Mathematical framework for large-scale brain network modeling in The Virtual Brain
Open accessAbstract
In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.
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
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?
Both highly parameterized simulations and sparser analytic predictions of FC appear comparably effective.
“Models can involve highly parameterized simulations of regional activity (Hansen et al., 2015; Messe et al., 2014; Sanz-Leon et al., 2015), or sparser analytic predictions of FC using graph metrics of mutual communication (Goni et al., 2014; Grayson et al., 2016) (although, both approaches appear to be comparably effective; see Figure 1D).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Sanz-Leon et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319 · see citing works
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323 · see citing works
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 · see citing works
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688 · see citing works
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 · see citing works
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 · see citing works
Spontaneous 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 · see citing works
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.24.9868 · Cited by 6,483 · see citing works
Ogata, K., & Brewer, J. W. (1971) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.3426465 · Cited by 6,370 · see citing works
Impulses and Physiological States in Theoretical Models of Nerve Membrane
Fitzhugh, R. (1961) · Biophysical Journal · doi:10.1016/s0006-3495(61)86902-6 · Cited by 6,199 · see citing works
The minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253 · see citing works
Numerical solution of stochastic differential equations
Kloeden, P. E. 1., & Platen, E. (2016) · doi:10.5194/gmd-2016-45-rc1 · Cited by 5,119 · see citing works
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684 · see citing works
An Active Pulse Transmission Line Simulating Nerve Axon
Nagumo, J., Arimoto, S., & Yoshizawa, S. (1962) · Proceedings of the IRE · doi:10.1109/jrproc.1962.288235 · Cited by 4,278 · see citing works
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 · see citing works
Dynamical Systems in Neuroscience
Izhikevich, E. M. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2526.001.0001 · Cited by 3,776 · see citing works
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.13.5951 · Cited by 3,681 · see citing works
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/879716 · Cited by 3,625 · see citing works
Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons
Wilson, H. R., & Cowan, J. D. (1972) · Biophysical Journal · doi:10.1016/s0006-3495(72)86068-5 · Cited by 3,579 · see citing works
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 · see citing works
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · see citing works
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750 · see citing works
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,605 · see citing works
Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1
Boynton, G., Engel, S., Glover, G., & Heeger, D. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-13-04207.1996 · Cited by 2,522 · see citing works
Voltage oscillations in the barnacle giant muscle fiber
Morris, C., & Lecar, H. (1981) · Biophysical Journal · doi:10.1016/s0006-3495(81)84782-0 · Cited by 2,302 · see citing works
Singh, Y., Janardhanan, S., & Al, S. Y. E. (2010) · Cited by 2,176 · see citing works
Dynamics of pattern formation in lateral-inhibition type neural fields
Амари, Ш.-И. (1977) · Biological Cybernetics · doi:10.1007/bf00337259 · Cited by 2,086 · see citing works
A model of neuronal bursting using three coupled first order differential equations
Hindmarsh, J. L., & Rose, R. M. (1984) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1984.0024 · Cited by 1,953 · see citing works
Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem.
Sarvas, J. (1987) · Physics in Medicine and Biology · doi:10.1088/0031-9155/32/1/004 · Cited by 1,945 · see citing works
Time course EPI of human brain function during task activation
Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., & Hyde, J. S. (1992) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910250220 · Cited by 1,935 · see citing works
Discrete Differential-Geometry Operators for Triangulated 2-Manifolds
Meyer, M., Desbrun, M., Schröder, P., & Barr, A. H. (2003) · Mathematics and visualization · doi:10.1007/978-3-662-05105-4_2 · Cited by 1,910 · see citing works
A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue
Wilson, H. R., & Cowan, J. D. (1973) · Kybernetik · doi:10.1007/bf00288786 · Cited by 1,823 · see citing works
Network structure of cerebral cortex shapes functional connectivity on multiple time scales
Honey, C. J., Kötter, R., Breakspear, M. J., & Sporns, O. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701519104 · Cited by 1,683 · see citing works
Retinotopic organization in human visual cortex and the spatial precision of functional MRI
Engel, S. A. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.2.181 · Cited by 1,606 · see citing works
Logothetis, N. K., & Wandell, B. A. (2004) · Annual Review of Physiology · doi:10.1146/annurev.physiol.66.082602.092845 · Cited by 1,568 · see citing works
Mass action in the nervous system
Freeman, W. J. (1975) · Cited by 1,480 · see citing works
Glasser, M. F., & Van Essen, D. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2180-11.2011 · Cited by 1,447 · see citing works
The human connectome: a complex network
Sporns, O. (2011) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2010.05888.x · Cited by 1,399 · see citing works
Adult neurogenesis and functional plasticity in neuronal circuits
Lledo, P., Alonso, M., & Grubb, M. S. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1867 · Cited by 1,364 · see citing works
Modeling the hemodynamic response to brain activation
Buxton, R. B., Uludaǧ, K., Dubowitz, D. J., & Liu, T. T. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.013 · Cited by 1,222 · see citing works
Cortex: Statistics and Geometry of Neuronal Connectivity
Braitenberg, V., & Schüz, A. (1998) · doi:10.1007/978-3-662-03733-1 · Cited by 1,178 · cited 1× · see citing works
“This approximation is strictly correct only for the short fibers running within the gray matter (Braitenberg and Schüz, 1998).”
