive function in Parkinson’s patients. Statement of significance Sleep — particularly spindle activity — is critical for memory consolidation, a core cognitive process. Changes to the architecture and oscillations of sleep are well documented in Parkinson’s disease (PD) and have been associated with worse overall cognition. However, whether altered sleep plays a causal role in this relationship, by directly interfering with sleep-dependent cognitive processes, or whether it represents a mere epiphenomenon of advancing disease, remains unknown. Our study is the first to investigate a possible direct relationship between sleep and cognition in PD. We show that sleep spindles and their temporal clustering into ‘trains’ relate to impairments in overnight declarative memory consolidation in patients. These findings are an important first step towards identifying modifiable sources of 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".