0096 Changes in Network Criticality and Directed Functional Connectivity as Sleep Progresses: Insights into the Restorative Functions of Sleep
Bibliographic record
Abstract
Abstract Introduction Sleep plays a crucial role in restoring the brain’s functions and preparing it for the cognitive demands of the next day. However, little is known about how the brain’s functional dynamics support these functions. This study aims to investigate how network criticality and directed functional connectivity changes as sleep progresses across the night. Methods We recorded overnight polysomnography using 256-channel electroencephalography (EEG) in 16 adults (50.2±19.1 years, 6 females). We extracted the first and last episodes of each sleep stage (N=86), as well as wakefulness before and after the sleep episode (N=32), with a minimal duration of 3.5 minutes. To assess criticality, we calculated chaoticity, proximity to edge-of-chaos criticality (PECC), the pair correlation function (PCF), and Lempel-Ziv complexity (LZC). Additionally, the directed phase lag index (dPLI) was used to calculate the feedback dominance index (FDI) in the alpha frequency band (8-13 Hz). Two-way repeated measures ANOVAs, with post-hoc tests using Tukey’s correction, were used to compare sleep-wake stages and timing (first vs. last episode). Results Across all sleep and wakefulness stages, PCF showed higher values in the later episodes than in the earlier ones (p< 0.001). LZC showed similar results, with later episodes having higher complexity than earlier ones across all states (p< 0.01). Finally, FDI was higher towards the end of the night compared to the beginning across all states (p< 0.05), but only for the left hemisphere. Chaoticity and PECC showed no significant changes between first and last episodes. Conclusion These findings suggest that sleep may lead to a gradual restoration of criticality, complexity and feedback-dominant connectivity as the night progresses. The increase of PCF suggests that the brain’s activity moves closer to a critical point, and the higher LZC indicates more complexity in later episodes, both pointing to more optimal information processing, adaptability, and computational efficiency. Finally, the increase of FDI suggests that sleep restores anterior-to-posterior information flow as the night advances, potentially supporting the reintegration of distributed neural networks necessary for cognitive functioning and consciousness upon awakening. Support (if any)
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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) | 0.002 | 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".