Investigating the electrophysiological basis of resting state networks using magnetoencephalography

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

Consistent 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

Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI

Greicius, 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

Neuronal 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

A computational model‐based analysis of basal ganglia pathway changes in Parkinson’s disease inferred from resting‐state fMRI

Maith, O., Villagrasa, F., Dinkelbach, H. Ü., Baladron, J., Horn, A., Irmen, F., Kühn, A. A., & Hamker, F. H. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14868 · Cited by 26

A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain

Fauchon, C., Kim, J. A., El‐Sayed, R., Osborne, N. R., Rogachov, A., Cheng, J. C., Hemington, K. S., Bosma, R. L., Dunkley, B. T., Oh, J., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03967-9 · Cited by 33

Enhanced behavioral performance through interareal gamma and beta synchronization

Parto-Dezfouli, M., Vezoli, J., Bosman, C. A., & Fries, P. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113249 · Cited by 29

A Multimodal Perspective on the Composition of Cortical Oscillations

Rönnqvist, K. C., McAllister, C. J., Woodhall, G. L., Stanford, I. M., & Hall, S. D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00132 · Cited by 29

Network dynamics in the healthy and epileptic developing brain

Rosch, R. E., Baldeweg, T., Moeller, F., & Baier, G. (2017) · Network Neuroscience · doi:10.1162/netn_a_00026 · Cited by 27

Structural damage in early preterm brain changes the electric resting state networks

Omidvarnia, A., Metsäranta, M., Lano, A., & Vanhatalo, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.091 · Cited by 28

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Wirsich, J., Amico, E., Giraud, A., Goñi, J., & Sadaghiani, S. (2020) · Network Neuroscience · doi:10.1162/netn_a_00135 · Cited by 27

Benchmarking metrics for inferring functional connectivity from multi-channel EEG and MEG: A simulation study

Yu, M. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0018826 · Cited by 30

Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents

Safar, K., Zhang, J., Emami, Z., Gharehgazlou, A., Ibrahim, G. M., & Dunkley, B. T. (2021) · Brain Communications · doi:10.1093/braincomms/fcab044 · Cited by 26

A 3D atlas of functional human brain energetic connectome based on neuropil distribution

Yu, Y., Akif, A., Hermán, P., Cao, M., Rothman, D. L., Carson, R. E., Agarwal, D., Evans, A. C., & Hyder, F. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac322 · Cited by 27

Co-variations of cerebral blood volume and single neurons discharge during resting state and visual cognitive tasks in non-human primates

Claron, J., Provansal, M., Salardaine, Q., Tissier, P., Dizeux, A., Deffieux, T., Picaud, S. A., Tanter, M., Arcizet, F., & Pouget, P. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112369 · Cited by 26

Functional Cortical Hubs in the Eyes-Closed Resting Human Brain from an Electrophysiological Perspective Using Magnetoencephalography

Jin, S., Jeong, W., Seol, J., Kwon, J., & Chung, C. K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068192 · Cited by 28

Frontal and Parietal Cortices Show Different Spatiotemporal Dynamics across Problem-solving Stages

Tschentscher, N., & Hauk, O. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00960 · Cited by 26

Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts

Zhigalov, A., Heinilä, E., Parviainen, T. M., Parkkonen, L. T., & Hyvärinen, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.014 · Cited by 26

The dynamic modular fingerprints of the human brain at rest

Kabbara, A., Paban, V., & Hassan, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117674 · Cited by 27

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Liu, Z., de Zwart, J. A., Chang, C., Duan, Q., van Gelderen, P., & Duyn, J. H. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht164 · Cited by 22

Effect of Zolpidem in the Aftermath of Traumatic Brain Injury: An MEG Study

Sripad, P., Rosenberg, J., Boers, F., Filß, C., Galldiks, N., Langen, K., Clauss, R. P., Shah, N. J., & Dammers, J. (2020) · Case Reports in Neurological Medicine · doi:10.1155/2020/8597062 · Cited by 25

Noise-driven multistability vs deterministic chaos in phenomenological semi-empirical models of whole-brain activity

