The brain's large-scale networks reorganize constantly, even at rest. EEG microstates—brief periods of stable scalp electrical activity—correspond to fMRI-defined resting-state networks and exhibit scale-free, long-range temporal correlations. This paper proposes modeling microstate sequences with nonlinear stochastic differential equations that form a noisy network attractor. A single-layer network of four nodes reproduces transition probabilities between microstates but not the heavy-tailed residence time distributions. A two-layer network with a hidden layer captures these heavy tails and long-range correlations. Fitting these models to EEG data recorded inside and outside an MRI scanner shows that separating EEG from fMRI machine noise causes a loss of information, reflected in differences in the long tail of dwell-time distributions.
The stream of consciousness is thought to be composed of discrete brain states, reflected in EEG microstates—transient, stable patterns of global neuronal activity lasting fractions of seconds. In 23 surgical patients, high-density EEG was recorded continuously from wakefulness to unconsciousness induced by step-wise increasing propofol concentrations. Under surgical anesthesia, microstate sequences became sparser, longer-lasting, and less complex. However, moderate sedation initially increased the temporal dynamics and complexity of microstates, producing a distinctive U-shaped pattern that may correspond to paradoxical excitation. These findings suggest that normal consciousness relies on a metastable balance between order and chaos, enabling flexible state transitions, and that altered consciousness reflects changes in this balance.