Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
Open accessAbstract
We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time-scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from N=80 participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity.
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
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?
Varying degrees of functional connectivity between richly connected networks have been found to be accompanied by periods of either high or low modularity.
“Intriguingly, with respect to global workspaces, varying degrees of functional connectivity between richly connected networks have been found to be accompanied by periods of either high or low modularity (Betzel et al., 2016), consistent with a potential functional significance of integrating information across otherwise isolated sub-systems.”
From An Integrated World Modeling Theory (IWMT) of Consciousness (2020) · cites this paper as Betzel et al., 2016
The Results section, “The resting state alternates between periods of unexpectedly strong/weak connectivity and high/low modularity” (page unavailable), links fluctuations in functional connections to periods of differing modularity. But the paper does not characterize the connections in that finding as being between “richly connected networks”; its Results section on excursion locations instead emphasizes connections within visual and somatomotor networks. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
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 · see citing works
Self-citationThe organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182 · see citing works
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 · see citing works
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304 · see citing works
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748 · see citing works
Functional and Effective Connectivity: A Review
Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415 · see citing works
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · see citing works
Self-citationResolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024 · see citing works
Tracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992 · see citing works
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962 · see citing works
Self-citationFisher, R. A. (1915) · Biometrika · doi:10.1093/biomet/10.4.507 · Cited by 2,159 · see citing works
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048 · see citing works
Satterthwaite, T., Elliott, M., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M., Eickhoff, S., Hakonarson, H., Gur, R., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,900 · see citing works
Network modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878 · see citing works
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808 · see citing works
Networks of the Brain: Quantitative Analysis and Modeling
Sporns, O. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/8476.001.0001 · Cited by 1,750 · see citing works
Self-citationDynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705 · see citing works
Network structure of cerebral cortex shapes functional connectivity on multiple time scales
Honey, C. J., Kötter, R., Breakspear, M. J., & Sporns, O. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701519104 · Cited by 1,683 · see citing works
Self-citationYan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461 · see citing works
Self-citationThe Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery
Calhoun, V. D., Miller, R. L., Pearlson, G. D., & Adalı, T. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.015 · Cited by 1,388 · see citing works
Fisher, R. A. (1915) · Biometrika · doi:10.2307/2331838 · Cited by 1,334 · see citing works
Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334 · see citing works
Self-citationDamaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., et al. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.07.003 · Cited by 1,066 · see citing works
Performance of modularity maximization in practical contexts
Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937 · see citing works
Functional system and areal organization of a highly sampled individual human brain
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M.-Y., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U. F., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.037 · Cited by 911 · see citing works
Consensus clustering in complex networks
Lancichinetti, A., & Fortunato, S. (2012) · Scientific Reports · doi:10.1038/srep00336 · Cited by 898 · see citing works
Weight-conserving characterization of complex functional brain networks
Rubinov, M., & Sporns, O. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.069 · Cited by 871 · see citing works
Self-citationThe NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry
Nooner, K. B., Colcombe, S., Tobe, R. H., Mennes, M., Benedict, M. M., Moreno, A. L., Panek, L. J., Brown, S., Zavitz, S. T., Li, Q., et al. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00152 · Cited by 855 · see citing works
On spurious and real fluctuations of dynamic functional connectivity during rest
Leonardi, N., & Van De Ville, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.007 · Cited by 828 · see citing works
Time-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753 · see citing works
Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis
Tagliazucchi, E., Balenzuela, P., Fraiman, D., & Chialvo, D. R. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00015 · Cited by 716 · see citing works
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.22058 · Cited by 715 · see citing works
Signature of consciousness in the dynamics of resting-state brain activity
Barttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., Jarraya, B., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418031112 · Cited by 670 · see citing works
Zuo, X., & Xing, X.-X. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.009 · Cited by 657 · see citing works
Self-citationLiu, X., & Duyn, J. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216856110 · Cited by 647 · see citing works
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?