Piccinini, J., Ipiña, I. P., Laufs, H., Kringelbach, M. L., Deco, G., Perl, Y. S., & Tagliazucchi, E. (2021) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0025543 · Cited by 25

Oscillatory correlates of autobiographical memory

Knyazev, G. G., Savostyanov, A. N., Bocharov, A. V., Дорошева, Е. А., Tamozhnikov, S. S., & Сапрыгин, А. Е. (2014) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2014.12.006 · Cited by 28

Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses

Mayhew, S. D., & Bagshaw, A. P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.051 · Cited by 26

Age of onset determines intrinsic functional brain architecture in Friedreich ataxia

Naeije, G., Wens, V., Coquelet, N., Sjøgård, M., Goldman, S., Pandolfo, M., & Tiège, X. D. (2019) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.50966 · Cited by 25

Investigating the interaction between EEG and fNIRS: A multimodal network analysis of brain connectivity

Blanco, R., Koba, C., & Crimi, A. (2024) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102416 · Cited by 24

Using structural connectivity to augment community structure in EEG functional connectivity

Glomb, K., Mullier, E., Carboni, M., Rubega, M., Iannotti, G., Tourbier, S., Seeber, M., Vulliémoz, S., & Hagmann, P. (2020) · Network Neuroscience · doi:10.1162/netn_a_00147 · Cited by 22

Origin of the Time Lag Phenomenon and the Global Signal in Resting-State fMRI

Amemiya, S., Takao, H., & Abe, O. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.596084 · Cited by 25

Predicting MEG resting-state functional connectivity from microstructural information

Messaritaki, E., Foley, S. F., Schiavi, S., Magazzini, L., Routley, B. C., Jones, D. K., & Singh, K. D. (2021) · Network Neuroscience · doi:10.1162/netn_a_00187 · Cited by 23

Spatial heterogeneity of the relation between resting‐state connectivity and blood flow: An important consideration for pharmacological studies

Khalili‐Mahani, N., van Osch, M. J. P., de Rooij, M., Beckmann, C. F., van Buchem, M. A., Dahan, A., van Gerven, J. M., & Rombouts, S. A. R. B. (2012) · Human Brain Mapping · doi:10.1002/hbm.22224 · Cited by 24

A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length

Sommariva, S., Sorrentino, A., Piana, M., Pizzella, V., & Marzetti, L. (2017) · Brain Topography · doi:10.1007/s10548-017-0609-4 · Cited by 24

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug‐resistant epilepsy: A machine learning approach

Ntolkeras, G., Makaram, N., Bernabei, M., Vega, A. C. D. L., Bolton, J. B., Madsen, J. R., Stone, S. S. D., Pearl, P. L., Papadelis, C. L., Grant, P. E., et al. (2024) · Epilepsia · doi:10.1111/epi.17898 · Cited by 23

Transient spectral events in resting state MEG predict individual task responses

Becker, R., Vidaurre, D., Quinn, A. J., Abeysuriya, R., Jones, Ō. P., Jbabdi, S., & Woolrich, M. W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116818 · Cited by 23

Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales

Godfrey, M., & Singh, K. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117551 · Cited by 25

State-Related Changes in MEG Functional Connectivity Reveal the Task-Positive Sensorimotor Network

Bardouille, T., & Boe, S. G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048682 · Cited by 24

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients

Demuru, M., van Duinkerken, E., Fraschini, M., Marrosu, F., Snoek, F. J., Barkhof, F., Klein, M., Diamant, M., & Hillebrand, A. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.06.001 · Cited by 25

Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images

Rosa, P. S. L., Brooks, T. L., Deych, E., Shands, B., Prior, F., Larson‐Prior, L. J., & Shannon, W. D. (2015) · Statistics in Medicine · doi:10.1002/sim.6757 · Cited by 24

Global signal modulation of single-trial fMRI response variability: Effect on positive vs negative BOLD response relationship

Mayhew, S. D., Mullinger, K. J., Ostwald, D., Porcaro, C., Bowtell, R. W., Bagshaw, A. P., & Francis, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.077 · Cited by 23

