Cited by 938Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsNeural dynamics and brain function

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Investigating the electrophysiological basis of resting state networks using magnetoencephalography

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Brookes, Matthew J., Woolrich, Mark, Luckhoo, Henry, Price, Darren, Hale, Joanne R., Stephenson, Mary C., Barnes, Gareth R., Smith, Stephen M., Morris, Peter G.·2011·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1112685108PMC PMC3189080

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Abstract

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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Self-citation
Reviewer #1 (Public Review): A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings
(2024) · eLife · doi:10.7554/elife.97107.2.sa1
A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordingsPreprint
Das, P., He, M., & Purdon, P. L. (2024) · eLife · doi:10.7554/elife.97107.2
Author response: A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings
Das, P., He, M., & Purdon, P. L. (2024) · eLife · doi:10.7554/elife.97107.2.sa0
eLife Assessment: A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings
Luo, H. (2024) · eLife · doi:10.7554/elife.97107.2.sa2
Reviewer #2 (Public Review): A multiplex of connectome trajectories enables several connectivity patterns in parallel
(2024) · eLife · doi:10.7554/elife.98777.1.sa0
A multiplex of connectome trajectories enables several connectivity patterns in parallelPreprint
Mostame, P., Wirsich, J., Alderson, T. H., Ridley, B., Giraud, A., Carmichael, D. W., Vulliémoz, S., Guye, M., Lemieux, L., & Sadaghiani, S. (2024) · eLife · doi:10.7554/elife.98777.1
eLife Assessment: A multiplex of connectome trajectories enables several connectivity patterns in parallel
Fornito, A. (2024) · eLife · doi:10.7554/elife.98777.1.sa2

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: Brookes, Matthew J.
Woolrich, Mark
Price, Darren
Stephenson, Mary C.
Barnes, Gareth R.
Smith, Stephen M.
Morris, Peter G.
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
DOI: 10.1073/pnas.1112685108
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

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Investigating the electrophysiological basis of resting state networks using magnetoencephalography

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