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Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations
Open accessAbstract
Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.
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“Present BNMs only allow for a very limited number of parameters to be estimated, e.g., a single global scaling factor of connection strength (Deco et al., 2013).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Deco et al., 2013
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Cites
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
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
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 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
Self-citationSpontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Self-citationFunctional and Effective Connectivity: A Review
Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415
Predicting human resting-state functional connectivity from structural connectivity
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811168106 · Cited by 3,307
Self-citation
Cited by
Sorted by citations
Items 101–200 of 573
Cabral, J., Fernandes, H. M., van Hartevelt, T. J., James, A. C., Kringelbach, M. L., & Deco, G. (2013) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4851117 · Cited by 77
Self-citationShu, N., Duan, Y., Xia, M., Schoonheim, M. M., Huang, J., Ren, Z., Sun, Z., Ye, J., Dong, H., Shi, F., et al. (2016) · Scientific Reports · doi:10.1038/srep29383 · Cited by 80
Yousefi, B., & Keilholz, S. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117827 · Cited by 78
neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling
Cakan, C., Jajcay, N., & Obermayer, K. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09931-9 · Cited by 81
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
NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity
Deligianni, F., Carmichael, D. W., Zhang, H. G., Clark, C. A., & Clayden, J. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153404 · Cited by 73
Neuronal Origin of the Temporal Dynamics of Spontaneous BOLD Activity Correlation
Matsui, T., Murakami, T., & Ohki, K. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy045 · Cited by 79
Lim, S. I., Radicchi, F., van den Heuvel, M. P., & Sporns, O. (2019) · Scientific Reports · doi:10.1038/s41598-019-39243-w · Cited by 73
Injury alters intrinsic functional connectivity within the primate spinal cord
Chen, L. M., Mishra, A., Yang, P., Wang, F., & Gore, J. C. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1424106112 · Cited by 72
Gilson, M., Deco, G., Friston, K. J., Hagmann, P., Mantini, D., Betti, V., Romani, G. L., & Corbetta, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.061 · Cited by 72
Self-citationFriedemann Pulvermüller, A., & Garagnani, M. (2014) · Cortex · doi:10.1016/j.cortex.2014.02.015 · Cited by 74
Meier, J. M., Perdikis, D., Blickensdörfer, A., Stefanovski, L., Liu, Q., Maith, O., Dinkelbach, H. Ü., Baladron, J., Hamker, F. H., & Ritter, P. (2022) · Experimental Neurology · doi:10.1016/j.expneurol.2022.114111 · Cited by 73
Panda, R., Thibaut, A., López‐González, A., Escrichs, A., Bahri, M. A., Hillebrand, A., Deco, G., Laureys, S., Gosseries, O., Annen, J., et al. (2022) · eLife · doi:10.7554/elife.77462 · Cited by 69
Self-citationStructural connectome topology relates to regional BOLD signal dynamics in the mouse brain
Sethi, S. S., Zerbi, V., Wenderoth, N., Fornito, A., & Fulcher, B. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4979281 · Cited by 72
The Digital Twin Brain: A Bridge between Biological and Artificial Intelligence
Xiong, H., Chu, C., Fan, L., Song, M. X., Zhang, J., Ma, Y., Zheng, R., Zhang, J., Yang, Z., & Jiang, T. (2023) · Intelligent Computing · doi:10.34133/icomputing.0055 · Cited by 69
Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches
Sadaghiani, S., & Wirsich, J. (2019) · Network Neuroscience · doi:10.1162/netn_a_00114 · Cited by 67
Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry
Murray, J. D., Demirtaş, M., & Anticevic, A. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.07.004 · Cited by 67
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03330-y · Cited by 79
Pineda‐Pardo, J. A., Bruña, R., Woolrich, M. W., Marcos, A., Nobre, A. C., Maestú, F., & Vidaurre, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.08.002 · Cited by 66
Melozzi, F., Woodman, M., Jirsa, V., & Bernard, C. (2017) · eNeuro · doi:10.1523/eneuro.0111-17.2017 · Cited by 68
Discovering the Computational Relevance of Brain Network Organization
Ito, T., Hearne, L. J., Mill, R. D., Cocuzza, C. V., & Cole, M. W. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.10.005 · Cited by 67