Phase shift invariant imaging of coherent sources (PSIICOS) from MEG data

Ossadtchi, A. E., Altukhov, D., & Jerbi, K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.031 · Cited by 23

Attenuated Post-Movement Beta Rebound Associated With Schizotypal Features in Healthy People

Hunt, B. A. E., Liddle, E. B., Gascoyne, L. E., Magazzini, L., Routley, B. C., Singh, K. D., Morris, P. G., Brookes, M. J., & Liddle, P. F. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby117 · Cited by 23

Temporal‐plus epilepsy in children: A connectomic analysis in magnetoencephalography

Martire, D. J., Wong, S. M., Workewych, A. M., Pang, E. W., Boutros, S., Smith, M. L., Ochi, A., Otsubo, H., Sharma, R., Widjaja, E., et al. (2020) · Epilepsia · doi:10.1111/epi.16591 · Cited by 23

Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia

Sanfratello, L., Houck, J. M., & Calhoun, V. D. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.05.007 · Cited by 23

Electrophysiological resting state networks of predominantly akinetic-rigid Parkinson patients: Effects of dopamine therapy

Schneider, L., Seeger, V., Timmermann, L., & Florin, E. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102147 · Cited by 22

Abnormal organization of inhibitory control functional networks in future binge drinkers

Antón‐Toro, L. F., Bruña, R., Suárez‐Méndez, I., Correas, Á., García‐Moreno, L. M., & Maestú, F. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.108401 · Cited by 23

Data-Driven Classification of Spectral Profiles Reveals Brain Region-Specific Plasticity in Blindness

Lubinus, C., Orpella, J., Keitel, A., Gudi-Mindermann, H., Engel, A. K., Roeder, B., & Rimmele, J. M. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa370 · Cited by 24

Electrophysiological signatures of resting state networks predict cognitive deficits in stroke

Romeo, Z., Mantini, D., Durgoni, E., Passarini, L., Meneghello, F., & Zorzi, M. (2021) · Cortex · doi:10.1016/j.cortex.2021.01.019 · Cited by 22

Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts

Hindriks, R., & Tewarie, P. K. B. (2023) · Communications Biology · doi:10.1038/s42003-023-04648-x · Cited by 21

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Afnan, J., von Ellenrieder, N., Lina, J., Pellegrino, G., Arcara, G., Cai, Z., Hedrich, T., Abdallah, C., Khajehpour, H., Frauscher, B., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120158 · Cited by 22

Spontaneous brain oscillations as neural fingerprints of working memory capacities: A resting-state MEG study

Oswald, V., Zerouali, Y., Boulet‐Craig, A., Krajinović, M., Laverdière, C., Sinnett, D., Jolicœur, P., Lippé, S., Jerbi, K., & Robaey, P. (2017) · Cortex · doi:10.1016/j.cortex.2017.09.021 · Cited by 22

The dynamics of human cognition: Increasing global integration coupled with decreasing segregation found using iEEG

Cruzat, J., Deco, G., Campo, A. T., Príncipe, A., Costa, A., Kringelbach, M. L., & Rocamora, R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.064 · Cited by 21

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks

Colclough, G. L., Woolrich, M. W., Harrison, S. J., Rojas-López, P. A., Valdes-Sosa, P. A., & Smith, S. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.077 · Cited by 21

A new computational approach to estimate whole-brain effective connectivity from functional and structural MRI, applied to language development

Hahn, G., Skeide, M. A., Mantini, D., Ganzetti, M., Alain Destexhe, A., Friederici, A. D., & Deco, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-44909-6 · Cited by 25

Reduced parietal alpha power and psychotic symptoms: Test-retest reliability of resting-state magnetoencephalography in schizophrenia and healthy controls

Candelaria‐Cook, F. T., Schendel, M. E., Ojeda, C. J., Bustillo, J., & Stephen, J. M. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.10.023 · Cited by 22

Neuronal and Physiological Correlation to Hemodynamic Resting-State Fluctuations in Health and Disease

