The relationship between oscillatory EEG activity and the laminar-specific BOLD signal
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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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The proposed directionality of oscillations is consistent with laminar fMRI profiles, LFP-based phase relationships, and neuromodulation effects.
“Such findings are consistent with the observable laminar profile of fMRI (187), LFP-based phase relationship (184), and effects of neuromodulation (188).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [187]
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“Another hybrid modelling approach could be supported by simultaneous EEG-fMRI measurements (Scheeringa et al., 2016).”
MethodSharoh, D., van Mourik, T., Bains, L. J., Segaert, K., Weber, K., Peter Hagoort, A., & Norris, D. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907858116 · Cited by 77 · see citing works
Self-citationElucidating relations between fMRI, ECoG, and EEG through a common natural stimulus
Haufe, S., DeGuzman, P., Henin, S. E., Arcaro, M. J., Honey, C. J., Hasson, U., & Parra, L. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.016 · Cited by 77 · see citing works
“, 1999) and likely depend on the stimulus (Scheeringa et al., 2016).”
Shafiei, G., Baillet, S., & Mišić, B. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001735 · Cited by 72 · see citing works
Laminar (f)MRI: A short history and future prospects
Norris, D. G., & Polimeni, J. R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.082 · Cited by 67 · see citing works
Self-citationShedding new light on disorders of consciousness diagnosis: The dynamic functional connectivity
Naro, A., Bramanti, A., Leo, A., Cacciola, A., Manuli, A., Bramanti, P., & Calabrò, R. S. (2018) · Cortex · doi:10.1016/j.cortex.2018.03.029 · Cited by 68 · see citing works
Words affect visual perception by activating object shape representations
Noorman, S., Neville, D. A., & Simanova, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-32483-2 · Cited by 67 · see citing works
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Self-citationNon-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms
Bonaiuto, J. J., Rossiter, H. E., Meyer, S. S., Adams, N., Little, S. J., Callaghan, M. F., Dick, F., Bestmann, S., & Barnes, G. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.068 · Cited by 55 · see citing works
“While high resolution functional magnetic resonance imaging (fMRI) can resolve laminar-specific activity (Chen et al., 2013; Goense et al., 2012; Guidi et al., 2016; Huber et al., 2015; Kok et al., 2016; Koopmans et al., 2011, 2010; Olman et al., 2012; Scheeringa et al., 2016), it cannot measure dynamics at a millisec…”
BackgroundSpatial specificity of alpha oscillations in the human visual system
Popov, T. G., Gips, B., Kästner, S., & Jensen, O. B. (2019) · Human Brain Mapping · doi:10.1002/hbm.24712 · Cited by 56 · see citing works
“Several studies highlight a relationship between the hemodynamic response and oscillatory activity (Hermes, Nguyen, & Winawer, 2017; Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001; Scheeringa et al., 2011; Scheeringa, Koopmans, van Mourik, Jensen, & Norris, 2016).”
Self-citationDissecting the Function of Hippocampal Oscillations in a Human Anxiety Model
Khemka, S., Barnes, G. R., Dolan, R. J., & Bach, D. R. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1834-16.2017 · Cited by 53 · see citing works
“The power effect in the current study was broadly limited to gamma band (24 –150 Hz), and oscillations in this frequency range show a robust relationship with BOLD responses (Boorman et al., 2015; Hutchison et al., 2015; Scheeringa et al., 2016), rendering the two findings rather consistent.”
ResultBayesian fusion and multimodal DCM for EEG and fMRI
Wei, H., Jafarian, A., Zeidman, P., Vladimir Litvak, A., Razi, A., Hu, D., & Friston, K. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116595 · Cited by 54 · see citing works
“These neuronal afferents further characterise a laminar specification of neurovascular architecture that may be useful for modelling high resolution fMRI (Heinzle et al., 2016; Markuerkiaga et al., 2016; Scheeringa et al., 2016; Huber et al., 2018; Kashyap et al., 2018; Uludag and Blinder, 2018).”
BackgroundThe neurophysiology of ketamine: an integrative review
McMillan, R. L., & Muthukumaraswamy, S. D. (2020) · Reviews in the Neurosciences · doi:10.1515/revneuro-2019-0090 · Cited by 49 · see citing works
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GABAergic System Dysfunction and Challenges in Schizophrenia Research
Jahangir, M., Zhou, J., Lang, B., & Wang, X. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.663854 · Cited by 52 · see citing works
Laminar fMRI: What can the time domain tell us?
