The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture
Open accessAbstract
Functional coupling across distributed brain regions varies across task contexts, yet there are stable features. To better understand the range and central tendencies of network configurations, coupling patterns were explored using functional MRI (fMRI) across 14 distinct continuously performed task states ranging from passive fixation to increasingly demanding classification tasks. Mean global correlation profiles across the cortex ranged from 0.69 to 0.82 between task states. Network configurations from both passive fixation and classification tasks similarly predicted task coactivation patterns estimated from meta-analysis of the literature. Thus, even across markedly different task states, central tendencies dominate the coupling configurations. Beyond these shared components, distinct task states displayed significant differences in coupling patterns in response to their varied demands. One possibility is that anatomical connectivity provides constraints that act as attractors pulling network configurations towards a limited number of robust states. Reconfigurable coupling modes emerge as significant modifications to a core functional architecture.
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
“Together, these results suggest that a globally efficient, integrated network architecture supports fast, effective computation throughout the cognitive processing stream (Krienen et al., 2014), potentially through the facilitation of parallel processing mechanisms (Sigman and Dehaene, 2008).”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Krienen 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
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Self-citationDissociable 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
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Self-citationCorrespondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Colman (2022) · doi:10.1073/pnas · Cited by 4,370
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.
Fries, P. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.08.011 · Cited by 4,284
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5675 · Cited by 4,212
Research works printed but not linked: 1
- Cole MW, 2013, Multi-state task functional connectivity reveals the human brain's dynamic network architecture and stable functional backbone
Cited by
Sorted by citations
Items 101–200 of 373
Robust Resilience of the Frontotemporal Syntax System to Aging
Campbell, K. L., Samu, D., Davis, S. W., Geerligs, L., Mustafa, A., & TYLER, L. K. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4561-15.2016 · Cited by 63
Hutchison, R. M., & Morton, J. B. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.08.006 · Cited by 54
Robust dynamic brain coactivation states estimated in individuals
Peng, X., Liu, Q., Hubbard, C. S., Wang, D., Zhu, W., Fox, M., & Liu, H. (2023) · Science Advances · doi:10.1126/sciadv.abq8566 · Cited by 52
Lynch, L. K., Lu, K., Wen, H., Zhang, Y., Saykin, A. J., & Liu, Z. (2018) · Human Brain Mapping · doi:10.1002/hbm.24335 · Cited by 54
The Benefit of Slice Timing Correction in Common fMRI Preprocessing Pipelines
Parker, D. B., & Razlighi, Q. R. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00821 · Cited by 62
Connectome-based models predict attentional control in aging adults
Fountain‐Zaragoza, S., Samimy, S., Rosenberg, M. D., & Prakash, R. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.074 · Cited by 55
Reznik, D., Trampel, R., Weiskopf, N., Witter, M. P., & Doeller, C. F. (2023) · Neuron · doi:10.1016/j.neuron.2023.05.029 · Cited by 52
Li, F., Yi, C., Song, L., Jiang, Y., Peng, W., Si, Y., Zhang, T., Zhang, R., Yao, D., Zhang, Y., et al. (2018) · Brain Topography · doi:10.1007/s10548-018-0688-x · Cited by 49
Tasks activating the default mode network map multiple functional systems
Mancuso, L., Cavuoti-Cabanillas, S., Liloia, D., Manuello, J., Buzi, G., Duca, S., Cauda, F., & Costa, T. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02467-0 · Cited by 53
Ageing and the resting state: is cognition obsolete?
