The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Time-resolved resting-state brain networks
Open accessAbstract
Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
“recent experiments using functional MRI data have demonstrated that global brain signals transition between states of high and low connectivity strength over time (Zalesky et al., 2014) and that these fluctuations are related to coordinated patterns of network topology (Betzel et al., 2015)”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Zalesky et al., 2014
In the citing paper
“In keeping with Zalesky et al. (Zalesky et al., 2014), VAR model order was set at 11, appropriately mimicking the expected temporal signature of the BOLD response in 0.72s TR data.”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Zalesky et al., 2014
Citations
Cites
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
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
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
Cited by
Sorted by citations
Items 601–700 of 750
Computational Models of Dysconnectivity in Large-Scale Resting-State Networks
Demirtaş, M., & Deco, G. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809825-7.00004-3 · Cited by 3
Amyloid causes intermittent network disruptions in cognitively intact older subjects
Mueller, S. G. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9869-1 · Cited by 3
A Data-Driven Method to Dissect the Dynamics of the Causal Influence in Complex Dynamical Systems
Mwaffo, V., Keshavan, J., Hedrick, T. L., & Humbert, S. (2018) · Complexity in Engineering (COMPENG) · doi:10.1109/compeng.2018.8536232 · Cited by 3
Electromagnetic radiation modulates synchronization in cortical neurons of Macaque brain
Vinaya, M., & Ignatius, R. P. (2019) · International Journal of Modern Physics C · doi:10.1142/s0129183119500475 · Cited by 3
Brain network analyses in clinical neuroscience
Sokolov, A. A., Granziera, C., Fischi-Gómez, E., Preti, M. G., Ryvlin, P., Ville, D. V. D., & Friston, K. J. (2019) · Swiss Archives of Neurology Psychiatry and Psychotherapy · doi:10.4414/sanp.2019.03074 · Cited by 3
Yan, C.-G., Yang, Z., Colcombe, S. J., Zuo, X., & Milham, M. P. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/048405 · Cited by 5
Spontaneous emergence of behaviorally relevant motifs in human motor cortexPreprint
Livne, T., Kim, D., Metcalf, N. V., Shulman, G. L., & Corbetta, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.25.353326 · Cited by 3
Fu, Z., Liu, J., Salman, M. S., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Calhoun, V. D. (2022) · Research Square · doi:10.21203/rs.3.rs-1514598/v1 · Cited by 3
Saviola, F., Tambalo, S., Cabalo, D. G., Novello, L., Pierotti, E., Rabini, G., Dodich, A., Turella, L., Ville, D. V. D., & Jovicich, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.26.520413 · Cited by 3
Static and dynamic functional connectomes represent largely similar informationPreprint
Matkovič, A., Anticevic, A., Murray, J. D., & Repovš, G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.24.525348 · Cited by 3
Zhang, S., Larsen, B., Sydnor, V. J., Zeng, T., An, L., Yan, X., Kong, R., Kong, X., Gur, R. C., Gur, R. E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.22.546023 · Cited by 3
Colloquium: Multiscale modeling of brain network organization
Presigny, C., & Fallani, F. D. V. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.031002 · Cited by 66
Liu, Z., Xia, H., & Chen, A. (2024) · GeroScience · doi:10.1007/s11357-024-01366-y · Cited by 2
Nobukawa, S., Nishimura, H., & Yamanishi, T. (2018) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2018.8482070 · Cited by 2
Human Intelligence and Network Neuroscience
Barbey, A. K. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.009 · Cited by 2
Motifs, Small Worlds, and Network Economy
(2016) · Elsevier eBooks · doi:10.1016/b978-0-12-407908-3.00008-x · Cited by 2
