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

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.

Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

“…However, the accuracy of these methods in conjunction with graphical network analysis is not yet established, and the good approximation of planar gradiometer topography to underlying cortical sources provides sufficient resolution to test our current hypotheses. Simulation studies confirm that multivariate auto-regression modelling is more robust in sensor space ( Michalareas et al , 2013 ), while the use of lagged interaction measures from planar gradiometer data are less sensitive to field-spread ( Pereira et al , 2017 ). Our own simulations provided further evidence that the analysis of sensor space graph metrics accords with source space generators of the data.…”

“…An advantage of this method for MEG/EEG is that it is less sensitive to the field spread that otherwise inflates instantaneous correlation metrics ( Baccalá and Sameshima, 2001 ; van Dellen et al , 2013 ; Colclough et al , 2016 ). When combined with the focal field-of-view of planar gradiometers, simulation studies confirm that multivariate autoregressive modelling minimizes field spread while remaining veridical to source–space interactions ( Pereira et al , 2017 ). Indeed, our own simulations confirmed that this approach can recover average local efficiency of source-level networks, provided signal-to-noise ratio is sufficiently high ( Supplementary Fig.…”

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)

“…The interfering factors to be considered in MEG data analysis are field spread (FS) and volume conduction (VC). Because of the topographical representation of magnetic field beyond the source, signal can be picked up at some distance and it is termed as field spread (Silva Pereira et al, 2017 ). Due to FS, a signal from one underlying source can be present in multiple time series.…”

A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

“…is computed for nodes i and j, where < • > denotes averaging over time [45]. One may note that the PLI takes values between 0 (random phase relations or perfect synchrony) and 1 (perfect phase locking).…”