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Predictive Coding: A more cognitive process than we thought?
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“BOLD signals have also been found to correlate with gamma power particularly in super fi cial layers of cortex [114].”
Zarubin, G., Gundlach, C., Nikulin, V. V., Villringer, A., & Bogdan, M. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00366 · Cited by 45 · see citing works
Higher and deeper: Bringing layer fMRI to association cortex
Finn, E. S., Huber, L., & Bandettini, P. A. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101930 · Cited by 47 · see citing works
“To date, nearly all layer fMRI studies have focused on unimodal cortex: visual (Kok et al., 2016; Muckli et al., 2015; Scheeringa et al., 2016), auditory (De Martino et al., 2015; Moerel et al., 2019), motor * Corresponding author at: Department of Psychological and Brain Sciences, Dartmouth…”
BackgroundMultimodal EEG-fMRI: advancing insight into large-scale human brain dynamics
Chang, C., & Chen, J. E. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100279 · Cited by 45 · see citing works
Naturalistic stimuli: A paradigm for multiscale functional characterization of the human brain
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Strategies and prospects for cortical depth dependent T2 and T2* weighted BOLD fMRI studies
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Self-citationComparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG
Jafarian, A., Vladimir Litvak, A., Cagnan, H., Friston, K. J., & Zeidman, P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116734 · Cited by 43 · see citing works
“There is considerable interest in associating the BOLD response with specific layers of the cortical column, and the laminar specificity of forward and backward connections (e.g. Scheeringa and Fries, 2017; Lawrence et al., 2017; Duyn, 2012; Scheeringa et al., 2016).”
BackgroundPinti, P., SIDDIQUI, M., Levy, A. D., Jones, E. J. H., & Tachtsidis, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-83420-9 · Cited by 43 · see citing works
Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI
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Contribution of animal models toward understanding resting state functional connectivity
Pais‐Roldán, P., Matéo, C., Pan, W., Acland, B. T., Kleinfeld, D., Snyder, L. H., Yu, X., & Keilholz, S. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118630 · Cited by 33 · see citing works
Laminar signal extraction over extended cortical areas by means of a spatial GLM
van Mourik, T., van der Eerden, J. P. J. M., Bazin, P., & Norris, D. G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212493 · Cited by 36 · see citing works
Self-citationDecoding the circuitry of consciousness: From local microcircuits to brain-scale networks
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“Some animal and human studies also suggested that different cortical regions could generate their own alpha oscillations (Lopes Da Silva et al., 1977; Bollimunta et al., 2008, 2011; Scheering et al., 2016).”
BackgroundKnudsen, L., Bailey, C. J., Blicher, J. U., Yang, Y. T., Zhang, P., & Lund, T. E. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120011 · Cited by 34 · see citing works
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Challenges and opportunities of mesoscopic brain mapping with fMRI
Bandettini, P. A., Huber, L., & Finn, E. S. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.06.002 · Cited by 33 · see citing works
The interplay between attention and long‐term memory in affective habituation
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Conflict- and error-related theta activities are coupled to BOLD signals in different brain regions
Beldzik, E., Ullsperger, M., Domagalik, A., & Marek, T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119264 · Cited by 31 · see citing works
When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI
Scrivener, C. L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.636424 · Cited by 30 · see citing works
“Combining this with EEG can therefore facilitate precise mapping between EEG signals and BOLD activation across individual layers of the cortex (e.g., Scheeringa et al., 2016), with greater detail than can be achieved at lower field strengths.”
BackgroundMicheli, C., Schepers, I. M., Özker, M., Yoshor, D., Beauchamp, M. S., & Rieger, J. W. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13992 · Cited by 28 · see citing works
Ultra-High Field Imaging of Human Visual Cognition
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“electrophysiological oscillation band, plays a special role over and above other bands in fastconducted inter-ICN connectivity, which might be linked to cortical layer specific preferences of the gamma-band (Scheeringa et al., 2016; Scheeringa and Fries, 2017).”
BackgroundModelling the Human Cortex in Three Dimensions
Kuehn, E., & Sereno, M. I. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.010 · Cited by 25 · see citing works
“Another promising way forward would involve combining measures of blood flow/blood volume with electrophysiological signals, which would allow deeper insights into frequency-dependent, microcircuit interactions [12].”
