How reliable are MEG resting-state connectivity metrics?

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.

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

“…Please see Table S1 for example of consistency of our results. We also checked that important hubs for all frequency bands are in line with previous published studies (Brookes et al, 2011; Hipp et al, 2012; Colclough et al, 2016) as shown in Fig. S8.…”

“…1. Previous studies (humans and primates) have demonstrated the validity and functional significance of these synchronous envelope amplitude modulations (Brookes et al, 2011, 2016; Vidal et al, 2012; Wang et al, 2012; Colclough et al, 2016) for both oscillatory and broadband signals.…”

“…We used a method based upon envelope correlations to reliably estimate synchronicity between different cortical labels (Colclough et al, 2016). In contrast to phase-based connectivity metrics envelope correlations measure how the amplitude of an envelope within a frequency band is synchronously modulated over time across distinct cortical regions, as illustrated in the fourth panel of Fig.…”

Functional network dynamics in a neurodevelopmental disorder of known genetic origin

“…The MEG sensor data were then low pass filtered at 4–30 Hz, to focus on slower frequencies only. This is the frequency range at which amplitude correlations have been shown to produce robust resting‐state brain networks and better distinguish true from spurious connectivity (Colclough et al, ; Luckhoo et al, ). For each subject, source space activity was then estimated at every point of an 8 mm whole‐brain grid using a linearly constrained minimum variance (LCMV) scalar beamformer that combines information across both sensor types and accounts for the reduction in dimensionality induced by the signal‐space separation method (Van Veen et al, 1997; Woolrich, Hunt, Groves, & Barnes, 2011).…”

Optimising experimental design for MEG resting state functional connectivity measurement

“…For example, Wens et al (2014) showed that whilst group level connectivity within several well-known networks is stable, there is significant variability at the individual subject level. Colclough et al (2016) tested the between session repeatability of a large number of functional connectivity measurements, showing that although group level inference is reliable, network metrics vary across individuals.…”