Ripples in scalp EEGs of children: co-occurrence with sleep-specific transients and occurrence across sleep stages
Bibliographic record
Abstract
Study Objectives: A dialogue between hippocampal ripples (80-250 Hz) and neocortical sleep-specific transients is important for memory consolidation. Physiological neocortical ripples can be recognized in scalp EEGs of children. We investigated how often scalp-EEG recorded ripples co-occur with different types of sleep-specific transients, the distribution and spatial extent of ripples with and without co-occurring sleep-specific transients, and the occurrence of ripples across sleep stages. Methods: We marked ripples in daytime sleep-EEGs of 19 children and determined for each ripple if it co-occurred with a sleep-specific transient. We compared the distribution of ripples without co-occurring sleep-specific transients to the distribution of all ripples. We estimated the spatial extent of simultaneously occurring ripples by counting how many EEG regions they comprised. We compared ripple rate per sleep stage using Friedman's analysis of variance and Wilcoxon signed-rank test. Results: 74.4 % of ripples co-occurred with sleep-specific transients: 27.8 % with vertex waves, 14.7 % with hypnagogic hypersynchrony, 13.7 % with slow waves, 12.2 % with spindles, and 6.0 % with K-complexes. Ripples without co-occurring sleep-specific transients showed the same central dominance but a significantly less pronounced midline dominance than the overall distribution pattern. Spatial extent was larger when ripples co-occurred with sleep-specific transients. Ripple rates during nonrapid eye movement (N) sleep stages N1 and N2 were higher than during N3 (T = 22.00, p = 0.02 and T = 23.00, p = 0.01). Conclusions: Scalp-EEG recorded physiological ripples co-occur with various sleep-specific EEG-transients, especially with vertex waves. These ripples occur most frequently during light sleep.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".