Cited by 828Functional Brain Connectivity StudiesMental Health Research TopicsAdvanced MRI Techniques and Applications

The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.

On spurious and real fluctuations of dynamic functional connectivity during rest

Open access

Leonardi, Nora, Van De Ville, Dimitri·2015·NeuroImage

DOI 10.1016/j.neuroimage.2014.09.007

Open this paper in CoChat — free account Open PDF

Abstract

Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cut-off frequencies for filtering-are not yet systematically studied. In this letter we provide the fundamental theory from signal processing to address these parameter choices when estimating and interpreting dynFC. We guide the reader through several illustrative cases, both simple analytical models and experimental fMRI BOLD data. First, we show how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals, even for deterministic signals with a fixed relationship. Second, we study how real fluctuations of dynFC can be explained using a frequency-based view, which is particularly instructive for signals with multiple frequency components such as fMRI BOLD, demonstrating that fluctuations in sliding-window correlation emerge by interaction between frequency components similar to the phenomenon of beat frequencies. We conclude with practical guidelines for the choice and impact of the window length.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2014.09.007

Cites

20 of 20 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Tracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992
Dynamic functional connectivity: Promise, issues, and interpretations
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., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Decoding subject-driven cognitive states with whole-brain connectivity patterns
Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr099 · Cited by 1,843
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808
Learning sculpts the spontaneous activity of the resting human brain
Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., & Corbetta, M. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0902455106 · Cited by 777
Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.22058 · Cited by 732
Temporally-independent functional modes of spontaneous brain activity
Smith, S. M., Miller, K. L., Moeller, S., Xu, J., Auerbach, E. J., Woolrich, M. W., Beckmann, C. F., Jenkinson, M., Andersson, J., Glasser, M. F., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121329109 · Cited by 702
Generating surrogate data for time series with several simultaneously measured variables
Prichard, D., & Theiler, J. P. (1994) · Physical Review Letters · doi:10.1103/physrevlett.73.951 · Cited by 639
A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia
Sakoğlu, Ü., Pearlson, G. D., Kiehl, K. A., Wang, Y. M., Michael, A. M., & Calhoun, V. D. (2010) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-010-0197-8 · Cited by 623
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 401
Self-citation

Flagged first, then by citations

Show all 21 references

1 of the 21 references couldn’t be matched to a known paper.

