Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution

Abstract

The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach.Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI).Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected.Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.

Direction of information flow in large-scale resting-state networks is frequency-dependent

“…An atlas-based beamformer approach was adopted to project MEG data from sensor level to source space (14). First, the coregistered MRI was spatially normalized to a template MRI using the New Segment toolbox in SPM8 (63).…”

“…To obtain a single time series for an ROI, we used each ROI's centroid as representative for that ROI, with the centroid defined here as the voxel within the ROI that is nearest, in terms of Euclidean distance, to all other voxels in the ROI [see Fig. S6 and Table S6 for comparison with an approach based on selection of the voxel with maximum activation (14)]. …”

“…Indeed, interactions in large-scale functional networks have been observed using Electroencephalography, Magnetoencephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI) (e.g., refs. [11][12][13][14]. However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state.…”

Structure‐function relationships in the visual system in multiple sclerosis: an MEG and OCT study

“…An atlas‐based beamforming approach was adopted to map MEG data from sensor level to source space 23. The exact beamforming procedure has been described in previous work16 and can also be found in the Data S1.…”

The relationship between MEG and fMRI

“…There are also many other ways to image connectivity including phase lag index (Stam et al, 2007, Hillebrand et al, 2012, imaginary coherence (Nolte et al, 2004b, Sekihara et al, 2011, synchronisation likelihood (Stam and van Dijk, 2002) and others. It is beyond the scope of this review to list all of the possible metrics for functional coupling in MEG; however this abbreviated list provides some indication of the myriad metrics that are available.…”