The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Tracking Whole-Brain Connectivity Dynamics in the Resting State
Open accessAbstract
Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Mounting evidence suggests that the functional connectome exhibits temporal instability that allows it to flexibly switch between multiple different configurations within a scanning session (Allen et al., 2014; Chang and Glover, 2010; Kang et al., 2011; Shen et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Allen et al., 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Least squares quantization in PCM
Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
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
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
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
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
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
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355
Flagged first, then by citations
Research works printed but not linked: 4
- Baker, 2012, Investigating the temporal dynamics of resting state connectivity with MEG
- Chang, 2012, Dynamics of resting-state functional connectivity associated with heart rate variability
- Gonzalez-Castillo, 2012, Detection of task transitions on 45 min long continuous multi-task runs using whole brain connectivity
- Starck, 2012, Temporally varying connectivity between ICA default-mode sub-networks—ASD vs. controls
Cited by
Sorted by citations
Items 401–500 of 1,003
Comparing MEG and high-density EEG for intrinsic functional connectivity mapping
Coquelet, N., Tiège, X. D., Destoky, F., Roshchupkina, L., Bourguignon, M., Goldman, S., Peigneux, P., & Wens, V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116556 · Cited by 66
Determining the number of states in dynamic functional connectivity using cluster validity indexes
Vergara, V. M., Salman, M. S., Abrol, A., Espinoza, F. A., & Calhoun, V. D. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108651 · Cited by 73
Self-citationA toolbox for brain network construction and classification (BrainNetClass)
Zhou, Z., Chen, X., Zhang, Y., Hu, D., Qiao, L., Yu, R., Yap, P., Pan, G., Zhang, H., & Shen, D. G. (2020) · Human Brain Mapping · doi:10.1002/hbm.24979 · Cited by 69
Cognitive and Neural State Dynamics of Narrative Comprehension
Song, H., Park, B., Park, H., & Shim, W. M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0037-21.2021 · Cited by 91
Subcortical-cortical dynamical states of the human brain and their breakdown in stroke
Favaretto, C., Allegra, M., Deco, G., Metcalf, N. V., Griffis, J. C., Shulman, G. L., Brovelli, A., & Corbetta, M. (2022) · Nature Communications · doi:10.1038/s41467-022-32304-1 · Cited by 77
Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks
Brovelli, A., Badier, J., Bonini, F., Bartoloméi, F., Coulon, O., & Auzias, G. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1672-16.2016 · Cited by 70
Cisler, J. M. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00029 · Cited by 71
Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models
Ting, C., Ombao, H., Samdin, S. B., & Salleh, S. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2780185 · Cited by 72
Shedding new light on disorders of consciousness diagnosis: The dynamic functional connectivity
Naro, A., Bramanti, A., Leo, A., Cacciola, A., Manuli, A., Bramanti, P., & Calabrò, R. S. (2018) · Cortex · doi:10.1016/j.cortex.2018.03.029 · Cited by 71
Cerebellar-cerebral dynamic functional connectivity alterations in major depressive disorder
Zhu, D., Yang, Y., Zhang, Y., Wang, C., Wang, Y., Zhang, C., Zhao, W., & Zhu, J. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.06.062 · Cited by 71
Dynamic changes in brain lateralization correlate with human cognitive performance
Wu, X., Kong, X., Vatansever, D., Liu, Z., Zhang, K., Sahakian, B. J., Robbins, T. W., Feng, J., Thompson, P. M., & Zhang, J. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001560 · Cited by 72
Rytty, R., Nikkinen, J., Paavola, L. E., Elseoud, A. A., Moilanen, V., Visuri, A., Tervonen, O. A., Renton, A. E., Traynor, B. J., Kiviniemi, V. J., et al. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00461 · Cited by 74
