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      Graph-based analysis of brain connectivity in schizophrenia

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      PLoS ONE
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          Abstract

          The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were calculated for the original EEG data and following current source density (CSD) transformation, re-referencing using the average reference electrode (AVERAGE) and reference electrode standardization techniques (REST). The statistical analysis of adjacency matrices was carried out using indices based on graph theory. Both CSD and REST reduced the influence of volume conducted currents. The largest group differences in connectivity were observed for the alpha band. Schizophrenic patients showed reduced connectivity strength, as well as a lower clustering coefficient and shorter characteristic path length for both measures of phase synchronization following CSD transformation or REST re-referencing. Reduced synchronization was accompanied by increased directional flow from the occipital region for the alpha band. Following the REST re-referencing, the sources of alpha activity were located at parietal rather than occipital derivations. The results of PLV and DTF demonstrated group differences in fronto-posterior asymmetry following CSD transformation, while for PLI the differences were significant only using REST. The only analysis that identified group differences in inter-hemispheric asymmetry was DTF calculated for REST. Our results suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, may be a useful tool in the study of disconnectivity disorders such as schizophrenia.

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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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              Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization.

              Cortical activity and perception are not driven by the external stimulus alone; rather sensory information has to be integrated with various other internal constraints such as expectations, recent memories, planned actions, etc. The question is how large scale integration over many remote and size-varying processes might be performed by the brain. We have conducted a series of EEG recordings during processes thought to involve neuronal assemblies of varying complexity. While local synchronization during visual processing evolved in the gamma frequency range, synchronization between neighboring temporal and parietal cortex during multimodal semantic processing evolved in a lower, the beta1 (12-18 Hz) frequency range, and long range fronto-parietal interactions during working memory retention and mental imagery evolved in the theta and alpha (4-8 Hz, 8-12 Hz) frequency range. Thus, a relationship seems to exist between the extent of functional integration and the synchronization-frequency. In particular, long-range interactions in the alpha and theta ranges seem specifically involved in processing of internal mental context, i.e. for top-down processing. We propose that large scale integration is performed by synchronization among neurons and neuronal assemblies evolving in different frequency ranges.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: ResourcesRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 November 2017
                2017
                : 12
                : 11
                : e0188629
                Affiliations
                [1 ] Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
                [2 ] Department of Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warsaw, Poland
                University of Electronic Science and Technology of China, CHINA
                Author notes

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

                Author information
                http://orcid.org/0000-0002-6821-350X
                Article
                PONE-D-17-24237
                10.1371/journal.pone.0188629
                5708839
                29190759
                86405bef-ce0b-4f7e-905e-01f77a2ff53a
                © 2017 Olejarczyk, Jernajczyk

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 June 2017
                : 10 November 2017
                Page count
                Figures: 16, Tables: 5, Pages: 28
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Schizophrenia
                Engineering and Technology
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                Electrophysiological Techniques
                Brain Electrophysiology
                Electroencephalography
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                Research and Analysis Methods
                Mathematical and Statistical Techniques
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                Custom metadata
                Data are held in a public repository: Olejarczyk, E.; Jernajczyk, W. (2017) EEG in schizophrenia. RepOD. http://dx.doi.org/10.18150/repod.0107441.

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