Back to Search Start Over

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability.

Authors :
Bagdasarov, Armen
Brunet, Denis
Michel, Christoph M.
Gaffrey, Michael S.
Source :
Brain Topography; Jul2024, Vol. 37 Issue 4, p496-513, 18p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08960267
Volume :
37
Issue :
4
Database :
Complementary Index
Journal :
Brain Topography
Publication Type :
Academic Journal
Accession number :
178066325
Full Text :
https://doi.org/10.1007/s10548-024-01043-5