Microstates in resting-state EEG: Current status and future directions

Abstract

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable “microstates” that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease.

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

“…Previous studies have consistently reported that EEG microstates range in duration from 60-120 ms (Koenig et al, 2002); for a review see (Khanna et al, 2015), in accordance with previous findings regarding the duration required for conscious experience. For example, sequentially presented stimuli are not perceived as separate when they follow each other within less than 80 ms (Efron, 1970).…”

“…Microstate map A exhibits a left-right orientation, map B exhibits a right-left orientation, map C exhibits an anterior-posterior orientation, and map D exhibits a fronto-central maximum. Even if more cluster maps are selected, these four maps seem to consistently dominate the data across different age ranges, conditions (e.g., sleep and hypnosis), and pathological states (Khanna et al, 2015).…”

“…Usually, the following temporal parameters of microstates are calculated: (1) the average duration that a given microstate remains stable, (2) the frequency of occurrence for each microstate independent of its individual duration, (3) the fraction of total recording time for which a given microstate is dominant (i.e., coverage), (4) the global variance explained by each microstate, and (5) the transition probabilities of a given microstate to any other microstate (see reviews by (Khanna et al, 2015;. If the cluster analysis is performed separately for conditions or groups, the topographical shape of the different microstate maps can also be compared across…”

“…The scale-free properties of microstate time series, which span the timescales characteristic of EEG microstate changes and fMRI BOLD oscillations, explain how information that can be observed at such different timescales is intertwined. Several studies have referred to , who utilized a more conventional approach, when interpreting the significance of specific microstate changes in different states and pathologies (see review by (Khanna et al, 2015). However, given recent discussions regarding the anatomical overlap of various fMRI RSNs in time and space (Karahanoğlu and Van De Ville, 2015;Smith et al, 2012) and the subdivisions of these networks (particularly the default mode network) (Andrews-…”

EEG microstates during different phases of Transcendental Meditation practice

“…Due to the very high time resolution of the EEG, the microstate analysis allows the isolation of microstates with a typical duration of 60-120 milliseconds. Cluster analyses of the microstate topographies showed that in eyes closed resting EEG, four classes of microstate topographies explain a large amount (up to 90%) of the variance in the data (Khanna et al 2015;Koenig et al 2002;Wackermann et al 1993); they also replicate well over a large age range (from 6 to 80 years) and over different studies (see Koenig et al 2002). In addition to their duration, the EEG microstates are characterized by the parameters coverage and occurrence that describe the percentage of time spent and the number of occurrences per second respectively of microstates of a given class.…”