Hindriks, R., Adhikari, M. H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N. K., & Deco, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.055 · Cited by 632 · see citing works
Calhoun, V. D., & Adalı, T. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2211076 · Cited by 543 · see citing works
Functional connectivity dynamics: modeling the switching behavior of the resting state
Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.001 · Cited by 528 · see citing works
Cognitive relevance of the community structure of the human brain functional coactivation network
Crossley, N. A., Mechelli, A., Vértes, P. E., Winton‐Brown, T., Patel, A. X., Ginestet, C. E., McGuire, P. K., & Bullmore, E. T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220826110 · Cited by 449 · see citing works
Comparing Community Structure to Characteristics in Online Collegiate Social Networks
Red, V., Kelsic, E. D., Mucha, P. J., & Porter, M. A. (2011) · SIAM Review · doi:10.1137/080734315 · Cited by 436 · see citing works
On the use of correlation as a measure of network connectivity
Zalesky, A., Fornito, A., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.001 · Cited by 430 · see citing works
A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain
de Pasquale, F., Penna, S. D., Snyder, A. Z., Marzetti, L., Pizzella, V., Romani, G. L., & Corbetta, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.031 · Cited by 413 · see citing works
Rashid, B., Damaraju, E., Pearlson, G. D., & Calhoun, V. D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00897 · Cited by 399 · see citing works
Enhanced repertoire of brain dynamical states during the psychedelic experience
Tagliazucchi, E., Carhart-Harris, R. L., Leech, R., Nutt, D., & Chialvo, D. R. (2014) · Human Brain Mapping · doi:10.1002/hbm.22562 · Cited by 399 · see citing works
Leonardi, N., Richiardi, J., Gschwind, M., Simioni, S., Annoni, J., Schluep, M., Vuilleumier, P. O., & Ville, D. V. D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.019 · Cited by 390 · see citing works
Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.052 · Cited by 369 · see citing works
Task-Based Core-Periphery Organization of Human Brain Dynamics
Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003171 · Cited by 356 · see citing works
Dynamic functional connectivity of the default mode network tracks daydreaming
Kucyi, A., & Davis, K. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.044 · Cited by 355 · see citing works
EEG correlates of time-varying BOLD functional connectivity
Chang, C. E., Liu, Z., Chen, M. C., Liu, X., & Duyn, J. H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.049 · Cited by 344 · see citing works
Karahanoğlu, F. I., & Ville, D. V. D. (2015) · Nature Communications · doi:10.1038/ncomms8751 · Cited by 320 · see citing works
Beyond the Connectome: The Dynome
Kopell, N., Gritton, H. J., Whittington, M. A., & Kramer, M. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.016 · Cited by 316 · see citing works
DynamicBC : A MATLAB Toolbox for Dynamic Brain Connectome Analysis
Liao, W., Wu, G., Xu, Q., Ji, G., Zhang, Z., Zang, Y., & Lu, G. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0253 · Cited by 304 · see citing works
Dynamic BOLD functional connectivity in humans and its electrophysiological correlates
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., & Laufs, H. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00339 · Cited by 285 · see citing works
Analysis of community structure in networks of correlated data
Gómez, S., Pablo Jensen, B., & Arenas, À. (2009) · Physical Review E · doi:10.1103/physreve.80.016114 · Cited by 261 · see citing works
Towards a statistical test for functional connectivity dynamics
Zalesky, A., & Breakspear, M. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.047 · Cited by 257 · see citing works
Tracking the Brain's Functional Coupling Dynamics over Development
Hutchison, R. M., & Morton, J. B. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4638-14.2015 · Cited by 214 · see citing works
EEG Alpha Power Modulation of fMRI Resting-State Connectivity
Scheeringa, R., Petersson, K. M., Kleinschmidt, A. K., Jensen, O. B., & BASTIAANSEN, M. C. M. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0088 · Cited by 204 · see citing works
