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Dynamic functional connectivity: Promise, issues, and interpretations
Open accessAbstract
The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.
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“Endogenous fluctuations in cortical networks, which are rapid and highly non-stationary (Hutchison et al., 2013), could affect comprehension by altering the connectivity of these networks.”
From Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension (2018) · cites this paper as Hutchison et al., 2013
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Items 201–247 of 247
Wu, L., Eichele, T., & Calhoun, V. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.053 · Cited by 126
Self-citationSQUID systems for biomagnetic imaging
Pizzella, V., Penna, S. D., Gratta, C. D., & Romani, G. L. (2001) · Superconductor Science and Technology · doi:10.1088/0953-2048/14/7/201 · Cited by 126
Pan, W., Thompson, G. J., Magnuson, M. E., Majeed, W., Jaeger, D., & Keilholz, S. D. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0014 · Cited by 119
Self-citationPhillips, J. M., Vinck, M. A., Everling, S., & Womelsdorf, T. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht050 · Cited by 117
Self-citationHlinka, J., Alexakis, C., Diukova, A., Liddle, P. F., & Auer, D. P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.002 · Cited by 116
P4‐158: Nonstationarity in the ‘resting brain's’ modular architecture
Jones, D. T., Vemuri, P., Murphy, M. C., Gunter, J. L., Senjem, M. L., Machulda, M. M., Przybelski, S. A., Gregg, B. E., Kantarci, K., Knopman, D. S., et al. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.05.1862 · Cited by 116
Robust Transient Dynamics and Brain Functions
Rabinovich, M. I., & Varona, P. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00024 · Cited by 111
Dynamic Coordination in the Brain
(2010) · The MIT Press eBooks · doi:10.7551/mitpress/9780262014717.001.0001 · Cited by 109
Spontaneous Brain Activity Relates to Autonomic Arousal
Fan, J., Xu, P., Van Dam, N. T., Eilam-Stock, T., Gu, X., Luo, Y.-J., & Hof, P. R. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.1172-12.2012 · Cited by 107
Neural-Cardiac Coupling in Threat-Evoked Anxiety
Dalton, K. M., Kalin, N. H., Grist, T. M., & Davidson, R. J. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/0898929054021094 · Cited by 104
Visualizing the human connectome
Margulies, D. S., Böttger, J., Watanabe, A., & Gorgolewski, K. J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.111 · Cited by 102
Dynamic magnetic resonance inverse imaging of human brain function
Lin, F., Wald, L. L., Ahlfors, S. P., Hämäläinen, M., Kwong, K. K., & Belliveau, J. W. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20997 · Cited by 102
The contribution of electrophysiology to functional connectivity mapping
Schölvinck, M. L., Leopold, D. A., Brookes, M. J., & Khader, P. H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.010 · Cited by 101
Self-citationPrinciples of Brain Dynamics: Global State Interactions
Rabinovich, M. I., Friston, K. J., & Varona, P. (2012) · Cited by 99
Raz, G., Winetraub, Y., Jacob, Y., Kinreich, S., Maron‐Katz, A., Shaham, G., Podlipsky, I., Gilam, G., Soreq, E., & Hendler, T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.084 · Cited by 97
Allen, J. J. B., & Cohen, M. X. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00232 · Cited by 94
Hughes, S. W. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00193 · Cited by 89
MR-Encephalography: Fast multi-channel monitoring of brain physiology with magnetic resonance
Hennig, J., Zhong, K., & Speck, O. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.036 · Cited by 83
Fukushima, M., Saunders, R. C., Leopold, D. A., Mishkin, M., & Averbeck, B. B. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.014 · Cited by 71
Self-citationJönsson, P., & Sonnby‐Borgström, M. (2003) · Biological Psychology · doi:10.1016/s0301-0511(02)00114-x · Cited by 67
The synchronization of spontaneous BOLD activity predicts extraversion and neuroticism
Wei, L., Duan, X., Yang, Y., Liao, W., Gao, Q. C., Ding, J.-R., Zhang, Z., Zeng, W., Li, Y., Lu, G., et al. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.060 · Cited by 65
New dimensions of interneuronal specialization unmasked by principal cell heterogeneity
Krook-Magnuson, E., Varga, C., Lee, S.-H., & Soltesz, I. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2011.10.005 · Cited by 63
EEG-fMRI Methods for the Study of Brain Networks during Sleep
Duyn, J. H. (2012) · Frontiers in Neurology · doi:10.3389/fneur.2012.00100 · Cited by 61
Self-citationBottger, J., Schafer, A., Lohmann, G., Villringer, A., & Margulies, D. S. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.114 · Cited by 59
Caffeine increases the temporal variability of resting-state BOLD connectivity in the motor cortex
Rack-Gomer, A. L., & Liu, T. T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.001 · Cited by 59
Neuronal Synchronization and Thalamocortical Rhythms during Sleep, Wake, and Epilepsy
Timofeev, I., Bazhenov, M., Seigneur, J., & B, T. J. S. A. (2012) · Oxford University Press eBooks · doi:10.1093/med/9780199746545.003.0013 · Cited by 55
Magnuson, M. E., Majeed, W., & Keilholz, S. D. (2010) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22295 · Cited by 53
Self-citationCanolty, R. T., Ganguly, K., & Carmena, J. M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002809 · Cited by 52
Endogenous human brain dynamics recover slowly following cognitive effortPreprint
Suckling, J., Barnes, A., & Bullmore, E. T. (2008) · Nature Precedings · doi:10.1038/npre.2008.2665.1 · Cited by 50
Neuronal Synchronization and Thalamocortical Rhythms in Sleep, Wake and Epilepsy
Timofeev, I., Bazhenov, M., Seigneur, J., & B, T. J. S. A. (2012) · Europe PMC (PubMed Central) · Cited by 50
Simultaneous fMRI and Electrophysiology in the Rodent Brain
Pan, W., Thompson, G. J., Magnuson, M. E., Majeed, W., Jaeger, D., & Keilholz, S. D. (2010) · Journal of Visualized Experiments · doi:10.3791/1901 · Cited by 44
Self-citationAn EEG study of brain connectivity dynamics at the resting state.
Dimitriadis, S. I., Laskaris, N., Tsirka, V., Vourkas, M., & Micheloyannis, S. (2012) · PubMed · Cited by 37
McAvoy, M. P., Larson‐Prior, L. J., Ludwikow, M., Zhang, D., Snyder, A. Z., Gusnard, D. L., Raichle, M. E., & d’Avossa, G. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00900.2011 · Cited by 34
Functional connectivity: eigenimages and multivariate analyses
Friston, K. J., & Büchel, C. (2007) · Elsevier eBooks · doi:10.1016/b978-012372560-8/50037-1 · Cited by 34
A novel approach for global noise reduction in resting-state fMRI: APPLECOR
Marx, M., Pauly, K. B., & Chang, C. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.040 · Cited by 25
Self-citationPeng, T., Niazy, R. K., Payne, S. J., & Wise, R. G. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.013 · Cited by 22
Laufs, H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.034 · Cited by 19
The Dynamical and Structural Basis of Brain Activity
Deco, G., Jirsa, V., & Friston, K. J. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/9108.003.0003 · Cited by 19
Noebels, J. L., Avoli, M., Ma, R., et al. (2012) · Cited by 18
Robinson, L. F., Peña, V., & Kushnir, Y. (2008) · Theoretical and Applied Climatology · doi:10.1007/s00704-007-0351-z · Cited by 16
T2 vertebral bone marrow changes after space flight
LeBlanc, A., Lin, C., Evans, H., Shackelford, L. C., Martin, C., & Hedrick, T. D. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199903)41:3 · Cited by 12
Functional connectivity eigennetworks reveal different brain dynamics in multiple sclerosis patients
Leonardi, N., Richiardi, J., & Ville, D. V. D. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556528 · Cited by 9
Supplementary Material to: Tracking Whole-Brain Connectivity Dynamics in the Resting State.
Allen, E. A., Damaraju, E., Plis, S., et al. (2013) · Cited by 4
Self-citationLarkman (2001) · doi:10.1002/1522-2586(200102)13:2
Glover (2000) · doi:10.1002/1522-2594(200007)44:1
Carretero (1998) · doi:10.1175/1520-0477(1998)079
Flagged first, then by citations
Research works printed but not linked: 1
- Merritt, 2013, The electrical basis of dynamic functional connectivity measured with sliding window correlation., Proc ISMRM
Cited by
Sorted by citations
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123
Self-citationIntrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564
Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334
Self-citation
Metadata
- Authors
- 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., Handwerker, D. A., Keilholz, S., Kiviniemi, V., Leopold, D. A., de Pasquale, F., Sporns, O., Walter, M., & Chang, C.
- Affiliations
- Western University (CA)
- York University (CA)
- Mind Research Network (US)
- National Institutes of Health (US)
- Washington University in St. Louis (US)
- Stanford Medicine (US)
- Georgia Institute of Technology (US)
- Oulu University Hospital (FI)
- Indiana University Bloomington (US)
- Center for Behavioral Brain Sciences (DE)
- Corresponding author
- Hutchison, R. Matthew
- Chang, Catie
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 360-378
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3807588
- OpenAlex
- W1976623182
- Cited by
- 2,962
- References
- 247
- Citations by year
- 2026: 233 · 2025: 283 · 2024: 272 · 2023: 251 · 2022: 265 · 2021: 282 · 2020: 278 · 2019: 233 · 2018: 227 · 2017: 213 · 2016: 192 · 2015: 120 · 2014: 90 · 2013: 18
- FWCI
- 75.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- Funders
- Canadian Institute of Health Research
- Intramural Research Program of the National Institute of Neurological Disorders and Stroke, National Institutes of Health
- 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 fMRI, temporal dynamics of brain activity, large-scale brain organization, neural correlates of cognition, behavioral correlates, methodological considerations
- MeSH terms
- Animals, Blood Flow Velocity, Brain, Cerebrovascular Circulation, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Anatomic, Models, Neurological, Nerve Net, Synaptic Transmission, Connectome
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