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

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.

EEG Resting-State Networks in Dementia with Lewy Bodies Associated with Clinical Symptoms

“…Exact low-resolution brain electromagnetic tomography (eLORETA) is a linear inverse solution that reconstructs cortical electrical activity from the scalp EEG data with correct localization [19-23]. Thus, eLORETA with EEG data has been widely used in neuroscience research [19, 20, 24-26]. Recent studies on cortical activity using fMRI or eLORETA have revealed that cortical sources work together in distant regions even in the resting state and construct a set of resting state networks (RSNs), which subserve specific brain functions [20, 27-30].…”

“…Thus, eLORETA with EEG data has been widely used in neuroscience research [19, 20, 24-26]. Recent studies on cortical activity using fMRI or eLORETA have revealed that cortical sources work together in distant regions even in the resting state and construct a set of resting state networks (RSNs), which subserve specific brain functions [20, 27-30]. In DLB, fMRI studies have reported that spatial configuration of RSNs was preserved but some activities of RSNs changed compared to healthy subjects [31, 32].…”

Detection of independent functional networks during music listening using electroencephalogram and sLORETA-ICA

“…technique that allows the identification of intracerebral networks on the basis of scalp EEG measures [19]. The advantage of this technique is that the identified networks are represented across the different frequency bands.…”

“…The 'networks' we have identified are brain areas, which conjointly are activated and/or deactivated. In the following we describe and paraphrase the sLORETAfunctional independent component analysis (fICA) method according to the description given in the paper of Aoki et al [19]. The EEG recordings of each participant are first transformed to the frequency domain, resulting in a set of cross-spectral EEG matrices, for each frequency band and for each condition.…”

“…ICA of EEG is based on the premise that each electrode on the scalp records a linear sum of various underlying electrocortical signals, as well as electromyographic, electro-ocular, electrocardiographic, and movement artifacts. Thus, ICs representing neural activity or artifacts can now be separated from ICs representing artifacts [19].…”