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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 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.
Title: | Detection of EEG-resting state independent networks by eLORETA-ICA method |
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Author/s: |
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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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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.
Item ID: | 41072 |
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DC Identifier: | https://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/fnh... |
Deposited by: | Memoria Investigacion |
Deposited on: | 22 Apr 2017 09:42 |
Last Modified: | 22 Apr 2017 09:42 |