Han, H., Jiang, J., Gu, L., Gan, J. Q., & Wang, H. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad33b1 · Cited by 3
Contrasting topologies of synchronous and asynchronous functional brain networks
McIntyre, C. C., Bahrami, M., Shappell, H. M., Lyday, R. G., Fish, J., Bollt, E. M., & Laurienti, P. J. (2024) · Network Neuroscience · doi:10.1162/netn_a_00413 · Cited by 2
Zhao, Z., Li, R., Wu, Y., Li, M., & Wu, D. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101496 · Cited by 3
Magnetoencephalography Dimensionality Reduction Informed by Dynamic Brain States
Cathignol, A. E., Kusch, L., Angiolelli, M., Lopez, E. T., Polverino, A., Romano, A., Sorrentino, G., Jirsa, V., Rabuffo, G., & Sorrentino, P. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70128 · Cited by 2
Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients
Zanin, M., Belkoura, S., Gómez, J., Álfaro, C., & Cano, J. (2020) · Brain Topography · doi:10.1007/s10548-020-00799-w · Cited by 2
Characterizing Motif Dynamics of Electric Brain Activity Using Symbolic Analysis
Zanin, M., & Papo, D. (2014) · Entropy · doi:10.3390/e16115654 · Cited by 2
Huang, Y., & Yu, Z. (2022) · Entropy · doi:10.3390/e24020152 · Cited by 2
Temporal and Spectral Signatures of the Default Mode Network
de Pasquale, F., & Marzetti, L. (2019) · Springer eBooks · doi:10.1007/978-3-030-00087-5_20 · Cited by 2
Time-varying functional connectivity as Wishart processes
Kampman, O. P., Ziminski, J., Afyouni, S., van der Wilk, M., & Kourtzi, Z. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00184 · Cited by 2
Трифонов, М. И., & Panasevich, E. A. (2018) · Российский физиологический журнал им И М Сеченова · doi:10.7868/s0869813918070110 · Cited by 2
Fukushima, M., Betzel, R. F., He, Y., Zuo, X., & Sporns, O. (2015) · arXiv (Cornell University) · Cited by 2
Slavakis, K., Salsabilian, S., Wack, D. S., Muldoon, S. F., Baidoo-Williams, H. E., Vettel, J. M., Cieslak, M., & Grafton, S. T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.07767 · Cited by 2
Truncated Gaussian-Mixture Variational AutoEncoderPreprint
Zhao, Q., Honnorat, N., Adeli, E., & Pohl, K. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.03717 · Cited by 2
Dynamic Graph Metrics: Tutorial, Toolbox, and TalePreprint
Sizemore, A. E., & Bassett, D. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10643 · Cited by 3
Metabolism modulates network synchrony in the aging brainPreprint
Weistuch, C., Mujica-Parodi, L. R., Amgalan, A., & Dill, K. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.17.047233 · Cited by 3
Kopál, J., Hlinka, J., Despouy, E., Valton, L., Denuelle, M., Sol, J., Curot, J., & Barbeau, E. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.30.360529 · Cited by 2
On the topochronic map of the human brain dynamicsPreprint
Sorrentino, P., Petkoski, S., Sparaco, M., Lopez, E. T., Signoriello, E., Baselice, F., Bonavita, S., Pirozzi, M. A., Quarantelli, M., Sorrentino, G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.01.447872 · Cited by 2
Fu, Z., Liu, J., Salman, M. S., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Calhoun, V. D. (2022) · Research Square · doi:10.21203/rs.3.rs-1742626/v1 · Cited by 2
Romano, A., Lopez, E. T., Cipriano, L., Liparoti, M., Minino, R., Polverino, A., Cavaliere, C., Aiello, M., Granata, C., Sorrentino, G., et al. (2022) · medRxiv · doi:10.1101/2022.11.11.22282206 · Cited by 2
Brain flexibility increases during the peri-ovulatory phase of the menstrual cyclePreprint
Liparoti, M., Cipriano, L., Lopez, E. T., Polverino, A., Minino, R., Sarno, L., Sorrentino, G., Lucidi, F., & Sorrentino, P. (2023) · medRxiv · doi:10.1101/2023.06.19.23291595 · Cited by 2
Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease
Englert, R., Kincses, B., Kotikalapudi, R., Gallitto, G., Li, J., Hoffschlag, K., Woo, C., Wager, T. D., Timmann, D., Bingel, U., et al. (2024) · eLife · doi:10.1101/2023.11.03.565516 · Cited by 4
