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. 2024 Jul;37(4):496-513.
doi: 10.1007/s10548-024-01043-5. Epub 2024 Mar 2.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Armen Bagdasarov  1 , Denis Brunet  2   3 , Christoph M Michel  2   3 , Michael S Gaffrey  4   5   6
Affiliations

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Armen Bagdasarov et al. Brain Topogr. 2024 Jul.

Abstract

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Keywords: Infants; Microstates; Reliability; Resting-state; Source localization; Tutorial.

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Conflict of interest statement

Christoph M. Michel serves as the editor of the journal in which this manuscript is being submitted as well as a guest editor of the Call for Papers / Special Issue in which this manuscript is being submitted. Further, all authors of the current manuscript have previously published with Lucie Bréchet who is also a guest editor. The authors declare that they have no other known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Five infant microstates show similar topographies across varying EEG data durations. Note. Microstates are polarity-invariant (i.e., colors do not indicate polarity).
Fig. 2
Fig. 2
Split-half reliability of temporal properties
Fig. 3
Fig. 3
Split-half reliability of transition probabilities. Note. Bars indicate the minimum and maximum Spearman-Brown split-half reliability coefficient values for transition probabilities across transition directions for each data direction
Fig. 4
Fig. 4
Split-half reliability of neural sources
See this image and copyright information in PMC

References

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