Campbell, K. L., & Schacter, D. L. (2016) · Language Cognition and Neuroscience · doi:10.1080/23273798.2016.1227858 · Cited by 49
Intrinsic functional connectivity is organized as three interdependent gradients
Zhang, J., Abiose, O., Katsumi, Y., Touroutoglou, A., Dickerson, B. C., & Barrett, L. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-51793-7 · Cited by 47
Exploring distinct default mode and semantic networks using a systematic ICA approach
Jackson, R. L., Cloutman, L. L., & Ralph, M. A. L. (2019) · Cortex · doi:10.1016/j.cortex.2018.12.019 · Cited by 49
Gerchen, M. F., & Kirsch, P. (2017) · Human Brain Mapping · doi:10.1002/hbm.23762 · Cited by 47
GEFF: Graph embedding for functional fingerprinting
Abbas, K., Amico, E., Svaldi, D. O., Tipnis, U., Duong-Tran, D. A., Liu, M., Rajapandian, M., Harezlak, J., Ances, B. M., & Goñi, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117181 · Cited by 43
A large-scale structural and functional connectome of social mentalizing
Wang, Y., Metoki, A., Xia, Y., Zang, Y., Yong He, A., & Olson, I. R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118115 · Cited by 48
Neural networks supporting switching, hypothesis testing, and rule application
Liu, Z., Braunlich, K., Wehe, H. S., & Seger, C. A. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.07.019 · Cited by 43
Kobeleva, X., Firbank, M., Peraza, L. R., Gallagher, P., Thomas, A. J., Burn, D. J., O'Brien, J. T., & Taylor, J. (2017) · Cortex · doi:10.1016/j.cortex.2017.02.016 · Cited by 42
Top-down cortical interactions in visuospatial attention
Meehan, T. P., Bressler, S. L., Tang, W., Astafiev, S. V., Sylvester, C. M., Shulman, G. L., & Corbetta, M. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1390-6 · Cited by 43
Park, H., Pae, C., Friston, K. J., Jang, C., Razi, A., Zeidman, P., Chang, W. S., & Chang, J. W. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00346 · Cited by 44
Effective connectivity during working memory and resting states: A DCM study
Jung, K., Friston, K. J., Pae, C., Choi, H., Tak, S., Choi, Y. K., Park, B., Park, C.-A., Cheong, C., & Park, H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.067 · Cited by 43
High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility
Sneve, M. H., Grydeland, H., Rosa, M. G. P., Paus, T., Chaplin, T. A., Walhovd, K. B., & Fjell, A. M. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy268 · Cited by 41
Lateral parietal cortex in the generation of behavior: Implications for apathy
Tumati, S., Martens, S., de Jong, B. M., & Aleman, A. (2018) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2018.12.003 · Cited by 40
A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction
Spronk, M., Keane, B. P., Ito, T., Kulkarni, K. R., Ji, J. L., Anticevic, A., & Cole, M. W. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa242 · Cited by 43
Betti, V., Corbetta, M., de Pasquale, F., Wens, V., & Penna, S. D. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1089-17.2018 · Cited by 40
Supramodal Mechanisms of the Cognitive Control Network in Uncertainty Processing
Wu, T., Spagna, A., Chen, C., Schulz, K. P., Hof, P. R., & Fan, J. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa189 · Cited by 37
Modelling subject variability in the spatial and temporal characteristics of functional modes
Harrison, S. J., Bijsterbosch, J. D., Segerdahl, A. R., Fitzgibbon, S. P., Farahibozorg, S.-R., Duff, E. P., Smith, S. M., & Woolrich, M. W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117226 · Cited by 42
Centralized and distributed cognitive task processing in the human connectome
Amico, E., Arenas, À., & Goñi, J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00072 · Cited by 35
Activity flow underlying abnormalities in brain activations and cognition in schizophrenia
Hearne, L. J., Mill, R. D., Keane, B. P., Repovš, G., Anticevic, A., & Cole, M. W. (2021) · Science Advances · doi:10.1126/sciadv.abf2513 · Cited by 34
Fuentes‐Claramonte, P., Martín-Subero, M., Salgado‐Pineda, P., Alonso-Lana, S., Moreno‐Alcázar, A., Argila‐Plaza, I., Santo-Angles, A., Albajes‐Eizagirre, A., Anguera, M., Capdevila, A., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0209376 · Cited by 37
BOLD cofluctuation ‘events’ are predicted from static functional connectivity
Ladwig, Z., Seitzman, B. A., Dworetsky, A., Yu, Y., Adeyemo, B., Smith, D. M., Petersen, S. E., & Gratton, C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119476 · Cited by 32