Mikuni, N., Nagamine, T., Ikeda, A., Terada, K., Taki, W., Kimura, J., Kikuchi, H., & Shibasaki, H. (1997) · NeuroImage · doi:10.1006/nimg.1997.0272 · Cited by 190 · see citing works
Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks
Bazzi, M., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. (2016) · Multiscale Modeling and Simulation · doi:10.1137/15m1009615 · Cited by 188 · see citing works
Network structure shapes spontaneous functional connectivity dynamics
Shen, K., Hutchison, R. M., Bezgin, G., Everling, S., & McIntosh, A. R. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4903-14.2015 · Cited by 171 · see citing works
A Connectome Computation System for discovery science of brain
Xu, T., Yang, Z., Jiang, L., Xing, X.-X., & Zuo, X. (2015) · 中国科学通报：英文版 · doi:10.1007/s11434-014-0698-3 · Cited by 167 · see citing works
Self-citationEmergence of Stable Functional Networks in Long-Term Human Electroencephalography
Chu, C. J., Kramer, M., Pathmanathan, J. S., Bianchi, M. T., Westover, M. B., Wizon, L., & Cash, S. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5669-11.2012 · Cited by 161 · see citing works
Dynamic network structure of interhemispheric coordination
Doron, K. W., Bassett, D. S., & Gazzaniga, M. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216402109 · Cited by 153 · see citing works
Community Detection for Correlation Matrices
MacMahon, M., & Garlaschelli, D. (2015) · Physical Review X · doi:10.1103/physrevx.5.021006 · Cited by 146 · see citing works
Task-related modulation of functional connectivity variability and its behavioral correlations
Elton, A., & Gao, W. (2015) · Human Brain Mapping · doi:10.1002/hbm.22847 · Cited by 137 · see citing works
Stable long-range interhemispheric coordination is supported by direct anatomical projections
Shen, K., Mišić, B., Cipollini, B., Bezgin, G., Buschkuehl, M., Hutchison, R. M., Jaeggi, S. M., Kross, E. F., Peltier, S., Everling, S., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1503436112 · Cited by 119 · see citing works
Emergence of Persistent Networks in Long-Term Intracranial EEG Recordings
Kramer, M., Eden, U. T., Lepage, K. Q., Kolaczyk, E. D., Bianchi, M. T., & Cash, S. S. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2287-11.2011 · Cited by 118 · see citing works
The spatial structure of resting state connectivity stability on the scale of minutes
Gonzalez-Castillo, J., Handwerker, D. A., Robinson, M. E., Hoy, C. W., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00138 · Cited by 116 · see citing works
BETZEL, R. F., GRIFFA, A., AVENA-KOENIGSBERGER, A., GOÑI, J., THIRAN, J.-P., HAGMANN, P., & SPORNS, O. (2013) · Network Science · doi:10.1017/nws.2013.19 · Cited by 113 · see citing works
Self-citationSynchronization dynamics and evidence for a repertoire of network states in resting EEG
Betzel, R. F., Erickson, M. A., Abell, M., O’Donnell, B. F., Hetrick, W. P., & Sporns, O. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00074 · Cited by 100 · see citing works
Self-citationTask-Based Core-Periphery Organisation of Human Brain DynamicsPreprint
Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.3555 · Cited by 81 · see citing works
Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints
Liégeois, R., Ziegler, E., Phillips, C. L. M., Geurts, P., Gómez, F. A., Bahri, M. A., Yeo, B. T. T., Soddu, A., Vanhaudenhuyse, A., Laureys, S., et al. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1083-y · Cited by 73 · see citing works
Functional Connectivity in MRI Is Driven by Spontaneous BOLD Events
Allan, T. W., Francis, S., Caballero‐Gaudes, C., Morris, P. G., Liddle, E. B., Liddle, P. F., Brookes, M. J., & Gowland, P. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124577 · Cited by 44 · see citing works
Thompson, W. H., & Fransson, P. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00398 · Cited by 41 · see citing works
Miller, R. L., Yaesoubi, M., & Calhoun, V. D. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944460 · Cited by 34 · see citing works
On the danger of detecting network states in white noise
Hlinka, J., & Hadrava, M. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00011 · Cited by 29 · see citing works
Dynamic fluctuations in integration and segregation within the human functional connectomePreprint
Shine, J. M., Bell, P. T., Koyejo, S., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2015) · arXiv (Cornell University) · Cited by 2 · see citing works
Detecting network states in white noise
