EEG microstate sequences exhibit precise temporal orchestration with frequency-specific alterations in multimer dynamics during reversible unconsciousness, with beta band sequences showing consistent increases in peak power and decreases in center frequency during both deep sedation and N3 sleep.
Key Findings
Results
Robust periodic components consistently emerged within microstate sequences across theta, alpha, beta, and gamma frequency bands and persisted across distinct states of consciousness.
Periodicity was observed across broadband and canonical frequency bands including theta, alpha, beta, and gamma bands.
These periodic components were found during both wakefulness and reversible unconsciousness (anesthesia and sleep).
The Chaos Game Representation (CGR) spectral analysis framework was used to detect these periodic components.
Periodicity persisted across distinct consciousness states, suggesting a fundamental property of microstate sequences.
Results
Multimer structure and conditional duration distribution constitute the underlying mechanism of microstate periodicity, as demonstrated by both surrogate data deconstruction and hierarchical generative model reconstruction.
Surrogate data analysis was used to deconstruct and test the contribution of multimer structure to periodicity.
A hierarchical generative model was used to reconstruct and confirm the mechanism.
Both approaches provided converging evidence pointing to multimer structure and conditional duration distribution as the generative mechanisms.
These findings establish the mechanistic basis for the observed spectral periodicities in microstate sequences.
Results
Temporal smoothing of EEG microstate sequences abolishes the intrinsic periodic components.
Application of temporal smoothing to microstate sequences eliminated the periodic components identified in CGR spectra.
This finding has methodological implications, suggesting that commonly used temporal smoothing procedures remove genuine temporal structure.
The result indicates that intrinsic periodicity is a real feature of unsmoothed microstate sequences rather than an artifact.
Results
During both deep sedation and N3 sleep, the beta band microstate sequence exhibited a consistent increase in peak power and a decrease in center frequency, producing highly characteristic patterns in CGR spectra.
The beta band alterations were observed consistently across two distinct forms of reversible unconsciousness: deep sedation (anesthesia) and N3 sleep.
Changes included both an increase in peak power and a decrease in center frequency within the beta band microstate sequence.
These changes resulted in 'highly characteristic patterns in the CGR spectra' specific to unconscious states.
The convergence across anesthesia and sleep strengthens the finding as a potential neurophysiological biomarker for consciousness assessment.
Results
A data-driven algorithm was developed to extract multimers and calculate their metrics, revealing distinct, frequency-dependent alterations in multimer dynamics during reversible unconsciousness.
The algorithm was described as 'data-driven' for extracting multimers from EEG microstate sequences.
Multimer metrics were calculated and compared across consciousness states.
Alterations in multimer dynamics were frequency-dependent, differing across theta, alpha, beta, and gamma bands.
The transition to unconsciousness was associated with 'a shift towards specific dynamical regimes' in multimer dynamics.
Methods
A spectral analysis framework based on Chaos Game Representation (CGR) was employed to investigate multimer-based dynamics of EEG microstates.
CGR was applied to analyze microstate sequences across broadband and canonical frequency bands.
The framework was applied to data from both anesthesia (deep sedation) and sleep (N3 stage) paradigms.
CGR enabled detection of periodic components and characterization of spectral patterns in discrete microstate sequences.
The method provided a 'solid methodological foundation for investigating higher-order temporal structures.'
Results
The study identified that microstate sequences exhibit preserved temporal hierarchy during reversible unconsciousness despite frequency-specific alterations in dynamical regimes.
Temporal hierarchy was preserved across both anesthesia and sleep states.
Frequency-specific changes were identified rather than a global collapse of temporal organization.
The findings offer 'promising neurophysiological biomarkers for consciousness assessment.'
Results provide 'novel insights into the temporal organization of large-scale neural dynamics.'
What This Means
This research suggests that the brain's large-scale electrical activity, as measured by EEG, follows precise, repeating temporal patterns that can be detected by analyzing sequences of 'microstates' — brief, recurring patterns of brain activity lasting fractions of a second. Using a mathematical framework called Chaos Game Representation (CGR), the researchers found that these patterns repeat periodically across multiple brain frequency bands (including theta, alpha, beta, and gamma), and that this periodicity arises from the structure of recurring microstate combinations called 'multimers.' Importantly, a common data processing step called temporal smoothing was found to erase these genuine patterns, which has implications for how EEG microstate analyses should be conducted.
During unconsciousness — whether induced by anesthesia or occurring naturally during deep (N3) sleep — the researchers found specific changes in the beta frequency band: the periodicity became stronger (higher peak power) and slowed down (lower center frequency). These changes appeared in both sleep and anesthesia, suggesting they may reflect something fundamental about the loss of consciousness rather than being specific to just one condition. The researchers also identified that different frequency bands showed different patterns of change during unconsciousness, indicating that the transition from wakefulness to unconsciousness involves a shift in specific dynamical regimes rather than a simple global shutdown of brain activity.
This research suggests that multimer-based analysis of EEG microstates could provide useful biomarkers for assessing states of consciousness, potentially relevant to monitoring anesthesia depth or characterizing sleep stages. The methodological framework developed here — including the data-driven multimer extraction algorithm and CGR spectral analysis — offers new tools for studying how the brain organizes activity over time, and may help researchers better understand the neural basis of consciousness.
Wang C, Zhou D. (2026). Preserved temporal hierarchy but frequency-specific alterations in dynamical regimes of EEG microstate multimers during reversible unconsciousness.. NeuroImage. https://doi.org/10.1016/j.neuroimage.2026.121781