Sorted by citations

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
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
Brain network dynamics are hierarchically organized in time
Vidaurre, D., Smith, S. M., & Woolrich, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705120114 · Cited by 883
Dynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?
Hindriks, R., Adhikari, M. H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N. K., & Deco, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.055 · Cited by 632
Rethinking segregation and integration: contributions of whole-brain modelling
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3963 · Cited by 658
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks
Dixon, M. L., De La Vega, A., Mills, C., Andrews-Hanna, J., Spreng, R. N., Cole, M. W., & Christoff, K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715766115 · Cited by 595
On the stability of BOLD fMRI correlations
Laumann, T. O., Snyder, A. Z., Mitra, A., Gordon, E. M., Gratton, C., Adeyemo, B., Gilmore, A. W., Nelson, S. M., Berg, J. J., Greene, D. J., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw265 · Cited by 516
Increased global integration in the brain after psilocybin therapy for depression
(2022) · doi:10.1038/s41591-022-01744-z · Cited by 445
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
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 443
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
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
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 364
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 351
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 343
The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity
Cohen, J. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.036 · Cited by 306
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
Resting brain dynamics at different timescales capture distinct aspects of human behavior
Liégeois, R., Li, J., Kong, R., Orbán, C., Ville, D. V. D., Ge, T., Sabuncu, M. R., & Yeo, B. T. T. (2019) · Nature Communications · doi:10.1038/s41467-019-10317-7 · Cited by 300
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 304
A Hitchhiker's Guide to Functional Magnetic Resonance Imaging
Soares, J. M., Magalhães, R. J. D. S., Moreira, P. S., Sousa, A., Ganz, E., Sampaio, A., Alves, V., Marques, P., & Sousa, N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00515 · Cited by 304
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 264
Towards a statistical test for functional connectivity dynamics
Zalesky, A., & Breakspear, M. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.047 · Cited by 257
High-amplitude cofluctuations in cortical activity drive functional connectivity
Esfahlani, F. Z., Jo, Y., Faskowitz, J., Byrge, L. A., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005531117 · Cited by 256
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.001 · Cited by 249
Principles of dynamic network reconfiguration across diverse brain states
Shine, J. M., & Poldrack, R. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.010 · Cited by 247
Interactions between the default network and dorsal attention network vary across default subsystems, time, and cognitive states
Dixon, M. L., Andrews‐Hanna, J. R., Spreng, R. N., Irving, Z. C., Mills, C., Girn, M., & Kalina Christoff, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.073 · Cited by 239
Multilayer modeling and analysis of human brain networks
Domenico, M. D. (2017) · GigaScience · doi:10.1093/gigascience/gix004 · Cited by 232
Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns
Liu, J., Liao, X., Xia, M., & Yong He, A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23890 · Cited by 212
More than just statics: temporal dynamics of intrinsic brain activity predicts the suicidal ideation in depressed patients
Li, J., Duan, X., Cui, Q., Chen, H., & Liao, W. (2019) · Psychological Medicine · doi:10.1017/s0033291718001502 · Cited by 208
Comparing test-retest reliability of dynamic functional connectivity methods
Choe, A. S., Nebel, M. B., Barber, A. D., Cohen, J. R., Xu, Y., Pekar, J. J., Caffo, B. S., & Lindquist, M. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.005 · Cited by 197
Multilayer network switching rate predicts brain performance
Pedersen, M., Zalesky, A., Omidvarnia, A., & Jackson, G. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1814785115 · Cited by 198
Replicability of time-varying connectivity patterns in large resting state fMRI samples
Abrol, A., Damaraju, E., Miller, R. L., Stephen, J. M., Claus, E. D., Mayer, A. R., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.020 · Cited by 192
Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function
Braun, U., Schäfer, A., Bassett, D. S., Rausch, F., Schweiger, J. I., Bilek, E., Erk, S., Romanczuk‐Seiferth, N., Grimm, O., Geiger, L. S., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608819113 · Cited by 189
Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states
Wang, C., Ong, J. L., Patanaik, A., Zhou, J. H., & Chee, M. W. L. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1523980113 · Cited by 191
From maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177
Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions
Nomi, J. S., Farrant, K., Damaraju, E., Rachakonda, S., Calhoun, V. D., & Uddin, L. Q. (2016) · Human Brain Mapping · doi:10.1002/hbm.23135 · Cited by 180
Functional Network Dynamics of the Language System
Chai, L. R., Mattar, M. G., Blank, I., Fedorenko, E. G., & Bassett, D. S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw238 · Cited by 169
Detection of functional brain network reconfiguration during task-driven cognitive states
Telesford, Q. K., Lynall, M.-E., Vettel, J. M., Miller, M. B., Grafton, S. T., & Bassett, D. S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.078 · Cited by 171
Estimation of dynamic functional connectivity using multiplication of temporal derivatives
Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.064 · Cited by 166
State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility
Douw, L., Wakeman, D. G., Tanaka, N., Liu, H., & Stufflebeam, S. M. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.09.034 · Cited by 162
Dynamic changes of amplitude of low‐frequency fluctuations in patients with generalized anxiety disorder
Cui, Q., Sheng, W., Chen, Y., Pang, Y., Lu, F., Tang, Q., Han, S., Shen, Q., Wang, Y., Xie, A., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24902 · Cited by 159
Positive affect, surprise, and fatigue are correlates of network flexibility
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00425-z · Cited by 178
Tools of the trade: estimating time-varying connectivity patterns from fMRI data
Iraji, A., Faghiri, A., Lewis, N., Fu, Z., Rachakonda, S., & Calhoun, V. D. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa114 · Cited by 155
Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
Zhang, Y., Zhang, H., Chen, X., Lee, S., & Shen, D. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-06509-0 · Cited by 154
Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations
Liao, W., Chen, H., Li, J., Ji, G., Wu, G., Long, Z., Xu, Q., Duan, X., Cui, Q., & Biswal, B. B. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2904555 · Cited by 143
Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model
Wang, J., Wang, Y., Huang, H., Jia, Y., Zheng, S., Zhong, S., Chen, G., Huang, L., & Huang, R. (2019) · Psychological Medicine · doi:10.1017/s003329171900028x · Cited by 139
Dynamic functional connectivity and brain metastability during altered states of consciousness
Cavanna, F., Vilas, M. G., Palmucci, M., & Tagliazucchi, E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.065 · Cited by 137
Structure–function relationships during segregated and integrated network states of human brain functional connectivity
Fukushima, M., Betzel, R. F., He, Y., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1539-3 · Cited by 137
Intracranial Electrophysiology Reveals Reproducible Intrinsic Functional Connectivity within Human Brain Networks
Kucyi, A., Schrouff, J., Bickel, S., Foster, B. L., Shine, J. M., & Parvizi, J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0217-18.2018 · Cited by 134
Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation
Liao, W., Li, J., Duan, X., Cui, Q., Chen, H., & Chen, H. (2018) · Human Brain Mapping · doi:10.1002/hbm.24235 · Cited by 132
Chronnectomic patterns and neural flexibility underlie executive function
Nomi, J. S., Vij, S. G., Dajani, D. R., Steimke, R., Damaraju, E., Rachakonda, S., Calhoun, V. D., & Uddin, L. Q. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.026 · Cited by 131
Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium
Long, Y., Cao, H., Yan, C., Chen, X., Li, L., Castellanos, F. X., Bai, T., Bo, Q., Chen, G., Chen, N., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102163 · Cited by 133
Modeling and interpreting mesoscale network dynamics
Khambhati, A. N., Sizemore, A. E., Betzel, R. F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.029 · Cited by 127
Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)
Dimitriadis, S. I., Salis, C. I., Tarnanas, I., & Linden, D. E. J. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00028 · Cited by 117
Animal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization
Mandino, F., Cerri, D. H., Garin, C. M., Straathof, M., van Tilborg, G. A. F., Chakravarty, M. M., Dhénain, M., Dijkhuizen, R. M., Gozzi, A., Heß, A., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00078 · Cited by 115
Predicting individual brain maturity using dynamic functional connectivity
Qin, J., Chen, S.-G., Hu, D., Zeng, L.-L., Fan, Y.-M., Chen, X.-P., & Shen, H. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00418 · Cited by 117
Individual differences and time-varying features of modular brain architecture
Liao, X., Cao, M., Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.066 · Cited by 113
Childhood stress, grown-up brain networks: corticolimbic correlates of threat-related early life stress and adult stress response
Kaiser, R. H., Clegg, R. A., Goer, F., Pechtel, P., Beltzer, M., Vitaliano, G. D., Olson, D. P., Teicher, M. H., & Pizzagalli, D. A. (2017) · Psychological Medicine · doi:10.1017/s0033291717002628 · Cited by 122