Whole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169 · see citing works
The Magnetic Properties and Structure of Hemoglobin, Oxyhemoglobin and Carbonmonoxyhemoglobin
Pauling, L. C., & Coryell, C. D. (1936) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.22.4.210 · Cited by 1,163 · see citing works
Jansen, B. H., & Rit, V. G. (1995) · Biological Cybernetics · doi:10.1007/bf00199471 · Cited by 1,159 · see citing works
Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics
Friston, K. J., Mechelli, A., Turner, R., & Price, C. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0630 · Cited by 1,131 · see citing works
Brain Graphs: Graphical Models of the Human Brain Connectome
Bullmore, E. T., & Bassett, D. S. (2011) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-040510-143934 · Cited by 1,130 · see citing works
Gong, G., He, Y., Concha, L., Lebel, C., Gross, D. W., Evans, A. C., & Beaulieu, C. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn102 · Cited by 1,112 · see citing works
The dynamic brain: from spiking neurons to neural masses and cortical fields
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000092 · Cited by 1,084 · see citing works
Self-citationMiezin, F. M., Maccotta, L., Ollinger, J., Petersen, S., & Buckner, R. (2000) · NeuroImage · doi:10.1006/nimg.2000.0568 · Cited by 1,033 · see citing works
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
Wong, K.-F., & Wang, X.-J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3733-05.2006 · Cited by 1,029 · see citing works
The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal
Logothetis, N. K. (2002) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2002.1114 · Cited by 952 · see citing works
BigBrain: An Ultrahigh-Resolution 3D Human Brain Model
Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-É., Bludau, S., Bazin, P., Lewis, L. B., Oros‐Peusquens, A., et al. (2013) · Science · doi:10.1126/science.1235381 · Cited by 879 · see citing works
EEG and MEG: forward solutions for inverse methods
Mosher, J. C., Leahy, R. M., & Lewis, P. S. (1999) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.748978 · Cited by 845 · see citing works
A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation
Buxton, R. B., & Frank, L. R. (1997) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-199701000-00009 · Cited by 808 · see citing works
On the nature of seizure dynamics
Jirsa, V., Stacey, W. C., Quilichini, P., Ivanov, A., & Bernard, C. (2014) · Brain · doi:10.1093/brain/awu133 · Cited by 760 · see citing works
Self-citationKey role of coupling, delay, and noise in resting brain fluctuations
Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kötter, R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901831106 · Cited by 756 · see citing works
Self-citationTime-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753 · see citing works
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743 · see citing works
Model of brain rhythmic activity
Lopes, F. H. D. S., Hoeks, A. P. G., Smits, H., & Zetterberg, L. H. (1974) · Kybernetik · doi:10.1007/bf00270757 · Cited by 734 · see citing works
Tetrahedral mesh generation from volumetric binary and grayscale images
Fang, Q., & Boas, D. A. (2009) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2009.5193259 · Cited by 730 · see citing works
A neural mass model for MEG/EEG:
Olivier David, A., & Friston, K. J. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.015 · Cited by 705 · see citing works
Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors
Deco, G., & Jirsa, V. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2523-11.2012 · Cited by 680 · see citing works
Self-citationNeural networks as spatio-temporal pattern-forming systems
Ermentrout, B. (1998) · Reports on Progress in Physics · doi:10.1088/0034-4885/61/4/002 · Cited by 672 · see citing works
Can structure predict function in the human brain?