Spontaneous functional network dynamics and associated structural substrates in the human brain
Liao, X., Yuan, L., Zhao, T., Dai, Z., Shu, N., Xia, M., Yang, Y., Evans, A. C., & Yong He, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00478 · Cited by 68
Zöller, D., Schaer, M., Scariati, E., Padula, M. C., Eliez, S., & Ville, D. V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.064 · Cited by 72
Network connectivity correlates of variability in fluid intelligence performance
Santarnecchi, E., Emmendorfer, A. K., Tadayon, S., Rossi, S., Rossi, A., & Pascual‐Leone, Á. (2017) · Intelligence · doi:10.1016/j.intell.2017.10.002 · Cited by 70
Dynamic Connectivity Patterns in Conscious and Unconscious Brain
Ma, Y., Hamilton, C. M., & Zhang, N. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0464 · Cited by 71
Functional connectivity and the sleep-deprived brain
Chee, M. W. L., & Zhou, J. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.02.009 · Cited by 68
Zhao, J., Huang, J., Zhi, D., Yan, W., Ma, X., Yang, X., Li, X., Ke, Q., Jiang, T., Calhoun, V. D., et al. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108756 · Cited by 72
Self-citationDimitriadis, S. I., Laskaris, N., & Micheloyannis, S. (2015) · Cognitive Neurodynamics · doi:10.1007/s11571-015-9330-8 · Cited by 67
A new algorithm for spatiotemporal analysis of brain functional connectivity
Mheich, A., Hassan, M., Khalil, M. A., Berrou, C., & Wendling, F. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.01.002 · Cited by 68
Alaerts, K., Nayar, K., Kelly, C. A. M., Raithel, J., Milham, M. P., & Martino, A. D. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv029 · Cited by 66
Formal Models of the Network Co-occurrence Underlying Mental Operations
Bzdok, D., Varoquaux, G. P., Grisel, O., Eickenberg, M., Poupon, C., & Thirion, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004994 · Cited by 61
Chang, S., Chow, H. M., Wieland, E. A., & McAuley, J. D. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.08.021 · Cited by 68
Khalili‐Mahani, N., Rombouts, S. A. R. B., van Osch, M. J. P., Duff, E. P., Carbonell, F., Nickerson, L. D. H., Becerra, L. R., Dahan, A., Evans, A. C., Soucy, J., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23516 · Cited by 63
Perri, C. D., Amico, E., Heine, L., Annen, J., Martial, C., Larroque, S. K., Soddu, A., Marinazzo, D., & Laureys, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23826 · Cited by 67
Dynamic mode decomposition of resting-state and task fMRI
Casorso, J., Kong, X., Wang, C., Ville, D. V. D., Yeo, B. T. T., & Liégeois, R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.019 · Cited by 71
Abrol, A., Fu, Z., Du, Y., & Calhoun, V. D. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856500 · Cited by 67
Self-citationIntrinsic connectome organization across temporal scales: New insights from cross-modal approaches
Sadaghiani, S., & Wirsich, J. (2019) · Network Neuroscience · doi:10.1162/netn_a_00114 · Cited by 67
Brain functional network modeling and analysis based on fMRI: a systematic review
Wang, Z., Xin, J., Wang, Z., Yao, Y., Zhao, Y., & Qian, W. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09630-5 · Cited by 68
You, K., & Park, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117464 · Cited by 71
Few-Shot Domain-Adaptive Anomaly Detection for Cross-Site Brain Images
Su, J., Shen, H., Peng, L., & Hu, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3125686 · Cited by 68
A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG
Mahyari, A., Zoltowski, D. M., Bernat, E. M., & Aviyente, S. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2553960 · Cited by 66
Test-Retest Reliability of “High-Order” Functional Connectivity in Young Healthy Adults
Zhang, H., Chen, X., Zhang, Y., & Shen, D. G. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00439 · Cited by 67
Brookes, M. J., Groom, M. J., Liuzzi, L., Hill, R. M., Smith, H. J. F., Briley, P. M., Hall, E. L., Hunt, B. A. E., Gascoyne, L. E., Taylor, M. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.008 · Cited by 66
Reineberg, A. E., Gustavson, D. E., Benca‐Bachman, C. E., Banich, M. T., & Friedman, N. P. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01600 · Cited by 73
