Slowing of electroencephalogram in rapid eye movement sleep behavior disorder
Bibliographic record
Abstract
Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by a loss of atonia and an increase in phasic muscle activity during REM sleep, leading to complex nocturnal motor behaviors. Brainstem structures responsible for the pathogenesis of RBD are also implicated in cortical activation. To verify the hypothesis that electroencephalogram (EEG) activation will be impaired in RBD, we performed quantitative analyses of waking and REM sleep EEG in 15 idiopathic RBD patients and 15 age- and gender-matched healthy subjects. During wakefulness, RBD patients showed a considerably higher theta power in frontal, temporal, and occipital regions with a lower beta power in the occipital region. The dominant occipital frequency was significantly lower in RBD. During REM sleep, beta power in the occipital region was lower in RBD. This study shows for the first time an impaired cortical activation during both wakefulness and REM sleep in idiopathic RBD, despite an absence of changes on sleep architecture compared with controls. EEG slowing in these patients may represent an early sign of central nervous system dysfunction, perhaps paralleled by subclinical cognitive deficits. The topographical distribution of EEG slowing and possible pathophysiological mechanisms are discussed in light of the known association between RBD and neurodegenerative disorders.
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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 | 0.001 | 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".