Allouch, S., Kabbara, A., Duprez, J., Khalil, M. A., Modolo, J., & Hassan, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120006 · Cited by 39
Challenges in the measurement and interpretation of dynamic functional connectivity
Laumann, T. O., Snyder, A. Z., & Gratton, C. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00366 · Cited by 37
Mostame, P., & Sadaghiani, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117051 · Cited by 35
Luo, W., Greene, A. S., & Constable, R. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118332 · Cited by 34
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 40
Latent functional connectivity underlying multiple brain states
McCormick, E. M., Arnemann, K. L., Ito, T., Hanson, S. J., & Cole, M. W. (2022) · Network Neuroscience · doi:10.1162/netn_a_00234 · Cited by 35
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
Mostame, P., & Sadaghiani, S. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2155-20.2020 · Cited by 30
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
Beyond 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
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
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
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
Parker, D. B., & Razlighi, Q. R. (2019) · Scientific Reports · doi:10.1038/s41598-019-50483-8 · Cited by 31
Avalanche criticality in individuals, fluid intelligence, and working memory
Xu, L., Feng, J., & Yu, L. (2022) · Human Brain Mapping · doi:10.1002/hbm.25802 · Cited by 35
Salvo, J. J., Holubecki, A. M., & Braga, R. M. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.05.003 · Cited by 30
Capouskova, K., Zamora‐López, G., Kringelbach, M. L., & Deco, G. (2023) · Human Brain Mapping · doi:10.1002/hbm.26511 · Cited by 28
Attention reorganizes connectivity across networks in a frequency specific manner
Kwon, S., Watanabe, M., Fischer, E., & Bartels, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.014 · Cited by 28
Koyama, M. S., Molfese, P. J., Milham, M. P., Mencl, W. E., & Pugh, K. R. (2020) · Brain and Language · doi:10.1016/j.bandl.2020.104835 · Cited by 28
Alarcón, G., Pfeifer, J. H., Fair, D. A., & Nagel, B. J. (2018) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2018.00073 · Cited by 31
Konkle, T., & Caramazza, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw287 · Cited by 26
Liu, P., Yang, W., Zhuang, K., Wei, D., Yu, R., Huang, X., & Qiu, J. (2020) · Neurobiology of Stress · doi:10.1016/j.ynstr.2020.100285 · Cited by 28
Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods
Litwińczuk, M. C., Trujillo‐Barreto, N. J., Muhlert, N., Cloutman, L. L., & Woollams, A. M. (2022) · Brain Connectivity · doi:10.1089/brain.2022.0036 · Cited by 31
Functional neural network configuration in late childhood varies by age and cognitive state
Le, T. M., Huang, A., O’Rawe, J. F., & Leung, H. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100862 · Cited by 30
Brain network mechanisms of visual shape completion
Keane, B. P., Barch, D. M., Mill, R. D., Silverstein, S. M., Krekelberg, B., & Cole, M. W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118069 · Cited by 25
Network dynamics in the healthy and epileptic developing brain
Rosch, R. E., Baldeweg, T., Moeller, F., & Baier, G. (2017) · Network Neuroscience · doi:10.1162/netn_a_00026 · Cited by 27
Adolescent sex differences in cortico-subcortical functional connectivity during response inhibition
Chung, Y. S., Calhoun, V. D., & Stevens, M. C. G. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00718-y · Cited by 27
Busler, J. N., Yanes, J. A., Bird, R. T., Reid, M. A., & Robinson, J. L. (2019) · Experimental Brain Research · doi:10.1007/s00221-019-05595-y · Cited by 25
Zheng, Y., Farahibozorg, S.-R., Gong, W., Rafipoor, H., Jbabdi, S., & Smith, S. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119418 · Cited by 24
Combining task-evoked and spontaneous activity to improve pre-operative brain mapping with fMRI
Fox, M., Qian, T., Madsen, J. R., Wang, D., Li, M., Ge, M., Zuo, H., Groppe, D. M., Mehta, A. D., Hong, B., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.030 · Cited by 25
Data-Driven Extraction of a Nested Model of Human Brain Function
Bolt, T., Nomi, J. S., Yeo, B. T. T., & Uddin, L. Q. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0323-17.2017 · Cited by 23