Honey, C. J., Thivierge, J.-P., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.071 · Cited by 629 · see citing works
Brunel, N., & Wang, X.-J. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1011204814320 · Cited by 620 · see citing works
Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases
Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J., & Coalson, T. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr291 · Cited by 616 · see citing works
Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry
Polonsky, A., Blake, R., Braun, J., & Heeger, D. J. (2000) · Nature Neuroscience · doi:10.1038/80676 · Cited by 589 · see citing works
Resting-brain functional connectivity predicted by analytic measures of network communication
Goñi, J., van den Heuvel, M. P., Avena‐Koenigsberger, A., de Mendizábal, N. V., Betzel, R. F., Griffa, A., Hagmann, P., Corominas‐Murtra, B., Thiran, J., & Sporns, O. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315529111 · Cited by 579 · see citing works
Deco, G., Ponce-Alvarez, A., Mantini, D., Romani, G. L., Hagmann, P., & Corbetta, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1091-13.2013 · Cited by 576 · see citing works
Ogawa, S., & Lee, T. (1990) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910160103 · Cited by 565 · see citing works
Modeling the impact of lesions in the human brain
Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000408 · Cited by 561 · see citing works
Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire
Ghosh, A., Rho, Y.-A., McIntosh, A. R., Kötter, R., & Jirsa, V. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000196 · Cited by 550 · see citing works
Self-citationBrunel, N., & Wang, X. (2014) · Journal of Computational Neuroscience · doi:10.1007/s10827-014-0506-8 · Cited by 538 · see citing works
Functional connectivity dynamics: modeling the switching behavior of the resting state
Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.001 · Cited by 528 · see citing works
Self-citationBroadband criticality of human brain network synchronization
Kitzbichler, M. G., Smith, M. L., Christensen, S. R., & Bullmore, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000314 · Cited by 527 · see citing works
The Virtual Brain: a simulator of primate brain network dynamics
Sanz‐Leon, P., Knock, S. A., Woodman, M., Domide, L., Mersmann, J., McIntosh, A. R., & Jirsa, V. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00010 · Cited by 516 · see citing works
Self-citationRole of local network oscillations in resting-state functional connectivity
Cabral, J., Hugues, E., Sporns, O., & Deco, G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.010 · Cited by 515 · see citing works
Field Theory of Electromagnetic Brain Activity
Jirsa, V., & Haken, H. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.960 · Cited by 487 · see citing works
Self-citationPropagation and stability of waves of electrical activity in the cerebral cortex
Robinson, P. A., RENNIE, C. J., & Wright, J. J. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.56.826 · Cited by 486 · see citing works
Dynamics of large-scale brain activity in normal arousal states and epileptic seizures
Robinson, P. A., RENNIE, C. J., & ROWE, D. L. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.65.041924 · Cited by 464 · see citing works
Breakspear, M. J., Roberts, J. A., Terry, J. R., Rodrigues, S., Mahant, N., & Robinson, P. A. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhj072 · Cited by 463 · see citing works
Trends and properties of human cerebral cortex: Correlations with cortical myelin content
Glasser, M. F., Goyal, M. S., Preuss, T. M., Raichle, M. E., & Essen, D. C. V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.060 · Cited by 456 · see citing works
Mapping the human connectome at multiple scales with diffusion spectrum MRI
Cammoun, L., Gigandet, X., Meskaldji, D. E., Thiran, J., Sporns, O., Kim, Q., Maeder, P. P., Meuli, R. A., & Hagmann, P. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.09.031 · Cited by 443 · see citing works
Comparing hemodynamic models with DCM
Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.040 · Cited by 441 · see citing works
Imaging human connectomes at the macroscale
Craddock, R. C., Jbabdi, S., Yan, C.-G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., & Milham, M. P. (2013) · Nature Methods · doi:10.1038/nmeth.2482 · Cited by 419 · see citing works
Current concepts in neuroanatomical tracing
Köbbert, C., Apps, R., Bechmann, I., Lanciego, J. L., Mey, J., & Thanos, S. (2000) · Progress in Neurobiology · doi:10.1016/s0301-0082(00)00019-8 · Cited by 419 · see citing works
Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals
Wendling, F., Bellanger, J.-J., Bartoloméi, F., & Chauvel, P. (2000) · Biological Cybernetics · doi:10.1007/s004220000160 · Cited by 410 · see citing works
Fast, accurate algorithm for numerical simulation of exponentially correlated colored noise
Fox, R. F., Gatland, I. R., Roy, R., et al. (1988) · Physical Review A · doi:10.1103/physreva.38.5938 · Cited by 409 · see citing works
Fiber Dissection Technique: Lateral Aspect of the Brain
Türe, U., Yasargil, M. G., Friedman, A. H., & Al‐Mefty, O. (2000) · Neurosurgery · doi:10.1097/00006123-200008000-00028 · Cited by 401 · see citing works
Numerical solution of stochastic differential equations
Talay, D. (1994) · Stochastics and stochastics reports · doi:10.1080/17442509408833885 · Cited by 398 · see citing works
