Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.

Details

Serval ID
serval:BIB_26781B910A96
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.
Journal
Brain connectivity
Author(s)
Silva Pereira S., Hindriks R., Mühlberg S., Maris E., van Ede F., Griffa A., Hagmann P., Deco G.
ISSN
2158-0022 (Electronic)
ISSN-L
2158-0014
Publication state
Published
Issued date
11/2017
Peer-reviewed
Oui
Volume
7
Number
9
Pages
541-557
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
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.

Keywords
Brain model, Coherence, Graph theory, Human brain connectivity, Magnetoencephalography (MEG), field spread, network-theoretical analysis, resting-state MEG, sensor-level functional networks, topology reconstruction, whole-brain model
Pubmed
Create date
07/09/2017 15:10
Last modification date
20/08/2019 13:05
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