Scientific article

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

Published inNeuroscience and biobehavioral reviews, vol. 49, p. 105-113
Publication date2015

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.

  • Brain/growth & development/physiology/physiopathology
  • Electroencephalography/methods
  • Humans
  • Rest
Citation (ISO format)
KHANNA, Arjun et al. Microstates in resting-state EEG: current status and future directions. In: Neuroscience and biobehavioral reviews, 2015, vol. 49, p. 105–113. doi: 10.1016/j.neubiorev.2014.12.010
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Article (Published version)
ISSN of the journal0149-7634

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