Self-citationSchedlbauer, A. M., & Ekstrom, A. D. (2019) · Network Neuroscience · doi:10.1162/netn_a_00102 · Cited by 24
Working memory load-dependent changes in cortical network connectivity estimated by machine learning
Eryilmaz, H., Dowling, K. F., Hughes, D., Rodriguez-Thompson, A., Tanner, A. S., Huntington, C., Coon, W. G., & Roffman, J. L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116895 · Cited by 24
Banjac, S., Roger, É., Pichat, C., Cousin, É., Mosca, C., Lamalle, L., Krainik, A., Kahane, P., & Baciu, M. V. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102702 · Cited by 24
Chen, J.-Z., Tam, A., Kebets, V., Orban, C., Ooi, L. Q. R., Marek, S. A., Dosenbach, N. U. F., Eickhoff, S., Bzdok, D., Holmes, A., et al. (2020) · bioRxiv · doi:10.1101/2020.06.24.168724 · Cited by 24
Self-citationUnderstanding neural flexibility from a multifaceted definition
Yin, D., & Kaiser, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118027 · Cited by 24
Rushmore, J., Sylvain Bouix, A., Kubicki, M. R., Yogesh Rathi, A., Yeterian, E. H., & Makris, N. (2022) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2022.1035420 · Cited by 25
Soreq, E., Violante, I. R., Daws, R. E., & Hampshire, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22199-9 · Cited by 23
Age differences in functional network reconfiguration with working memory training
Iordan, A. D., Moored, K., Katz, B., Cooke, K. A., Buschkuehl, M., Jaeggi, S. M., Polk, T. A., Peltier, S., Jonides, J., & Reuter‐Lorenz, P. A. (2021) · Human Brain Mapping · doi:10.1002/hbm.25337 · Cited by 26
Task-specific network interactions across key cognitive domains
Williams, K. A., Numssen, O., & Hartwigsen, G. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab531 · Cited by 24
Mill, R. D., Hamilton, J. L., Winfield, E. C., Lalta, N., Chen, R. H., & Cole, M. W. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001686 · Cited by 22
Hierarchical dynamics of informational patterns and decision-making
Varona, P., & Rabinovich, M. I. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.0475 · Cited by 25
Temporal Dynamics of Functional Brain States Underlie Cognitive Performance
Gu, H., Schulz, K. P., Fan, J., & Yang, Y. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa350 · Cited by 21
Kim, H. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.04.007 · Cited by 23
Predicting individual variability in task‐evoked brain activity in schizophrenia
Tik, N., Livny, A., Gal, S., Gigi, K., Tsarfaty, G., Weiser, M., & Tavor, I. (2021) · Human Brain Mapping · doi:10.1002/hbm.25534 · Cited by 21
Masked features of task states found in individual brain networks
Porter, A., Nielsen, A. N., Dorn, M., Dworetsky, A., Edmonds, D., & Gratton, C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac247 · Cited by 22
What can atypical language hemispheric specialization tell us about cognitive functions?
Cai, Q., & der Haegen, L. V. (2015) · Neuroscience Bulletin · doi:10.1007/s12264-014-1505-5 · Cited by 21
Within-individual precision mapping of brain networks exclusively using task data
Du, J., Tripathi, V., Elliott, M. L., Ladopoulou, J., Sun, W., Eldaief, M. C., & Buckner, R. L. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.029 · Cited by 24
Self-citationOptimal Control Costs of Brain State Transitions in Linear Stochastic Systems
Kamiya, S., Kawakita, G., Sasai, S., Kitazono, J., & Oizumi, M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1053-22.2022 · Cited by 20
Frontoparietal Activity Interacts With Task-Evoked Changes in Functional Connectivity
Hwang, K., Shine, J. M., & D’Esposito, M. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy011 · Cited by 25
Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S., DeRamus, T. P., Kochunov, P. V., Adhikari, B. M., Belger, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.03.506487 · Cited by 20
Zhang, H., Zhao, R., Hu, X., Guan, S., Margulies, D. S., Meng, C., & Biswal, B. B. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02564-0 · Cited by 23
Peng, Y., Zheng, Y., Ziwen, Y., Guo, J., Fan, C., Li, C., Deng, J., Song, S., Qiao, J., & Wang, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1242543 · Cited by 20
Meng, Q., Zhu, Y., Yuan, Y., Ni, R., Li, Y., Liu, J., & Bu, J. (2023) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2023.100411 · Cited by 16
Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses
Nunes, A. S., Moiseev, A., Kozhemiako, N., Cheung, T., Ribary, U., & Doesburg, S. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116386 · Cited by 19
