The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
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.
Overview
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
Cites
Statistics
Verification
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?
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Least squares quantization in PCM
Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
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
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355
Research works printed but not linked: 4
- Baker, 2012, Investigating the temporal dynamics of resting state connectivity with MEG
- Chang, 2012, Dynamics of resting-state functional connectivity associated with heart rate variability
- Gonzalez-Castillo, 2012, Detection of task transitions on 45 min long continuous multi-task runs using whole brain connectivity
- Starck, 2012, Temporally varying connectivity between ICA default-mode sub-networks—ASD vs. controls
Cited by
Sorted by citations
Items 801–900 of 1,003
Disrupted Resting-State Functional Connectivity in Hippocampal Subregions After Sleep Deprivation
Zhao, R., Zhang, X., Zhu, Y., Fei, N., Sun, J., Liu, P., Yang, X., & Qin, W. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.11.049 · Cited by 33
Birn, R. M., Converse, A. K., Rajala, A. Z., Alexander, A. L., Block, W. F., McMillan, A. B., Christian, B. T., Filla, C., Murali, D., Hurley, S. A., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2513-18.2018 · Cited by 34
What Can Computational Models Contribute to Neuroimaging Data Analytics?
Popovych, O. V., Manos, T., Hoffstaedter, F., & Eickhoff, S. B. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00068 · Cited by 33
Abreu, R., Leal, A., & Figueiredo, P. (2019) · Scientific Reports · doi:10.1038/s41598-018-36976-y · Cited by 33
Sanfratello, L., Houck, J. M., & Calhoun, V. D. (2019) · Brain Connectivity · doi:10.1089/brain.2018.0608 · Cited by 34
Self-citationTracking the Main States of Dynamic Functional Connectivity in Resting State
Zhou, Q., Zhang, L., Feng, J., & Lo, C. Z. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00685 · Cited by 33
Refined measure of functional connectomes for improved identifiability and prediction
Cai, B., Zhang, G., Hu, W., Zhang, A., Zillé, P., Zhang, Y., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019) · Human Brain Mapping · doi:10.1002/hbm.24741 · Cited by 30
Self-citationZhu, Y., Liu, J., Mathiak, K., Ristaniemi, T., & Cong, F. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2953971 · Cited by 33
Chen, Y., Tang, J. H., Chen, Y., Farrand, J., Craft, M., Carlson, B. W., & Yuan, H. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.560878 · Cited by 33
State-related neural influences on fMRI connectivity estimation
Martin, C. G., He, B. J., & Chang, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118590 · Cited by 34
Nonlinear functional network connectivity in resting functional magnetic resonance imaging data
Motlaghian, S. M., Belger, A., Bustillo, J., Ford, J. M., Iraji, A., Lim, K. O., Mathalon, D. H., Mueller, B. A., OʼLeary, D. S., Pearlson, G. D., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25972 · Cited by 45
Self-citationZarkali, A., Luppi, A. I., Stamatakis, E. A., Reeves, S., McColgan, P., Leyland, L., Lees, A. J., & Weil, R. S. (2022) · Communications Biology · doi:10.1038/s42003-022-03903-x · Cited by 36
Long, Y., Ouyang, X., Yan, C., Wu, Z., Huang, X., Pu, W., Cao, H., Liu, Z., & Palaniyappan, L. K. (2023) · Human Brain Mapping · doi:10.1002/hbm.26202 · Cited by 32
Lu, J., Zhang, X., Wang, Y., Cheng, Y., Shu, Z., Wang, J., Zhu, Z., Liu, P., Yu, Y., Wu, J., et al. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3242263 · Cited by 33
Abnormal dynamic community structure of the salience network in depression
Wei, M., Qin, J., Yan, R., Bi, K., Liu, C., Yao, Z., & Lü, Q. (2016) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25429 · Cited by 32