Hunyadi, B., Woolrich, M. W., Quinn, A. J., Vidaurre, D., & Vos, M. D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.082 · Cited by 66
Luppi, A. I., Golkowski, D., Ranft, A., Ilg, R., Jordan, D., Menon, D. K., & Stamatakis, E. A. (2021) · Human Brain Mapping · doi:10.1002/hbm.25405 · Cited by 62
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03330-y · Cited by 79
Melozzi, F., Woodman, M., Jirsa, V., & Bernard, C. (2017) · eNeuro · doi:10.1523/eneuro.0111-17.2017 · Cited by 68
Sendi, M. S. E., Zendehrouh, E., Miller, R. L., Fu, Z., Du, Y., Liu, J., Mormino, E. C., Salat, D. H., & Calhoun, V. D. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2020.593263 · Cited by 63
Self-citationIndividualized event structure drives individual differences in whole-brain functional connectivity
Betzel, R. F., Cutts, S. A., Greenwell, S. A., Faskowitz, J., & Sporns, O. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118993 · Cited by 61
Mapping and comparing fMRI connectivity networks across species
Pagani, M., Gutierrez‐Barragan, D., de Guzman, A. E., Xu, T., & Gozzi, A. (2023) · Communications Biology · doi:10.1038/s42003-023-05629-w · Cited by 68
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
Diversity of functional connectivity patterns is reduced in propofol‐induced unconsciousness
Lee, H., Noh, G., Joo, P., Choi, B., Silverstein, B. H., Kim, M., Wang, J., Jung, W., & Kim, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23708 · Cited by 66
Cerebral cartography and connectomics
Sporns, O. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0173 · Cited by 65
Alonso, S., Deco, G., Horst, G. J. T., & Cabral, J. (2020) · Frontiers in Neural Circuits · doi:10.3389/fncir.2020.570583 · Cited by 58
Static and Dynamic Intrinsic Connectivity following Mild Traumatic Brain Injury
Mayer, A. R., Ling, J. M., Allen, E. A., Klimaj, S. D., Yeo, R. A., & Hanlon, F. M. (2014) · Journal of Neurotrauma · doi:10.1089/neu.2014.3542 · Cited by 64
Self-citationJi, G., Yu, F., Liao, W., & Wang, K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.035 · Cited by 64
A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data
Warnick, R., Guindani, M., Erhardt, E. B., Allen, E. A., Calhoun, V. D., & Vannucci, M. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1379404 · Cited by 64
Self-citationKarahanoğlu, F. I., & Ville, D. V. D. (2017) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2017.09.008 · Cited by 61
Al-Sharoa, E. M., Al-khassaweneh, M., & Aviyente, S. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2854676 · Cited by 63
Sun, W. W., & Li, L. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1527701 · Cited by 64
Fam, J., Sun, Y., Qi, P., Lau, R. C., Feng, L., Kua, E. H., & Mahendran, R. (2019) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12972 · Cited by 67
Han, S., He, Z., Duan, X., Tang, Q., Chen, Y., Yang, Y., Pang, Y., Nan, X., Cui, Q., & Chen, H. (2018) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2018.12.017 · Cited by 63
Ultra-high field (10.5 T) resting state fMRI in the macaque
Yacoub, E. S., Grier, M. D., Auerbach, E. J., Lagore, R., Harel, N., Adriany, G., Zilverstand, A., Hayden, B. Y., Heilbronner, S. R., Uǧurbil, K., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117349 · Cited by 62
A spatiotemporal complexity architecture of human brain activity
Krohn, S., von Schwanenflug, N., Waschke, L., Romanello, A., Gell, M., Garrett, D. D., & Finke, C. (2023) · Science Advances · doi:10.1126/sciadv.abq3851 · Cited by 65
Metastability demystified — the foundational past, the pragmatic present and the promising future
Hancock, F., Rosas, F. E., Luppi, A. I., Zhang, M., Mediano, P. A. M., Cabral, J., Deco, G., Kringelbach, M. L., Breakspear, M. J., Kelso, J. A. S., et al. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00883-1 · Cited by 92
Xie, H., Calhoun, V. D., González-Castillo, J., Damaraju, E., Miller, R. L., Bandettini, P. A., & Mitra, S. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.050 · Cited by 61
Self-citationRácz, F. S., Stylianou, O., Mukli, P., & Eke, A. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00049 · Cited by 66
Fang, F. Z., Potter, T. B. H., Nguyen, T. P., & Zhang, Y. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065720500513 · Cited by 60
