Transient topographical dynamics of the electroencephalogram predict brain connectivity and behavioural responsiveness during drowsiness
As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of frontoparietal alpha networks and the emergence of frontoparietal theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth.\n\nAuthor summaryHow do we lose responsiveness as we fall asleep? As we become sleepy, our ability to react to external stimuli disappears gradually. Here we sought to understand the rapid fluctuations in brain electrical activity that predict the loss of responsiveness as participants fell asleep while performing a word classification task. We analysed the patterns of connectivity between anterior and posterior brain regions observed during wakefulness in alpha band and showed that this connectivity shifted to slower theta frequencies as participants became unresponsive. We also investigated the dynamics of brain electrical microstates, which represent an alphabet of quasi-stable global brain states with lifetimes of 10-100 milliseconds, and found that the temporal dynamics of microstates slowed down when participants became unresponsive. Using machine learning, we further showed that microstate dynamics prior to a stimulus predict whether subjects will respond to it. We integrated microstates and connectivity for the first time to show that a specific microstate captures connectivity patterns correlated with unresponsiveness during this transition. We conclude that falling asleep is accompanied by a millisecond-level interplay between distinct brain networks, and suggest a renewed focus on fine-grained temporal scales in the study of transitions between levels of consciousness.