Tracking whole-brain connectivity dynamics in the resting state

Allen, Elena A.; Damaraju, Eswar; Plis, Sergey M.; Erhardt, Erik B.; Eichele, Tom; Calhoun, Vince D.

doi:10.1093/cercor/bhs352

Cited by 2,992Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

Tracking whole-brain connectivity dynamics in the resting state

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Allen, Elena A., Damaraju, Eswar, Plis, Sergey M., Erhardt, Erik B., Eichele, Tom, Calhoun, Vince D.·2014·Cerebral Cortex

DOI 10.1093/cercor/bhs352PMC PMC3920766

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Abstract

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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The functional connectome exhibits temporal instability that allows flexible switching between multiple configurations within a scanning session.
“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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BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
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Structural and Functional Brain Networks: From Connections to Cognition
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Multi-task connectivity reveals flexible hubs for adaptive task control
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Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience
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Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation
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Rethinking segregation and integration: contributions of whole-brain modelling
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Dynamic reconfiguration of the default mode network during narrative comprehension
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Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?
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Questions and controversies in the study of time-varying functional connectivity in resting fMRI
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Deep learning for neuroimaging: a validation study
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Functional connectivity dynamics: modeling the switching behavior of the resting state
Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.001 · Cited by 528 · see citing works
On the stability of BOLD fMRI correlations
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Human consciousness is supported by dynamic complex patterns of brain signal coordination
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Fast transient networks in spontaneous human brain activity
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The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core
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Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery
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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 · see citing works
Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411 · see citing works
Cognitive Network Neuroscience
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419 · see citing works
Dynamic cooperation and competition between brain systems during cognitive control
Cocchi, L., Zalesky, A., Fornito, A., & Mattingley, J. B. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.08.006 · Cited by 429 · see citing works
Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest
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 · see citing works
Dynamic Resting-State Functional Connectivity in Major Depression
Kaiser, R. H., Whitfield-Gabrieli, S., Dillon, D. G., Goer, F., Beltzer, M., Minkel, J., Smoski, M., Dichter, G., & Pizzagalli, D. A. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2015.352 · Cited by 438 · see citing works
NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders
Du, Y., Fu, Z., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Gao, S., Xing, Y., Lin, D., Salman, M. S., Rahaman, M. A., Abrol, A., Chen, J., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102375 · Cited by 433 · see citing works
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Small-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 431 · see citing works
The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread
Jirsa, V., Proix, T., Perdikis, D., Woodman, M., Wang, H., Gonzalez-Martinez, J. A., Bernard, C., Bénar, C., Guye, M., Chauvel, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.049 · Cited by 434 · see citing works
The graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415 · see citing works
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders
Deco, G., & Kringelbach, M. L. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.034 · Cited by 402 · see citing works
Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects
Rashid, B., Damaraju, E., Pearlson, G. D., & Calhoun, V. D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00897 · Cited by 399 · see citing works
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Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest
Leonardi, N., Richiardi, J., Gschwind, M., Simioni, S., Annoni, J., Schluep, M., Vuilleumier, P. O., & Ville, D. V. D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.019 · Cited by 390 · see citing works
How Local Excitation-Inhibition Ratio Impacts the Whole Brain Dynamics
Deco, G., Ponce‐Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5068-13.2014 · Cited by 377 · see citing works
Task-Based Core-Periphery Organization of Human Brain Dynamics
Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003171 · Cited by 356 · see citing works
Human brain networks function in connectome-specific harmonic waves
Atasoy, S., Donnelly, I., & Pearson, J. (2016) · Nature Communications · doi:10.1038/ncomms10340 · Cited by 381 · see citing works
Resting brains never rest: computational insights into potential cognitive architectures
Deco, G., Jirsa, V., & McIntosh, A. R. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2013.03.001 · Cited by 368 · see citing works
Spectrally resolved fast transient brain states in electrophysiological data
Vidaurre, D., Quinn, A. J., Baker, A. P., Dupret, D., Tejero-Cantero, Á., & Woolrich, M. W. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.047 · Cited by 375 · see citing works