Hlinka, J., & Hadrava, M. (2015) · BMC Neuroscience · doi:10.1186/1471-2202-16-s1-p92 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Sporns O Networks of the Brain 2011 MIT press
- Watts DJ Strogatz SH Collective dynamics of small-worldnetworks nature 1998 393 6684 440 442 9623998
Cited by
Sorted by citations
The dynamic functional connectome: State-of-the-art and perspectives
Preti, M. G., Bolton, T. A. W., & Ville, D. V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.061 · Cited by 1,425 · see citing works
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886 · see citing works
Graph theory methods: applications in brain networks
Sporns, O. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/osporns · Cited by 643 · see citing works
Self-citationDixon, M. L., De La Vega, A., Mills, C., Andrews-Hanna, J., Spreng, R. N., Cole, M. W., & Christoff, K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715766115 · Cited by 595 · see citing works
Human consciousness is supported by dynamic complex patterns of brain signal coordination
Demertzi, A., Tagliazucchi, E., Dehaene, S., Deco, G., Barttfeld, P., Raimondo, F., Martial, C., Fernández-Espejo, D., Rohaut, B., Voss, H. U., et al. (2019) · Science Advances · doi:10.1126/sciadv.aat7603 · Cited by 513 · see citing works
Betzel, R. F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.006 · Cited by 587 · see citing works
Self-citationSmall-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 431 · see citing works
Can brain state be manipulated to emphasize individual differences in functional connectivity?
Finn, E., Scheinost, D., Finn, D., Shen, X.-L., Papademetris, X., & Constable, R. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.064 · Cited by 361 · see citing works
Resting brain dynamics at different timescales capture distinct aspects of human behavior
Liégeois, R., Li, J., Kong, R., Orbán, C., Ville, D. V. D., Ge, T., Sabuncu, M. R., & Yeo, B. T. T. (2019) · Nature Communications · doi:10.1038/s41467-019-10317-7 · Cited by 288 · see citing works
High-amplitude cofluctuations in cortical activity drive functional connectivity
Esfahlani, F. Z., Jo, Y., Faskowitz, J., Byrge, L. A., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005531117 · Cited by 250 · see citing works
Self-citationHigher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration
Schultz, D. H., & Cole, M. W. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0358-16.2016 · Cited by 268 · see citing works
Optimally controlling the human connectome: the role of network topology
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016) · Scientific Reports · doi:10.1038/srep30770 · Cited by 247 · see citing works
Self-citationFrom regions to connections and networks: new bridges between brain and behavior
Mišić, B., & Sporns, O. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.05.003 · Cited by 243 · see citing works
Self-citationA Graph Signal Processing Perspective on Functional Brain Imaging
Huang, W., Bolton, T. A. W., Medaglia, J. D., Bassett, D. S., Ribeiro, A., & Ville, D. V. D. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2798928 · Cited by 247 · see citing works
Principles of dynamic network reconfiguration across diverse brain states
Shine, J. M., & Poldrack, R. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.010 · Cited by 242 · see citing works
Dixon, M. L., Andrews‐Hanna, J. R., Spreng, R. N., Irving, Z. C., Mills, C., Girn, M., & Kalina Christoff, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.073 · Cited by 228 · see citing works
Multilayer modeling and analysis of human brain networks
Domenico, M. D. (2017) · GigaScience · doi:10.1093/gigascience/gix004 · Cited by 221 · see citing works
Dynamics of large-scale electrophysiological networks: A technical review
O’Neill, G. C., Tewarie, P. K. B., Vidaurre, D., Liuzzi, L., Woolrich, M. W., & Brookes, M. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.003 · Cited by 214 · see citing works
Liu, J., Liao, X., Xia, M., & Yong He, A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23890 · Cited by 205 · see citing works
Replicability of time-varying connectivity patterns in large resting state fMRI samples
Abrol, A., Damaraju, E., Miller, R. L., Stephen, J. M., Claus, E. D., Mayer, A. R., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.020 · Cited by 193 · see citing works
