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      Is Open Access

      IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

      review-article

      a , * , b , c , d , e , f , g , h , i , j , k , l , m , n , o , p , q , r , s

      Clinical Neurophysiology

      Elsevier

      AEF, auditory evoked field, BOLD, blood-level oxygen dependent, CKC, corticokinematic coherence, CMC, cortex–muscle coherence, DCM, dynamic causal modeling, EEG, electroencephalography, ECD, equivalent current dipole, ECoG, electrocorticography, fMRI, functional magnetic resonance imaging, HE, hepatic encephalopathy, IAP, intracarotid amobarbital procedure, ICA, independent component analysis, IES, intracutaneous epidermal electrical stimulation, ISI, interstimulus interval, MEG, magnetoencephalography, MNE, minimum norm estimate, MRI, magnetic resonance imaging, MUSIC, multiple signal classification, SEF, somatosensory evoked field, SNR, signal-to-noise ratio, SQUID, superconducting quantum interference device, SSS, signal-space separation, STN, subthalamic nucleus, TMS, transcranial magnetic stimulation, tSSS, temporo-spatial signal space separation, VEF, visual evoked field, Magnetoencephalography, Electroencephalography, Clinical neurophysiology, Evoked and event-related responses, Transient and steady-state responses, Spontaneous brain activity, Neural oscillations, Analysis and interpretation, Artifacts, Source modeling, Epilepsy, Preoperative evaluation, Stroke, Pain, Traumatic brain injury, Parkinson’s disease, Hepatic encephalopathy, Alzheimer’s disease and dementia, Neuropsychiatric disorders, Brain maturation and development, Dyslexia, Guidelines

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          Highlights

          • The main principles of magnetoencephalography (MEG) and the value of combined MEG and EEG are discussed.

          • Established and some potential future clinical applications of MEG are reviewed.

          • Practical guidelines for clinical MEG examinations are presented.

          Abstract

          Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible.

          This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations.

          In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.

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          Most cited references 272

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          Abnormal neural oscillations and synchrony in schizophrenia.

          Converging evidence from electrophysiological, physiological and anatomical studies suggests that abnormalities in the synchronized oscillatory activity of neurons may have a central role in the pathophysiology of schizophrenia. Neural oscillations are a fundamental mechanism for the establishment of precise temporal relationships between neuronal responses that are in turn relevant for memory, perception and consciousness. In patients with schizophrenia, the synchronization of beta- and gamma-band activity is abnormal, suggesting a crucial role for dysfunctional oscillations in the generation of the cognitive deficits and other symptoms of the disorder. Dysfunctional oscillations may arise owing to anomalies in the brain's rhythm-generating networks of GABA (gamma-aminobutyric acid) interneurons and in cortico-cortical connections.
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            Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.

             Cornelis Stam (corresponding) ,  Guido Nolte,  Andreas Daffertshofer (2007)
            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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              Über das Elektrenkephalogramm des Menschen

               Hans Berger (1929)
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                Author and article information

                Contributors
                Journal
                Clin Neurophysiol
                Clin Neurophysiol
                Clinical Neurophysiology
                Elsevier
                1388-2457
                1872-8952
                1 August 2018
                August 2018
                : 129
                : 8
                : 1720-1747
                Affiliations
                [a ]Department of Art, Aalto University, Helsinki, Finland
                [b ]McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
                [c ]Wellcome Centre for Human Neuroimaging, University College of London, London, UK
                [d ]Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
                [e ]Clinical Neuroscience, Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
                [f ]Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, UK
                [g ]Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Germany
                [h ]Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
                [i ]Harvard Medical School, Boston, MA, USA
                [j ]NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                [k ]Centre for Human Brain Health, University of Birmingham, Birmingham, UK
                [l ]Department of Integrative Physiology, National Institute of Physiological Sciences, Okazaki, Japan
                [m ]Department of Functional Neurology and Epileptology, Neurological Hospital & University of Lyon, Lyon, France
                [n ]Department of Epileptology, Tohoku University, Sendai, Japan
                [o ]Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
                [p ]Department of Neuroscience, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
                [q ]Institute of Clinical Neuroscience and Medical Psychology, and Department of Neurology, Heinrich-Heine-University, Düsseldorf, Germany
                [r ]Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, USA
                [s ]Department of Physics, University of Washington, Seattle, WA, USA
                Author notes
                [* ]Corresponding author at: Department of Art, School of Arts, Design and Architecture, Aalto University, PO Box 31000, FI-00076 Aalto, Helsinki, Finland. riitta.hari@ 123456aalto.fi
                Article
                S1388-2457(18)30657-6
                10.1016/j.clinph.2018.03.042
                6045462
                29724661
                5da75a96-a28e-413d-a4e0-348f02179670
                © 2018 International Federation of Clinical Neurophysiology. Elsevier Ireland Ltd. All rights reserved.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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