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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.
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“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
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Metadata
- 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
- 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
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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 ›
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