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Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA

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  • Stéphane Sockeel
  • Denis Schwartz
  • Mélanie Pélégrini-Issac
  • Habib Benali

Abstract

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.

Suggested Citation

  • Stéphane Sockeel & Denis Schwartz & Mélanie Pélégrini-Issac & Habib Benali, 2016. "Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.
  • Handle: RePEc:plo:pone00:0146845
    DOI: 10.1371/journal.pone.0146845
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    References listed on IDEAS

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    1. Yohan Attal & Denis Schwartz, 2013. "Assessment of Subcortical Source Localization Using Deep Brain Activity Imaging Model with Minimum Norm Operators: A MEG Study," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-14, March.
    2. Nikos K. Logothetis & Jon Pauls & Mark Augath & Torsten Trinath & Axel Oeltermann, 2001. "Neurophysiological investigation of the basis of the fMRI signal," Nature, Nature, vol. 412(6843), pages 150-157, July.
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