Detection of EEG-resting state independent networks by eLORETA-ICA method

Aoki, Yasunori and Ishii, Ryouhei and Pascual Marqui, Roberto D. and Canuet Delis, Leonides and Ikeda, Shunichiro and Hata, Masahiro and Imajo, Kaoru and Matsuzaki, Haruyasu and Musha, Toshimitsu and Asada, Takashi and Iwase, Masao and Takeda, M. (2015). Detection of EEG-resting state independent networks by eLORETA-ICA method. "Frontiers in Human Neuroscience", v. 9 (n. 31); pp. 1-12. ISSN 1662-5161. https://doi.org/10.3389/fnhum.2015.00031.

Description

Title: Detection of EEG-resting state independent networks by eLORETA-ICA method
Author/s:
  • Aoki, Yasunori
  • Ishii, Ryouhei
  • Pascual Marqui, Roberto D.
  • Canuet Delis, Leonides
  • Ikeda, Shunichiro
  • Hata, Masahiro
  • Imajo, Kaoru
  • Matsuzaki, Haruyasu
  • Musha, Toshimitsu
  • Asada, Takashi
  • Iwase, Masao
  • Takeda, M.
Item Type: Article
Título de Revista/Publicación: Frontiers in Human Neuroscience
Date: 10 February 2015
ISSN: 1662-5161
Volume: 9
Subjects:
Freetext Keywords: eLORETA-ICA, LORETA, resting state network, independent component analysis, ICA, EEG
Faculty: Centro de Tecnología Biomédica (CTB) (UPM)
Department: Tecnología Fotónica y Bioingeniería
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called ?Resting State independent Networks? (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RS-independent-Ns and their interactions in all frequency bands. We applied exact low resolution brain electromagnetic tomography-ICA (eLORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RS-independent-Ns in alpha, beta and gamma frequency bands. Next, taking into account previous neuroimaging findings, five RS-independent-Ns were identified: (1) the visual network in alpha frequency band, (2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) in alpha and beta frequency bands and left posterior dorsal visual pathway (DVP) in alpha frequency band, (3) self-referential processing network, characterized by a negative correlation between the medial prefrontal cortex (mPFC) in beta frequency band and right temporoparietal junction (TPJ) in alpha frequency band, (4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus in alpha frequency band; and (5) sensorimotor network in beta and gamma frequency bands. We selected eLORETA-ICA which has many advantages over the other network visualization methods and overall findings indicate that eLORETA-ICA with EEG data can identify five RS-independent-Ns in their intrinsic frequency bands, and correct correlations within RS-independent-Ns.

More information

Item ID: 41072
DC Identifier: http://oa.upm.es/41072/
OAI Identifier: oai:oa.upm.es:41072
DOI: 10.3389/fnhum.2015.00031
Official URL: http://journal.frontiersin.org/article/10.3389/fnhum.2015.00031/full
Deposited by: Memoria Investigacion
Deposited on: 22 Apr 2017 09:42
Last Modified: 22 Apr 2017 09:42
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