Panitz, D. Y., Mendelsohn, A., Cabral, J., & Berkovich‐Ohana, A. (2023) · Research Square · doi:10.21203/rs.3.rs-3038477/v1 · Cited by 2
Muller, A. M., Manning, V., Wong, C. Y. F., & Pennington, D. (2024) · medRxiv · doi:10.1101/2024.09.26.24314399 · Cited by 2
Zhong, P. (2023) · Biometrics · doi:10.1111/biom.13844 · Cited by 1
New Advances in Magnetic Resonance Imaging
Larrivee, D. C. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.111259 · Cited by 2
Uncovering dynamic human brain phase coherence networks
Olsen, A. S., Brammer, A., Fisher, P. M., & Mørup, M. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2518287123 · Cited by 1
Time Persistence of the FMRI Resting-State Functional Brain Networks
Guo, S., Levy, O., Dvir, H., Kang, R., Li, D., Havlin, S., & Axelrod, V. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1570-24.2025 · Cited by 1
Wu, Y., Li, R., Jiang, G., Yang, N., Liu, M., Chen, Y., Chen, Z., Yu, K., Yin, Y., Xu, S., et al. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1510321 · Cited by 1
Zhang, W., Si, Q., Guan, Z., Cao, L., Wang, M., Zhao, C., Sun, L., Zhang, X., Zhang, Z., Li, C., et al. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1536658 · Cited by 2
Targeted Time‐Varying Functional Connectivity
Alonso, S., Cocchi, L., Hearne, L. J., Shine, J. M., & Vidaurre, D. (2025) · Human Brain Mapping · doi:10.1002/hbm.70157 · Cited by 1
de Kloe, T., Fazal, Z., Kohn, N., Norris, D. G., Menon, R. S., Llera, A., & Beckmann, C. F. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.58 · Cited by 1
Multifractal-dynamic reliability of white matter in rs-fMRI and relationships to gray matter
Guan, S., Wei, H., Meng, C., & Biswal, B. B. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119932 · Cited by 1
Zhang, C., Sun, F., & Yang, Y. Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108365 · Cited by 1
Exploring functional connectivity at different timescales with multivariate mode decomposition
Morante, M., Frølich, K., & Rehman, N. U. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1653007 · Cited by 1
Soleimani, N., Wiafe, S.-L., Iraji, A., Pearlson, G. D., & Calhoun, V. D. (2025) · Network Neuroscience · doi:10.1162/netn.a.505 · Cited by 1
GBrain:A Graph Signal Processing Approach to EEG Based Attention Detection
Majanini, S. A., Khalil, M. A., Kabbara, A., & Sheikh, H. A. (2025) · International Conference on Advances in Biomedical Engineering (ICABME) · doi:10.1109/icabme66883.2025.11211822 · Cited by 1
Ma, X., Belmonte, M. K., Zhao, Y., & Zhao, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70423 · Cited by 2
Klugah‐Brown, B., Li, Y., Wu, H., Yang, Z., Hagan, A., Agoalikum, E., Wang, P., & Biswal, B. B. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03549-9 · Cited by 4
Prasad, K. M. R., Bowei, O., & Theis, N. D. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106834 · Cited by 1
Falb, P., Reed, M. B., Klug, S., Murgaš, M., Godbersen, G. M., Schmidt, C., Nics, L., Hacker, M., Lanzenberger, R. R., & Hahn, A. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122163 · Cited by 1
Clustering brain-network-connectivity states using kernel partial correlations
Slavakis, K., Salsabilian, S., Wack, D. S., Muldoon, S. F., Baidoo-Williams, H. E., Vettel, J. M., Cieslak, M., & Grafton, S. T. (2016) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2016.7869039 · Cited by 1
Dissociation of categorical and coordinate spatial relations on dynamic network organization states
Hao, X., Chen, Z., Huang, T., Song, Y., Kong, X., & Liu, J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.972375 · Cited by 1
Dynamic functional connectivity
França, L. G. S., Fenn‐Moltu, S., & Batallé, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00006-3 · Cited by 1
Adversarial U-Network for Predicting Blood Oxygen Level-Dependent Time Series
Bao, C., Zheng, W., Zhang, Q., Yang, S., Yao, Z., & Hu, B. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385495 · Cited by 1