Reconfiguration patterns of large-scale brain networks in motor imagery
Zhang, T., Wang, F., Li, M., Li, F., Tan, Y. H. V., Zhang, Y., Yang, H., Biswal, B. B., Yao, D., & Xu, P. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1786-y · Cited by 19
Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states
Colenbier, N., Sareen, E., Puntas, T. D. Á., Griffa, A., Pellegrino, G., Mantini, D., Marinazzo, D., Arcara, G., & Amico, E. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120021 · Cited by 18
Zhang, H., Meng, C., Di, X., Wu, X., & Biswal, B. B. (2023) · Network Neuroscience · doi:10.1162/netn_a_00314 · Cited by 19
Mindfulness Meditation and Network Neuroscience: Review, Synthesis, and Future Directions
Prakash, R. S., Shankar, A., Tripathi, V., Yang, W. F. Z., Fisher, M. E., Bauer, C. C. C., Betzel, R. F., & Sacchet, M. D. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.11.005 · Cited by 24
Resting-state network topology and planning ability in healthy adults
Vriend, C., Wagenmakers, M. J., van den Heuvel, O. A., & van der Werf, Y. D. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-02004-6 · Cited by 19
Disentangling common from specific processing across tasks using task potency
Chauvin, R. J., Mennes, M., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.059 · Cited by 19
Controversies and progress on standardization of large-scale brain network nomenclaturePreprint
Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A., et al. (2022) · doi:10.31219/osf.io/25za6 · Cited by 18
Self-citationPopp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2025) · Communications Biology · doi:10.1038/s42003-025-08231-4 · Cited by 17
The secret life of predictive brains: what's spontaneous activity for?Preprint
Pezzulo, G., Zorzi, M., & Corbetta, M. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/qus3h · Cited by 19
Yan, Y., Hulbert, J. C., Zhuang, K., Liu, W., Wei, D., Qiu, J., Anderson, M. C., & Yang, W. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac336 · Cited by 16
Mapping brain state-dependent sensory responses across the mouse cortex
Montagni, E., Resta, F., Tort‐Colet, N., Scaglione, A., Mazzamuto, G., Alain Destexhe, A., Pavone, F. S., & Mascaro, A. L. A. (2024) · iScience · doi:10.1016/j.isci.2024.109692 · Cited by 15
Dynamic and stationary brain connectivity during movie watching as revealed by functional MRI
Di, X., Zhang, Z., Xu, T., & Biswal, B. B. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02522-w · Cited by 17
Morin, T., Moore, K. N., Isenburg, K., Ma, W., & Stern, C. E. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac457 · Cited by 17
Kashyap, R., Bhattacharjee, S., Yeo, B. T. T., & Chen, S. A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24873 · Cited by 15
Self-citationLi, H., Booth, J. R., Feng, X., Wei, N., Zhang, M., Zhang, J., Zhong, H., Lu, C., Liu, L., Ding, G., et al. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107630 · Cited by 15
Metadata
- Authors
- Krienen, F. M., Yeo, B. T. T., & Buckner, R. L.
- Affiliations
- Harvard University (US)
- National University of Singapore (SG)
- Corresponding author
- Buckner, Randy L.
- Venue
- Philosophical Transactions of the Royal Society B Biological Sciences
- Publisher
- The Royal Society
- Published
- 2014-10-05
- Volume
- 369
- Issue
- 1653
- Pages
- 20130526
- ISSN
- 0962-8436 (ISSN-L)
- 1471-2970
- PMC
- PMC4150301
- OpenAlex
- W2061662249
- Cited by
- 372
- References
- 81
- Citations by year
- 2026: 21 · 2025: 22 · 2024: 33 · 2023: 33 · 2022: 37 · 2021: 41 · 2020: 34 · 2019: 41 · 2018: 34 · 2017: 27 · 2016: 26 · 2015: 17 · 2014: 5
- FWCI
- 13.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Philosophical Transactions of the Royal Society B Biological Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1s10rr019307-01, 1s10rr021110-01a1)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015896)
- National Center for Research Resources (S10RR023401)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- functional coupling, brain network reconfiguration, cortical functional connectivity, task state, classification tasks
- MeSH terms
- Adult, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Nerve Net, Perception, Physical Stimulation, Psychomotor Performance, Word Association Tests, Cluster Analysis, Linear Models, Connectome
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