The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
The dynamic functional connectome: State-of-the-art and perspectives
Open accessAbstract
Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.
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.
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
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
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
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
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
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
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.036 · Cited by 2,387
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010) · Science · doi:10.1126/science.1184819 · Cited by 2,123
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Van Dijk, K. R. A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00783.2009 · Cited by 1,844
Dynamic 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
Resting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502
Mind-wandering as spontaneous thought: a dynamic framework
Christoff, K., Irving, Z. C., Fox, K. C. R., Spreng, R. N., & Andrews-Hanna, J. R. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.113 · Cited by 1,376
Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., & Kleinschmidt, A. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1831638100 · Cited by 1,028
Controllability of structural brain networks
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., et al. (2015) · Nature Communications · doi:10.1038/ncomms9414 · Cited by 912
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
Dynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791
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
Temporally-independent functional modes of spontaneous brain activity
Smith, S. M., Miller, K. L., Moeller, S., Xu, J., Auerbach, E. J., Woolrich, M. W., Beckmann, C. F., Jenkinson, M., Andersson, J., Glasser, M. F., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121329109 · Cited by 702
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
Dynamic reconfiguration of the default mode network during narrative comprehension
Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016) · Nature Communications · doi:10.1038/ncomms12141 · Cited by 588
Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network
Chen, T., Cai, W., Ryali, S., Supekar, K., & Menon, V. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002469 · Cited by 480
Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.001 · Cited by 249 · cited 4×
“Betzel et al. (2016) observed large variations of modularity, which was strong in periods when a large number of strong connections could be detected.”
InfluentialAwake reactivation predicts memory in humans
Staresina, B. P., Alink, A., Kriegeskorte, N., & Henson, R. N. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311989110 · Cited by 219
Estimation of dynamic functional connectivity using multiplication of temporal derivatives
Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.064 · Cited by 166
Predicting individual brain maturity using dynamic functional connectivity
Qin, J., Chen, S.-G., Hu, D., Zeng, L.-L., Fan, Y.-M., Chen, X.-P., & Shen, H. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00418 · Cited by 117
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
Kang, J., Wang, L., Yan, C., Wang, J., Liang, X., & He, Y. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.033 · Cited by 111
Falahpour, M., Thompson, W. K., Abbott, A. E., Jahedi, A., Mulvey, M. E., Datko, M., Liu, T. T., & Müller, R.-A. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0389 · Cited by 106
Cited by
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Cabral, J., Vidaurre, D., Marques, P., Magalhães, R., Silva Moreira, P., Miguel Soares, J., Deco, G., Sousa, N., & Kringelbach, M. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-05425-7 · Cited by 386
Exercise benefits on Alzheimer’s disease: State-of-the-science
Valenzuela, P. L., Castillo-García, A., Morales, J. S., de la Villa, P., Hampel, H., Emanuele, E., Lista, S., & Lucia, A. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101108 · Cited by 337
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271
Towards a new approach to reveal dynamical organization of the brain using topological data analysis
Saggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P. A., Carlsson, G., Glover, G., & Reiss, A. L. (2018) · Nature Communications · doi:10.1038/s41467-018-03664-4 · Cited by 253
Metadata
- Authors
- Preti, M. G., Bolton, T. A., & Van De Ville, D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 160
- Pages
- 41-54
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2563279629
- Cited by
- 1,405
- References
- 30
- FWCI
- 56.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