Characterization of cross-correlations in electrocorticograms of anesthetized mice
Adushkina, V. V., & Павлов, А. Н. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01288-y · Cited by 1
Geffen, A., Bland, N. S., & Sale, M. V. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00345 · Cited by 1
Geffen, A., Bland, N. S., & Sale, M. V. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70042 · Cited by 1
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2015) · arXiv (Cornell University) · Cited by 1
Aggarwal, P., & Gupta, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/172981 · Cited by 1
Transcriptional Signatures of the Hierarchical Chronnectome Organization in the Human BrainPreprint
Liu, J., Xia, M., Wang, X., Liao, X., & Yong He, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/637942 · Cited by 1
Dynamic Connectivity without Sliding WindowsPreprint
Huang, S.-G., Chung, M. K., Carroll, I. C., & Goldsmith, H. H. (2019) · arXiv (Cornell University) · Cited by 1
Temporal Dynamic Model for Resting State fMRI Data: A Neural Ordinary Differential Equation approachPreprint
Wen, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08146 · Cited by 3
Lei, T., Liao, X., Chen, X., Zhao, T., Xu, Y., Xia, M., Zhang, J., Sun, X., Wei, Y., Men, W., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.03.433828 · Cited by 1
Can fMRI functional connectivity index dynamic neural communication?Preprint
Alonso, S., Llera, A., ter Horst, G., & Vidaurre, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.27.453965 · Cited by 1
Probabilistic Inference on Virtual Brain Models of DisordersPreprint
Hashemi, M., Ziaeemehr, A., Woodman, M., Petkoski, S., & Jirsa, V. (2024) · bioRxiv · doi:10.1101/2024.02.21.581243 · Cited by 1
Zhang, J., Posse, S., & Tatsuoka, C. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.18.599636 · Cited by 1
Cortical network reconfiguration aligns with shifts of basal ganglia and cerebellar influencePreprint
Nestor, K. (2024) · PubMed · doi:10.48550/arxiv.2408.07977 · Cited by 4
Brain Fluidity as a Functional Marker of Tau-Related Neurodegeneration in Alzheimer’s DiseasePreprint
Mazzara, C., Gambino, G., Duma, G. M., Neri, M., Giglia, G., Piccoli, T., Guajana, F., Comelli, A., Tuttolomondo, A., Migliore, M., et al. (2025) · medRxiv · doi:10.1101/2025.07.04.25330744 · Cited by 1
Functional Connectivity-based Attractor Dynamics in Rest, Task, and DiseasePreprint
Englert, R., Kincses, B., Kotikalapudi, R., Gallitto, G., Li, J., Hoffschlag, K., Woo, C., Wager, T. D., Timmann, D., Bingel, U., et al. (2025) · eLife · doi:10.7554/elife.98725.2 · Cited by 1
Fast clustering for scalable statistical analysis on structured imagesPreprint
Thirion, B., Hoyos-Idrobo, A., Kahn, J., & Varoquaux, G. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04898
Modeling functional network dynamics via multi-scale dictionary learning and network continuums
Li, X., Lin, B., Lv, J., Ye, J., & Liu, T. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493212
Demirtaş, M., Gilson, M., Murray, J. D., Popović, D., Vieta, E., Pintor, L., Prčkovska, V., Villoslada, P., & Deco, G. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/085274
Neural Substrates of Parkinson's Disease
Koshimori, Y. (2016) · TSpace (University of Toronto)
Riemannian multi-manifold modeling and clustering in brain networks
Slavakis, K., Salsabilian, S., Wack, D. S., Muldoon, S. F., Baidoo-Williams, H. E., Vettel, J. M., Cieslak, M., & Grafton, S. T. (2017) · Wavelets and Sparsity · doi:10.1117/12.2274405
Domain-general perspectives on the neurocognitive specialization of language
Castillo, G. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Dynamics of ketamine-related resting state functional connectivity changes in healthy controls
Spies, M., Klöbl, M., Höflich, A. S., Kranz, G. S., Vanicek, T., Hahn, A., Winkler, D., Kasper, S. F., & Lanzenberger, R. R. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.11.479