Vazquez, A. L., Murphy, M. C., & Kim, S. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0276 · Cited by 21

Motor effects of deep brain stimulation correlate with increased functional connectivity in Parkinson's disease: An MEG study

Boon, L. I., Hillebrand, A., Potters, W. V., de Bie, R. M. A., Prent, N., Bot, M., Schuurman, P. R., Stam, C. J., van Rootselaar, A., & Berendse, H. W. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102225 · Cited by 21

A multimodal magnetoencephalography 7 T fMRI and 7 T proton MR spectroscopy study in first episode psychosis

Gawne, T. J., Overbeek, G., Killen, J., Reid, M. A., Kraguljac, N. V., Denney, T. S., Ellis, C. A., & Lahti, A. C. (2020) · Schizophrenia · doi:10.1038/s41537-020-00113-4 · Cited by 22

The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI–EEG study

Zhang, J., Liu, D., Qian, S., Qu, X., Zhang, P., Ding, N., & Zang, Y. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac124 · Cited by 21

Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data

Idaji, M. J., Zhang, J., Stephani, T., Nolte, G., Müller, K., Villringer, A., & Nikulin, V. V. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119053 · Cited by 19

Comparison between EEG and MEG of static and dynamic resting‐state networks

Cho, S., van Es, M. W. J., Woolrich, M. W., & Gohil, C. (2024) · Human Brain Mapping · doi:10.1002/hbm.70018 · Cited by 21

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Silva, R. F., Plis, S., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pattichis, M. S., Adalı, T., & Calhoun, V. D. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2594945 · Cited by 22

Effortful control and resting state networks: A longitudinal EEG study

Knyazev, G. G., Savostyanov, A. N., Bocharov, A. V., Slobodskaya, H. R., Bairova, N. B., Tamozhnikov, S. S., & Stepanova, V. V. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.01.031 · Cited by 25

Assessment and elimination of the effects of head movement on MEG resting-state measures of oscillatory brain activity

Messaritaki, E., Koelewijn, L., Dima, D. C., Williams, G. M., Perry, G., & Singh, K. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.038 · Cited by 24

Development of Neuroimaging-Based Biomarkers in Psychiatry

Aydın, O., Ünal‐Aydın, P., & Arslan, A. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-32-9721-0_9 · Cited by 22

Diminished large-scale functional brain networks in absolute pitch during the perception of naturalistic music and audiobooks

Brauchli, C., Leipold, S., & Jäncke, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116513 · Cited by 23

The Functional Alterations in Top-Down Attention Streams of Parkinson’s disease Measured by EEG

Yoo, H. B., de la Concha, E. O., Ridder, D. D., Pickut, B. A., & Vanneste, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-29036-y · Cited by 21

HD-EEG for tracking sub-second brain dynamics during cognitive tasks

Mheich, A., Dufor, O., Yassine, S., Kabbara, A., Biraben, A., Wendling, F., & Hassan, M. (2021) · Scientific Data · doi:10.1038/s41597-021-00821-1 · Cited by 21

Neural mechanisms of mental schema: a triplet of delta, low beta/spindle and ripple oscillations

Ohki, T., & Takei, Y. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13844 · Cited by 20

Spontaneous activity competes with externally evoked responses in sensory cortex

Karvat, G., Alyahyay, M., & Diester, I. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023286118 · Cited by 23

Spectral Independent Component Analysis with noise modeling for M/EEG source separation

Ablin, P., Cardoso, J.-F., & Gramfort, A. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109144 · Cited by 20

Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes

Quinn, A. J., Green, G. G. R., & Hymers, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118330 · Cited by 20

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

Farahibozorg, S.-R., Bijsterbosch, J. D., Gong, W., Jbabdi, S., Smith, S. M., Harrison, S. J., & Woolrich, M. W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118513 · Cited by 21

Memory traces of long-range coordinated oscillations in the sleeping human brain

Piantoni, G., van der Werf, Y. D., Jensen, O. B., & Someren, E. J. W. V. (2014) · Human Brain Mapping · doi:10.1002/hbm.22613 · Cited by 25