When makes you unique: Temporality of the human brain fingerprint
Ville, D. V. D., Farouj, Y., Preti, M. G., Liégeois, R., & Amico, E. (2021) · Science Advances · doi:10.1126/sciadv.abj0751 · Cited by 108
Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles
Xue, K., Liang, S., Yang, B., Zhu, D., Xie, Y., Qin, W., Liu, F., Zhang, Y., & Yu, C. (2020) · Psychological Medicine · doi:10.1017/s0033291720003876 · Cited by 112
Sensory-motor cortices shape functional connectivity dynamics in the human brain
Kong, X., Kong, R., Orbán, C., Wang, P., Zhang, S., Anderson, K. M., Holmes, A. J., Murray, J. D., Deco, G., van den Heuvel, M. P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26704-y · Cited by 115
Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia
Dong, D., Duan, M., Wang, Y., Zhang, X., Jia, X., Li, Y., Xin, F., Yao, D., & Luo, C. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy232 · Cited by 108
Differential patterns of dynamic functional connectivity variability of striato–cortical circuitry in children with benign epilepsy with centrotemporal spikes
Li, R., Liao, W., Yu, Y., Chen, H., Guo, X., Tang, Y., & Chen, H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23910 · Cited by 103
Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan
Battaglia, D. A., Boudou, T., Hansen, E. C. A., Lombardo, D., Chettouf, S., Daffertshofer, A., McIntosh, A. R., Zimmermann, J., Ritter, P., & Jirsa, V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117156 · Cited by 98
Dynamic functional connectivity analysis reveals decreased variability of the default‐mode network in developing autistic brain
He, C., Chen, Y., Jian, T., Chen, H., Guo, X., Wang, J., Wu, L., Chen, H., & Duan, X. (2018) · Autism Research · doi:10.1002/aur.2020 · Cited by 101
Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network
Kottaram, A., Johnston, L. A., Cocchi, L., Ganella, E. P., Everall, I., Pantelis, C., Ramamohanarao, K., & Zalesky, A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24519 · Cited by 100
Assessment of dynamic functional connectivity in resting‐state fMRI using the sliding window technique
Savva, A. D., Mitsis, G. D., & Matsopoulos, G. K. (2019) · Brain and Behavior · doi:10.1002/brb3.1255 · Cited by 110
Prediction of stimulus-independent and task-unrelated thought from functional brain networks
Kucyi, A., Esterman, M. S., Capella, J., Green, A., Uchida, M., Biederman, J., Gabrieli, J. D. E., Valera, E. M., & Whitfield, S. (2021) · Nature Communications · doi:10.1038/s41467-021-22027-0 · Cited by 99
A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity
Menon, S. S., & Krishnamurthy, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-42090-4 · Cited by 95
Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification
Venkatesh, M., JáJá, J. F., & Pessoa, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116398 · Cited by 98
Attention Bias in Rumination and Depression: Cognitive Mechanisms and Brain Networks
Kaiser, R. H., Snyder, H. R., Goer, F., Clegg, R. A., Ironside, M. L., & Pizzagalli, D. A. (2018) · Clinical Psychological Science · doi:10.1177/2167702618797935 · Cited by 104
Functional networks of the brain: from connectivity restoration to dynamic integration
Hramov, A. E., Frolov, N. S., Maksimenko, V. A., Kurkin, S., Kazantsev, V., & Pisarchik, A. N. (2020) · Physics-Uspekhi · doi:10.3367/ufne.2020.06.038807 · Cited by 100
Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series
Sporns, O., Faskowitz, J., Teixeira, A. S., Cutts, S. A., & Betzel, R. F. (2021) · Network Neuroscience · doi:10.1162/netn_a_00182 · Cited by 98
Functional Connectivity of the Brain Across Rodents and Humans
Xu, N., LaGrow, T. J., Anumba, N., Lee, A., Zhang, X., Yousefi, B., Bassil, Y., Clavijo, P. G., Sharghi, V. K., Maltbie, E. A., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.816331 · Cited by 100
Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions
Keilholz, S. D., Caballero‐Gaudes, C., Bandettini, P. A., Deco, G., & Calhoun, V. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0543 · Cited by 92
Dynamic effective connectivity
Zarghami, T. S., & Friston, K. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116453 · Cited by 90
Changes in functional connectivity dynamics associated with vigilance network in taxi drivers
Shen, H., Li, Z., Qin, J., Liu, Q., Wang, L., Zeng, L., Li, H., & Hu, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.010 · Cited by 91
Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S. D., & Cohen, J. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00037 · Cited by 95
Altered inter‐ and intrahemispheric functional connectivity dynamics in autistic children
Guo, X., Duan, X., Chen, H., He, C., Xiao, J., Fan, Y., Fan, Y.-S., Guo, J., & Chen, H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24812 · Cited by 90
The integration of the internal and external milieu in the insula during dynamic emotional experiences
Nguyen, V. T., Breakspear, M. J., Hu, X., & Guo, C. C. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.078 · Cited by 90
On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis
Pedersen, M., Omidvarnia, A., Zalesky, A., & Jackson, G. D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.020 · Cited by 89
Individual structural features constrain the mouse functional connectome
Melozzi, F., Bergmann, E., Harris, J. A., Kahn, I., Jirsa, V., & Bernard, C. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906694116 · Cited by 89
Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state
Mokhtari, F., Akhlaghi, M. I., Simpson, S. L., Guorong Wu, A., & Laurienti, P. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.001 · Cited by 85
Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan
Xia, Y., Chen, Q., Shi, L., Li, M., Gong, W., Chen, H., & Qiu, J. (2018) · Human Brain Mapping · doi:10.1002/hbm.24385 · Cited by 85
White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter
Wang, P., Wang, J., Michael, A., Wang, Z., Klugah‐Brown, B., Meng, C., & Biswal, B. B. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab181 · Cited by 81
Reduction in gray matter of cerebellum in schizophrenia and its influence on static and dynamic connectivity
He, H., Luo, C., Luo, Y., Duan, M., Yi, Q., Biswal, B. B., & Yao, D. (2018) · Human Brain Mapping · doi:10.1002/hbm.24391 · Cited by 82
Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity
Lydon‐Staley, D. M., Ćirić, R., Satterthwaite, T. D., & Bassett, D. S. (2018) · Network Neuroscience · doi:10.1162/netn_a_00071 · Cited by 86
Test-retest reliability of dynamic functional connectivity in resting state fMRI
Zhang, C., Baum, S. A., Adduru, V. R., Biswal, B. B., & Michael, A. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.021 · Cited by 84
Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling
Ryali, S., Supekar, K., Chen, T., Kochalka, J., Cai, W., Nicholas, J., Padmanabhan, A., & Menon, V. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005138 · Cited by 82
Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems
Griffa, A., Ricaud, B., Benzi, K., Bresson, X., Daducci, A., Vandergheynst, P., Thiran, J., & Hagmann, P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.015 · Cited by 82
On wakefulness fluctuations as a source of BOLD functional connectivity dynamics
Haimovici, A., Tagliazucchi, E., Balenzuela, P., & Laufs, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-06389-4 · Cited by 81
Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder
Han, S., Cui, Q., Wang, X., Li, L., Li, D., He, Z., Guo, X., Fan, Y., Guo, J., Sheng, W., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25017 · Cited by 81
A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs
Patel, A. X., & Bullmore, E. T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.052 · Cited by 77
Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models
Shappell, H. M., Caffo, B. S., Pekar, J. J., & Lindquist, M. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.013 · Cited by 77
Dynamic Reorganization of Functional Connectivity Reveals Abnormal Temporal Efficiency in Schizophrenia
Sun, Y., Collinson, S. L., Suckling, J., & Sim, K. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby077 · Cited by 77
Infraslow Electroencephalographic and Dynamic Resting State Network Activity
Grooms, J. K., Thompson, G. J., Pan, W.-J., Billings, J., Schumacher, E. H., Epstein, C. M., & Keilholz, S. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0492 · Cited by 74
Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints
Liégeois, R., Ziegler, E., Phillips, C. L. M., Geurts, P., Gómez, F. A., Bahri, M. A., Yeo, B. T. T., Soddu, A., Vanhaudenhuyse, A., Laureys, S., et al. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1083-y · Cited by 74
Stability of dynamic functional architecture differs between brain networks and states
Li, L., Lu, B., & Yan, C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116230 · Cited by 76

Authors: Leonardi, N., & Van De Ville, D.
Affiliations: University of Geneva (CH)
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2015-01
Volume: 104
Pages: 430-436
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2014.09.007
OpenAlex: W2142566135
Cited by: 828
References: 21
Citations by year: 2026: 63 · 2025: 86 · 2024: 73 · 2023: 80 · 2022: 73 · 2021: 83 · 2020: 95 · 2019: 80 · 2018: 57 · 2017: 77 · 2016: 34 · 2015: 25
FWCI: 25.42
Type: journal article
Language: en
Update notices: erratum (2015-01-01)
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Archive ouverte UNIGE (University of Geneva) (publishedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
Funders: Swiss National Science Foundation (PP00P2-146318)
Center for Biomedical Imaging (CIBM)
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: dynamic functional connectivity, resting-state brain networks, sliding window correlation, window size, beat frequency, frequency-based analysis, network dynamics, signal processing theory
MeSH terms: Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Nerve Net, Signal Processing, Computer-Assisted, Artifacts

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 ›

Are you an author, or do you see a problem with this page? Request a review

On spurious and real fluctuations of dynamic functional connectivity during rest

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks