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      Dynamic brain network states in human generalized spike-wave discharges

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          Abstract

          Generalised spike-wave discharges (GSW) in idiopathic generalised epilepsy (IGE) appear to have abrupt onset on EEG. However, in rodent models, GSW emerge during evolving brain network states. Using EEG-fMRI, Tangwiriyasakul et al. reveal that GSW onset in human IGE, as in rodent models, emerges during evolving brain network states.

          Abstract

          Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus ( P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections ( P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls ( P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.

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          Most cited references38

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          Weight-conserving characterization of complex functional brain networks.

          Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction.

            Triggering functional MRI (fMRI) image acquisition immediately after an EEG event can provide information on the location of the event generator. However, EEG artifact associated with pulsatile blood flow in a subject inside the scanner may obscure EEG events. This pulse artifact (PA) has been widely recognized as a significant problem, although its characteristics are unpredictable. We have investigated the amplitude, distribution on the scalp, and frequency of occurrence of this artifact. This showed large interindividual variations in amplitude, although PA is normally largest in the frontal region. In five of six subjects, PA was greater than 50 microV in at least one of the temporal, parasagittal, and central channels analyzed. Therefore, we developed and validated a method for removing PA. This subtracts an averaged PA waveform calculated for each electrode during the previous 10 s. Particular attention has been given to reliable ECG peak detection and ensuring that the average PA waveform is free of other EEG artifacts. Comparison of frequency spectra for EEG recorded outside and inside the scanner, with and without PA subtraction, showed a clear reduction in artifact after PA subtraction for all four frequency ranges analyzed. As further validation, lateralized epileptiform spikes were added to recordings from inside and outside the scanner: PA subtraction significantly increased the proportion of these spikes that were correctly identified and decreased the number of false spike detections. We conclude that in some subjects, EEG/fMRI studies will be feasible only using PA subtraction. Copyright 1998 Academic Press.
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              Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia.

              An increasing number of schizophrenia studies have been examining electroencephalography (EEG) data using time-frequency analysis, documenting illness-related abnormalities in neuronal oscillations and their synchronization, particularly in the gamma band. In this article, we review common methods of spectral decomposition of EEG, time-frequency analyses, types of measures that separately quantify magnitude and phase information from the EEG, and the influence of parameter choices on the analysis results. We then compare the degree of phase locking (ie, phase-locking factor) of the gamma band (36-50 Hz) response evoked about 50 milliseconds following the presentation of standard tones in 22 healthy controls and 21 medicated patients with schizophrenia. These tones were presented as part of an auditory oddball task performed by subjects while EEG was recorded from their scalps. The results showed prominent gamma band phase locking at frontal electrodes between 20 and 60 milliseconds following tone onset in healthy controls that was significantly reduced in patients with schizophrenia (P = .03). The finding suggests that the early-evoked gamma band response to auditory stimuli is deficiently synchronized in schizophrenia. We discuss the results in terms of pathophysiological mechanisms compromising event-related gamma phase synchrony in schizophrenia and further attempt to reconcile this finding with prior studies that failed to find this effect.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                October 2018
                28 August 2018
                28 August 2018
                : 141
                : 10
                : 2981-2994
                Affiliations
                [1 ] Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London, UK
                [2 ] Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
                [3 ] King’s College Hospital, London, UK
                Author notes
                Correspondence to: Professor Mark P. Richardson Institute of Psychiatry Psychology and Neuroscience King’s College London Maurice Wohl Clinical Neuroscience Institute 5 Cutcombe Road London SE5 9RT UK E-mail: mark.richardson@ 123456kcl.ac.uk

                Chayanin Tangwiriyasakul, Suejen Perani, David W. Carmichael and Mark P. Richardson authors contributed equally to this work.

                Article
                awy223
                10.1093/brain/awy223
                6158757
                30169608
                292243b2-f681-411c-83d9-a999d91acd8a
                © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 March 2018
                : 25 June 2018
                : 15 July 2018
                Page count
                Pages: 14
                Funding
                Funded by: Medical Research Council 10.13039/501100000265
                Award ID: MR/K013998/1
                Funded by: Action Medical Research 10.13039/501100000317
                Award ID: SP4646
                Funded by: Engineering and Physical Sciences Research Council 10.13039/501100000266
                Award ID: EP/M001393/1
                Funded by: Brain Products
                Funded by: Engineering and Physical Sciences Research Council Centre for Predictive Modelling in Healthcare
                Award ID: EP/N014391/1
                Funded by: Medical Research Council Centre for Neurodevelopmental Disorders
                Award ID: MR/N026063/1
                Funded by: NIHR 10.13039/100006662
                Funded by: Biomedical Research Centre at South London
                Funded by: Maudsley NHS Foundation Trust
                Funded by: NIHR 10.13039/100006662
                Funded by: Wellcome Trust King’s Clinical Research Facility
                Funded by: Centre for Neuroimaging King’s College London
                Funded by: National Institute of Health Research Great Ormond Street Hospital Biomedical Research Centre
                Categories
                Original Articles

                Neurosciences
                idiopathic/genetic generalized epilepsy,generalized spike-wave,eeg-functional mri,functional brain network,pre-ictal

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