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      The Quest for EEG Power Band Correlation with ICA Derived fMRI Resting State Networks

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          Abstract

          The neuronal underpinnings of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) resting state networks (RSNs) are still unclear. To investigate the underlying mechanisms, specifically the relation to the electrophysiological signal, we used simultaneous recordings of electroencephalography (EEG) and fMRI during eyes open resting state (RS). Earlier studies using the EEG signal as independent variable show inconclusive results, possibly due to variability in the temporal correlations between RSNs and power in the low EEG frequency bands, as recently reported (Goncalves et al., 2006, 2008; Meyer et al., 2013). In this study we use three different methods including one that uses RSN timelines as independent variable to explore the temporal relationship of RSNs and EEG frequency power in eyes open RS in detail. The results of these three distinct analysis approaches support the hypothesis that the correlation between low EEG frequency power and BOLD RSNs is instable over time, at least in eyes open RS.

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          Most cited references21

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          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Electrophysiological signatures of resting state networks in the human brain.

            Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
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              Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data.

              In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.
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                Author and article information

                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                25 June 2013
                2013
                : 7
                : 315
                Affiliations
                [1] 1Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour , Nijmegen, Netherlands
                [2] 2MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente , Twente, Netherlands
                [3] 3Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen , Essen, Netherlands
                Author notes

                Edited by: Simon Daniel Robinson, Medical University of Vienna, Austria

                Reviewed by: Andrew P. Bagshaw, University of Birmingham, UK; Marco Buiatti, INSERM, France

                *Correspondence: Matthias Christoph Meyer, Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Kapittelweg 29, 6525EN Nijmegen, Netherlands e-mail: matthias.meyer@ 123456donders.ru.nl
                Article
                10.3389/fnhum.2013.00315
                3691889
                23805098
                f55f409b-4b1e-4b61-b3c6-bf94309bf14a
                Copyright © 2013 Meyer, Janssen, Van Oort, Beckmann and Barth.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 20 January 2013
                : 10 June 2013
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 34, Pages: 9, Words: 5547
                Categories
                Neuroscience
                Original Research

                Neurosciences
                combined eeg-fmri,resting state,source modeling,rsn,ica,ecp,ihm
                Neurosciences
                combined eeg-fmri, resting state, source modeling, rsn, ica, ecp, ihm

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