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.
Highlights
The brain intrinsically interacts between distant regions, building cortical networks during motor and cognitive tasks
11 independent components (ICs) were most consistent with physiological assumption that is topography and frequency of some known networks and artifacts such as electromyogram is at frontal or temporal cortex in gamma frequency band, we selected 11 as the number of components
INDEPENDENT COMPONENT 5 IC5 was found at the right occipitotemporal cortex in alpha frequency band and at the right ventral prefrontal cortex (vPFC) in beta frequency band with left posterior occipito-parietal cortex, caudal intraparietal sulcus (cIPS) and MT+ in alpha frequency band (Figure 2)
Summary
The brain intrinsically interacts between distant regions, building cortical networks during motor and cognitive tasks. The so called default mode network (DMN) is known to be active during resting and attenuates during task performance. It has been reported that several cortical networks cooperate with each other positively or negatively during performance of complex cognitive tasks (Spreng and Schacter, 2012). These functional networks have been investigated by lesional and anatomical studies and during functional tasks with functional magnetic resonance imaging (fMRI), which measures regional cerebral blood flow (rCBF) changes. One mathematical method called independent component analysis (ICA) have received growing attention (Bell and Sejnowski, 1995; Hyvärinen and Oja, 2000).
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