Brain network dynamics in high-functioning individuals with autism
Watanabe, T., & Rees, G. (2017) · Nature Communications · doi:10.1038/ncomms16048 · Cited by 165 · see citing works
Neural oscillations and brain stimulation in Alzheimer’s disease
Jafari, Z., Kolb, B. E., & Mohajerani, M. H. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101878 · Cited by 176 · see citing works
Douw, L., Wakeman, D. G., Tanaka, N., Liu, H., & Stufflebeam, S. M. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.09.034 · Cited by 162 · see citing works
Northoff, G., & Duncan, N. W. (2016) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2016.08.003 · Cited by 158 · see citing works
Lord, L.-D., Stevner, A. B. A., Deco, G., & Kringelbach, M. L. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0283 · Cited by 161 · see citing works
Positive affect, surprise, and fatigue are correlates of network flexibility
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00425-z · Cited by 158 · see citing works
Self-citationYan, C., Yang, Z., Colcombe, S. J., Zuo, X., & Milham, M. P. (2017) · 中国科学通报：英文版 · doi:10.1016/j.scib.2017.09.015 · Cited by 150 · see citing works
Self-citationSafron, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00030 · Cited by 133 · see citing works
“Intriguingly, with respect to global workspaces, varying degrees of functional connectivity between richly connected networks have been found to be accompanied by periods of either high or low modularity (Betzel et al., 2016), consistent with a potential functional significance of integrating information across otherw…”
BackgroundChen, B., Xu, T., Zhou, C., Wang, L., Yang, N., Wang, Z., Dong, H.-M., Yang, Z., Zang, Y., Zuo, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144963 · Cited by 136 · see citing works
Self-citationFukushima, M., Betzel, R. F., He, Y., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1539-3 · Cited by 136 · see citing works
Self-citationSun, J., Liu, Z., Rolls, E. T., Chen, Q., Yao, Y., Yang, W., Wei, D., Zhang, Q., Zhang, J., Feng, J., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy010 · Cited by 136 · see citing works
Long, Y., Cao, H., Yan, C., Chen, X., Li, L., Castellanos, F. X., Bai, T., Bo, Q., Chen, G., Chen, N., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102163 · Cited by 131 · see citing works
From static to temporal network theory: Applications to functional brain connectivity
Thompson, W. H., Brantefors, P., & Fransson, P. (2017) · Network Neuroscience · doi:10.1162/netn_a_00011 · Cited by 123 · see citing works
Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks
Shafiei, G., Zeighami, Y., Clark, C. A., Coull, J. T., Nagano‐Saito, A., Leyton, M., Dagher, A., & Mišić, B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy264 · Cited by 119 · see citing works
Individual differences and time-varying features of modular brain architecture
Liao, X., Cao, M., Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.066 · Cited by 117 · see citing works
Harlalka, V., Bapi, R. S., Vinod, P. K., & Roy, D. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00006 · Cited by 110 · see citing works
Controversies and progress on standardization of large-scale brain network nomenclature
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. R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00323 · Cited by 119 · see citing works
Self-citationTime-resolved structure-function coupling in brain networks
Liu, Z.-Q., Vázquez-Rodríguez, B., Spreng, R. N., Bernhardt, B. C., Betzel, R. F., & Mišić, B. (2022) · Communications Biology · doi:10.1038/s42003-022-03466-x · Cited by 103 · see citing works
Self-citationBetzel, R. F., Bertolero, M. A., Gordon, E. M., Gratton, C., Dosenbach, N. U. F., & Bassett, D. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.003 · Cited by 108 · see citing works
Self-citationGenetic variants in Alzheimer disease — molecular and brain network approaches
Gaiteri, C., Mostafavi, S., Honey, C. J., Jager, P. L. D., & Bennett, D. A. (2016) · Nature Reviews Neurology · doi:10.1038/nrneurol.2016.84 · Cited by 96 · see citing works
Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series
Sporns, O., Faskowitz, J., Teixeira, A. S., Cutts, S. A., & Betzel, R. F. (2021) · Network Neuroscience · doi:10.1162/netn_a_00182 · Cited by 92 · see citing works