Magnetoencephalography in the study of brain dynamics

Pizzella, V. (2014) · Functional Neurology · doi:10.11138/fneur/2014.29.4.241 · Cited by 21

Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses

Hirschmann, J. V., Baillet, S., Woolrich, M. W., Schnitzler, A., Vidaurre, D., & Florin, E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116374 · Cited by 17

MEG Intersubject Phase Locking of Stimulus-Driven Activity during Naturalistic Speech Listening Correlates with Musical Training

Puschmann, S., Regev, M., Baillet, S., & Zatorre, R. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0932-20.2020 · Cited by 20

Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

Xu, N., Shan, W., Qi, J., Wu, J., & Wang, Q. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.649074 · Cited by 19

Alterations in the Topology of Functional Connectomes Are Associated with Post-Traumatic Stress Disorder and Blast-Related Mild Traumatic Brain Injury in Combat Veterans

Rowland, J. A., Stapleton-Kotloski, J. R., Martindale, S. L., Rogers, E., Ord, A. S., Godwin, D. W., & Taber, K. H. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2020.7450 · Cited by 18

Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study

Tagawa, M., Takei, Y., Kato, Y., Suto, T., Hironaga, N., Ohki, T., Takahashi, Y., Fujihara, K., Sakurai, N., Ujita, K., et al. (2022) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13362 · Cited by 19

Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution

Cai, C., Long, Y., Ghosh, S., Hashemi, A., Gao, Y.-J., Diwakar, M., Haufe, S., Sekihara, K., Wu, W., & Nagarajan, S. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3256963 · Cited by 19

Reliability of resting-state functional connectivity in the human spinal cord: Assessing the impact of distinct noise sources

Kaptan, M., Horn, U., Vannesjo, S. J., Mildner, T., Weiskopf, N., Finsterbusch, J., Brooks, J. C. W., & Eippert, F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120152 · Cited by 16

Identification of the Default Mode Network with electroencephalography

Fomina, T., Hohmann, M. R., Schölkopf, B., & Grosse‐Wentrup, M. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320143 · Cited by 22

Rhythmic Components in Extracranial Brain Signals Reveal Multifaceted Task Modulation of Overlapping Neuronal Activity

van der Meij, R., van Ede, F., & Maris, E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154881 · Cited by 18

Development of human electrophysiological brain networks

Briley, P. M., Liddle, E. B., Groom, M. J., Smith, H. J. F., Morris, P. G., Colclough, G. L., Brookes, M. J., & Liddle, P. F. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00293.2018 · Cited by 19

A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

Vieira, B. H., Dubois, J., Calhoun, V. D., & Salmón, C. E. G. (2021) · Human Brain Mapping · doi:10.1002/hbm.25656 · Cited by 18

Deep learning for neuroimaging: a validation studyPreprint

Plis, S., Hjelm, D., Salakhutdinov, R., & Calhoun, V. D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5847 · Cited by 19

Investigating intrinsic connectivity networks using simultaneous BOLD and CBF measurements

Mayhew, S. D., Mullinger, K. J., Bagshaw, A. P., Bowtell, R. W., & Francis, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.042 · Cited by 17

The role of sub-second neural events in spontaneous brain activity

Florin, E., Watanabe, M., & Logothetis, N. K. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.10.006 · Cited by 19

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

Zhu, Y., Wang, X., Mathiak, K., Toiviainen, P., Ristaniemi, T., Xu, J., Chang, Y., & Cong, F. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065721500015 · Cited by 18

Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children

Dyck, D. V., Deconinck, N., Aeby, A., Baijot, S., Coquelet, N., Trotta, N., Rovaï, A., Goldman, S., Urbain, C. M., Wens, V., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118368 · Cited by 18

Human Brain Oscillations: From Physiological Mechanisms to Analysis and Cognition

Jensen, O. B., Spaak, E., & Zumer, J. M. (2019) · Springer eBooks · doi:10.1007/978-3-030-00087-5_17 · Cited by 22

EEG and Neuronal Activity Topography analysis can predict effectiveness of shunt operation in idiopathic normal pressure hydrocephalus patients

