EEG Patterns Prior to Motor Activations of Parasomnias: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Non-rapid eye movement (NREM) parasomnias are defined as abnormal nocturnal behaviors that typically arise from the NREM sleep stage 3 during the first sleep cycle. The polysomnographic studies showed an increase in sleep fragmentation and an atypical slow wave activity (SWA) in participants with NREM parasomnias compared to healthy controls. To date, the pathophysiology of NREM parasomnias is still poorly understood. The recent investigation of the EEG patterns immediately before parasomnia events could shed light on the motor activations' processes. This systematic review aims to summarize empirical evidence about these studies and provide an overview of the methodological issues. METHODS: A systematic literature search was carried out in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The documents obtained were evaluated using the Newcastle-Ottawa Scale (NOS). RESULTS: Nine studies were included in the qualitative synthesis. The major evidence revealed an increased slow frequency EEG activity immediately before the motor activations in frontal and central areas and increased beta activity in the anterior cingulate cortices. DISCUSSION: The investigation of EEG patterns before parasomniac episodes could provide new insight into the study of NREM parasomnia pathophysiology. The high- and low-frequency EEG increase before the episodes could represent a predictive electrophysiological pattern of the motor activations' onset. Overall, identifying specific sleep markers before parasomnias might also help differentiate between NREM parasomnias and other motor sleep disorders. Different methodological protocols should be integrated for overcoming the lack of consistent empirical findings. Thus, future studies should focus on the topographical examination of canonical EEG frequency bands to better understand spatial and time dynamics before the episodes and identify the networks underlying the onset of activations.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".