Can Slow-Wave Sleep Enhancement Improve Memory? A Review of Current Approaches and Cognitive Outcomes.
Bibliographic record
Abstract
Slow-wave sleep (SWS) is involved in the overnight consolidation of declarative memories. Recent efforts using auditory stimulation, slow-oscillatory transcranial direct current stimulation (so-tDCS), and pharmacological agents have targeted sleep slow-waves as a method for enhancing cognitive performance. However, no studies thus far have integrated current evidence to provide a preliminary review of the effects of SWS enhancement on memory and other cognitive outcomes. The objective of this review was to synthesize the results of recent experimental studies that have used auditory stimulation, electrical, and pharmacological methods to boost both SWS and cognitive performance. A systematic review was done to identify and consolidate all currently existing empirical studies in this area. We found that each stimulation method could enhance slow-wave power and/or SWS duration in human subjects. Closed-loop, in-phase auditory stimulation enhanced verbal declarative memory in healthy adults. Electrical stimulation using so-tDCS showed some efficacy in promoting verbal declarative memory, picture recognition memory, and location memory. Interleukin-6 and sodium oxybate enhanced declarative verbal memory, while tiagabine and sodium oxybate improved some non-memory measures of cognitive performance. There is some evidence that so-tDCS can also improve certain cognitive outcomes in clinical populations. Overall, future studies should recruit larger sample sizes drawn from more diverse populations, and determine clinical significance and effect sizes of each enhancement methodology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".