ArticleNeuroImage2021
Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Oscillatory edge connectivity in pain-related regions supports machine learning identification of migraine.The journal of headache and pain · 2026Article
- Temporal stability and neural complexity in resting-state MEG predict migraine phenotypes.The journal of headache and pain · 2025Article
- Detecting Event-Related Spectral Perturbations in Right-Handed Sensorimotor Cortical Responses Using OPM-MEG.Bioengineering (Basel, Switzerland) · 2025Article
- Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral-Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals.Brain sciences · 2025Article
- Altered neuromagnetic activity under visual stimuli in migraine: a multi-frequency magnetoencephalography study.Frontiers in neurology · 2025Article
- Article
- Multi-modal and multi-model interrogation of large-scale functional brain networks.NeuroImage · 2023Article
- Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?Network neuroscience (Cambridge, Mass.) · 2022Article
- Alterations in the default mode network in rolandic epilepsy with mild spike-wave index in non-rapid eye movement sleep.Frontiers in neuroscience · 2022Article
- Predicting MEG resting-state functional connectivity from microstructural information.Network neuroscience (Cambridge, Mass.) · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or 'irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to 'scale frequencies, f
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Registered trials
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.