4.7 Article

How reliable are MEG resting-state connectivity metrics?

期刊

NEUROIMAGE
卷 138, 期 -, 页码 284-293

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2016.05.070

关键词

MEG; Source leakage; Magnetic field spread; Functional connectivity; Network analysis; Connectome

资金

  1. McDonnell Center for Systems Neuroscience at Washington University
  2. Research Councils UK Digital Economy Programme (Centre for Doctoral Training in Healthcare Innovation) [EP/G036861/1]
  3. Medical Research Council UK New Investigator Grant [MR/M006301/1]
  4. Welcome Trust [098369/Z/12/Z, 092753]
  5. National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals Trust Oxford University
  6. MRC UK MEG Partnership Grant [MR/K005464/1]
  7. National Institute for Health Research (NIHR) Oxford Biomedical Research Centre
  8. 16 NIH Institutes and Centers [1U54MH091657]
  9. MRC [MR/K005464/1, MR/M006301/1] Funding Source: UKRI
  10. Engineering and Physical Sciences Research Council [1243649] Funding Source: researchfish
  11. Medical Research Council [MR/K005464/1, MR/M006301/1] Funding Source: researchfish

向作者/读者索取更多资源

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. (C) 2016 The Authors. Published by Elsevier Inc.

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