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EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States

Abstract

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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Acknowledgements

We acknowledge the Portuguese Science Foundation (FCT) for financial support through Project PTDC/SAUENB/112294/2009, Project PTDC/EEIELC/3246/2012, Grant LARSyS UID/EEA/50009/2019, and the Doctoral Grant PD/BD/105777/2014, and thank the support of Centre d’Imagerie BioMédicale (CIBM) of the UNIL, UNIGE, HUG, CHUV, EPFL and the Leenaards and Jeantet Foundations.

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Correspondence to Patrícia Figueiredo.

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10548_2020_805_MOESM1_ESM.pdf

Figure S1: EEG model with the second highest accuracy (MS4 topographies). (A) For each dFC state (illustrated by the connectivity matrix at the top row; values normalized between -1 and 1 for visualization purposes), the average microstate topographies across windows with the same dFC state label are shown, ordered by their GEV values (from top to bottom). (B) Confusion matrix summarizing the results of the leave-one-subject-out classification procedure, for each class (i.e., dFC state) separately. The number of observations (i.e., dFC windows) correctly (diagonal) and incorrectly (off-diagonal) classified are shown, together with the class-specific recall, precision, false recovery rate and false negative rate, and the overall accuracy. (PDF 2160 kb)

10548_2020_805_MOESM2_ESM.docx

Table S1: Performance of each dFC state identification method in terms of the minimum BIC values found when varying the methods’ parameters (number of dFC states k in k-means and PCA, and also the regularization parameter λ in the DL approaches), and their respective optimal parameters (k* and λ*). (DOCX 12 kb)

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Abreu, R., Jorge, J., Leal, A. et al. EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States. Brain Topogr 34, 41–55 (2021). https://doi.org/10.1007/s10548-020-00805-1

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Keywords

  • Simultaneous EEG-fMRI
  • EEG microstates
  • fMRI dynamic functional connectivity
  • Random forests