Overview of coupled map lattices
Kaneko, K. (1992) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.165869 · Cited by 386 · see citing works
Mass action in the nervous system
Walter, D. O. (1976) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(76)90110-3 · Cited by 377 · see citing works
Dynamics of Controlled Mechanical Systems with Delayed Feedback
Hu, H., & Wang, Z. (2002) · Springer eBooks · doi:10.1007/978-3-662-05030-9 · Cited by 369 · see citing works
Prediction of electroencephalographic spectra from neurophysiology
Robinson, P. A., RENNIE, C. J., Wright, J. J., Bahramali, H., Gordon, E., & ROWE, D. L. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.63.021903 · Cited by 366 · see citing works
Dynamical consequences of lesions in cortical networks
Honey, C. J., & Sporns, O. (2008) · Human Brain Mapping · doi:10.1002/hbm.20579 · Cited by 353 · see citing works
Exploring the network dynamics underlying brain activity during rest
Cabral, J., Kringelbach, M. L., & Deco, G. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.12.005 · Cited by 351 · see citing works
Characterizing brain anatomical connections using diffusion weighted MRI and graph theory
Iturria‐Medina, Y., Canales-Rodríguez, E. J., Melie‐García, L., Valdes-Hernandez, P. A., Martínez‐Montes, E., Alemán‐Gómez, Y., & Sánchez‐Bornot, J. M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.012 · Cited by 341 · see citing works
Mechanisms of evoked and induced responses in MEG/EEG
Olivier David, A., Kilner, J. M., & Friston, K. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.034 · Cited by 339 · see citing works
The brain wave equation: a model for the EEG
Nunez, P. L. (1974) · Mathematical Biosciences · doi:10.1016/0025-5564(74)90020-0 · Cited by 338 · see citing works
Exact results for the Kuramoto model with a bimodal frequency distribution
Martens, E. A., Barreto, E., Strogatz, S. H., et al. (2009) · Physical Review E · doi:10.1103/physreve.79.026204 · Cited by 330 · see citing works
Anatomically motivated modeling of cortical laminae
Wähnert, M., Dinse, J., Weiss, M., Streicher, M., Wähnert, P., Geyer, S., Turner, R., & Bazin, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.078 · Cited by 319 · see citing works
Cabral, J., Luckhoo, H., Woolrich, M. W., Joensson, M., Mohseni, H., Baker, A. P., Kringelbach, M. L., & Deco, G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.047 · Cited by 312 · see citing works
Modelling event-related responses in the brain
Olivier David, A., Harrison, L. M., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.030 · Cited by 299 · see citing works
Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla
Shmuel, A., Yacoub, E., Chaimow, D., Logothetis, N. K., & Ugurbil, K. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.12.030 · Cited by 294 · see citing works
Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms
Freyer, F., Roberts, J. A., Becker, R., Robinson, P. A., Ritter, P., & Breakspear, M. J. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6693-10.2011 · Cited by 289 · see citing works
A brief history of human brain mapping
Raichle, M. E. (2009) · Trends in Neurosciences · doi:10.1016/j.tins.2008.11.001 · Cited by 286 · see citing works
Fine structure of neural spiking and synchronization in the presence of conduction delays
Ermentrout, B., & Kopell, N. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.3.1259 · Cited by 282 · see citing works
A novel approach to the human connectome: Ultra-high resolution mapping of fiber tracts in the brain
Axer, M., Amunts, K., Gräßel, D., Palm, C., Dammers, J., Axer, H., Pietrzyk, U., & Zilles, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.075 · Cited by 280 · see citing works
Metastable chimera states in community-structured oscillator networks
Shanahan, M. (2010) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3305451 · Cited by 274 · see citing works
Dynamics of the brain at global and microscopic scales: Neural networks and the EEG
Wright, J. J., & Liley, D. T. J. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00042679 · Cited by 273 · see citing works
Kötter, R. (2004) · Neuroinformatics · doi:10.1385/ni:2:2:127 · Cited by 273 · see citing works
The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging
Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0120 · Cited by 268 · see citing works
Self-citationA spatially continuous mean field theory of electrocortical activity
Liley, D. T. J., Cadusch, P. J., & Dafilis, M. P. (2002) · Network Computation in Neural Systems · doi:10.1080/net.13.1.67.113 · Cited by 261 · see citing works
A neurophysiologically-based mathematical model of flash visual evoked potentials
Jansen, B. H., Zouridakis, G., & Brandt, M. E. (1993) · Biological Cybernetics · doi:10.1007/bf00224863 · Cited by 258 · see citing works
Multiple Bumps in a Neuronal Model of Working Memory
Laing, C. R., Troy, W. C., Gutkin, B. S., & Ermentrout, B. (2002) · SIAM Journal on Applied Mathematics · doi:10.1137/s0036139901389495 · Cited by 258 · see citing works
Unified neurophysical model of EEG spectra and evoked potentials
RENNIE, C. J., Robinson, P. A., & Wright, J. J. (2002) · Biological Cybernetics · doi:10.1007/s00422-002-0310-9 · Cited by 238 · see citing works
A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics
Jirsa, V., & Haken, H. (1997) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(96)00166-2 · Cited by 236 · see citing works
