Spectral and network investigation reveals distinct power and connectivity patterns between phasic and tonic REM sleep
Bibliographic record
Abstract
Although rapid eye movement (REM) sleep is often thought of as a singular state, it consists of two substates, phasic and tonic REM, defined by the presence (respectively absence) of bursts of rapid eye movements. These two substates have distinct EEG signatures and functional properties. However, whether they exhibit regional specificities remains unknown. Using intracranial EEG recordings from 31 patients, we analyzed expert-labeled segments from tonic and phasic REM and contrasted them with wakefulness segments. We assessed the spectral and connectivity content of these segments using Welch's method to estimate power spectral density and the phase locking value to assess functional connectivity. Overall, we found a widespread power gradient between low and high frequencies (p < 0.05, Cohen's d = 0.17 ± 0.20), with tonic REM being dominated by lower frequencies (p < 0.01, d = 0.18 ± 0.08), and phasic REM by higher frequencies (p < 0.01, d = 0.18 ± 0.19). However, some regions, such as the occipito-temporal areas as well as medial frontal regions, exhibit opposite trends. Connectivity was overall higher in all bands except in the low and high ripple frequency bands in most networks during tonic REM (p < 0.01, d = 0.08 ± 0.09) compared to phasic REM. Yet, functional connections involving the visual network were always stronger during phasic REM when compared to tonic REM. These findings highlight the spatiotemporal heterogeneity of REM sleep which is consistent with the concept of focal sleep in humans.
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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 | 0.001 |
| 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) | 0.000 | 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".