The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Time-resolved resting-state brain networks
Open accessAbstract
Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.
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“recent experiments using functional MRI data have demonstrated that global brain signals transition between states of high and low connectivity strength over time (Zalesky et al., 2014) and that these fluctuations are related to coordinated patterns of network topology (Betzel et al., 2015)”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Zalesky et al., 2014
In the citing paper
“In keeping with Zalesky et al. (Zalesky et al., 2014), VAR model order was set at 11, appropriately mimicking the expected temporal signature of the BOLD response in 0.72s TR data.”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Zalesky et al., 2014
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Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
A default mode of brain function
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Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Distributed hierarchical processing in the primate cerebral cortex
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Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
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The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
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Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734
Self-citationGRETNA: a graph theoretical network analysis toolbox for imaging connectomics
Wang, J., Wang, X., Xia, M., Liao, X., Evans, A. C., & Yong He, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00386 · Cited by 1,439
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 1,454
The 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 1,408
Metadata
- Authors
- Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M.
- Affiliations
- The University of Melbourne (AU)
- The University of Queensland (AU)
- QIMR Berghofer Medical Research Institute (AU)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2014-06-30
- Volume
- 111
- Issue
- 28
- Pages
- 10341-10346
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4104861
- OpenAlex
- W1996196481
- Cited by
- 753
- References
- 79
- Citations by year
- 2026: 18 · 2025: 57 · 2024: 49 · 2023: 49 · 2022: 58 · 2021: 82 · 2020: 74 · 2019: 74 · 2018: 65 · 2017: 98 · 2016: 64 · 2015: 51 · 2014: 13
- FWCI
- 42.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Funders
- National Institutes of Health (5u54mh091657-05)
- National Institute of Mental Health (1U54 MH091657, U54-MH091657)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- resting-state brain networks, dynamic functional connectivity, brain network topology, default mode network, frontoparietal network, network hubs, information transmission efficiency, metabolic cost, brain dynamics, Human Connectome Project
- MeSH terms
- Adult, Brain, Humans, Magnetic Resonance Imaging, Male, Mental Processes, Nerve Net, Radiography
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