Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution
Open accessAbstract
The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach. Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI). Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected. Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.
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In the resting state, electrophysiological source imaging has uncovered frequency-dependent functional networks and their dynamics.
“In the resting state, ESI has uncovered frequency-dependent functional networks and their dynamics (86–88).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [86]
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Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity
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- Authors
- Hillebrand, A., Barnes, G. R., Bosboom, J. L., Berendse, H. W., & Stam, C. J.
- Affiliations
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- Wellcome Centre for Human Neuroimaging (GB)
- OLVG (NL)
- Corresponding author
- Hillebrand, Arjan
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 59
- Issue
- 4
- Pages
- 3909-3921
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3382730
- OpenAlex
- W2096221603
- Cited by
- 416
- References
- 152
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 19 · 2023: 32 · 2022: 36 · 2021: 36 · 2020: 36 · 2019: 36 · 2018: 37 · 2017: 37 · 2016: 30 · 2015: 22 · 2014: 26 · 2013: 28 · 2012: 11
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Royal Society
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- resting-state functional connectivity, phase lag index, volume conduction, eyes-closed resting state, alpha band, beta band, gamma band, resting-state networks, brain network topology
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Nerve Net, Neural Pathways, Rest, Magnetoencephalography
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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