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Investigating the electrophysiological basis of resting state networks using magnetoencephalography
Open accessAbstract
In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level–dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.
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“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 [87]
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Metadata
- Authors
- Brookes, M. J., Woolrich, M., Luckhoo, H., Price, D., Hale, J. R., Stephenson, M. C., Barnes, G. R., Smith, S. M., & Morris, P. G.
- Affiliations
- University of Nottingham (GB)
- Warneford Hospital (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- John Radcliffe Hospital (GB)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-09-19
- Volume
- 108
- Issue
- 40
- Pages
- 16783-16788
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3189080
- OpenAlex
- W2122418437
- Cited by
- 938
- References
- 32
- Citations by year
- 2026: 33 · 2025: 36 · 2024: 60 · 2023: 58 · 2022: 72 · 2021: 68 · 2020: 91 · 2019: 85 · 2018: 74 · 2017: 64 · 2016: 67 · 2015: 66 · 2014: 73 · 2013: 57 · 2012: 31
- FWCI
- 26.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust
- Leverhulme Trust
- University College London
- University of Nottingham
- Medical Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- resting-state networks, magnetoencephalography, independent component analysis, BOLD signal, functional connectivity
- MeSH terms
- Brain, Electrophysiology, Hemodynamics, Humans, Membrane Potentials, Models, Biological, Nerve Net, Magnetoencephalography
About these checks
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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