Evidence map›Paper›PMID 27262239›Full record

ArticleNeuroImage2016

How reliable are MEG resting-state connectivity metrics?

G L Colclough, M W Woolrich, P K Tewarie, M J Brookes, A J Quinn, S M Smith

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 215 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
215citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

215 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Tracking electrophysiological signatures of Alzheimer's disease: a systematic review of multimodal studies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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  17. Preoperative MEG reveals differential brain network characteristics in drug-resistant epilepsy patients based on vagus nerve stimulation response.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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155 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

G L ColcloughOxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK; Dept. Engineering Sciences, University of Oxford, Parks Rd, Oxford, UK. Electronic address: giles.colclough@ohba.ox.ac.uk.
M W WoolrichOxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK.
P K TewarieSir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK.
M J BrookesSir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK.
A J QuinnOxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK.
S M SmithCentre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK.

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Medical Research Council MR/K005464/1Medical Research Council MR/M006301/1NIMH NIH HHS U54 MH091657Wellcome Trust 092753
6 · The paper itself

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.

Indexed as

AlgorithmsAdultCerebral CortexConnectomeFemaleHumansMagnetoencephalographyMaleNerve NetReproducibility of ResultsRestSensitivity and SpecificityConnectomeFunctional connectivityMagnetic field spreadMEGNetwork analysisSource leakage

Identifiers

PMID27262239
PMCPMC5056955

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.