Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales.
Functional connectivity
MEG
Multi-scale rank-Vector entropy
Resting state
Variability
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 02 2021
01 02 2021
Historique:
received:
30
03
2020
revised:
08
10
2020
accepted:
03
11
2020
pubmed:
14
11
2020
medline:
2
3
2021
entrez:
13
11
2020
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 33186722
pii: S1053-8119(20)31036-3
doi: 10.1016/j.neuroimage.2020.117551
pmc: PMC7836237
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
117551Subventions
Organisme : Medical Research Council
ID : MR/K005464/1
Pays : United Kingdom
Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
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