Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.
Springer Science and Business Media LLC
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Comsa, I. M., Bekinschtein, T. A., & Chennu, S. (2019). Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.. Brain Topogr, 32 (2), 315-331. https://doi.org/10.1007/s10548-018-0689-9
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 alpha networks and the emergence of 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.
Neural Pathways, Humans, Electroencephalography, Alpha Rhythm, Theta Rhythm, Acoustic Stimulation, Brain Mapping, Data Interpretation, Statistical, Behavior, Psychomotor Performance, Reaction Time, Adult, Female, Male, Young Adult, Machine Learning, Sleepiness
Is supplemented by: https://doi.org/10.17863/CAM.33597
External DOI: https://doi.org/10.1007/s10548-018-0689-9
This record's URL: https://www.repository.cam.ac.uk/handle/1810/287271
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/