A Dance of Neural Rhythms: Coupling of Slow Oscillations and Spindle Activity During Sleep, in Relation to Memory and Ageing
Bibliographic record
Abstract
Sleep undergoes both quantitative and qualitative changes across the lifespan, which accompanies age-related declines in memory. Contemporary research suggests that synchronized cross-frequency coupling (CFC) of slow oscillations (SO) and spindles during sleep is a neural mechanism of overnight memory consolidation, and it has been hypothesized that an age-related “de-coupling” may contribute to memory impairments in older adults. However, evidence for this ageing hypothesis is currently lacking, as most available studies of SO-spindle CFC and memory focus on young adults, with few studies available to directly compare younger and older groups. \nThe goal of my dissertation is to enhance current thinking about SO-spindle CFC and its relations with ageing and overnight memory consolidation. This work includes two interrelated studies, both examining SO-spindle CFC during sleep in relation to performance on a sleep-dependent declarative memory task. The first study, with 25 healthy seniors, showed that SO-spindle CFC was stable across two recording nights, and that a slow spindle coupling phase closer to the SO up-state predicted better memory consolidation. An exploratory analysis also highlighted potential interactive effects between distinct CFC measures in predicting memory. My second study builds on the analyses from Study 1 with a new sample of 16 older adults compared to 16 young adults, all who completed an expanded 3-night protocol that included a similar memory task and a cognitive control task. This second study showcased age-group differences in coupling during sleep but did not find similarly strong evidence for relations between CFC and memory. \nOverall, this work demonstrates 1) a preserved association between certain measures of CFC during sleep and memory in older adults; 2) that coupling dynamics may differ between younger and older adults, but the overall magnitude of coupling may not; and 3) how relations between CFC, ageing, and memory can vary across distinct analytic contexts. Results are discussed in reference to the impact of ageing on brain oscillations and CFC, distinctions in the methods between my two studies as compared to the extant literature, and the impact of even small changes in signal processing decisions on coupling metrics and their association with memory and ageing.
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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".