Self-citationKeilholz, S. D., Caballero‐Gaudes, C., Bandettini, P. A., Deco, G., & Calhoun, V. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0543 · Cited by 94 · see citing works
Yu, Q., Du, Y., Chen, J., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Adalı, T., Pearlson, G. D., & Calhoun, V. D. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2825200 · Cited by 93 · see citing works
Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S. D., & Cohen, J. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00037 · Cited by 92 · see citing works
Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics
Bolton, T. A. W., Morgenroth, E., Preti, M. G., & Ville, D. V. D. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.005 · Cited by 89 · see citing works
Dopamine depletion alters macroscopic network dynamics in Parkinson’s disease
Shine, J. M., Bell, P. T., Matar, E., Poldrack, R. A., Lewis, S. J. G., Halliday, G. M., & O’Callaghan, C. (2019) · Brain · doi:10.1093/brain/awz034 · Cited by 85 · see citing works
Temporal stability of functional brain modules associated with human intelligence
Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24807 · Cited by 83 · see citing works
Self-citationRyali, S., Supekar, K., Chen, T., Kochalka, J., Cai, W., Nicholas, J., Padmanabhan, A., & Menon, V. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005138 · Cited by 79 · see citing works
Griffa, A., Ricaud, B., Benzi, K., Bresson, X., Daducci, A., Vandergheynst, P., Thiran, J., & Hagmann, P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.015 · Cited by 79 · see citing works
On wakefulness fluctuations as a source of BOLD functional connectivity dynamics
Haimovici, A., Tagliazucchi, E., Balenzuela, P., & Laufs, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-06389-4 · Cited by 80 · see citing works
TbCAPs: A toolbox for co-activation pattern analysis
Bolton, T. A. W., Tuleasca, C., Wotruba, D., Rey, G., Dhanis, H., Gauthier, B., Delavari, F., Morgenroth, E., Gaviria, J., Blondiaux, E., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116621 · Cited by 77 · see citing works
Sun, Y., Collinson, S. L., John Suckling, A., & Sim, K. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby077 · Cited by 76 · see citing works
Changes in dynamic functional connections with aging
Tian, L., Li, Q., Wang, C., & Yu, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.040 · Cited by 76 · see citing works
Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics
Pope, M., Fukushima, M., Betzel, R. F., & Sporns, O. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109380118 · Cited by 67 · see citing works
Self-citationThe significance of negative correlations in brain connectivity
Zhan, L., Jenkins, L. M., Wolfson, O., GadElkarim, J., Nocito, K., Thompson, P. M., Ajilore, O., Chung, M. K., & Leow, A. (2017) · The Journal of Comparative Neurology · doi:10.1002/cne.24274 · Cited by 67 · see citing works
Characterizing dynamic local functional connectivity in the human brain
Deng, L., Sun, J., Cheng, L., & Tong, S. (2016) · Scientific Reports · doi:10.1038/srep26976 · Cited by 65 · see citing works
Karahanoğlu, F. I., & Ville, D. V. D. (2017) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2017.09.008 · Cited by 61 · see citing works
Changes in functional connectivity dynamics with aging: A dynamical phase synchronization approach
Nobukawa, S., Kikuchi, M., & Takahashi, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.008 · Cited by 61 · see citing works
Bolton, T. A. W., Jochaut, D., Giraud, A., & Ville, D. V. D. (2018) · Human Brain Mapping · doi:10.1002/hbm.24009 · Cited by 61 · see citing works
Structural connectome and connectivity lateralization of the multimodal vestibular cortical network
Indovina, I., Bosco, G., Riccelli, R., Maffei, V., Lacquaniti, F., Passamonti, L., & Toschi, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117247 · Cited by 61 · see citing works
Saravanan, A., Parida, S., Murugan, M., Reddy, M. S., Elumalai, P. V., & Dash, S. K. (2022) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2022.101630 · Cited by 59 · see citing works
Dynamic synergetic configurations of resting-state networks in ADHD
Kaboodvand, N., Iravani, B., & Fransson, P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116347 · Cited by 58 · see citing works
Song, H., Shim, W., & Rosenberg, M. (2022) · bioRxiv · doi:10.7554/elife.85487 · Cited by 62 · see citing works
Living on the edge: network neuroscience beyond nodes