A survey of brain functional network extraction methods using fMRI data
Du, Y., Fang, S., He, X., & Calhoun, V. D. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.05.011 · Cited by 33
Self-citationAtomic dynamic functional interaction patterns for characterization of ADHD
Ou, J., Lian, Z., Xie, L. Y., Li, X., Wang, P., Hao, Y., Zhu, D., Jiang, R., Wang, Y., Chen, Y., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22548 · Cited by 32
Su, J., Shen, H., Zeng, L., Qin, J., Liu, Z., & Hu, D. (2016) · Neuroreport · doi:10.1097/wnr.0000000000000622 · Cited by 33
Functional Connectivity’s Degenerate View of Brain Computation
Marrelec, G., Messé, A., Giron, A., & Rudrauf, D. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005031 · Cited by 32
Vergara, V. M., Mayer, A. R., Damaraju, E., & Calhoun, V. D. (2017) · Brain and Behavior · doi:10.1002/brb3.809 · Cited by 32
Self-citationKawagoe, T., Onoda, K., & Yamaguchi, S. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13787 · Cited by 30
Fu, Z., Tu, Y., Di, X., Biswal, B. B., Calhoun, V. D., & Zhang, Z. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00593 · Cited by 32
Self-citationZhang, Y., Zhang, S., Ide, J. S., Hu, S., Zhornitsky, S., Wang, W., Dong, G., Tang, X., & Li, C. R. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.03.016 · Cited by 31
Two-year dynamic functional network connectivity in clinically isolated syndrome
Rocca, M. A., de la Cruz, M. H., Valsasina, P., Mesaroš, Š., Martinović, V., Ivanović, J., Drulovic, J. S., & Filippi, M. (2019) · Multiple Sclerosis Journal · doi:10.1177/1352458519837704 · Cited by 30
Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis
Zhao, Q., Honnorat, N., Adeli, E., Pfefferbaum, A., Sullivan, E. V., & Pohl, K. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_68 · Cited by 32
Dynamic reconfiguration of the functional brain network after musical training in young adults
Li, Q., Wang, X., Wang, S., Xie, Y., Li, X., Xie, Y., & Li, S. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01867-z · Cited by 32
Li, K., Luo, X., Zeng, Q., Jiaerken, Y., Wang, S., Xu, X., Xu, X., Xu, J., Wang, C., Zhou, J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55452-9 · Cited by 31
Faghiri, A., Iraji, A., Damaraju, E., Belger, A., Ford, J. M., Mathalon, D. H., McEwen, S. C., Mueller, B. A., Pearlson, G. D., Preda, A., et al. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108600 · Cited by 32
Self-citationSimos, N. J., Dimitriadis, S. I., Kavroulakis, E., Manikis, G. C., Bertsias, G. K., Simos, P. G., Maris, T. G., & Papadaki, E. Z. (2020) · Brain Sciences · doi:10.3390/brainsci10110777 · Cited by 31
Dynamic Brain Connectivity in Resting State Functional MR Imaging
Jalilianhasanpour, R., Ryan, D. H., Agarwal, S., Beheshtian, E., Gujar, S. K., Pillai, J. J., & Sair, H. I. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.09.004 · Cited by 32
Dini, H., Sendi, M. S. E., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Fu, Z., Espinoza, R. T., Narr, K. L., Qi, S., Abbott, C., van Rooij, S. J. H., Riva‐Posse, P., et al. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.689488 · Cited by 32
Self-citationDynamic changes of large-scale resting-state functional networks in major depressive disorder
Zhang, J., Cui, H., Yang, H., Li, Y., Xu, D., Zhao, T., Wu, H., Du, Z., Huang, W., Wang, C., et al. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110369 · Cited by 33
Xie, H., Beaty, R. E., Jahanikia, S., Geniesse, C., Sonalkar, N., & Saggar, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118531 · Cited by 33
Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective
Zhu, J., Zeng, Q., Shi, Q., Li, J., Dong, S., Lai, C., & Cheng, G. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.710735 · Cited by 32
Owen, L. L. W., Chang, T. H., & Manning, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-25876-x · Cited by 34
