The effectiveness of auditory stimulation in sleep varies with thalamocortical spindle phase
Bibliographic record
Abstract
Slow oscillations and sleep spindles are neural events that both occur during non-rapid eye movement sleep and are implicated in sleep-dependent memory consolidation. Their temporal co-occurrence, or 'coupling', is thought to support sleep-dependent memory processes. The roles of these neural events can be explored through non-invasive brain stimulation techniques. Closed-loop auditory stimulation, which precisely times sounds to enhance or disrupt neural events, can induce slow oscillation and spindle activity, improving memory in some individuals. While spindle-targeted stimulation is now feasible, the effect of slow oscillation-spindle coupling and spindle phase on neurophysiological outcomes remains unexplored. This study investigates how spindle phase timing relates to the neurophysiological effects of closed-loop auditory stimulation timed to slow oscillation up-states. A secondary aim is to characterize predictors of inter-individual differences in stimulation effectiveness. Electroencephalography data collected across multiple nights were analysed from 16 healthy adults, with stimulation delivered at the slow oscillation up-state or withheld (sham condition). Results show that while slow wave activity shows enhancement with minimal phase-dependency, temporally-coordinated spindle activity emerges only in the peak and rising phases. In contrast, trough stimulation delays spindle activity, and stimulation during the falling phase produces no enhanced spindle activity. Across subjects, strength of slow wave and spindle activity was correlated at detection in each frequency band separately, but amplitude at detection did not predict response strength. These findings refine our understanding of sleep oscillation dynamics and inform future uses of closed-loop stimulation, with a view to advancing fundamental science and potentially restoring sleep and memory functions in clinical applications.
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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 | 0.002 |
| 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".