EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

Abstract

SummaryThe present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of largescale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales.The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…In contrast to microstate temporal dynamics, the topographic configurations of microstates are strikingly consistent across published reports (Michel and Koenig, 2018). For the present study, microstate clusters A through E matched canonical patterns found in the literature (Michel and Koenig, 2018), including those observed in studies using data-driven approaches to determine an optimal number of clusters (Bréchet et al, 2019;Custo et al, 2017). Together, these five clusters explained 85% of the variance among subject-level cluster centroids, and 63% of the topographic variance (on average) seen in participants' resting EEG recordings.…”

“…By extension, the sequencing and temporal dynamics of changes in global brain states can be described in terms of the electric field strength and topographic configuration of a succession of microstates over time. Research has consistently identified between 4 and 7 data-driven clusters of microstates that account for a large proportion of the observed variance in EEG time series during periods of quiet rest (Michel and Koenig, 2018). The topographic configurations of clusters of resting EEG microstates are remarkably consistent within and between individuals, and the same clusters have been commonly identified across studies.…”

“…One way to characterize the large-scale dynamics of brain networks is to quantify organized patterns of topographic voltage configurations in broadband scalp-recorded EEG (Michel and Koenig, 2018). When examined across brief timescales, the spatial distributions of certain head-surface voltage topographies can be seen to emerge as quasi-stable patterns in the ongoing signal.…”

“…During these periods, which typically last between 40 and 120 milliseconds, a particular topographic configuration will tend to predominate, before transitioning rapidly to other momentary quasi-stable configurations. These periods of topographic stability, which have been termed microstates (Lehmann et al, 1987;Wackermann et al, 1993), reflect transient brain states of phase-synchronized neuronal activity that can be useful for describing the activity of global cortical networks and their alternating temporal dynamics (for a review of microstate methodology, see Michel and Koenig, 2018).…”

EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

“…Clinical conditions include schizophrenia (Koenig et al, 1999), Alzheimer's disease (Nishida et al, 2013), and narcolepsy (Kuhn et al, 2015; Drissi et al, 2016). An overview of the field has recently been published in two reviews (Khanna et al, 2015; Michel and Koenig, 2017). …”