Mizuno, Y., Cai, W., Supekar, K., Makita, K., Takiguchi, S., Tomoda, A., & Menon, V. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119332 · Cited by 33
Synchronous high-amplitude co-fluctuations of functional brain networks during movie-watching
Tanner, J., Faskowitz, J., Byrge, L. A., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00026 · Cited by 30
Quevenco, F. C., Schreiner, S. J., Preti, M. G., Bergen, J. M. G. V., Kirchner, T., Wyss, M. T., Steininger, S. C., Gietl, A. F., Leh, S. E., Buck, A., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101776 · Cited by 31
Dynamic Functional Network Analysis in Mild Traumatic Brain Injury
Hou, W., Rhodes, C. S., Jiang, L., Roys, S., Zhuo, J., JáJá, J. F., & Gullapalli, R. P. (2019) · Brain Connectivity · doi:10.1089/brain.2018.0629 · Cited by 35
Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L., Cruzat, J., Roseman, L., Nutt, D. J., Deco, G., Kringelbach, M. L., Stamatakis, E. A., & Kuceyeski, A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.14.444193 · Cited by 30
Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG
Deng, J., Sun, B., Kavcic, V., Liu, M., Giordani, B., & Li, T. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13411 · Cited by 31
DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks
Thapaliya, B., Miller, R. L., Chen, J., Wang, Y., Akbaş, E., Sapkota, R., Ray, B., Suresh, P., Ghimire, S., Calhoun, V. D., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103462 · Cited by 33
Self-citationDynamic regional phase synchrony (DRePS)
Omidvarnia, A., Pedersen, M., Walz, J. M., Vaughan, D. N., Abbott, D. F., & Jackson, G. D. (2016) · Human Brain Mapping · doi:10.1002/hbm.23151 · Cited by 32
Zhuang, X., Walsh, R. R., Sreenivasan, K. R., Yang, Z., Mishra, V. R., & Cordes, D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.019 · Cited by 31
Zhang, Y., Guo, G., & Yuan, T. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00484 · Cited by 29
Dynamic functional connectivity states characterize NREM sleep and wakefulness
Zhou, S., Zou, G., Xu, J., Su, Z., Zhu, H., Zou, Q., & Gao, J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24770 · Cited by 29
Wu, M., Li, F., Wu, Y., Tieying, Z., Gao, J., Xu, P., & Luo, B. (2020) · Aging and Disease · doi:10.14336/ad.2019.0606 · Cited by 31
Du, M., Zhang, L., Li, L., Ji, E., Han, X., Huang, G., Liang, Z., Shi, L., Yang, H., & Zhang, Z. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.04.005 · Cited by 32
Jiang, F., Jin, H., Gao, Y., Xie, X., Cummings, J., Raj, A., & Nagarajan, S. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119131 · Cited by 30
Han, S., Xu, Y., Guo, H., Fang, K., Wei, Y., Liu, L., Cheng, J., Zhang, Y., & Cheng, J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac163 · Cited by 31
Jiang, Y., Yuan, T., Chen, Y., Guo, P., Lian, T., Liu, Y., Liu, W., Bai, Y., Zhang, Q., Zhang, W., et al. (2022) · Translational Neurodegeneration · doi:10.1186/s40035-022-00327-9 · Cited by 31
Accurate classification of schizophrenia patients based on novel resting-state fMRI features
Arbabshirani, M. R., Castro, E., & Calhoun, V. D. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945163 · Cited by 31
Self-citationBeyond the evoked/intrinsic neural process dichotomy
Bolt, T., Anderson, M. L., & Uddin, L. Q. (2017) · Network Neuroscience · doi:10.1162/netn_a_00028 · Cited by 35
Yue, J.-L., Li, P., Shi, L., Lin, X., Sun, H., & Lü, L. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.02.025 · Cited by 33
Zöller, D., Bolton, T. A. W., Karahanoğlu, F. I., Eliez, S., Schaer, M., & Ville, D. V. D. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2863944 · Cited by 30
Modulation of resting-state network connectivity by verbal divergent thinking training
Fink, A., Benedek, M., Koschutnig, K., Papousek, I., Weiss, E. M., Bagga, D., & Schöpf, V. (2018) · Brain and Cognition · doi:10.1016/j.bandc.2018.10.008 · Cited by 34