It’s about time: Linking dynamical systems with human neuroimaging to understand the brain
John, Y. J., Sawyer, K. S., Srinivasan, K., Müller, E. J., Munn, B., & Shine, J. M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00230 · Cited by 71
Whole-Brain Network Models: From Physics to Bedside
Pathak, A., Roy, D., & Banerjee, A. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.866517 · Cited by 66
Rácz, F. S., Stylianou, O., Mukli, P., & Eke, A. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00049 · Cited by 66
Murray, J. D., & Anticevic, A. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.10.021 · Cited by 64
Wang, J., Becker, B., Wang, L., Li, H., Zhao, X., & Jiang, T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.001 · Cited by 61
Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease
Khan, A. F., Adewale, Q., Baumeister, T. R., Carbonell, F., Zilles, K., Palomero‐Gallagher, N., Iturria‐Medina, Y., & Initiative, A. D. N. (2021) · Brain · doi:10.1093/brain/awab375 · Cited by 61
Ší́p, V., Petkoski, S., Hashemi, M., Dickscheid, T., Amunts, K., & Jirsa, V. (2023) · Science Advances · doi:10.1126/sciadv.abq7547 · Cited by 64
Functional connectivity change as shared signal dynamics
Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G., & Anticevic, A. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.11.011 · Cited by 60
Ito, T., Yang, G. R., Laurent, P. A., Schultz, D. H., & Cole, M. W. (2022) · Nature Communications · doi:10.1038/s41467-022-28323-7 · Cited by 78
Arsiwalla, X. D., Zucca, R., Betella, A., Martínez, E. F., Dalmazzo, D., Omedas, P., Deco, G., & Verschure, P. F. M. J. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00002 · Cited by 60
Self-citationTimescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain
Fallon, J., Ward, P. G. D., Parkes, L., Oldham, S., Arnatkevičiūtė, A., Fornito, A., & Fulcher, B. (2020) · Network Neuroscience · doi:10.1162/netn_a_00151 · Cited by 66
Zhang, S., Larsen, B., Sydnor, V. J., Zeng, T., An, L., Yan, X., Kong, R., Kong, X., Gur, R. C., Gur, R. E., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2318641121 · Cited by 67
Self-citationModel-based whole-brain effective connectivity to study distributed cognition in health and disease
Gilson, M., Zamora‐López, G., Pallarés, V., Adhikari, M. H., Senden, M., Campo, A. T., Mantini, D., Corbetta, M., Deco, G., & Insabato, A. (2019) · Network Neuroscience · doi:10.1162/netn_a_00117 · Cited by 62
Self-citationWang, J., Zhang, J., Rong, M., Wei, X., Zheng, D., Fox, P. T., Eickhoff, S. B., & Jiang, T. (2016) · Human Brain Mapping · doi:10.1002/hbm.23311 · Cited by 58
Larivière, S., de Wael, R. V., Paquola, C., Hong, S., Mišić, B., Bernasconi, N., Bernasconi, A., Bonilha, L., & Bernhardt, B. C. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0587 · Cited by 51
Priority coding in the visual system
Rust, N. C., & Cohen, M. R. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00582-9 · Cited by 59
Understanding brain states across spacetime informed by whole-brain modelling
Vohryzek, J., Cabral, J., Vuust, P., Deco, G., & Kringelbach, M. L. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0247 · Cited by 56
Self-citationConcurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics
Wirsich, J., Giraud, A., & Sadaghiani, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116998 · Cited by 57
Connectome spectral analysis to track EEG task dynamics on a subsecond scale
Glomb, K., Rué‐Queralt, J., Pascucci, D., Defferrard, M., Tourbier, S., Carboni, M., Rubega, M., Vulliémoz, S., Plomp, G., & Hagmann, P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117137 · Cited by 53
Self-citationFunctional connectivity arises from a slow rhythmic mechanism
Li, J. M., Bentley, W. J., & Snyder, L. H. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419837112 · Cited by 53
Burt, J. B., Preller, K. H., Demirtaş, M., Ji, J. L., Krystal, J. H., Vollenweider, F. X., Anticevic, A., & Murray, J. D. (2021) · eLife · doi:10.7554/elife.69320 · Cited by 48
Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy
Park, B., Larivière, S., Rodríguez‐Cruces, R., Royer, J., Tavakol, S., Wang, Y., Caciagli, L., Caligiuri, M. E., Gambardella, A., Concha, L., et al. (2021) · Brain · doi:10.1093/brain/awab417 · Cited by 52
Nakagawa, T., Woolrich, M. W., Luckhoo, H., Joensson, M., Mohseni, H., Kringelbach, M. L., Jirsa, V., & Deco, G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.009 · Cited by 52
Self-citationMomi, D., Ozdemir, R. A., Tadayon, E., Boucher, P. O., Domenico, A. D., Fasolo, M., Shafi, M. M., Pascual‐Leone, Á., & Santarnecchi, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-90663-z · Cited by 51
