title: Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI.
abstract: Functional magnetic resonance imaging (fMRI) studies utilizing measures of hemodynamic signal, such as the blood oxygenation level-dependent (BOLD) signal, have discovered that resting-state brain activities are organized into multiple large-scale functional networks, coined as resting-state networks (RSNs). However, an important limitation of the available fMRI studies is that hemodynamic signals only provide an indirect measure of the neuronal activity. In contrast, electroencephalography (EEG) directly measures electrophysiological activity of the brain. However, little is known about the brain-wide organization of such spontaneous neuronal population signals at the resting state. It is not entirely clear if or how the network structure built upon slowly fluctuating hemodynamic signals is represented in terms of fast, dynamic, and spontaneous neuronal activity. In this study, we investigated the electrophysiological representation of RSNs from simultaneously acquired EEG and fMRI data in the resting human brain. We developed a data-driven analysis approach that reconstructed multiple large-scale electrophysiological networks from high-resolution EEG data alone. The networks derived from EEG were then compared with RSNs independently derived from simultaneously acquired fMRI in their spatial structures as well as temporal dynamics. Results reveal spatially and temporally specific electrophysiological correlates for the fMRI-RSNs. Findings suggest that the spontaneous activity of various large-scale cortical networks is reflected in macroscopic EEG potentials.
authors.affiliation: 1 Laureate Institute for Brain Research , Tulsa, Oklahoma.
authors.forename: Han
authors.initials: H
authors.lastname: Yuan
authors.affiliation: 1 Laureate Institute for Brain Research , Tulsa, Oklahoma.|2 School of Electrical and Computer Engineering, University of Oklahoma , Norman, Oklahoma.|3 Center for Biomedical Engineering, University of Oklahoma , Norman, Oklahoma.
authors.forename: Lei
authors.initials: L
authors.lastname: Ding
authors.affiliation: 2 School of Electrical and Computer Engineering, University of Oklahoma , Norman, Oklahoma.
authors.forename: Min
authors.initials: M
authors.lastname: Zhu
authors.affiliation: 1 Laureate Institute for Brain Research , Tulsa, Oklahoma.
authors.forename: Vadim
authors.initials: V
authors.lastname: Zotev
authors.affiliation: 1 Laureate Institute for Brain Research , Tulsa, Oklahoma.
authors.forename: Raquel
authors.initials: R
authors.lastname: Phillips
authors.affiliation: 1 Laureate Institute for Brain Research , Tulsa, Oklahoma.|3 Center for Biomedical Engineering, University of Oklahoma , Norman, Oklahoma.|4 College of Engineering, University of Oklahoma , Norman, Oklahoma.
authors.forename: Jerzy
authors.initials: J
authors.lastname: Bodurka
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008564:Membrane Potentials / Q000502:physiology; D009415:Nerve Net / Q000502:physiology; D012146:Rest / Q000502:physiology; D062211:Spatio-Temporal Analysis; D013223:Statistics as Topic / Q000379:methods*
keywords: brain connectivity; dynamics; electroencephalography (EEG); functional magnetic resonance imaging (fMRI); resting-state networks; source analysis