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      EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

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          Abstract

          The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both methods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under anaesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that, while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterised by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions.

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          Most cited references41

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          Testing for nonlinearity in time series: the method of surrogate data

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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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              Breakdown of cortical effective connectivity during sleep.

              When we fall asleep, consciousness fades yet the brain remains active. Why is this so? To investigate whether changes in cortical information transmission play a role, we used transcranial magnetic stimulation together with high-density electroencephalography and asked how the activation of one cortical area (the premotor area) is transmitted to the rest of the brain. During quiet wakefulness, an initial response (approximately 15 milliseconds) at the stimulation site was followed by a sequence of waves that moved to connected cortical areas several centimeters away. During non-rapid eye movement sleep, the initial response was stronger but was rapidly extinguished and did not propagate beyond the stimulation site. Thus, the fading of consciousness during certain stages of sleep may be related to a breakdown in cortical effective connectivity.
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                Author and article information

                Contributors
                laurasophie.imperatori@gmail.com
                giulioberna@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 June 2019
                20 June 2019
                2019
                : 9
                : 8894
                Affiliations
                [1 ]ISNI 0000 0004 1790 9464, GRID grid.462365.0, MoMiLab Research Unit, , IMT School for Advanced Studies Lucca, ; Lucca, Italy
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Psychology, , University of Cambridge, ; Cambridge, United Kingdom
                [3 ]ISNI 0000 0001 0423 4662, GRID grid.8515.9, Center for Investigation and Research on Sleep, , Lausanne University Hospital, ; Lausanne, Switzerland
                [4 ]ISNI 0000 0001 2232 2818, GRID grid.9759.2, School of Computing, , University of Kent, ; Chatham Maritime, United Kingdom
                [5 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Clinical Neurosciences, , University of Cambridge, ; Cambridge, United Kingdom
                [6 ]ISNI 0000 0004 1756 8209, GRID grid.144189.1, University Hospital of Pisa, ; Pisa, Italy
                [7 ]GRID grid.440617.0, Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, ; Santiago, Chile
                [8 ]ISNI 0000 0001 2224 0804, GRID grid.411964.f, The Neuropsychology and Cognitive Neurosciences Research Center (CINPSI Neurocog), Universidad Católica del Maule, ; Talca, Chile
                Author information
                http://orcid.org/0000-0002-0227-1600
                http://orcid.org/0000-0001-5184-6477
                http://orcid.org/0000-0003-2061-9719
                Article
                45289
                10.1038/s41598-019-45289-7
                6586889
                31222021
                432619b9-2bba-4e42-ba59-bf2eb6a75466
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 January 2019
                : 3 June 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003196, Ministero della Salute (Ministry of Health, Italy);
                Award ID: GR-2011-02347383
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: PZ00P3_173955
                Award Recipient :
                Funded by: Divesa Foundation Switzerland, Pierre-Mercier Foundation for Science and the Bourse Pro-Femme of the University of Lausanne
                Categories
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                © The Author(s) 2019

                Uncategorized
                dynamical systems,biophysical models
                Uncategorized
                dynamical systems, biophysical models

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