Liu, Z., Han, F., & Wang, Q. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-022-07298-6 · Cited by 52
Neural and metabolic basis of dynamic resting state fMRI
Thompson, G. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.010 · Cited by 57
Altered Dynamic Functional Network Connectivity in Frontal Lobe Epilepsy
Klugah‐Brown, B., Luo, C., He, H., Jiang, S., Armah, G. K., Wu, Y., Li, J., Yin, W., & Yao, D. (2018) · Brain Topography · doi:10.1007/s10548-018-0678-z · Cited by 51
Deco, G., van Hartevelt, T. J., Fernandes, H. M., Stevner, A. B. A., & Kringelbach, M. L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.047 · Cited by 47
Self-citationHomeostatic plasticity and emergence of functional networks in a whole-brain model at criticality
Rocha, R. P., Koçillari, L., Suweis, S., Corbetta, M., & Maritan, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33923-9 · Cited by 50
Self-citationMacroscale and microcircuit dissociation of focal and generalized human epilepsies
Weng, Y., Larivière, S., Caciagli, L., de Wael, R. V., Rodríguez‐Cruces, R., Royer, J., Xu, Q., Bernasconi, N., Bernasconi, A., Yeo, B. T. T., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-0958-5 · Cited by 46
Bridging Levels of Understanding in Schizophrenia Through Computational Modeling
Anticevic, A., Murray, J. D., & Barch, D. M. (2015) · Clinical Psychological Science · doi:10.1177/2167702614562041 · Cited by 51
Roy, D., Sigala, R., Breakspear, M. J., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0252 · Cited by 47
Self-citationXia, X., Fan, L., Chen, C., Eickhoff, S. B., Chen, J., Li, H., & Jiang, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23636 · Cited by 48
Tewarie, P. K. B., Prasse, B., Meier, J. M., Santos, F. A. N., Douw, L., Schoonheim, M. M., Stam, C. J., Mieghem, P. V., & Hillebrand, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116805 · Cited by 45
A unified framework for multimodal structure–function mapping based on eigenmodes
Deslauriers‐Gauthier, S., Zucchelli, M., Frigo, M., & Rachid Deriche, B. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101799 · Cited by 45
Deng, S., Li, J., Yeo, B. T. T., & Gu, S. (2022) · Communications Biology · doi:10.1038/s42003-022-03196-0 · Cited by 49
Deco, G., Lynn, C. W., Perl, Y. S., & Kringelbach, M. L. (2023) · Physical review. E · doi:10.1103/physreve.108.064410 · Cited by 45
Self-citationA network control theory pipeline for studying the dynamics of the structural connectome
Parkes, L., Kim, J. Z., Stiso, J., Brynildsen, J. K., Cieslak, M., Covitz, S. C., Gur, R. E., Gur, R. C., Pasqualetti, F., Shinohara, R. T., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01023-w · Cited by 54
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.033 · Cited by 46
Role of white-matter pathways in coordinating alpha oscillations in resting visual cortex
Hindriks, R., Woolrich, M. W., Luckhoo, H., Joensson, M., Mohseni, H., Kringelbach, M. L., & Deco, G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.057 · Cited by 46
Self-citationArbabyazd, L., Shen, K., Wang, Z., Hofmann‐Apitius, M., Ritter, P., McIntosh, A. R., Battaglia, D. A., & Jirsa, V. (2021) · eNeuro · doi:10.1523/eneuro.0475-20.2021 · Cited by 44
Learning how network structure shapes decision-making for bio-inspired computing
Schirner, M., Deco, G., & Ritter, P. (2023) · Nature Communications · doi:10.1038/s41467-023-38626-y · Cited by 55
Self-citationAnalytical Operations Relate Structural and Functional Connectivity in the Brain
Saggio, M. L., Ritter, P., & Jirsa, V. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157292 · Cited by 43
Resting state networks in empirical and simulated dynamic functional connectivity
Glomb, K., Ponce‐Alvarez, A., Gilson, M., Ritter, P., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.065 · Cited by 42
Self-citationDoes the brain behave like a (complex) network? I. Dynamics
Papo, D., & Buldú, J. M. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.006 · Cited by 44
The Hopf whole-brain model and its linear approximation
Ponce‐Alvarez, A., & Deco, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-53105-0 · Cited by 46
Self-citationStructure-function clustering in multiplex brain networks
Crofts, J. J., Forrester, M., & O’Dea, R. D. (2016) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/116/18003 · Cited by 47
Reduced emergent character of neural dynamics in patients with a disrupted connectome
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119926 · Cited by 41
Luppi, A. I., Cabral, J., Cofré, R., Mediano, P. A. M., Rosas, F. E., Qureshi, A. Y., Kuceyeski, A. F., Tagliazucchi, E., Raimondo, F., Deco, G., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120162 · Cited by 44