Betzel, R. F., Faskowitz, J., & Sporns, O. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.08.009 · Cited by 60 · see citing works
Self-citationKeerativittayayut, R., Aoki, R., Sarabi, M. T., Jimura, K., & Nakahara, K. (2018) · eLife · doi:10.7554/elife.32696 · Cited by 52 · see citing works
Safron, A. (2021) · Entropy · doi:10.3390/e23060783 · Cited by 52 · see citing works
Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.044 · Cited by 53 · see citing works
Self-citationTemporal fluctuations in the brain’s modular architecture during movie-watching
Betzel, R. F., Byrge, L. A., Esfahlani, F. Z., & Kennedy, D. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116687 · Cited by 55 · see citing works
Self-citationEstimation and validation of individualized dynamic brain models with resting state fMRI
Singh, M. F., Braver, T. S., Cole, M. W., & Ching, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117046 · Cited by 47 · see citing works
Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals
Hahn, G., Zamora‐López, G., Uhrig, L., Tagliazucchi, E., Laufs, H., Mantini, D., Kringelbach, M. L., Jarraya, B., & Deco, G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117470 · Cited by 50 · see citing works
Functional hypergraph uncovers novel covariant structures over neurodevelopment
Gu, S., Yang, M., Medaglia, J. D., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23631 · Cited by 46 · see citing works
Dynamic reorganization of intrinsic functional networks in the mouse brain
Grandjean, J., Preti, M. G., Bolton, T. A. W., Buerge, M., Seifritz, E., Pryce, C. R., Ville, D. V. D., & Rudin, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.026 · Cited by 48 · see citing works
Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data
Chen, J. E., Rubinov, M., & Chang, C. E. (2017) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2017.06.009 · Cited by 47 · see citing works
Petrican, R., Taylor, M. J., & Grady, C. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.025 · Cited by 43 · see citing works
Brain network segregation and integration during an epoch-related working memory fMRI experiment
Fransson, P., Schiffler, B. C., & Thompson, W. H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.040 · Cited by 42 · see citing works
Resting state networks in empirical and simulated dynamic functional connectivity
Glomb, K., Ponce‐Alvarez, A., Gilson, M., Ritter, P., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.065 · Cited by 42 · see citing works
Esfahlani, F. Z., Byrge, L., Tanner, J. C., Sporns, O., Kennedy, D. P., & Betzel, R. F. (2021) · bioRxiv · doi:10.1016/j.neuroimage.2022.119591 · Cited by 50 · see citing works
Self-citationLin, S., Vavasour, I. M., Kosaka, B., Li, D. K. B., Traboulsee, A. L., MacKay, A. L., & McKeown, M. J. (2018) · Human Brain Mapping · doi:10.1002/hbm.24343 · Cited by 42 · see citing works
Psychological resilience correlates with EEG source-space brain network flexibility
Paban, V., Modolo, J., Mheich, A., & Hassan, M. (2019) · Network Neuroscience · doi:10.1162/netn_a_00079 · Cited by 42 · see citing works
Petkoski, S., Ritter, P., & Jirsa, V. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac500 · Cited by 41 · see citing works
Greenwell, S. A., Faskowitz, J., Pritschet, L., Santander, T., Jacobs, E. G., & Betzel, R. F. (2023) · Network Neuroscience · doi:10.1162/netn_a_00307 · Cited by 43 · see citing works
Self-citationDynamic primitives of brain network interaction
Schirner, M., Kong, X., Yeo, B. T. T., Deco, G., & Ritter, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118928 · Cited by 42 · see citing works
Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study
Li, F., Jiang, L., Liao, Y., Si, Y., Yi, C., Zhang, Y., Zhu, X., Yang, Z., Yao, D., Cao, Z., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0d41 · Cited by 41 · see citing works
Cortico-subcortical interactions in overlapping communities of edge functional connectivity
Chumin, E. J., Faskowitz, J., Esfahlani, F. Z., Jo, Y., Merritt, H., Tanner, J., Cutts, S. A., Pope, M., Betzel, R. F., & Sporns, O. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118971 · Cited by 41 · see citing works
Self-citationTCD2: Tree-based community detection in dynamic social networks
Mishra, S., Singh, S. S., Mishra, S., & Biswas, B. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114493 · Cited by 39 · see citing works
