Cited by 162Neural dynamics and brain functionVisual perception and processing mechanismsAdvanced MRI Techniques and Applications

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The relationship between oscillatory EEG activity and the laminar-specific BOLD signal

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Scheeringa, René, Koopmans, Peter J., van Mourik, Tim, Jensen, Ole, Norris, David G.·2016·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1522577113PMC PMC4914170

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Abstract

Significance: In this study, we demonstrate that α-, β-, and γ-oscillations can be related to activity in different cortical layers in the human brain. These cortical layers are believed to instantiate feedforward and feedback information streams. Electrophysiological oscillations in α- (8–12 Hz), β- (15–30 Hz), and γ- (40–100 Hz) bands have also been associated with different roles in feedforward and feedback processes and to different cortical layers in nonhuman animals. By simultaneously recording laminar functional MRI (fMRI) and EEG, we could directly link oscillatory signals to activity in cortical layers in humans. This research provides an important neural basis for noninvasive research into the role of cortical layers in information processing using laminar fMRI alone or combined with EEG.

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Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496
Unified segmentation
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
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
Fast and robust fixed-point algorithms for independent component analysis
Hyvärinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,304
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Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
Spatial registration and normalization of images
Friston, K. J., Ashburner, J., Frith, C., Poline, J.-B., Heather, J. D., & Frackowiak, R. S. J. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030303 · Cited by 3,744
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612

Flagged first, then by citations

Show all 74 references

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T Van Mourik PJ Koopmans DG Norris Improved cortical boundary registration for locally distorted fMRI. Proceedings of the ISMRM-ESMRBM Joint Annual Meeting 2014 (Curran Assoc Red Hook NY) p 903. (2015).

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Authors: Scheeringa, R., Koopmans, P. J., van Mourik, T., Jensen, O., & Norris, D. G.
Affiliations: Radboud University Nijmegen (NL)
Donders Institute for Brain Cognition and Behaviour, Radboud University, NL-6500 HB Nijmegen, The Netherlands;
Erwin L. Hahn Institute for Magnetic Resonance Imaging (DE)
Corresponding author: Scheeringa, René
ORCID: Scheeringa, René
Koopmans, Peter J.
Jensen, Ole
Norris, David G.
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2016-05-31
Volume: 113
Issue: 24
Pages: 6761-6766
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1522577113
PMC: PMC4914170
OpenAlex: W2410089593
Cited by: 162
References: 74
Citations by year: 2026: 14 · 2025: 13 · 2024: 8 · 2023: 12 · 2022: 13 · 2021: 26 · 2020: 21 · 2019: 21 · 2018: 13 · 2017: 17 · 2016: 4
FWCI: 11.67
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.pnas.org/preview_site/misc/userlicense.xhtml (vor, 183 d)
Locations: Proceedings of the National Academy of Sciences (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
University of Twente Research Information (publishedVersion, other-oa)
Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion)
University of Twente Research Information (submittedVersion, other-oa)
Funders: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (453-09-002, 451-12-021)
Wellcome Trust (WT100092MA)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: cortical oscillations, gamma oscillations, beta oscillations, alpha oscillations, visual cortex, EEG-fMRI correlation, high-resolution fMRI
MeSH terms: Adolescent, Adult, Alpha Rhythm, Beta Rhythm, Female, Humans, Magnetic Resonance Imaging, Male, Oxygen, Visual Cortex, Gamma Rhythm

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The relationship between oscillatory EEG activity and the laminar-specific BOLD signal

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