Gao, S., Mishne, G., & Scheinost, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25561 · Cited by 58
Dynamic thalamus parcellation from resting‐state fMRI data
Ji, B., Li, Z., Li, K., Li, L., Langley, J. A., Shen, H., Nie, S., Zhang, R., & Hu, X. P. (2015) · Human Brain Mapping · doi:10.1002/hbm.23079 · Cited by 60
Deco, G., Tagliazucchi, E., Laufs, H., Sanjuán, A., & Kringelbach, M. L. (2017) · eNeuro · doi:10.1523/eneuro.0106-17.2017 · Cited by 59
Hawco, C. S., Voineskos, A. N., Steeves, J. K. E., Dickie, E. W., Viviano, J. D., Downar, J. H., Blumberger, D. M., & Daskalakis, Z. J. (2018) · Cortex · doi:10.1016/j.cortex.2018.07.010 · Cited by 60
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
Bolton, T. A. W., Jochaut, D., Giraud, A., & Ville, D. V. D. (2018) · Human Brain Mapping · doi:10.1002/hbm.24009 · Cited by 63
Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis
d’Ambrosio, A., Valsasina, P., Gallo, A., Stefano, N. D., Pareto, D., Barkhof, F., Ciccarelli, O., Enzinger, C., Tedeschi, G., Stromillo, M. L., et al. (2019) · Multiple Sclerosis Journal · doi:10.1177/1352458519837707 · Cited by 62
Wang, J., Becker, B., Wang, L., Li, H., Zhao, X., & Jiang, T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.001 · Cited by 61
Representation learning of resting state fMRI with variational autoencoder
Kim, J., Zhang, Y., Han, K., Wen, Z., Choi, M., & Liu, Z. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118423 · Cited by 66
Hahn, A., Strandberg, T. O., Stomrud, E., Nilsson, M., van Westen, D., Palmqvist, S., Ossenkoppele, R., & Hansson, O. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz020 · Cited by 61
Estimating Whole-Brain Dynamics by Using Spectral Clustering
Cribben, I., & Yu, Y. (2016) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12169 · Cited by 62
Chen, G., Chen, P., Gong, J., Jia, Y., Zhong, S., Chen, F., Wang, J., Luo, Z., Qi, Z., Huang, L., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291720002378 · Cited by 59
Rocca, M. A., Valsasina, P., Meani, A., Gobbi, C., Zecca, C., Rovira, À., Sastre‐Garriga, J., KEARNEY, H. F., Ciccarelli, O., Matthews, L., et al. (2021) · Neurology · doi:10.1212/wnl.0000000000011494 · Cited by 60
Spatial and temporal functional connectivity changes between resting and attentive states
Bray, S., Arnold, A. E. G. F., Levy, R., & Iaria, G. (2014) · Human Brain Mapping · doi:10.1002/hbm.22646 · Cited by 60
Sleimen-Malkoun, R., Perdikis, D., Müller, V., Blanc, J.-L., Huys, R., Temprado, J.-J., & Jirsa, V. (2015) · eNeuro · doi:10.1523/eneuro.0067-14.2015 · Cited by 66
Sourty, M., Thoraval, L., Roquet, D., Armspach, J., Foucher, J., & Blanc, F. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00060 · Cited by 64
The energy landscape underpinning module dynamics in the human brain connectome
Ashourvan, A., Gu, S., Mattar, M. G., Vettel, J. M., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.067 · Cited by 59
Cai, J., Liu, A., Mi, T., Garg, S., Trappe, W. K., McKeown, M. J., & Wang, Z. J. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2875456 · Cited by 60
Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model
Zhang, G., Cai, B., Zhang, A., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2929959 · Cited by 63
Self-citationPrinciples and open questions in functional brain network reconstruction
Korhonen, O., Zanin, M., & Papo, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25462 · Cited by 59
Gorgolewski, K. J., Mendes, N., Wilfling, D., Wladimirow, E., Gauthier, C. J., Bonnen, T., Ruby, F. J. M., Trampel, R., Bazin, P., Cozatl, R., et al. (2015) · Scientific Data · doi:10.1038/sdata.2014.54 · Cited by 60
Fu, Z., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Turner, J. A., Du, Y., Assaf, M., Pearlson, G. D., & Calhoun, V. D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25205 · Cited by 63
Self-citationTemporal complexity of fMRI is reproducible and correlates with higher order cognition
Omidvarnia, A., Zalesky, A., Sina Mansour, L., Ville, D. V. D., Jackson, G. D., & Pedersen, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117760 · Cited by 60
Zhang, P., Jiang, Y., Liu, G., Jiao, H., Wang, J., Ma, L., Hu, W., & Zhang, J. (2021) · The Journal of Headache and Pain · doi:10.1186/s10194-021-01354-z · Cited by 60