Predicting Individual Brain Maturity Using Window-Based Dynamic Functional Connectivity
Hu, D., & Zeng, L. (2019) · Springer eBooks · doi:10.1007/978-981-32-9523-0_4
Wang, X., Jiao, Y., & Li, L. (2019) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22396
Time varying connectivity across the brain changes as a function of nicotine abstinence statePreprint
Fedota, J. R., Ross, T. J., Castillo, J., McKenna, M. R., Matous, A. L., Salmeron, B. J., Menon, V., & Stein, E. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.25.917617
Emotional Analysis Based on Dynamic Functional Brain Network
Wang, Z., Tong, Y., & Heng, X. (2019) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana.2019.00044
Santo, S., & Zhong, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01895
Impaired dynamic profile of brain recurring functional connectivity states in Alzheimer’s diseasePreprint
Balajoo, S. M., Asemani, D., Khadem, A. A., & Hamid Soltanian-zadeh, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.03.324673
Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery
Duncan, E. S., Shereen, A. D., Gentimis, T., & Small, S. L. (2020) · Neurorehabilitation and neural repair · doi:10.1177/1545968320981953
[About stepwise uterine devascularization in early post-partum haemorrhages].
Tsvetkov, K., Angelova, M., Monastirska, M., & Ts, T. (2008) · PubMed
Bardella, G., Giuffrida, V., Giarrocco, F., Brunamonti, E., Pani, P., & Ferraina, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.15.435381
Duda, M. (2021) · Deep Blue (University of Michigan) · doi:10.7302/3091
Assessment of temporal complexity in functional MRI between rest and task conditionsPreprint
Omidvarnia, A., Liégeois, R., Amico, E., Preti, M. G., Zalesky, A., & Ville, D. V. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.20.469367
Theories, Models, and Hypotheses
Barbey, A. K., Karama, S., & Haier, R. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.007
Jäncke, L. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735830092.793560898
Temporal and Spectral Signatures of the Default Mode Network
de Pasquale, F., & Marzetti, L. (2019) · Springer eBooks · doi:10.1007/978-3-319-62657-4_20-1
(2016) · Elsevier eBooks · doi:10.1016/b978-0-12-407908-3.10000-7
Author response: The structural connectome constrains fast brain dynamics
Sorrentino, P., Seguin, C., Rucco, R., Liparoti, M., Lopez, E. T., Bonavita, S., Quarantelli, M., Sorrentino, G., Jirsa, V., & Zalesky, A. (2021) · eLife · doi:10.7554/elife.67400.sa2
Barbey, A. K., Karama, S., & Haier, R. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.002
Metadata
- Authors
- Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M.
- Affiliations
- The University of Melbourne (AU)
- The University of Queensland (AU)
- QIMR Berghofer Medical Research Institute (AU)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2014-06-30
- Volume
- 111
- Issue
- 28
- Pages
- 10341-10346
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4104861
- OpenAlex
- W1996196481
- Cited by
- 753
- References
- 79
- Citations by year
- 2026: 18 · 2025: 57 · 2024: 49 · 2023: 49 · 2022: 58 · 2021: 82 · 2020: 74 · 2019: 74 · 2018: 65 · 2017: 98 · 2016: 64 · 2015: 51 · 2014: 13
- FWCI
- 42.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Funders
- National Institutes of Health (5u54mh091657-05)
- National Institute of Mental Health (1U54 MH091657, U54-MH091657)
- 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 Neuroimaging Techniques and Applications
- Keywords
- resting-state brain networks, dynamic functional connectivity, brain network topology, default mode network, frontoparietal network, network hubs, information transmission efficiency, metabolic cost, brain dynamics, Human Connectome Project
- MeSH terms
- Adult, Brain, Humans, Magnetic Resonance Imaging, Male, Mental Processes, Nerve Net, Radiography
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