Phase-Amplitude Coupling of Theta and Gamma Rhythms During Rapid Eye Movement Sleep Impacting Memory Across the Lifespan
Bibliographic record
Abstract
Phase-amplitude coupling (PAC) between brain oscillations is thought to be an underlying neural mechanism of memory consolidation. Oscillation coupling may become weaker with greater age, possibly explaining natural memory decline across the lifespan, as suggested by studies of PAC during non-rapid-eye-movement sleep. Theta-gamma PAC (TGC) during wake is correlated with stronger encoding and better recall. However, it is unclear how TGC during rapid-eye-movement (REM) sleep correlates with memory or changes with age. I aimed to find TGC during REM sleep (REM TGC) affecting sleep-dependent memory consolidation that changes with age-related memory decline. We recorded scalp electroencephalography of good sleeping younger and older adults. Oscillatory data was extracted from filtered electroencephalography signals. Before sleep, participants learned a declarative memory or non-memory control task, then retested the respective task after sleep to measure memory consolidation. Memory consolidation was better in younger, compared to older, adults. REM TGC strength, measured by a modulation index, was not different between age groups nor task nights. Faster gamma coupling in a frontal channel was positively correlated with and predicts improvements in memory consolidation in younger adults. Slower gamma coupling in a central channel was positively correlated with memory consolidation in older adults. Our results suggest REM TGC strength is stable across the lifespan. However, the strength of faster TGC in younger and of slower TGC in older adults may improve memory consolidation. These results uncover more about how REM sleep and REM TGC changes across the lifespan, in relation to memory. \n \n \nKeywords: rapid eye movement sleep, theta-gamma phase-amplitude coupling, sleep-dependent memory consolidation, aging
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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 | 0.000 | 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) | 0.001 | 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".