Age-Related Alterations in EEG Network Connectivity in Healthy Aging
Javaid, H., Kumarnsit, E., & Chatpun, S. (2022) · Brain Sciences · doi:10.3390/brainsci12020218 · Cited by 38 · see citing works
Community detection in complex networks: From statistical foundations to data science applications
Dey, A. K., Tian, Y., & Gel, Y. R. (2021) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1566 · Cited by 38 · see citing works
Causal decoding of individual cortical excitability states
Metsomaa, J., Belardinelli, P., Ermolova, M., Ziemann, U., & Zrenner, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118652 · Cited by 38 · see citing works
Douw, L., Quaak, M., Fitzsimmons, S., de Wit, S. J., van der Werf, Y. D., van den Heuvel, O. A., & Vriend, C. (2019) · Brain stimulation · doi:10.1016/j.brs.2019.10.017 · Cited by 37 · see citing works
Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs
Bolton, T. A. W., Tarun, A., Virginie Sterpenich, A., Schwartz, S., & Ville, D. V. D. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2755369 · Cited by 35 · see citing works
Rangaprakash, D., Dretsch, M. N., Katz, J. S., Denney, T. S., & Deshpande, G. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00803 · Cited by 34 · see citing works
Dynamic Functional Connectivity Strength Within Different Frequency-Band in Schizophrenia
Luo, Y., He, H., Duan, M., Huang, H., Hu, Z., Wang, H., Yao, G., Yao, D., Li, J., & Luo, C. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00995 · Cited by 34 · see citing works
Fukushima, M., & Sporns, O. (2020) · Communications Biology · doi:10.1038/s42003-020-01331-3 · Cited by 33 · see citing works
Self-citationSimulations to benchmark time-varying connectivity methods for fMRI
Thompson, W. H., Richter, C. G., Plavén‐Sigray, P., & Fransson, P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006196 · Cited by 35 · see citing works
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 36 · see citing works
Self-citationSakoğlu, Ü., Mete, M., Esquivel, J., Rubia, K., Briggs, R. W., & Adinoff, B. H. (2019) · Journal of Neuroscience Research · doi:10.1002/jnr.24421 · Cited by 34 · see citing works
Integrated world modeling theory expanded: Implications for the future of consciousness
Safron, A. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.642397 · Cited by 43 · see citing works
Dynamic functional connectivity states characterize NREM sleep and wakefulness
Zhou, S., Zou, G., Xu, J., Su, Z., Zhu, H., Zou, Q., & Gao, J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24770 · Cited by 29 · see citing works
Frequency-dependent organization of the brain’s functional network through delayed-interactions
Ziaeemehr, A., Zarei, M., Valizadeh, A., & Mirasso, C. R. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.003 · Cited by 31 · see citing works
Capouskova, K., Zamora‐López, G., Kringelbach, M. L., & Deco, G. (2023) · Human Brain Mapping · doi:10.1002/hbm.26511 · Cited by 29 · see citing works
- Authors
- Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O.
- Affiliations
- Indiana University Bloomington (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Betzel, Richard F.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 127
- Pages
- 287-297
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4755785
- OpenAlex
- W2173630155
- Cited by
- 249
- References
- 82
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 8 · 2023: 20 · 2022: 20 · 2021: 26 · 2020: 32 · 2019: 29 · 2018: 29 · 2017: 34 · 2016: 17 · 2015: 2
- FWCI
- 16.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Integrative Graduate Education
- Research Traineeship Training Program in the Dynamics of Brain–Body–Environment Systems at the Indiana University
- NIH (1 R01 AT009036-01)
- Uehara Memorial Foundation Postdoctoral Fellowship
- National Basic Research Program (973: 2015CB35170)
- Major Joint Fund for International Cooperation
- National Natural Science Foundation (81220108014)
- Chinese Academy of Sciences K.C. Wong Education Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- time-varying functional connectivity, resting-state functional connectivity, network modularity, dynamic brain networks, default mode network, community structure, Pearson correlation, visual network, sensorimotor network
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Rest
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
