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| Journal Article | FZJ-2024-02001 |
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2024
MIT Press
Cambridge, MA
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Please use a persistent id in citations: doi:10.1162/imag_a_00109 doi:10.34734/FZJ-2024-02001
Abstract: The resting human brain cycles through distinct states that can be analyzed using microstate analysis and electroencephalography(EEG) data. This approach classifies multichannel EEG data into spontaneously interchanging microstatesbased on topographic features. These microstates may be valuable biomarkers in neurodegenerative diseasessince they reflect the resting brain’s state. However, microstates do not provide information about the active neuralnetworks during the resting state. This article presents an alternative and complementary method for analyzingresting-stateEEG data and demonstrates its reproducibility and reliability. This method considers cerebral connectivitystates defined by phase synchronization and measured using the corrected imaginary phase-lockingvalue (ciPLV)based on source-reconstructedEEG recordings. We analyzed resting-stateEEG data from young, healthy participantsacquired on five consecutive days before and after a motor task. We show that our data reproduce microstatespreviously reported. Further, we reveal four stable topographic patterns over the multiple recording sessions in thesource connectivity space. While the classical microstates were unaffected by a preceding motor task, the connectivitystates were altered, reflecting the suppression of frontal activity in the post-movementresting state.
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