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

Authors :
Yasunori Aoki
Ryouhei Ishii
Pascual-Marqui, Roberto D.
Canuet, Leonides
Shunichiro Ikeda
Masahiro Hata
Kaoru Imajo
Haruyasu Matsuzaki
Toshimitsu Musha
Takashi Asada
Masao Iwase
Masatoshi Takeda
Source :
Frontiers in Human Neuroscience; Feb2015, Vol. 9, p1-12, 12p
Publication Year :
2015

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16625161
Volume :
9
Database :
Complementary Index
Journal :
Frontiers in Human Neuroscience
Publication Type :
Academic Journal
Accession number :
101963844
Full Text :
https://doi.org/10.3389/fnhum.2015.00031