Towards a dynamical understanding of microstate analysis of M/EEG data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00576873" target="_blank" >RIV/67985807:_____/23:00576873 - isvavai.cz</a>

Alternative codes found

RIV/00023752:_____/23:43921168

Result on the web

<a href="https://dx.doi.org/10.1016/j.neuroimage.2023.120371" target="_blank" >https://dx.doi.org/10.1016/j.neuroimage.2023.120371</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neuroimage.2023.120371" target="_blank" >10.1016/j.neuroimage.2023.120371</a>

Result language

angličtina

Original language name

Towards a dynamical understanding of microstate analysis of M/EEG data

Original language description

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three “classically” used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

<a href="/en/project/GA21-32608S" target="_blank" >GA21-32608S: Characterizing state repertoire and dynamics of spontaneous brain activity by neuroimaging methods</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neuroimage

ISSN

1053-8119

e-ISSN

1095-9572

Volume of the periodical

281

Issue of the periodical within the volume

November 2023

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

120371

UT code for WoS article

001083868400001

EID of the result in the Scopus database

2-s2.0-85171802503