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms
Cabral, J., Kringelbach, M. L., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.045 · Cited by 353 · see citing works
Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach
Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.052 · Cited by 369 · see citing works
Abnormal intrinsic brain functional network dynamics in Parkinson’s disease
Kim, J., Criaud, M., Cho, S. S., Díez‐Cirarda, M., Mihaescu, A. S., Coakeley, S., Ghadery, C., Valli, M., Jacobs, M. F., Houle, S., et al. (2017) · Brain · doi:10.1093/brain/awx233 · Cited by 367 · see citing works
The inferior parietal lobule and temporoparietal junction: A network perspective
Igelström, K. M., & Graziano, M. S. A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.01.001 · Cited by 367 · see citing works
Dynamic functional connectivity of the default mode network tracks daydreaming
Kucyi, A., & Davis, K. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.044 · Cited by 355 · see citing works
Exploring the network dynamics underlying brain activity during rest
Cabral, J., Kringelbach, M. L., & Deco, G. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.12.005 · Cited by 351 · see citing works
Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure
Liu, F., Wang, Y., Li, M., Wang, W., Li, R., Zhang, Z., Lu, G., & Chen, H. (2016) · Human Brain Mapping · doi:10.1002/hbm.23430 · Cited by 348 · see citing works
Discovering dynamic brain networks from big data in rest and task
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Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns
González-Castillo, J., Hoy, C. W., Handwerker, D. A., Robinson, M. E., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1501242112 · Cited by 344 · see citing works
Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity
Rashid, B., Arbabshirani, M. R., Damaraju, E., Cetin, M. S., Miller, R. L., Pearlson, G. D., & Calhoun, V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.051 · Cited by 328 · see citing works
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Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks
Karahanoğlu, F. I., & Ville, D. V. D. (2015) · Nature Communications · doi:10.1038/ncomms8751 · Cited by 320 · see citing works
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging
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Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders
Zhang, J., Cheng, W., Liu, Z., Zhang, K., Lei, X., Yao, Y., Becker, B., Liu, Y., Kendrick, K. M., Lu, G., et al. (2016) · Brain · doi:10.1093/brain/aww143 · Cited by 332 · see citing works
Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0
Voleti, V., Patel, K. B., Li, W., Perez Campos, C., Bharadwaj, S., Yu, H., Ford, C., Casper, M. J., Yan, R. W., Liang, W., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0579-4 · Cited by 320 · see citing works
DynamicBC : A MATLAB Toolbox for Dynamic Brain Connectome Analysis
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Anterior insula as a gatekeeper of executive control
Molnar-Szakacs, I., & Uddin, L. Q. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104736 · Cited by 307 · see citing works
Dynamic functional connectivity changes associated with dementia in Parkinson’s disease
Fiorenzato, E., Strafella, A. P., Kim, J., Schifano, R., Weis, L., Antonini, A., & Biundo, R. (2019) · Brain · doi:10.1093/brain/awz192 · Cited by 306 · see citing works
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states
Shakil, S., Lee, C., & Keilholz, S. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.074 · Cited by 296 · see citing works
Task-based dynamic functional connectivity: Recent findings and open questions
González-Castillo, J., & Bandettini, P. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.006 · Cited by 293 · see citing works
Network-Level Structure-Function Relationships in Human Neocortex
Mišić, B., Betzel, R. F., de Reus, M. A., van den Heuvel, M. P., Berman, M. G., McIntosh, A. R., & Sporns, O. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw089 · Cited by 277 · see citing works
Consciousness-specific dynamic interactions of brain integration and functional diversity
Luppi, A. I., Craig, M. M., Pappas, I., Finoia, P., Williams, G. B., Allanson, J., Pickard, J. D., Owen, A. M., Naci, L., Menon, D. K., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12658-9 · Cited by 251 · see citing works
Individual brain structure and modelling predict seizure propagation
Proix, T., Bartoloméi, F., Guye, M., & Jirsa, V. (2017) · Brain · doi:10.1093/brain/awx004 · Cited by 281 · see citing works
Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation
Yeo, B. T. T., Tandi, J., & Chee, M. W. L. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.018 · Cited by 265 · see citing works
Awakening: Predicting external stimulation to force transitions between different brain states
Deco, G., Cruzat, J., Cabral, J., Tagliazucchi, E., Laufs, H., Logothetis, N. K., & Kringelbach, M. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905534116 · Cited by 264 · see citing works
Interpreting temporal fluctuations in resting-state functional connectivity MRI
Liégeois, R., Laumann, T. O., Snyder, A. Z., Zhou, J. H., & Yeo, B. T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.012 · Cited by 256 · see citing works
EEG Signatures of Dynamic Functional Network Connectivity States
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Faster Independent Component Analysis by Preconditioning With Hessian Approximations
Ablin, P., Cardoso, J.-F., & Gramfort, A. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2844203 · Cited by 260 · see citing works
Towards a new approach to reveal dynamical organization of the brain using topological data analysis
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Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 34, 2154–2177. doi: 10.1093/cercor/bhs352. [Epub ahead of print
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2012)
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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.
ORCID: Damaraju, Eswar
Plis, Sergey M.
Erhardt, Erik B.
Calhoun, Vince D.
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
DOI: 10.1093/cercor/bhs352
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

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