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      Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

      review-article
      * ,
      Network Neuroscience
      MIT Press
      Connectome, Intrinsic, Multimodal, EEG, MEG, fMRI

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          Abstract

          The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take “baseline” intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

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          Most cited references124

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          Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.

          To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, the phase lag index (PLI), and compare its performance to the well-known phase coherence (PC), and to the imaginary component of coherency (IC). The PLI is a measure of the asymmetry of the distribution of phase differences between two signals. The performance of PLI, PC, and IC was examined in (i) a model of 64 globally coupled oscillators, (ii) an EEG with an absence seizure, (iii) an EEG data set of 15 Alzheimer patients and 13 control subjects, and (iv) two MEG data sets. PLI and PC were more sensitive than IC to increasing levels of true synchronization in the model. PC and IC were influenced stronger than PLI by spurious correlations because of common sources. All measures detected changes in synchronization during the absence seizure. In contrast to PC, PLI and IC were barely changed by the choice of different montages. PLI and IC were superior to PC in detecting changes in beta band connectivity in AD patients. Finally, PLI and IC revealed a different spatial pattern of functional connectivity in MEG data than PC. The PLI performed at least as well as the PC in detecting true changes in synchronization in model and real data but, at the same token and like-wise the IC, it was much less affected by the influence of common sources and active reference electrodes. Copyright 2007 Wiley-Liss, Inc.
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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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              Neuronal synchrony: a versatile code for the definition of relations?

              W. Singer (1999)
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                Author and article information

                Contributors
                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience
                MIT Press (One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu )
                2472-1751
                2020
                2020
                : 4
                : 1
                : 1-29
                Affiliations
                [1]Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
                [2]Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
                [3]Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                * Corresponding Author: sepideh.sadaghiani@ 123456gmail.com

                Handling Editor: Olaf Sporns

                Author information
                https://orcid.org/0000-0001-8800-3959
                https://orcid.org/0000-0003-0588-9710
                Article
                netn_a_00114
                10.1162/netn_a_00114
                7006873
                159eefeb-bd34-41d3-9f4b-e0a2b50e7e0a
                © 2019 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.

                History
                : 18 July 2019
                : 11 November 2019
                Page count
                Figures: 3, References: 205, Pages: 29
                Funding
                Funded by: National Institute of Mental Health, FundRef http://dx.doi.org/10.13039/100000025;
                Award ID: 1R01MH116226-01A1
                Categories
                Review Articles
                Custom metadata
                Sadaghiani, S., & Wirsich, J. (2020). Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches. Network Neuroscience, 4(1), 1–29. https://doi.org/10.1162/netn_a_00114

                connectome,intrinsic,multimodal,eeg,meg,fmri
                connectome, intrinsic, multimodal, eeg, meg, fmri

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