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.
| Original language | English |
|---|---|
| Pages (from-to) | 284-293 |
| Number of pages | 10 |
| Journal | NeuroImage |
| Volume | 138 |
| DOIs | |
| Publication status | Published - 1 Sept 2016 |
Bibliographical note
Funding Information:The authors would like to thank George O'Neill, Adam Baker and Diego Vidaurre for helpful comments. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University . G.L.C. is funded by the Research Councils UK Digital Economy Programme ( EP/G036861/1 , Centre for Doctoral Training in Healthcare Innovation ). M.J.B. and P.K.T. are funded by a Medical Research Council UK New Investigator Grant ( MR/M006301/1 ). S.M.S. is funded by a Welcome Trust Strategic Award ( 098369/Z/12/Z ). A.J.Q. is funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals Trust Oxford University . M.W.W. is funded by the Wellcome Trust ( 092753 ) and an MRC UK MEG Partnership Grant ( MR/K005464/1 ), and is also supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre . (The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.)
Publisher Copyright:
© 2016 The Authors
Keywords
- Connectome
- Functional connectivity
- Magnetic field spread
- MEG
- Network analysis
- Source leakage
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience