The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 501–600 of 938
Iandolo, G., Chourasia, N., Ntolkeras, G., Madsen, J. R., Papadelis, C. L., Grant, P. E., Pearl, P. L., Taffoni, F., & Tamilia, E. (2021) · Diagnostics · doi:10.3390/diagnostics11071234 · Cited by 17
Bruzzone, S. E. P., Lumaca, M., Brattico, E., Vuust, P., Kringelbach, M. L., & Bonetti, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-08521-5 · Cited by 19
Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks
Ruiz‐Rizzo, A. L., Neitzel, J., Müller, H. J., Sorg, C., & Finke, K. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00089 · Cited by 19
An Introduction to MEG Connectivity Measurements
Brookes, M. J., Woolrich, M. W., & Price, D. (2014) · Springer eBooks · doi:10.1007/978-3-642-33045-2_16 · Cited by 17
Self-citationHindriks, R. (2021) · Neuroimage Reports · doi:10.1016/j.ynirp.2021.100007 · Cited by 16
Averna, A., Marceglia, S. R. F., Priori, A., & Foffani, G. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00399-4 · Cited by 16
Unsupervised representation learning of spontaneous MEG data with nonlinear ICA
Zhu, Y., Parviainen, T. M., Heinilä, E., Parkkonen, L. T., & Hyvärinen, A. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120142 · Cited by 16
Marzetti, L., Basti, A., Guidotti, R., Baldassarre, A., Metsomaa, J., Zrenner, C., D’Andrea, A., Makkinayeri, S., Pieramico, G., Ilmoniemi, R., et al. (2024) · bioRxiv · doi:10.3390/biomedicines12050955 · Cited by 15
Milz, P., Pascual-Marqui, R. D., Lehmann, D., & Faber, P. L. (2016) · Brain Topography · doi:10.1007/s10548-016-0469-3 · Cited by 16
Branscheidt, M., Ejaz, N., Xu, J., Widmer, M., Harran, M. D., Cortés, J. C., Kitago, T., Celnik, P. A., Hernandez-Castillo, C. R., Diedrichsen, J., et al. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00148.2021 · Cited by 20
Reprint of ‘Noise contributions to the fMRI signal: An Overview'
Liu, T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.031 · Cited by 16
Scalable and Robust Tensor Decomposition of Spontaneous Stereotactic EEG Data
Li, J., Haldar, J. P., Mosher, J. C., Nair, D. R., Gonzalez-Martinez, J. A., & Leahy, R. M. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2875467 · Cited by 16
How Brain Arousal Mechanisms Work
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485 · Cited by 22
Tarailis, P., Blasio, F. M. D., Šimkutė, D., & Griškova-Bulanova, I. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12060896 · Cited by 15
Brain‐wide network analysis of resting‐state neuromagnetic data
Kida, T., Tanaka, E., Kakigi, R., & Inui, K. (2023) · Human Brain Mapping · doi:10.1002/hbm.26295 · Cited by 17
Zhu, Y., Liu, J., & Cong, F. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3277509 · Cited by 15
Spontaneous α Brain Dynamics Track the Episodic “When”
Azizi, L., Polti, I., & van Wassenhove, V. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0816-23.2023 · Cited by 15
Pigorini, A., Avanzini, P., Barborică, A., Bénar, C., Olivier David, A., Farisco, M., Keller, C. J., Manfridi, A., Mikulan, E., Paulk, A. C., et al. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110160 · Cited by 16
Rowland, J. A., Stapleton-Kotloski, J. R., Godwin, D. W., Hamilton, C. A., & Martindale, S. L. (2024) · Journal of Neurotrauma · doi:10.1089/neu.2023.0315 · Cited by 18
Magnetoencephalography in the study of epilepsy and consciousness
Foley, E., Cerquiglini, A., Cavanna, A. E., Nakubulwa, M. A., Furlong, P. L., Witton, C., & Seri, S. (2013) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2013.09.005 · Cited by 15
Lundqvist, M., Herman, P. A., Palva, J. M., Palva, S., Silverstein, D. N., & Lansner, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.080 · Cited by 14
Disrupted network connectivity in pediatric brain tumor survivors is a signature of injury
Gauvreau, S., Lefebvre, J., Bells, S., Laughlin, S., Bouffet, É., & Mabbott, D. (2019) · The Journal of Comparative Neurology · doi:10.1002/cne.24717 · Cited by 15
Linking 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
Tiège, X. D., Bourguignon, M., Piitulainen, H., & Jousmäki, V. (2020) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0000000000000481 · Cited by 15
Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention
Favaretto, C., Spadone, S., Sestieri, C., Betti, V., Cenedese, A., Penna, S. D., & Corbetta, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117781 · Cited by 13
Uncovering phase‐coupled oscillatory networks in electrophysiological data
van der Meij, R., Jacobs, J., & Maris, E. (2015) · Human Brain Mapping · doi:10.1002/hbm.22798 · Cited by 14
Interregional neural synchrony has similar dynamics during spontaneous and stimulus-driven states
Ghuman, A. S., van den Honert, R. N., & Martin, A. D. (2013) · Scientific Reports · doi:10.1038/srep01481 · Cited by 15
Saarinen, T., Jalava, A., Kujala, J., Stevenson, C. M., & Salmelin, R. H. (2015) · Human Brain Mapping · doi:10.1002/hbm.22784 · Cited by 15
Meng, L., & Xiang, J. (2016) · Brain and Development · doi:10.1016/j.braindev.2016.05.004 · Cited by 15
Under the Mind's Hood: What We Have Learned by Watching the Brain at Work
Nobre, A. C., & van Ede, F. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0742-19.2019 · Cited by 14
Frequency‐specific changes in the default mode network in patients with cingulate gyrus epilepsy
Leng, X., Xiang, J., Yang, Y., Yu, T., Qi, X., Zhang, X., Wu, S., & Wang, Y. (2020) · Human Brain Mapping · doi:10.1002/hbm.24956 · Cited by 17
Rosso, M., Fernández‐Rubio, G., Keller, P. E., Brattico, E., Vuust, P., Kringelbach, M. L., & Bonetti, L. (2025) · Advanced Science · doi:10.1002/advs.202413195 · Cited by 19
Neurophysiological mechanisms of focused attention meditation: A scoping systematic review
Lieberman, J. M., McConnell, P. A., Estarellas, M., & Sacchet, M. D. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.14 · Cited by 19
Microsleeps are Associated with Stage-2 Sleep Spindles from Hippocampal-Temporal Network
Jonmohamadi, Y., Poudel, G. R., Innes, C., & Jones, R. D. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500155 · Cited by 14
The Impact of the Geometric Correction Scheme on MEG Functional Topology at Rest
Penna, S. D., Corbetta, M., Wens, V., & de Pasquale, F. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01114 · Cited by 14
Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections
Ziaeemehr, A., & Valizadeh, A. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.608655 · Cited by 13
Temporal and Spectral Signatures of the Default Mode Network
de Pasquale, F., & Marzetti, L. (2014) · Springer eBooks · doi:10.1007/978-3-642-33045-2_20 · Cited by 14
Canonical granger causality between regions of interest
Ashrafulla, S., Haldar, J. P., Joshi, A. A., & Leahy, R. M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.056 · Cited by 14
Thalamocortical interactions underlying visual fear conditioning in humans
Lithari, C. D., Moratti, S., & Weisz, N. (2015) · Human Brain Mapping · doi:10.1002/hbm.22940 · Cited by 18
Estimating repetitive spatiotemporal patterns from resting-state brain activity data
Takeda, Y., Hiroe, N., Yamashita, O., & Sato, M.-A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.014 · Cited by 15
Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study
Sunaga, M., Takei, Y., Kato, Y., Tagawa, M., Suto, T., Hironaga, N., Ohki, T., Takahashi, Y., Fujihara, K., Sakurai, N., et al. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00597 · Cited by 14
Williams, N., Wang, S. H., Arnulfo, G., Nobili, L., Palva, S., & Palva, J. M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120036 · Cited by 14
Jia, Y., Jariwala, N., Hinkley, L. B. N., Nagarajan, S. S., & Subramaniam, K. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1077923 · Cited by 14
Liparoti, M., Cipriano, L., Lopez, E. T., Polverino, A., Minino, R., Sarno, L., Sorrentino, G., Lucidi, F., & Sorrentino, P. (2024) · Scientific Reports · doi:10.1038/s41598-023-49588-y · Cited by 13
Biomarker identification in bipolar disorder
Martella, F., Caporali, A., Macellaro, M., Cafaro, R., de Pasquale, F., Dell’Osso, B. M., & D’Addario, C. (2025) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2025.108823 · Cited by 13
Xu, W., Liao, P., Cao, M., White, D., Lyu, B., & Gao, J. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121182 · Cited by 18
Kyathanahally, S. P., Wang, Y., Calhoun, V. D., & Deshpande, G. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00074 · Cited by 12
Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography
Hindriks, R., Micheli, C., Bosman, C. A., Oostenveld, R., Lewis, C., Mantini, D., Fries, P., & Deco, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.010 · Cited by 13
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
Zarei, A. A., Jensen, W., Jadidi, A. F., Lontis, R., & Atashzar, S. F. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac59a1 · Cited by 13
Electrophysiological connectivity markers of preserved language functions in post-stroke aphasia
Shah‐Basak, P. P., Sivaratnam, G., Teti, S. D., Deschamps, T., Kielar, A., Jokel, R., & Meltzer, J. A. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103036 · Cited by 15
Nevado, Á., Hadjipapas, A., Kinsey, K., Moratti, S., Barnes, G. R., Holliday, I. E., & Green, G. G. R. (2012) · Neuroscience Letters · doi:10.1016/j.neulet.2012.02.007 · Cited by 13
Self-citationNing, L., & Yogesh Rathi, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2739740 · Cited by 13
Use of Network Analysis to Establish Neurosurgical Parameters in Gliomas and Epilepsy
Maesawa, S., Bagarinao, E., Fujii, M., Futamura, M., & Wakabayashi, T. (2016) · Neurologia medico-chirurgica · doi:10.2176/nmc.ra.2015-0302 · Cited by 13
Genuine cross-frequency coupling networks in human resting-state electrophysiological recordingsPreprint
Siebenhühner, F., Wang, S. H., Arnulfo, G., Lampinen, A., Nobili, L., Palva, J. M., & Palva, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/547638 · Cited by 13
Functional Connectivity Is Dominated by Aperiodic, Rather Than Oscillatory, Coupling
Monchy, N., Duprez, J., Houvenaghel, J., Legros, A. G., Voytek, B., & Modolo, J. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1041-25.2025 · Cited by 13
Schependom, J. V., Vidaurre, D., Costers, L., Sjøgård, M., Sima, D. M., Smeets, D., D'hooghe, M. B., D’haeseleer, M., Deco, G., Wens, V., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102632 · Cited by 12
Self-citationSynchronous Brain Dynamics Establish Brief States of Communality in Distant Neuronal Populations
Seeber, M., & Michel, C. M. (2021) · eNeuro · doi:10.1523/eneuro.0005-21.2021 · Cited by 15
Mertiens, S., Sure, M., Schnitzler, A., & Florin, E. (2023) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2023.1219334 · Cited by 12
Jeong, W., Kim, J. S., & Chung, C. K. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.09.009 · Cited by 12
Nugent, A. C., Luber, B. M., Carver, F. W., Robinson, S. E., Coppola, R., & Zarate, C. A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23417 · Cited by 12
Taberna, G. A., Samogin, J., Marino, M., & Mantini, D. (2021) · Brain Sciences · doi:10.3390/brainsci11060741 · Cited by 12
Cross-Frequency Brain Network Dynamics Support Pitch Change Detection
Samiee, S., Vuvan, D. T., Florin, E., Albouy, P., Peretz, I., & Baillet, S. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0630-21.2022 · Cited by 12
Zhang, F., Khan, A. F., Ding, L., & Yuan, H. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/acaccb · Cited by 12
Resting state connectivity mediates the relationship between collectivism and social cognition
Knyazev, G. G., Savostyanov, A. N., Bocharov, A. V., & Алексеевна, М. Е. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2017.12.002 · Cited by 12
Becker, R., & Hervais‐Adelman, A. (2020) · eNeuro · doi:10.1523/eneuro.0101-20.2020 · Cited by 11
Hindriks, R., Micheli, C., Mantini, D., & Deco, G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142091 · Cited by 11
Dark Control: Towards a Unified Account of Default Mode Function by Markov Decision ProcessesPreprint
Dohmatob, E., Dumas, G., & Bzdok, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/148890 · Cited by 12
Mongold, S. J., Piitulainen, H., Legrand, T., Ghinst, M. V., Naeije, G., Jousmäki, V., & Bourguignon, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119491 · Cited by 10
Polverino, A., Lopez, E. T., Liparoti, M., Minino, R., Romano, A., Cipriano, L., Trojsi, F., Jirsa, V., Sorrentino, G., & Sorrentino, P. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.04.003 · Cited by 11
Simola, J., Siebenhühner, F., Myrov, V. O., Kantojärvi, K., Paunio, T., Palva, J. M., Brattico, E., & Palva, S. (2022) · iScience · doi:10.1016/j.isci.2022.104985 · Cited by 11
Niazy, R. K., Cole, D. M., Beckmann, C. F., & Smith, S. M. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_14 · Cited by 11
Self-citationAmemiya, S., Takao, H., & Abe, O. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00065 · Cited by 9
Yıldırım, M., Hu, M., Le, N. M., 杉原, 弘., So, P. T. C., & Sur, M. (2020) · Biomedical Optics Express · doi:10.1364/boe.396962 · Cited by 11
Brain-wide neural co-activations in resting human
Ding, L., Shou, G., Cha, Y., Sweeney, J. A., & Yuan, H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119461 · Cited by 13
Luo, Z., Yin, E., Zeng, L., Shen, H., Su, J., Peng, L., Yan, Y., & Hu, D. (2024) · iScience · doi:10.1016/j.isci.2024.109206 · Cited by 10
Bocharov, A. V., Knyazev, G. G., Savostyanov, A. N., Сапрыгин, А. Е., Proshina, E. A., & Tamozhnikov, S. S. (2021) · Human Physiology · doi:10.1134/s0362119721010023 · Cited by 10
Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data
Khan, A. F., Zhang, F., Shou, G., Yuan, H., & Ding, L. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119460 · Cited by 11
Rhodes, N., Rier, L., Boto, E., Hill, R. M., & Brookes, M. J. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.8 · Cited by 11
Self-citationUnifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis
Hirayama, J.-I., Ogawa, T., & Hyvärinen, A. (2015) · Neural Computation · doi:10.1162/neco_a_00747 · Cited by 10
Mann‐Krzisnik, D., & Mitsis, G. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25902 · Cited by 9
31st Annual Computational Neuroscience Meeting: CNS*2022
Corsi, M., Sorrentino, P., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S., Jirsa, V., & Fallani, F. D. V. (2023) · Journal of Computational Neuroscience · doi:10.1007/s10827-022-00841-9 · Cited by 11
Nour, M. M., Liu, Y., Higgins, C., Woolrich, M. W., & Dolan, R. J. (2023) · Brain Communications · doi:10.1093/braincomms/fcad056 · Cited by 10
Self-citationMiskovic, V., Lynn, S. J., Bagg, J. O., Pouliot, J. J., Ríos, M., & Wackermann, J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.014 · Cited by 12
Lardone, A., Liparoti, M., Sorrentino, P., Minino, R., Polverino, A., Lopez, E. T., Bonavita, S., Lucidi, F., Sorrentino, G., & Mandolesi, L. (2022) · AIMS neuroscience · doi:10.3934/neuroscience.2022013 · Cited by 9
Multimodal based classification of schizophrenia patients
Cetin, M. S., Houck, J. M., Vergara, V. M., Miller, R. L., & Calhoun, V. D. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318931 · Cited by 10
Rahimi, M., Moradi, M. H., & Ghassemi, F. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.02.002 · Cited by 10
MEG, myself, and I: individual identification from neurophysiological brain activityPreprint
Castanheira, J. D. S., Perez, H. D. O., Mišić, B., & Baillet, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.18.431803 · Cited by 10
A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG
Gosti, G., Milanetti, E., Folli, V., de Pasquale, F., Leonetti, M., Corbetta, M., Ruocco, G., & Penna, S. D. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.027 · Cited by 9
Towards the automated detection of interictal epileptiform discharges with magnetoencephalography
Fernández-Martín, R., Feys, O., Juvené, E., Aeby, A., Urbain, C. M., Tiège, X. D., & Wens, V. (2023) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2023.110052 · Cited by 9
Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy
Wirsich, J., Iannotti, G. R., Ridley, B., Shamshiri, E., Sheybani, L., Grouiller, F., Bartoloméi, F., Seeck, M., Lazeyras, F., Ranjeva, J., et al. (2024) · Network Neuroscience · doi:10.1162/netn_a_00362 · Cited by 11
Validating MEG estimated resting-state connectome with intracranial EEG
Afnan, J., Cai, Z., Lina, J., Abdallah, C., Pellegrino, G., Arcara, G., Khajehpour, H., Frauscher, B., Gotman, J., & Grova, C. (2025) · Network Neuroscience · doi:10.1162/netn_a_00441 · Cited by 9
Alterations in the Magnetoencephalography Default Mode Effective Connectivity following Concussion
Reddy, D. D., Davenport, E. M., Yu, F. F., Wagner, B., Urban, J. E., Whitlow, C. T., Stitzel, J. D., & Maldjian, J. A. (2021) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7232 · Cited by 9
Cortical network formation based on subthalamic beta bursts in Parkinson's disease
Sure, M., Vesper, J., Schnitzler, A., & Florin, E. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119619 · Cited by 11
Opening Questions in Visual Working Memory
Nobre, A. C. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01920 · Cited by 10
Vallinoja, J., Nurmi, T., Jaatela, J., Wens, V., Bourguignon, M., Mäenpää, H., & Piitulainen, H. (2023) · Clinical Neurophysiology · doi:10.1016/j.clinph.2023.10.014 · Cited by 11
Hahm, J., Lee, H., Park, H., Kang, E., Kim, Y. K., Chung, C. K., Kang, H., & Lee, D. S. (2017) · Scientific Reports · doi:10.1038/srep41592 · Cited by 9
de la Salle, S., Choueiry, J., Shah, D. K., Bowers, H., McIntosh, J. F., Ilivitsky, V., Carroll, B., & Knott, V. (2020) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2020.173092 · Cited by 10
Rapidly spreading seizures arise from large-scale functional brain networks in focal epilepsy
Uehara, T., Shigeto, H., Mukaino, T., Yokoyama, J., Okadome, T., Yamasaki, R., Ogata, K., Mukae, N., Sakata, A., Tobimatsu, S., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118104 · Cited by 9
Intrinsic neural activity predisposes susceptibility to a body illusion
Hsu, T., Zhou, J., Yeh, S., Northoff, G., & Lane, T. J. (2022) · Cerebral Cortex Communications · doi:10.1093/texcom/tgac012 · Cited by 10
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
