Slow Spindle Trains During Daytime Naps are Associated with Improved Declarative Memory Consolidation
Bibliographic record
Abstract
Abstract Memory consolidation refers to the process by which newly encoded memories are strengthened and retained over time, and ample evidence indicates that sleep supports this process for both procedural and declarative memories. Although sleep spindles during non-rapid eye movement (NREM) sleep have been associated to consolidation, it remains unclear whether all spindle types contribute equally. Spindles vary in frequency and topography–slow spindles (≤12.5Hz) predominating over frontal regions, whereas fast spindles (>12.5Hz) peak parietally – and recent work suggests that procedural memory consolidation during overnight sleep is related to the temporal organization of spindles in ‘trains’ (i.e., events occurring <6s apart). Here we investigated whether a similar mechanism operates for declarative memory during daytime naps. Participants were assigned to a Nap (N=23) or No-Nap (N=15) group, and completed an object-spatial location task involving 36 item-location associations. Memory was assessed immediately after learning and again following a 90-minute nap or an equivalent wake period. Results showed that the Nap group exhibited significantly better delayed memory, as measured by combined recall-recognition score, and a greater proportion of participants maintained or improved their performance. In the Nap group, memory performance correlated with local spindle density at frontal and parietal sites, and, critically, with the proportion of slow spindles clustered in trains during NREM2. These findings suggest the temporal organization of slow spindles into clusters support declarative memory consolidation, pointing to a shared spindle-based mechanism across domains.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".