Cited by 184Functional Brain Connectivity StudiesNeural dynamics and brain functionAdvanced MRI Techniques and Applications

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The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations

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Florin, Esther, Baillet, Sylvain·2015·NeuroImage

DOI 10.1016/j.neuroimage.2015.01.054PMC PMC4387013

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Abstract

Functional imaging of the resting brain consistently reveals broad motifs of correlated blood oxygen level dependent (BOLD) activity that engages cerebral regions from distinct functional systems. Yet, the neurophysiological processes underlying these organized, large-scale fluctuations remain to be uncovered. Using magnetoencephalography (MEG) imaging during rest in 12 healthy subjects we analyze the resting state networks and their underlying neurophysiology. We first demonstrate non-invasively that cortical occurrences of high-frequency oscillatory activity are conditioned to the phase of slower spontaneous fluctuations in neural ensembles. We further show that resting-state networks emerge from synchronized phase-amplitude coupling across the brain. Overall, these findings suggest a unified principle of local-to-global neural signaling for long-range brain communication.

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Newey, W. K., & West, K. D. (1987) · Econometrica · doi:10.2307/1913610 · Cited by 16,936
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The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Correspondence of the brain's functional architecture during activation and rest
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The brainweb: phase synchronization and large-scale integration
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A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.
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Self-citation

Show all 50 references

Research works printed but not linked: 1

Mann, 1991, The chirplet transform: a generalization of Gabor's logon transform, Vis. Interface, Vol. 91, 3

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Authors: Florin, E., & Baillet, S.
Affiliations: Montreal Neurological Institute and Hospital (CA)
Corresponding author: Baillet, Sylvain
ORCID: Florin, Esther
Baillet, Sylvain
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2015-05
Volume: 111
Pages: 26-35
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2015.01.054
PMC: PMC4387013
OpenAlex: W2025209343
Cited by: 184
References: 50
Citations by year: 2026: 9 · 2025: 11 · 2024: 10 · 2023: 22 · 2022: 19 · 2021: 20 · 2020: 17 · 2019: 19 · 2018: 14 · 2017: 19 · 2016: 13 · 2015: 10
FWCI: 11.22
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Deutsche Forschungsgemeinschaft (DFG: FL 760 2-1)
Montreal Neurological Institute's National Bank Fellowship
Agence Nationale pour la Recherche (ANR-BLAN08-0250)
Killam Foundation
National Science and Engineering Research Council of Canada
NIH (2R01EB009048-05)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: resting-state networks, cross-frequency coupling, phase-amplitude coupling, neural oscillations, high-frequency oscillations, spontaneous neural fluctuations, magnetoencephalography
MeSH terms: Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Male, Nerve Net, Magnetoencephalography, Young Adult

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The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations

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