NREM sleep EEG slow waves in autistic and typically developing children: Morphological characteristics and scalp distribution
Bibliographic record
Abstract
Summary Autism is a developmental disorder with a neurobiological aetiology. Studies of the autistic brain identified atypical developmental trajectories that may lead to an impaired capacity to modulate electroencephalogram activity during sleep. We assessed the topography and characteristics of non‐rapid eye movement sleep electroencephalogram slow waves in 26 boys aged between 6 and 13 years old: 13 with an autism spectrum disorder and 13 typically developing. None of the participants was medicated, intellectually disabled, reported poor sleep, or suffered from medical co‐morbidities. Results are derived from a second consecutive night of polysomnography in a sleep laboratory. Slow waves (0.3–4.0 Hz; >75 µV) were automatically detected on artefact‐free sections of non‐rapid eye movement sleep along the anteroposterior axis in frontal, central, parietal and occipital derivations. Slow wave density (number per minute), amplitude (µV), slope (µV s −1 ) and duration (s) were computed for the first four non‐rapid eye movement periods. Slow wave characteristics comparisons between groups, derivations and non‐rapid eye movement periods were assessed with three‐way mixed ANOVAs. Slow wave density, amplitude, slope and duration were higher in anterior compared with most posterior derivations in both groups. Children with autism spectrum disorder showed lower differences in slow waves between recording sites along the anteroposterior axis than typically developing children. These group differences in the topography of slow wave characteristics were stable across the night. We propose that slow waves during non‐rapid eye movement sleep could be an electrophysiological marker of the deviant cortical maturation in autism linked to an atypical functioning of thalamo‐cortical networks.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".