Self-citationA fast method for forward computation of multiple-shell spherical head models
Berg, P., & Scherg, M. (1994) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)90113-9 · Cited by 236 · see citing works
Learning and comparing functional connectomes across subjects
Varoquaux, G., & Craddock, R. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.007 · Cited by 233 · see citing works
The Non-Random Brain: Efficiency, Economy, and Complex Dynamics
Sporns, O. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00005 · Cited by 230 · see citing works
Adding dynamics to the Human Connectome Project with MEG
Larson‐Prior, L. J., Oostenveld, R., Penna, S. D., Michalareas, G., Prior, F., Abbas Babajani-feremi, A., Schoffelen, J., Marzetti, L., de Pasquale, F., Pompeo, F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.056 · Cited by 226 · see citing works
Chimera states in heterogeneous networks
Laing, C. R. (2009) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3068353 · Cited by 216 · see citing works
A neural mass model of spectral responses in electrophysiology
Moran, R., Kiebel, S. J., Stephan, K. E., Reilly, R. B., Jean Daunizeau, A., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.032 · Cited by 214 · see citing works
Discrete laplace operator on meshed surfaces
Belkin, M. A., Sun, J. Z., & Wang, Y. (2008) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/1377676.1377725 · Cited by 214 · see citing works
Hellyer, P. J., Shanahan, M., Scott, G., Wise, R. J. S., Sharp, D. J., & Leech, R. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1853-13.2014 · Cited by 205 · see citing works
Dynamics of a neural system with a multiscale architecture
Breakspear, M. J., & Stam, C. J. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1643 · Cited by 205 · see citing works
Wendling, F., Bartoloméi, F., Bellanger, J.-J., et al. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00547-8 · Cited by 204 · see citing works
A model of the spatial-temporal characteristics of the alpha rhythm
VANROTTERDAM, A., LOPESDASILVA, F., VANDENENDE, J., et al. (1982) · Bulletin of Mathematical Biology · doi:10.1016/s0092-8240(82)80070-0 · Cited by 203 · see citing works
Bifurcation Analysis of Jansen's Neural Mass Model
Grimbert, F., & Faugeras, O. D. (2006) · Neural Computation · doi:10.1162/neco.2006.18.12.3052 · Cited by 196 · see citing works
The future of the human connectome
Essen, D. C. V., & Uǧurbil, K. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.032 · Cited by 195 · see citing works
Axer, M., Gräßel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Hütz, T., Eiben, B., Pietrzyk, U., Zilles, K., et al. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00034 · Cited by 193 · see citing works
Zhang, Z. (1995) · Physics in Medicine and Biology · doi:10.1088/0031-9155/40/3/001 · Cited by 192 · see citing works
Bastiani, M., Shah, N. J., Goebel, R., & Roebroeck, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.002 · Cited by 192 · see citing works
Modeling the effects of anesthesia on the electroencephalogram
Bojak, I., & Liley, D. T. J. (2005) · Physical Review E · doi:10.1103/physreve.71.041902 · Cited by 190 · see citing works
Modeling the outcome of structural disconnection on resting-state functional connectivity
Cabral, J., Hugues, E., Kringelbach, M. L., & Deco, G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.007 · Cited by 182 · see citing works
A model of the spatial-temporal characteristics of the alpha rhythm
van Rotterdam, A., Lopes, F. H. D. S., van den Ende, J., et al. (1982) · Bulletin of Mathematical Biology · doi:10.1007/bf02463252 · Cited by 182 · see citing works
Steyn‐Ross, M. L., Steyn‐Ross, D. A., Sleigh, J. W., & Liley, D. T. J. (1999) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.60.7299 · Cited by 178 · see citing works
Breakspear, M. J., Terry, J. R., & Friston, K. J. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x/14/4/305 · Cited by 176 · see citing works
CoCoMac 2.0 and the future of tract-tracing databases
Bakker, R., Wächtler, T., & Diesmann, M. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00030 · Cited by 176 · see citing works
Measuring and comparing brain cortical surface area and other areal quantities
Winkler, A. M., Sabuncu, M. R., Yeo, B. T. T., Fischl, B. R., Greve, D. N., Kochunov, P., Nichols, T. E., Blangero, J., & Glahn, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.026 · Cited by 176 · see citing works
Kriegeskorte, N., Cusack, R., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.059 · Cited by 175 · see citing works
The Wilson–Cowan model, 36 years later
Alain Destexhe, A., & B, T. J. S. A. (2009) · Biological Cybernetics · doi:10.1007/s00422-009-0328-3 · Cited by 170 · see citing works
From simple graphs to the connectome: Networks in neuroimaging
Sporns, O. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.085 · Cited by 170 · see citing works
Modal analysis of corticothalamic dynamics, electroencephalographic spectra, and evoked potentials
Robinson, P. A., Loxley, P., O’Connor, S. C., & RENNIE, C. J. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.63.041909 · Cited by 168 · see citing works
Realistically Coupled Neural Mass Models Can Generate EEG Rhythms
Sotero, R. C., Trujillo‐Barreto, N. J., Iturria‐Medina, Y., Carbonell, F., & Jiménez, J. C. (2007) · Neural Computation · doi:10.1162/neco.2007.19.2.478 · Cited by 161 · see citing works
The development of a noisy brain.
