Effect of field spread on resting-state magneto encephalography functional network analysis: a computational modeling study

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• dc.contributor.author Silva Pereira, Silvanaca
• dc.contributor.author Hindriks, Rikkertca
• dc.contributor.author Mühlberg, Stefanie, 1986-ca
• dc.contributor.author Maris, Ericca
• dc.contributor.author van Ede, Freekca
• dc.contributor.author Griffa, Alessandraca
• dc.contributor.author Hagmann, Patricca
• dc.contributor.author Deco, Gustavoca
• dc.date.accessioned 2018-01-15T09:47:22Z
• dc.date.available 2018-01-15T09:47:22Z
• dc.date.issued 2017
• dc.description.abstract A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.en
• dc.description.sponsorship R. Hindriks and G. Deco were funded by the European Research Council (Advanced Grant DYSTRUCTURE No.295129), the Spanish Research Project PSI2013-42091-P, the CONSOLIDER-INGENIO 2010 Program CSD2007-00012, and the FP7-ICT Brainscales (269921).
• dc.format.mimetype application/pdfca
• dc.identifier.citation Silva Pereira S, Hindriks R, Mühlberg S, Maris E, van Ede F, Griffa A, Hagmann P, Deco G. Effect of field spread on resting-state magneto encephalography functional network analysis: a computational modeling study. Brain Connect. 2017; 7 (9): 541-557. DOI: 10.1089/brain.2017.0525
• dc.identifier.doi http://dx.doi.org/10.1089/brain.2017.0525
• dc.identifier.issn 2158-0022
• dc.identifier.uri http://hdl.handle.net/10230/33622
• dc.language.iso eng
• dc.publisher Mary Ann Liebert, Incca
• dc.relation.ispartof Brain Connectivity. 2017; 7 (9): 541-557.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2013-42091-P
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269921
• dc.rights Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/brain.2017.0525
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Field spreaden
• dc.subject.keyword Network-theoretical analysisen
• dc.subject.keyword Resting-state MEGen
• dc.subject.keyword Sensor-level functional networksen
• dc.subject.keyword Topology reconstructionen
• dc.subject.keyword Whole-brain modelen
• dc.title Effect of field spread on resting-state magneto encephalography functional network analysis: a computational modeling studyca
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/submittedVersion