Aoki, Y., Kazui, H., Tanaka, T., Ishii, R., Wada, T., Ikeda, S., Hata, M., Canuet, L., Musha, T., Matsuzaki, H., et al. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.10.009 · Cited by 18

Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICAPreprint

Hälvä, H., Corff, S. L., Lehéricy, L., So, J., Zhu, Y., Gassiat, É., & Hyvärinen, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09620 · Cited by 18

Controversies and progress on standardization of large-scale brain network nomenclaturePreprint

Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A., et al. (2022) · doi:10.31219/osf.io/25za6 · Cited by 18

Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions

Alavash, M., Tune, S., & Obleser, J. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001410 · Cited by 18

Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm

Zhao, Y., Boley, M., Pelentritou, A., Karoly, P. J., Freestone, D. R., Liu, Y., Muthukumaraswamy, S. D., William, W., Liley, D. T. J., & Kuhlmann, L. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119592 · Cited by 16

Neuronal Coupling Modes Show Differential Development in the Early Cortical Activity Networks of Human Newborns

Yrjölä, P., Vanhatalo, S., & Tokariev, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1012-23.2024 · Cited by 19

Cortical Statistical Correlation Tomography of EEG Resting State Networks

Li, C., Yuan, H., Shou, G., Cha, Y., Sunderam, S., Besio, W. G., & Ding, L. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00365 · Cited by 18

The vast potential and bright future of neuroimaging

Wintermark, M., Colen, R. R., Whitlow, C. T., & Zaharchuk, G. (2018) · British Journal of Radiology · doi:10.1259/bjr.20170505 · Cited by 17

Effect of Cultural Priming on Social Behavior and EEG Correlates of Self-Processing

Knyazev, G. G., Алексеевна, М. Е., Savostyanov, A. N., Bocharov, A. V., & Сапрыгин, А. Е. (2018) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2018.00236 · Cited by 17

Effect of prewhitening in resting-state functional near-infrared spectroscopy data

Blanco, B., Molnar, M. T., & Caballero‐Gaudes, C. (2018) · Neurophotonics · doi:10.1117/1.nph.5.4.040401 · Cited by 21

Generalised Orthogonal Partial Directed Coherence as a Measure of Neural Information Flow During Meditation

Shaw, L., Mishra, S., & Routray, A. (2015) · Lecture notes in bioengineering · doi:10.1007/978-81-322-2256-9_13 · Cited by 17

Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG

Shou, G., Yuan, H., Li, C., Chen, Y., Chen, Y., & Ding, L. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7ad3 · Cited by 17

In Amyotrophic Lateral Sclerosis Blood Cytokines Are Altered, but Do Not Correlate with Changes in Brain Topology

Polverino, A., Rucco, R., Stillitano, I., Bonavita, S., Grimaldi, M., Minino, R., Pesoli, M., Trojsi, F., D’Ursi, A. M., Sorrentino, G., et al. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0741 · Cited by 18

Temporal modes of hub synchronization at rest

de Pasquale, F., Spadone, S., Betti, V., Corbetta, M., & Penna, S. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118005 · Cited by 17

Data‐driven MEG analysis to extract fMRI resting‐state networks

Pelzer, E. A., Sharma, A., & Florin, E. (2024) · Human Brain Mapping · doi:10.1002/hbm.26644 · Cited by 4

Analysis of Resting State according to the data of magnetoencephalography

Skiteva, L. I., Ushakov, V., & Verkhlyutov, V. M. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.11.116

Evaluation of data-driven MEG analysis approaches for the extraction of fMRI resting state networksPreprint

Pelzer, E. A., Sharma, A., & Florin, E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441916 · Cited by 2

Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI

Yuan, H., Ding, L., Zhu, M., Zotev, V. S., Phillips, R., & Bodurka, J. (2015) · Brain Connectivity · doi:10.1089/brain.2014.0336 · Cited by 98

Temporal dynamics of resting state brain connectivity as revealed by magnetoencephalography

Baker, A. P. (2014) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-vgkxy9poo