McIntosh, A. R., Kovacevic, N., Lippé, S., Garrett, D. D., Grady, C., & Jirsa, V. (2010) · PubMed · Cited by 160 · see citing works
Self-citationMechanisms of Cortical Electrical Activity and Emergence of Gamma Rhythm
RENNIE, C. J., Wright, J. J., & Robinson, P. A. (2000) · Journal of Theoretical Biology · doi:10.1006/jtbi.2000.2040 · Cited by 154 · see citing works
Spatiotemporal forward solution of the EEG and MEG using network modeling
Jirsa, V., Jantzen, K. J., Fuchs, A., et al. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.1009385 · Cited by 153 · see citing works
Self-citationBifurcation analysis of neural mass models: Impact of extrinsic inputs and dendritic time constants
Spiegler, A., Kiebel, S. J., Atay, F. M., & Knösche, T. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.081 · Cited by 150 · see citing works
Self-citationStefanescu, R. A., & Jirsa, V. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000219 · Cited by 149 · see citing works
Self-citationPerformance of a model for a local neuron population
Zetterberg, L. H., Kristiansson, L., & Mossberg, K. (1978) · Biological Cybernetics · doi:10.1007/bf00337367 · Cited by 142 · see citing works
Chauve, C., & Tannier, É. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000234 · Cited by 141 · see citing works
Simulation of electrocortical waves
Wright, J. J., & Liley, D. T. J. (1995) · Biological Cybernetics · doi:10.1007/bf00202790 · Cited by 140 · see citing works
Homogeneous nets of neuron-like elements
Амари, Ш.-И. (1975) · Biological Cybernetics · doi:10.1007/bf00339367 · Cited by 138 · see citing works
Mapping brains without coordinates
Kötter, R., & Wanke, E. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1625 · Cited by 137 · see citing works
Neural Mass Activity, Bifurcations, and Epilepsy
Touboul, J., Wendling, F., Chauvel, P., & Faugeras, O. D. (2011) · Neural Computation · doi:10.1162/neco_a_00206 · Cited by 134 · see citing works
Magnetoencephalography: A tool for functional brain imaging
H�m�l�inen, M. S. (1992) · Brain Topography · doi:10.1007/bf01129036 · Cited by 129 · see citing works
Cortical network dynamics with time delays reveals functional connectivity in the resting brain
Ghosh, A., Rho, Y.-A., McIntosh, A. R., Kötter, R., & Jirsa, V. (2008) · Cognitive Neurodynamics · doi:10.1007/s11571-008-9044-2 · Cited by 127 · see citing works
Self-citationSpatial relationship between neuronal activity and BOLD functional MRI
Kim, D., Ronen, I., Olman, C. A., Kim, S., Uǧurbil, K., & Toth, L. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.10.018 · Cited by 124 · see citing works
Forward Field Computation with OpenMEEG
Gramfort, A., Papadopoulo, T., Olivi, E., et al. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/923703 · Cited by 122 · see citing works
Feasibility of the homogeneous head model in the interpretation of neuromagnetic fields
Hämäläinen, M., & Sarvas, J. (1987) · Physics in Medicine and Biology · doi:10.1088/0031-9155/32/1/014 · Cited by 122 · see citing works
Chimeras in networks of planar oscillators
Laing, C. R. (2010) · Physical Review E · doi:10.1103/physreve.81.066221 · Cited by 116 · see citing works
Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations
Dumas, G., Chávez, M., Nadel, J., & Martinerie, J. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036414 · Cited by 116 · see citing works
Making sense of brain network data
Sporns, O. (2013) · Nature Methods · doi:10.1038/nmeth.2485 · Cited by 115 · see citing works
Metastability and chimera states in modular delay and pulse-coupled oscillator networks
Wildie, M., & Shanahan, M. (2012) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4766592 · Cited by 114 · see citing works
Modeling Brain Resonance Phenomena Using a Neural Mass Model
Spiegler, A., Knösche, T. R., Schwab, K., Haueisen, J., & Atay, F. M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002298 · Cited by 113 · see citing works
Self-citationStability and Bifurcations in Neural Fields with Finite Propagation Speed and General Connectivity
Atay, F. M., & Hutt, A. (2004) · SIAM Journal on Applied Mathematics · doi:10.1137/s0036139903430884 · Cited by 112 · see citing works
Coombes, S., Venkov, N. A., Shiau, L., Bojak, I., Liley, D. T. J., & Laing, C. R. (2007) · Physical Review E · doi:10.1103/physreve.76.051901 · Cited by 112 · see citing works
Shmuel, A., Chaimow, D., Raddatz, G., Ugurbil, K., & Yacoub, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.040 · Cited by 111 · see citing works
Steady states and global dynamics of electrical activity in the cerebral cortex
Robinson, P. A., RENNIE, C. J., Wright, J. J., & Bourke, P. D. (1998) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.58.3557 · Cited by 111 · see citing works
Histological Validation of DW-MRI Tractography in Human Postmortem Tissue
Seehaus, A. K., Roebroeck, A., Chiry, O., Kim, D.-S., Ronen, I., Bratzke, H., Goebel, R., & Galuske, R. A. W. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs036 · Cited by 109 · see citing works
On the Influence of Amplitude on the Connectivity between Phases
Daffertshofer, A., & van Wijk, B. C. M. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00006 · Cited by 109 · see citing works
Simulation of electrocortical waves
Wright, J. J., & Liley, D. T. J. (1995) · Biological Cybernetics · doi:10.1007/s004220050136 · Cited by 108 · see citing works
Breakspear, M. J., Terry, J. R., & Friston, K. J. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x_14_4_305 · Cited by 107 · see citing works
Neural Fields with Distributed Transmission Speeds and Long‐Range Feedback Delays
Atay, F. M., & Hutt, A. (2006) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/050629367 · Cited by 105 · see citing works