Ultra-High-Field Neuroimaging Reveals Fine-Scale Processing for 3D Perception
Ng, A. K. T., Jia, K., Goncalves, N. R., Zamboni, E., Kemper, V. G., Goebel, R., Welchman, A. E., & Kourtzi, Z. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0065-21.2021 · Cited by 25 · see citing works
“…studies provide evidence for the involvement of this circuit in the context of sensory processing (De Martino et al., 2015; Muckli et al., 2015; Gau et al., 2020; Jia et al., 2020; Zamboni et al., 2020) and visual attention ( Fracasso et al., 2016; Scheeringa et al., 2016; Lawrence et al., 2019a).”
Blanco, R., Koba, C., & Crimi, A. (2024) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102416 · Cited by 24 · see citing works
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Markuerkiaga, I., Marques, J. P., Bains, L. J., & Norris, D. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-81249-w · Cited by 21 · see citing works
Self-citationFraenz, C., Schlüter, C., Friedrich, P., Jung, R. E., Güntürkün, O., & Genç, E. (2021) · Intelligence · doi:10.1016/j.intell.2021.101545 · Cited by 21 · see citing works
Zaretskaya, N. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.101998 · Cited by 20 · see citing works
Cortical depth-dependent human fMRI of resting-state networks using EPIK
Pais‐Roldán, P., Yun, S. D., Palomero‐Gallagher, N., & Shah, N. J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1151544 · Cited by 20 · see citing works
“focusing on the visual cortex reported correlations between the function of specific layers imaged with fMRI 103 and particular EEG rhythms [40], reinforcing the potential for using fMRI to detect layer variability.”
BackgroundPrefrontal high gamma during a magnetoencephalographic working memory task
Carver, F. W., Rubinstein, D., Gerlich, A. H., Fradkin, S. I., Holroyd, T., & Coppola, R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24489 · Cited by 20 · see citing works
Lubbe, R. H. J. V. D., de Kleine, E., & Rataj, K. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.01.013 · Cited by 20 · see citing works
Trenado, C., González-Ramírez, A., Lizárraga-Cortés, V., Pedroarena‐Leal, N., Manjarrez, E., & Ruge, D. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00850 · Cited by 20 · see citing works
Fine-scale computations for adaptive processing in the human brain
Zamboni, E., Kemper, V. G., Goncalves, N. R., Jia, K., Karlaftis, V. M., Bell, S. J., Giorgio, J., Rideaux, R., Goebel, R., & Kourtzi, Z. (2020) · eLife · doi:10.7554/elife.57637 · Cited by 19 · see citing works
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“blood oxygenation) that are, however, correlated with local oscillatory activity in both lower and higher frequency bands [47, 48].”
BackgroundLin, Z., Tam, F., Churchill, N. W., Lin, F., MacIntosh, B. J., Schweizer, T. A., & Graham, S. J. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.663463 · Cited by 18 · see citing works
Evaluating the capabilities and challenges of layer-fMRI VASO at 3T
Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stöcker, T., Fernández‐Cabello, S., Poser, B. A., & Kronbichler, M. (2023) · Aperture Neuro · doi:10.52294/001c.85117 · Cited by 18 · see citing works
Liu, X., Pu, Y., Wu, D., Zhang, Z., Hu, X., & Liu, L. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00194 · Cited by 17 · see citing works
Meditation and the Brain in Health and Disease
Fox, K. C. R., & Cahn, B. R. (2020) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198808640.013.23 · Cited by 16 · see citing works
Prokopiou, P. C., Xifra‐Porxas, A., Kassinopoulos, M., Boudrias, M., & Mitsis, G. D. (2022) · Brain Topography · doi:10.1007/s10548-022-00898-w · Cited by 15 · see citing works
“Additional narrow frequency bands of the LFP spectrum, such as the delta (2-4 Hz) (de Munck et al., 2009), theta (5-7 Hz) (Scheeringa et al., 2008), beta (15-30 Hz) (Laufs et al., 2006), and gamma (30-80 Hz; Ebisch et al., 2005; Scheeringa et al., 2016, 2011) bands have also been used to this end.”
