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

AI Article Synopsis

  • Recent studies show that resting state data can reveal functional brain networks using independent component analysis (ICA), with fMRI traditionally used for this purpose, though EEG and MEG offer better temporal resolution.
  • This research aimed to identify resting state independent networks (RS-independent-Ns) in EEG data across various frequency bands (delta, theta, alpha, beta, gamma) in a sample of 80 healthy subjects and successfully found five distinct RS-independent-Ns in the alpha, beta, and gamma bands.
  • The identified networks include a visual network, dual-process networks related to visual perception and memory, a self-referential processing network, and a sensorimotor network, showcasing the effectiveness of eLORETA-ICA for examining brain

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4322703PMC
http://dx.doi.org/10.3389/fnhum.2015.00031DOI Listing

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