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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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Cites
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Self-citationConsistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Self-citationGreicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Self-citationNeuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581
Abnormal neural oscillations and synchrony in schizophrenia
Uhlhaas, P. J., & Singer, W. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2774 · Cited by 2,120
Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem.
Sarvas, J. (1987) · Physics in Medicine and Biology · doi:10.1088/0031-9155/32/1/004 · Cited by 1,945
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Alderson, T. H., Jun, S., Wirsich, J., Egan, M. K., ElSayed, S. W., Giakas, S. A., Mostame, P., Harper, J., Giraud, A., Malone, S. M., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2535464123
Ding, Y., Chen, Y., Tian, Y. H., Li, Z., Yang, H., Fang, K., Wen, B., Zheng, R., Li, S., Wei, Y., et al. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08307-3
del Cerro‐León, A., Shpakivska‐Bilán, D., Uceta, M., Maestú, F., García‐Moreno, L. M., & Antón‐Toro, L. F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.20.733186
Daniel, T. D. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1836602
Gu, L., Han, H., Wang, H., & Xiong, X. (2026) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2026.110864
Yang, R., Ru, X., Song, J., Zhao, Y., Fan, B., Na, S., & Gao, J. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122133
Wen, X., Han, B., Zhang, J., Gao, Y., Fengge, Y., Dou, F., Hao, Z., Li, H., & Hou, G. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122383
Oswald, V., Landry, M., Abdelhedi, H., Lippé, S., Robaey, P., & Jerbi, K. (2026) · iScience · doi:10.1016/j.isci.2026.117377
Rehbein, M. A., Kroker, T., Wollbrink, A., & Junghoefer, M. (2026) · Neuromethods · doi:10.1007/978-1-0716-5340-1_6
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Herrera-Pérez, S., Rossetti, P., Marín-Maicas, P., & Lázaro, E. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122204
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
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