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research article

EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States

Abreu, Rodolfo
•
Jorge, Joao  
•
Leal, Alberto
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2021
Brain Topography

Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.

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Type
research article
DOI
10.1007/s10548-020-00805-1
Web of Science ID

WOS:000587270700001

Author(s)
Abreu, Rodolfo
•
Jorge, Joao  
•
Leal, Alberto
•
Koenig, Thomas
•
Figueiredo, Patricia
Date Issued

2021

Publisher

SPRINGER

Published in
Brain Topography
Volume

34

Start page

41

End page

55

Subjects

Clinical Neurology

•

Neurosciences

•

Neurosciences & Neurology

•

simultaneous eeg-fmri

•

eeg microstates

•

fmri dynamic functional connectivity

•

random forests

•

physiological noise correction

•

bold signal

•

brain

•

registration

•

networks

•

robust

•

optimization

•

eeg/fmri

•

humans

•

cortex

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIFMET  
Available on Infoscience
November 24, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/173588
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