Self-citationPalombit, A., Silvestri, E., Volpi, T., Aiello, M., Cecchin, D., Bertoldo, A., & Corbetta, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119280 · Cited by 43
Self-citationVattikonda, A. N., Bapi, R. S., Banerjee, A., Deco, G., & Roy, D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.002 · Cited by 39
Self-citationDynamic primitives of brain network interaction
Schirner, M., Kong, X., Yeo, B. T. T., Deco, G., & Ritter, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118928 · Cited by 42
Self-citationSpontaneous brain network activity: Analysis of its temporal complexity
Pedersen, M., Omidvarnia, A., Walz, J. M., Zalesky, A., & Jackson, G. D. (2017) · Network Neuroscience · doi:10.1162/netn_a_00006 · Cited by 41
Towards an extended taxonomy of information dynamics via Integrated Information DecompositionPreprint
Mediano, P. A. M., Rosas, F. E., Luppi, A. I., Carhart-Harris, R. L., Bor, D., Seth, A. K., & Barrett, A. B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13186 · Cited by 40
Zhang, Z., Zhuo, K., Xiang, Q., Sun, Y. M., Suckling, J., Wang, J., Liu, D., & Sun, Y. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01398-4 · Cited by 41
Increased connectivity between sensorimotor and attentional areas in Parkinson’s disease
Onu, M., Badea, L., Roceanu, A. M., Tivarus, M. E., & Bajenaru, O. A. (2015) · Neuroradiology · doi:10.1007/s00234-015-1556-y · Cited by 40
Structure-Function Network Mapping and Its Assessment via Persistent Homology
Liang, H., & Wang, H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005325 · Cited by 38
Hearne, L. J., Lin, H., Sanz‐Leon, P., Tseng, W. I., Gau, S. S., Roberts, J. A., & Cocchi, L. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0554-6 · Cited by 39
Stramaglia, S., Pellicoro, M., Angelini, L., Amico, E., Aerts, H., Cortés, J. M., Laureys, S., & Marinazzo, D. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4978999 · Cited by 40
Generative models for clinical applications in computational psychiatry
Frässle, S., Yao, Y., Schöbi, D., Aponte, E. A., Heinzle, J., & Stephan, K. E. (2018) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1460 · Cited by 36
Wein, S., Deco, G., Tomé, A. M., Goldhacker, M., Malloni, W. M., Greenlee, M. W., & Lang, E. W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5573740 · Cited by 42
Self-citationKojori, E. S., Tomasi, D. G., & Volkow, N. D. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy144 · Cited by 41
The role of node dynamics in shaping emergent functional connectivity patterns in the brain
Forrester, M., Crofts, J. J., Sotiropoulos, S. N., Coombes, S., & O’Dea, R. D. (2020) · Network Neuroscience · doi:10.1162/netn_a_00130 · Cited by 34
Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis
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Self-citation
Metadata
- Authors
- Deco, G., Ponce-Alvarez, A., Mantini, D., Romani, G. L., Hagmann, P., & Corbetta, M.
- Affiliations
- Institució Catalana de Recerca i Estudis Avançats (ES)
- ETH Zurich (CH)
- University of Chieti-Pescara (IT)
- Centre Hospitalier Universitaire Vaudois (CH)
- Washington University in St. Louis (US)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2013-07-03
- Volume
- 33
- Issue
- 27
- Pages
- 11239-11252
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3718368
- OpenAlex
- W2127186958
- Cited by
- 576
- References
- 48
- Citations by year
- 2026: 43 · 2025: 65 · 2024: 41 · 2023: 36 · 2022: 58 · 2021: 46 · 2020: 46 · 2019: 34 · 2018: 51 · 2017: 40 · 2016: 31 · 2015: 43 · 2014: 34 · 2013: 8
- FWCI
- 21.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- SERVAL (Université de Lausanne) (publishedVersion, other-oa)
- IRIS (submittedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-sa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-sa)
- Padua Research Archive (University of Padova) (submittedVersion)
- ETH Zürich Research Collection
- Funders
- James S. McDonnell Foundation
- Fondation Leenaards
- National Institutes of Health
- National Institute of Mental Health (R01MH096482)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD061117)
- FP7 Ideas: European Research Council (295129)
- FP7 Information and Communication Technologies
- 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
- resting-state functional connectivity, structure-function relationship, noise propagation, statistical moments, neural dynamics, brain network dynamics
- MeSH terms
- Action Potentials, Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Rest, Linear Models, Young Adult
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