Closed-loop auditory stimulation (CLAS) does not improve sleep or declarative memory in chronic insomnia
Bibliographic record
Abstract
ABSTRACT Objective Investigate whether auditory closed-loop stimulation (CLAS) applied during sleep could be beneficial for sleep and declarative memory in individuals with chronic insomnia. Methods We performed a randomized crossover sham-controlled study on 27 individuals with chronic insomnia to assess changes in sleep and declarative memory between a night with CLAS (i.e., 2-ON-OFF blocks auditory tones locked to slow wave up-states during NREM) and a SHAM night. We conducted assessments of memory (word paired-associate learning task) and sleep (morning questionnaire, polysomnographic recordings) during both nights. Results We found that applying CLAS in a population of individuals with chronic insomnia led to an acute increase in SO amplitude after auditory stimulation. However, we found no beneficial effect of a single night of CLAS on subjective and objective sleep or declarative overnight memory performance. There was an association between the increase in SO density during CLAS with fewer markers of sleep fragmentation (i.e., sleep fragmentation index, arousals), suggesting interindividual differences in response to CLAS in chronic insomnia. Conclusions CLAS stimulation applied during NREM sleep in individuals with chronic insomnia is feasible but did not show consistent effects on EEG markers of sleep regulation. A subgroup of individuals with insomnia may be more responsive to the impact of CLAS on sleep maintenance.
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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) | 0.002 | 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".