Resting-state dynamic functional connectivity in major depressive disorder: A systematic review
Sun, S., Yan, C., Qu, S., Luo, G., Liu, X., Tian, F., Dong, Q., Li, X., & Hu, B. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.111076 · Cited by 61
Wilson, R. S., Mayhew, S. D., Rollings, D. T., Goldstone, A., Przeździk, I., Arvanitis, T. N., & Bagshaw, A. P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.061 · Cited by 59
Garcia‐Dias, R., Vieira, S., Pinaya, W. H. L., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00013-4 · Cited by 59
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
Open eyes and closed eyes elicit different temporal properties of brain functional networks
Weng, Y., Liu, X., Hu, H., Huang, H., Zheng, S., Chen, Q., Song, J., Cao, B., Wang, J., Wang, S., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117230 · Cited by 59
Iraji, A., Faghiri, A., Fu, Z., Rachakonda, S., Kochunov, P., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., Mueller, B. A., et al. (2021) · Network Neuroscience · doi:10.1162/netn_a_00196 · Cited by 56
Self-citationFunctional connectivity and oscillatory neuronal activity in the resting human brain
Ganzetti, M., & Mantini, D. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.02.032 · Cited by 61
Espinoza, F. A., Liu, J., Ciarochi, J., Turner, J. A., Vergara, V. M., Caprihan, A., Misiura, M. B., Johnson, H. J., Long, J. D., Bockholt, J. H., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24504 · Cited by 60
Self-citationMash, L. E., Reiter, M. A., Linke, A. C., Townsend, J., & Müller, R. (2018) · Developmental Neurobiology · doi:10.1002/dneu.22570 · Cited by 52
Yang, H., Zhang, H., Di, X., Wang, S., Meng, C., Tian, L., & Biswal, B. B. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118193 · Cited by 57
Song, H., Shim, W. M., & Rosenberg, M. D. (2023) · eLife · doi:10.7554/elife.85487 · Cited by 69
BOLD Granger Causality Reflects Vascular Anatomy
Webb, J., Ferguson, M. A., Nielsen, J. A., & Anderson, J. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0084279 · Cited by 58
Qiu, L., Xia, M., Cheng, B., Yuan, L., Kuang, W., Bi, F., Ai, H., Gu, Z., Lui, S., Huang, X., et al. (2018) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.170112 · Cited by 54
Connectivity dynamics from wakefulness to sleep
Damaraju, E., Tagliazucchi, E., Laufs, H., & Calhoun, V. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117047 · Cited by 54
Self-citationDiagnosis of early Alzheimer’s disease based on dynamic high order networks
Lei, B., Yu, S., Zhao, X., Frangi, A. F., Tan, E.-L., Elazab, A., Wang, T., & Wang, S. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00255-9 · Cited by 58
Cai, B., Zhang, G., Zhang, A., Xiao, L., Hu, W., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2021) · Human Brain Mapping · doi:10.1002/hbm.25394 · Cited by 56
Self-citation
Metadata
- Authors
- Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D.
- Affiliations
- Mind Research Network (US)
- Corresponding author
- Allen, Elena A.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-11-11
- Volume
- 24
- Issue
- 3
- Pages
- 663-676
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC3920766
- OpenAlex
- W2170702893
- Cited by
- 2,992
- References
- 95
- Citations by year
- 2026: 182 · 2025: 278 · 2024: 260 · 2023: 265 · 2022: 295 · 2021: 309 · 2020: 292 · 2019: 293 · 2018: 200 · 2017: 208 · 2016: 154 · 2015: 119 · 2014: 90 · 2013: 43 · 2012: 1
- FWCI
- 41.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Norges Forskningsråd
- National Institutes of Health
- National Institute on Drug Abuse (R01DA040487)
- National Institute of Biomedical Imaging and Bioengineering (R01EB000840, R01EB020407, R01EB005846, R01EB006841)
- National Center for Research Resources (P20RR021938)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- dynamic functional connectivity, resting-state functional connectivity, spatial independent component analysis, lateral parietal cortex, cingulate cortex, large-scale brain networks, spontaneous neural fluctuations
- MeSH terms
- Adolescent, Adult, Brain, Child, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Rest, Nonlinear Dynamics, Principal Component Analysis, Young Adult
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