Duc, N. T., & Lee, B. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0169 · Cited by 31
Wang, C., Wang, Y., Lau, W. K. W., Wei, X., Feng, X., Zhang, C., Liu, Y., Huang, R., & Zhang, R. (2021) · Depression and Anxiety · doi:10.1002/da.23195 · Cited by 31
Wang, Y., Lu, M., Liu, R., Wang, L., Wang, Y., Xu, L., Wu, K., Chen, C., Chen, T., Shi, X., et al. (2023) · Neural Plasticity · doi:10.1155/2023/8510213 · Cited by 32
Symmetry breaking organizes the brain’s resting state manifold
Fousek, J., Rabuffo, G., Gudibanda, K., Sheheitli, H., Petkoski, S., & Jirsa, V. (2024) · Scientific Reports · doi:10.1038/s41598-024-83542-w · Cited by 36
Age-Related Differences in the Dynamic Architecture of Intrinsic Networks
Madhyastha, T. M., & Grabowski, T. J. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0205 · Cited by 34
Wang, X., Li, L., Xu, T., & Ding, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140300 · Cited by 34
Connectivity-based change point detection for large-size functional networks
Jeong, S., Pae, C., & Park, H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.019 · Cited by 30
Real‐time estimation of dynamic functional connectivity networks
Monti, R. P., Lorenz, R., Braga, R. M., Anagnostopoulos, C., Leech, R., & Montana, G. (2016) · Human Brain Mapping · doi:10.1002/hbm.23355 · Cited by 31
Brueggen, K., Kasper, E., Dyrba, M., Bruno, D., Pomara, N., Ewers, M., Duering, M., Bürger, K., & Teipel, S. J. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00244 · Cited by 28
Linking brain network reconfiguration and intelligence: Are we there yet?
Girn, M., Mills, C., & Kalina Christoff, A. (2019) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2019.04.001 · Cited by 29
Xie, Y., Liu, T., Ai, J., Chen, D., Zhuo, Y., Zhao, G., Shuai, H., Wu, J., Han, Y., & Yan, T. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00118 · Cited by 30
Salience network dynamics underlying successful resistance of temptation
Steimke, R., Nomi, J. S., Calhoun, V. D., Stelzel, C., Paschke, L. M., Gaschler, R., Goschke, T., Walter, H., & Uddin, L. Q. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx123 · Cited by 30
Self-citationSavva, A. D., Kassinopoulos, M., Smyrnis, N., Matsopoulos, G. K., & Mitsis, G. D. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108519 · Cited by 30
Functional connectivity dynamics slow with descent from wakefulness to sleep
El-Baba, M., Lewis, D. J., Fang, Z., Owen, A. M., Fogel, S. M., & Morton, J. B. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224669 · Cited by 32
Chen, J., Sun, D., Shi, Y., Jin, W., Wang, Y., Xi, Q., & Ren, C. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00266-x · Cited by 29
Zhang, Z., Liu, G., Zheng, W., Shi, J., Liu, H., & Sun, Y. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102431 · Cited by 30
Coronel‐Oliveros, C., Cofré, R., & Orio, P. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008737 · Cited by 29
Calhoun, V. D., Pearlson, G. D., & Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T. (2021) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000967 · Cited by 30
Self-citationPorcaro, C., Renzo, A. D., Tinelli, E., Lorenzo, G. D., Seri, S., Lorenzo, C. D., Parisi, V., Caramia, F., Fiorelli, M., Piero, V. D., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98213-3 · Cited by 30
Yang, H., Zhang, H., Meng, C., Wohlschläger, A. M., Brandl, F., Di, X., Wang, S., Tian, L., & Biswal, B. B. (2022) · Human Brain Mapping · doi:10.1002/hbm.25884 · Cited by 30
Liu, X., Qiu, S., Wang, X., Chen, H., Tang, Y., & Qin, Y. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103332 · Cited by 30
Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network
Rosenthal, G., Sporns, O., & Avidan, G. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw279 · Cited by 33
Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders
Uddin, L. Q., & Karlsgodt, K. H. (2018) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2018.1443461 · Cited by 31
Sporns, O. (2020) · doi:10.1093/med/9780198713005.003.0013 · Cited by 29
Fu, Z., Liu, J.-Y., Salman, M., Sui, J., & Calhoun, V. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00151-8 · Cited by 34
Self-citationBrain states as wave-like motifs
Foster, M. L., & Scheinost, D. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.03.004 · Cited by 29
An fMRI study of implicit language learning in developmental language impairment
Plante, E., Patterson, D. K., Sandoval, M., Vance, C. J., & Asbjørnsen, A. E. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.01.027 · Cited by 29
Coquelet, N., Mary, A., Peigneux, P., Goldman, S., Wens, V., & Tiège, X. D. (2017) · Scientific Reports · doi:10.1038/s41598-017-13829-8 · Cited by 31
Lin, C., Yang, H., Knowlton, B. J., Wu, A. D., Iacoboni, M., Ye, Y.-L., Huang, S., & Chiang, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.081 · Cited by 30
Keinänen, T., Rytky, S., Korhonen, V. O., Huotari, N., Nikkinen, J., Tervonen, O. A., Palva, J. M., & Kiviniemi, V. J. (2018) · Journal of Neuroscience Research · doi:10.1002/jnr.24257 · Cited by 30
Detecting modular brain states in rest and task
Kabbara, A., Khalil, M. A., O’Neill, G. C., Dujardin, K., Traboulsi, Y. E., Wendling, F., & Hassan, M. (2019) · Network Neuroscience · doi:10.1162/netn_a_00090 · Cited by 31
Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism
Aggarwal, P., & Gupta, A. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.007 · Cited by 30
Teng, J., Ong, J. L., Patanaik, A., Tandi, J., Zhou, J. H., Chee, M. W. L., & Lim, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.004 · Cited by 30
Song, Z.-Y., Chen, J., Wen, Z., & Zhang, L. (2020) · Acta Radiologica · doi:10.1177/0284185120923270 · Cited by 29
Zhou, Z., Cai, B., Zhang, G., Zhang, A., Calhoun, V. D., & Wang, Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117190 · Cited by 31
Self-citationTu, M.-C., Hsu, Y., Yang, J., Huang, W., Deng, J. F., Lin, S.-Y., Lin, C.-Y. E., & Kuo, L. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00239 · Cited by 29
Saha, D. K., Damaraju, E., Rashid, B., Abrol, A., Plis, S., & Calhoun, V. D. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0794 · Cited by 29
Self-citationZhu, Y., Li, X., Qiao, Y., Shang, R., Shi, G., Shang, Y., & Guo, H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102163 · Cited by 29
Zheng, R., Chen, Y., Jiang, Y. B., Zhou, B., Li, S., Wei, Y., Wang, C., Han, S., Zhang, Y., & Cheng, J. (2022) · Journal of Neuroscience Research · doi:10.1002/jnr.25047 · Cited by 29
Modulatory Interactions of Resting-State Brain Functional Connectivity
Di, X., & Biswal, B. B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071163 · Cited by 29
Differences in dynamic and static functional connectivity between young and elderly healthy adults
Park, J. E., Jung, S. C., Ryu, K. H., Oh, J. Y., Kim, H. S., Choi, C.-G., Kim, S. J., & Shim, W. H. (2017) · Neuroradiology · doi:10.1007/s00234-017-1875-2 · Cited by 29
Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome
Peña-Gómez, C., Avena‐Koenigsberger, A., Sepulcre, J., & Sporns, O. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx170 · Cited by 33
Diminished neural network dynamics after moderate and severe traumatic brain injury
Gilbert, N., Bernier, R. A., Calhoun, V. D., Brenner, E. K., Grossner, E. C., Rajtmajer, S. M., & Hillary, F. G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197419 · Cited by 29
Self-citationEstimating dynamic brain functional networks using multi-subject fMRI data
Kundu, S., Jin, M., Pierce, J. E., McDowell, J. E., & Guo, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.045 · Cited by 29
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 ›
Are you an author, or do you see a problem with this page? Request a review