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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.
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“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
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Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study
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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 ›
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