Abstract
MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.
Highlights
Resting-state connectivity estimation with electrophysiology is an important tool for studying intrinsic brain activity
We focus on presenting results just from the alpha band because the differences between metrics were smallest for this band, presumably because it exhibits the largest signal to noise ratio
We find that the group-level connectivity matrices derived from the parcellation of 39 regions of interest (ROIs) all exhibit the basic structure of connectivity expected in the resting-state, with segregated sensory networks
Summary
Resting-state connectivity estimation with electrophysiology is an important tool for studying intrinsic brain activity. It is able to act in concert with resting-state functional magnetic resonance imaging (fMRI), both by deconstructing the oscillation-specific functional origin of resting-state networks (Brookes et al, 2011; Marzetti et al, 2013), and as a feasible tool for assessing the short-timescale fluctuations of connectivity at rest (Baker et al, 2014; de Pasquale et al, 2010, 2015). It acts as a complementary modality to fMRI and electrocorticograms for analyses of cortical communication patterns (de Pasquale et al, 2015), as well as of the fundamental differences in connectivity between healthy and diseased populations (Stam and van Straaten, 2012; Stam, 2014; van Straaten and Stam, 2013). Source estimates are highly spatially correlated, and this spatial leakage of inferred sources into their local neighbourhood can create the semblance of connectivity between
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