BackgroundSleep, learning, and memory in human research using noninvasive neuroimaging techniques
Uji, M., & Tamaki, M. (2022) · Neuroscience Research · doi:10.1016/j.neures.2022.12.013 · Cited by 16 · see citing works
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“The anatomical connections between layers are well established, so the examination of functional connectivity between layers of functionally related regions may provide valuable insights into local and interareal information flow for specific brain functions, such as the somatosensory functions studied here or visual…”
BackgroundLinking visual gamma to task‐related brain networks—a simultaneous EEG‐fMRI study
Beldzik, E., Domagalik, A., Beres, A. M., & Marek, T. (2019) · Psychophysiology · doi:10.1111/psyp.13462 · Cited by 16 · see citing works
Spectral fingerprints of facial affect processing bias in major depression disorder
Jiang, H., Hua, L., Dai, Z., Tian, S., Yao, Z., Lü, Q., & Popov, T. G. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz096 · Cited by 15 · see citing works
“, 2012; Fries, 2015) and correlate with the blood–oxygen level-dependent signal (positive correlation with gamma and negative correlation with alpha/beta) (Logothetis et al., 2001; Scheeringa et al., 2011; Scheeringa et al., 2016; Hermes et al., 2017).”
BackgroundDaniel, A. J., Smith, J. A., Spencer, G. S., Jorge, J., Bowtell, R. W., & Mullinger, K. J. (2018) · Human Brain Mapping · doi:10.1002/hbm.24396 · Cited by 14 · see citing works
Adapted cabling of an EEG cap improves simultaneous measurement of EEG and fMRI at 7T
Meyer, M. C., Scheeringa, R., WEBB, A. G., Petridou, N., Kraff, O., & Norris, D. G. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108518 · Cited by 12 · see citing works
“…et al., 2010; Porcaro et al., 2010; Scheeringa et al., 2009); the relation between resting state networks and EEG (Mantini et al., 2007; Moosmann et al., 2009; Sadaghiani et al., 2010; Scheeringa et al., 2008, 2012); and the neural underpinnings of the BOLD signal (Scheeringa et al., 2011a, 2016).”
BackgroundMethodInfluentialSelf-citationMeditation and the brain in health and diseasePreprint
Fox, K. C. R., & Cahn, B. R. (2018) · doi:10.31231/osf.io/m2sez · Cited by 13 · see citing works
Wu, C. W., Tsai, P.-J., Chen, S. C.-J., Li, C., Hsu, A., Wu, H.-Y., Ko, Y.-T., Hung, P.-C., Chang, C., Lin, C., et al. (2019) · Sleep and Biological Rhythms · doi:10.1007/s41105-019-00232-1 · Cited by 12 · see citing works
Improved cortical boundary registration for locally distorted fMRI scans
van Mourik, T., Koopmans, P. J., & Norris, D. G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223440 · Cited by 11 · see citing works
Self-citationUji, M., Jentzsch, I., Redburn, J., & Vishwanath, D. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.04.017 · Cited by 11 · see citing works
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Size of the spatial correlation between ECoG and fMRI activity
Piantoni, G., Hermes, D., Ramsey, N. F., & Petridou, N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118459 · Cited by 9 · see citing works
Relating neural oscillations to laminar fMRI connectivity in visual cortex
Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O. B., Norris, D. G., & Koopmans, P. J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac154 · Cited by 10 · see citing works
“This is further supported by our previous finding from this dataset that beta power correlates negatively with BOLD activity in deep layers (Scheeringa et al. 2016), which aligns well with observations in EEG and MEG that beta power, like alpha power, often decreases in regions that become actively…”
InfluentialSelf-citationChowdhury, M. E. H., Khandakar, A. A., Mullinger, K. J., Al-Emadi, N. A., & Bowtell, R. W. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00690 · Cited by 10 · see citing works
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“However, our results are not consistent with some studies in which anodal tDCS was effective in facilitating VPL (Olma et al., 2013; Sczesny-Kaiser et al., 2016; Van Meel, Daniels, de Beeck, & Baeck, 2016; Wu et al., 2023), or even impairing VPL with a short training session (Grasso, Tonolli,…”
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Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG–fMRIPreprint
Clausner, T., Marques, J. P., Scheeringa, R., & Bonnefond, M. (2025) · eLife · doi:10.1101/2024.07.31.605816 · Cited by 9 · see citing works
Self-citationDistinct alpha networks modulate different aspects of perceptual decision-making
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Neural basis of distractor resistance during visual working memory maintenance
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How to study spoken language understanding: a survey of neuroscientific methods
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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
- 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
- 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
- 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
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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