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Interictal networks in Magnetoencephalography

Authors

  • Urszula Malinowska,

    1. INSERM, Marseille, France
    2. Aix-Marseille Université, INS, Marseille, France
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  • Jean-Michel Badier,

    1. INSERM, Marseille, France
    2. Aix-Marseille Université, INS, Marseille, France
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  • Martine Gavaret,

    1. INSERM, Marseille, France
    2. Aix-Marseille Université, INS, Marseille, France
    3. APHM, Hôpital Timone, Service de neurophysiologie clinique, Marseille, France
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  • Fabrice Bartolomei,

    1. INSERM, Marseille, France
    2. Aix-Marseille Université, INS, Marseille, France
    3. APHM, Hôpital Timone, Service de neurophysiologie clinique, Marseille, France
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  • Patrick Chauvel,

    1. INSERM, Marseille, France
    2. Aix-Marseille Université, INS, Marseille, France
    3. APHM, Hôpital Timone, Service de neurophysiologie clinique, Marseille, France
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  • Christian-George Bénar

    Corresponding author
    1. INSERM, Marseille, France
    2. Aix-Marseille Université, INS, Marseille, France
    • Correspondence to: Christian-George Bénar, Institut des Neurosciences des Systèmes, UMR 1106, INSERM, Aix-Marseille Université, Faculté de Médecine La Timone, 27 Bd Jean Moulin, 13385 Marseille Cedex 05, France. E-mail: christian.benar@univ-amu.fr

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Abstract

Epileptic networks involve complex relationships across several brain areas. Such networks have been shown on intracerebral EEG (stereotaxic EEG, SEEG), an invasive technique. Magnetoencephalography (MEG) is a noninvasive tool, which was recently proven to be efficient for localizing the generators of epileptiform discharges. However, despite the importance of characterizing non-invasively network aspects in partial epilepsies, only few studies have attempted to retrieve fine spatiotemporal dynamics of interictal discharges with MEG. Our goal was to assess the relevance of magnetoencephalography for detecting and characterizing the brain networks involved in interictal epileptic discharges. We propose here a semi-automatic method based on independent component analysis (ICA) and on co-occurrence of events across components. The method was evaluated in a series of seven patients by comparing its results with networks identified in SEEG. On both MEG and SEEG, we found that interictal discharges can involve remote regions which are acting in synchrony. More regions were identified in SEEG (38 in total) than in MEG (20). All MEG regions were confirmed by SEEG when an electrode was present in the vicinity. In all patients, at least one region could be identified as leading according to our criteria. A majority (71%) of MEG leaders were confirmed by SEEG. We have therefore shown that MEG measurements can extract a significant proportion of the networks visible in SEEG. This suggests that MEG can be a useful tool for defining noninvasively interictal epileptic networks, in terms of regions and patterns of connectivity, in search for a “primary irritative zone.” Hum Brain Mapp 35:2789–2805, 2014. © 2013 Wiley Periodicals, Inc.

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