Effect of field spread on resting-state MEG functional network analysis: A computational modeling study
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Number of pages
SourceBrain Connectivity, 7, 9, (2017), pp. 541-557
Article / Letter to editor
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SW OZ DCC SMN
SubjectAction, intention, and motor control; DI-BCB_DCC_Theme 2: Perception, Action and Control
A popular way to analyze resting-state EEG and MEG data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time-series with 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 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 MRI-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 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 functional connectivity 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 to use lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.
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