Waxman, H. (2012) · Scholarpedia · doi:10.4249/scholarpedia.1451 · Cited by 104 · see citing works
Dynamic causal modeling with neural fields
Pinotsis, D. A., Moran, R., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.020 · Cited by 101 · see citing works
A coupled ordinary differential equation lattice model for the simulation of epileptic seizures
Larter, R. M., Speelman, B., & Worth, R. M. (1999) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166453 · Cited by 98 · see citing works
Liley, D. T. J., & Bojak, I. (2005) · PubMed · Cited by 98 · see citing works
Connecting Mean Field Models of Neural Activity to EEG and fMRI Data
Bojak, I., Oostendorp, T. F., Reid, A., & Kötter, R. (2010) · Brain Topography · doi:10.1007/s10548-010-0140-3 · Cited by 95 · see citing works
Neural field dynamics with local and global connectivity and time delay
Jirsa, V. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0260 · Cited by 94 · see citing works
Self-citationMean-field behavior in coupled oscillators with attractive and repulsive interactions
Hong, H., & Strogatz, S. H. (2012) · Physical Review E · doi:10.1103/physreve.85.056210 · Cited by 89 · see citing works
Hemodynamic Traveling Waves in Human Visual Cortex
Aquino, K. M., Schira, M. M., Robinson, P. A., Drysdale, P. M., & Breakspear, M. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002435 · Cited by 84 · see citing works
Nakagawa, T., Jirsa, V., Spiegler, A., McIntosh, A. R., & Deco, G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.055 · Cited by 81 · see citing works
Self-citationIntegrated MEG/EEG and fMRI model based on neural masses
Babajani, A., & Soltanian‐Zadeh, H. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.873748 · Cited by 76 · see citing works
Knock, S. A., McIntosh, A. R., Sporns, O., Kötter, R., Hagmann, P., & Jirsa, V. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2009.07.007 · Cited by 74 · see citing works
Self-citationAxonal Velocity Distributions in Neural Field Equations
Bojak, I., & Liley, D. T. J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000653 · Cited by 71 · see citing works
Synchrony and Clustering in Heterogeneous Networks with Global Coupling and Parameter Dispersion
Assisi, C. G., Jirsa, V., & Kelso, J. A. S. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.018106 · Cited by 70 · see citing works
Self-citationSteyn‐Ross, M. L., Steyn‐Ross, D. A., SLEIGH, J. W., et al. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.021902 · Cited by 67 · see citing works
Bezgin, G., Vakorin, V. A., van Opstal, A. J., McIntosh, A. R., & Bakker, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.013 · Cited by 63 · see citing works
The Mesoscopic Modeling of Burst Suppression during Anesthesia
Liley, D. T. J., & Walsh, M. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00046 · Cited by 63 · see citing works
Markounikau, V., Igel, C., Grinvald, A., & Jancke, D. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000919 · Cited by 62 · see citing works
Patchy propagators, brain dynamics, and the generation of spatially structured gamma oscillations
Robinson, P. A. (2006) · Physical Review E · doi:10.1103/physreve.73.041904 · Cited by 62 · see citing works
NONLINEAR INTERDEPENDENCE IN NEURAL SYSTEMS: MOTIVATION, THEORY, AND RELEVANCE
Breakspear, M. J., & Terry, J. R. (2002) · International Journal of Neuroscience · doi:10.1080/00207450290026193 · Cited by 62 · see citing works
Riera, J. J., Aubert, E., Iwata, K., Kawashima, R., Wan, X., & Ozaki, T. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1646 · Cited by 62 · see citing works
Systematic approximations of neural fields through networks of neural masses in the virtual brain
Spiegler, A., & Jirsa, V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.018 · Cited by 61 · see citing works
Self-citationThe functional geometry of local and horizontal connections in a model of V1
Bressloff, P. C., & Cowan, J. D. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2003.09.017 · Cited by 61 · see citing works
A data-driven framework for neural field modeling
Freestone, D. R., Aram, P., Dewar, M., Scerri, K., Grayden, D. B., & Kadirkamanathan, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.027 · Cited by 59 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Spiegler, 2015, The critical brain: dissipation and restin-state networks, PNAS
Cited by
Sorted by citations
The Kuramoto model in complex networks
Rodrigues, F. A., Peron, T., Ji, P., & Kurths, J. (2015) · Physics Reports · doi:10.1016/j.physrep.2015.10.008 · Cited by 1,026 · see citing works
“Other recent works have investigated further aspects of the relation between structure and dynamics of phase oscillators in anatomical and functional cortical networks [214, 378, 401–406], which includes studies on the effects of remote synchronization [407–409], a toolbox for large-scale simulations of brain dynamics…”
BackgroundLinking Structure and Function in Macroscale Brain Networks
Suárez, L. E., Markello, R. D., Betzel, R. F., & Mišić, B. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.008 · Cited by 745 · see citing works
Gradients of structure–function tethering across neocortex
Vázquez-Rodríguez, B., Suárez, L. E., Markello, R. D., Shafiei, G., Paquola, C., Hagmann, P., van den Heuvel, M. P., Bernhardt, B. C., Spreng, R. N., & Mišić, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903403116 · Cited by 458 · see citing works
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411 · see citing works
“Given that macroscale FC is modulated by both interregional connectivity strength as well as local population dynamics (Sanz-Leon et al., 2015), alterations in SC-FC coupling could potentially point to important shifts in the underlying drivers of various network phenomena.”
