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

Highlights

• EG microstates are quasi-stable periods of electric topography in multichannel EEG.

• Resting-state EEG is dominated by a small number of alternating microstates.

• EEG microstates are selectively modified across various neuropsychiatric illnesses.

• Each EEG microstate may reflect specific fMRI-probed resting-state network(s).

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.

Introduction

The pathophysiology of several neuropsychiatric and neurodegenerative disorders is linked to neurophysiological impairments which may be detectable well before manifestation of severe clinical symptoms (Ponomareva et al., 1998, Jelic et al., 2000, Avila et al., 2002). This suggests that longitudinal monitoring of brain's neurophysiological health may offer a valuable diagnostic and disease management strategy. To achieve this goal, however, we need to identify and establish cost-effective and reliable neurophysiological markers that have potential for translation to clinical practice.

With advancements in neurophysiological techniques and computational power, neurophysiologists continue to gain more insight into how the brain functions in health, and how function is disrupted in disease. Some of these techniques, such as functional magnetic resonance imaging (fMRI), have elucidated functional connectivity among specific brain regions organized into networks (van den Heuvel and Hulshoff Pol, 2010). The dynamic of these networks drives various classes of the brain's functions, and their disruption may be associated with pathophysiology of various neuropsychiatric illnesses (van den Heuvel and Hulshoff Pol, 2010). Electroencephalography (EEG) is a powerful and popular method that can also be used to examine network activity across the cortex in health and disease. EEG is inexpensive, and enables non-invasive assessment of neural activity resulting from both local and long-range neural coordination (Ingber and Nunez, 2011). In addition to low cost, EEG has millisecond temporal resolution, which is orders of magnitude finer than other neuroimaging modalities such as fMRI.

More than 80 years ago, Hans Berger coined the term EEG and for the first time recorded cortical oscillatory activity from the surface of the skull in humans (Berger, 1929). He described the potentiation and emergence of specific brain waves, today referred to as alpha oscillations (8–12 Hz), in posterior brain regions when human subjects were instructed to close their eyes. Since then, numerous studies have explored the association between various cortical frequency bands of delta (1–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (12–28 Hz), and gamma (>30 Hz) oscillations with different behavioral and disease states. Furthermore, due to the stochastic and multidimensional nature of EEG signals, a wide variety of analytical approaches have been proposed to quantify and discover different features of cortical oscillatory activity and the functional roles they serve. One common approach is to consider the EEG signal as a dynamical system that can be described in terms of its state and dynamics. The system state is the combination of all variables that describe the system at any given time t, and the system dynamics describe how the state changes over time.

One method of studying EEG, therefore, is by defining momentary states of the system based on variables of interest and describing changes in brain activity in terms of the modification of state characteristics, such as the duration or frequency of occurrence of specific states. For example, in nonlinear dynamical analysis, the EEG signal can be embedded into a so-called “state space” to derive values for the chaotic complexity (Wackermann et al., 1993) or synchronicity (Carmeli et al., 2005) of particular states. Several methods to define the entropy as a state characteristic of the EEG signal have been proposed to recognize ictal patterns (Kannathal et al., 2005). Microstate analysis, reviewed in this article, is another such method where states are defined by topographies of electric potentials over a multichannel electrode array.

In a seminal paper, Lehmann et al. demonstrated that the alpha frequency band (8–12 Hz) of the multichannel resting-state EEG signal can be parsed into a limited number of distinct quasi-stable states (Lehmann et al., 1987). These discrete states, called “microstates,” are defined by topographies of electric potentials recorded in a multichannel array over the scalp, which remain stable for 80–120 ms before rapidly transitioning to a different microstate. Unlike some other techniques, microstate analysis simultaneously considers the signal from all electrodes to create a global representation of a functional state. The rich syntax of the microstate time series offers a variety of new quantifications of the EEG signal with potential neurophysiological relevance. Indeed, many studies have since illustrated that characteristics of the EEG microstate time series vary across behavioral states (Stevens and Kircher, 1998, Lehmann et al., 2010), personality types (Schlegel et al., 2012), and neuropsychiatric disorders (Dierks et al., 1997, Lehmann et al., 2005, Kikuchi et al., 2011). Converging lines of evidence suggest that the microstate time series may provide insight about the neural activity of the brain in the resting state (Britz et al., 2010, Musso et al., 2010, Yuan et al., 2012). Microstate analysis of EEG may be a powerful, inexpensive, and clinically translatable neurophysiological method to study and assess global functional states of the brain in health and disease.

In this review, we first introduce the method of microstate analysis as it applies to resting-state EEG. We define resting-state EEG as recording from subjects that are not actively engaged in sensory or cognitive processing. A number of studies have examined EEG microstates during active tasks such as motor function and auditory processing (e.g. Günther et al., 1996), or during cognitive tasks (e.g. Stevens et al., 1997), in addition to event-related studies examining microstates time-locked to a stimulus (e.g. Ott et al., 2011); these are not reviewed here. Second, we discuss the functional interpretation of EEG microstates, both as reflections of resting-state brain activity and indicators of states of the brain. We then describe changes in resting-state microstates that have been associated with neuropsychiatric diseases and other altered brain states. We describe factors that can affect microstate dynamics, analysis, and interpretation. Finally, we conclude by summarizing future directions of microstate analysis.