Research Article: Reliability of Resting-State Microstate Features in Electroencephalography

Date Published: December 5, 2014

Publisher: Public Library of Science

Author(s): Arjun Khanna, Alvaro Pascual-Leone, Faranak Farzan, Thomas Koenig.


Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states (“microstates”) that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis.

We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach’s α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes.

The approach of identifying a single set of “global” microstate maps showed the highest reliability (mean Cronbach’s α>0.8, SEM ≈10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach’s α>0.9). All features had high test-retest reliability with 19 and 8 electrodes.

High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain’s neurophysiological health.

Partial Text

Neurophysiological impairments may precede the appearance of clinical symptomology in several neuropsychiatric illnesses [1]–[3]. Frequent and longitudinal monitoring of neurophysiological “biomarkers” could enable early detection of disease pathogenesis, and enhance understanding of the neurophysiological impairments underlying these disorders. Thus, there is great interest in developing techniques to detect neurophysiological biomarkers associated with impairments in the brain’s functional health.

After the data were preprocessed and epochs with artifacts removed, we had a mean of 127.87 seconds of data (SD  = 23.87, range  = 80–204) per recording that were submitted to microstate analysis from which we extracted the “original maps” at local maxima in the GFP curve. We chose a priori to cluster the original maps from each session into four microstates. Four microstate maps had a mean GEV of 69.93% (SD  = 3.58, range  = 65.34–77.99) across all recordings using TAAHC.

In this study, we sought to assess the test-retest reliability of resting-state EEG microstate analysis in healthy subjects over time. We used a number of variations of the method to determine the reliability and the degree of consistency among these approaches. This study has four major findings. First, we found that using a global set of microstates for all subjects yields average microstate durations, frequencies, and coverage fractions that have high Cronbach’s α, indicating excellent test-retest reliability. Second, we found that the use of global maps yields results that are in general more reliable than maps identified by session or by recording. Third, we showed that TAAHC and k-means clustering yield highly consistent results. Finally, we showed that microstate analysis can be reliably conducted with as few as 8 electrodes.

In this study, we found that when a global set of microstates is used to conduct microstate analysis over multiple sessions, resting-state EEG microstate analysis has high test-retest reliability in healthy subjects as measured by Cronbach’s α and SEM. We also determined the consistency of the k-means clustering and TAAHC algorithms in extracting microstate maps. Finally, we found that microstate analysis can be reliably conducted with as few as 8 electrodes.