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

By Arjan Hillebrand, Gareth Robert Barnes, Johannes L. W. Bosboom, Henk W. Berendse, Cornelis J. Stam. NeuroImage, 2012

414 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/9574eadc7da3a87186df7da83c6faebf98d8b54c

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In Cognitive Neuroscience

Citations in the corpus

A symmetric multivariate leakage correction for MEG connectomes (2015, positive): “In line with Hillebrand et al. (2012), we find an increased number of connections in regions associated with higher source power (occipital, parietal and temporal cortex in the alpha band; sensorimotor cortex in the beta-band).”
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, positive): “All calculations were performed using a commercial software package (MATLAB 7.12, The MathWorks Inc., Natick, MA, 2011).”
The trees and the forest: Characterization of complex brain networks with minimum spanning trees. (2014, positive): “These problems can be addressed by the use of MEG source space as a starting point of the network analysis (Hillebrand et al., 2012).”