BackgroundThe Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread
Jirsa, V., Proix, T., Perdikis, D., Woodman, M., Wang, H., Gonzalez-Martinez, J. A., Bernard, C., Bénar, C., Guye, M., Chauvel, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.049 · Cited by 434 · see citing works
Self-citationIndividual brain structure and modelling predict seizure propagation
Proix, T., Bartoloméi, F., Guye, M., & Jirsa, V. (2017) · Brain · doi:10.1093/brain/awx004 · Cited by 281 · see citing works
“A BNM (Sanz-Leon et al., 2015) was constructed by placing at each node of the parcellation a neural mass model able to reproduce the temporal seizure dynamics and to switch autonomously between interictal and ictal states, the so-called Epileptor model (Jirsa et al., 2014).”
MethodSelf-citationComputational models of epileptiform activity
Wendling, F., Benquet, P., Bartoloméi, F., & Jirsa, V. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.03.027 · Cited by 192 · see citing works
Self-citationInferring multi-scale neural mechanisms with brain network modelling
Schirner, M., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2018) · eLife · doi:10.7554/elife.28927 · Cited by 194 · see citing works
Self-citationTranslational perspectives for computational neuroimaging
Stephan, K. E., Iglesias, S., Heinzle, J., & Diaconescu, A. O. (2015) · Neuron · doi:10.1016/j.neuron.2015.07.008 · Cited by 169 · see citing works
“A notable example of the rapid development of large-scale BNMs for neuroimaging data is the ‘‘Virtual Brain’’ project (Jirsa et al., 2010; Sanz-Leon et al., 2015).”
BackgroundLocal structure-function relationships in human brain networks across the lifespan
Esfahlani, F. Z., Faskowitz, J., Slack, J., Mišić, B., & Betzel, R. F. (2022) · Nature Communications · doi:10.1038/s41467-022-29770-y · Cited by 172 · see citing works
Frässle, S., Lomakina, E. I., Razi, A., Friston, K. J., Buhmann, J. M., & Stephan, K. E. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.090 · Cited by 169 · see citing works
Kunze, T., Hunold, A., Haueisen, J., Jirsa, V., & Spiegler, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.015 · Cited by 162 · see citing works
“The time course of uPy(t) of each area constituted the output of each area in the network and was propagated to a set of 62 EEG sensors by means of a propagation matrix, which was already included in the according TVB dataset.”
MethodInfluentialSelf-citationMagnetoencephalography in Cognitive Neuroscience: A Primer
Groß, J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.001 · Cited by 160 · see citing works
“One example of this approach is implemented in the Virtual Brain software (https://thevirtualbrain.org/tvb/zwei; Deco et al., 2017; Sanz-Leon et al., 2015; Schirner et al., 2018).”
MethodThe quest for multiscale brain modeling
D'Angelo, E. U., & Jirsa, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.06.007 · Cited by 155 · see citing works
Self-citationProix, T., Jirsa, V., Bartoloméi, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-02973-y · Cited by 152 · see citing works
“Mixed connectivities in network architectures are common in large-scale brain models 39,40 .”
Self-citationAn automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data
Schirner, M., Rothmeier, S., Jirsa, V., McIntosh, A. R., & Ritter, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.055 · Cited by 150 · see citing works
Self-citationMapping how local perturbations influence systems-level brain dynamics
Gollo, L. L., Roberts, J. A., & Cocchi, L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.057 · Cited by 143 · see citing works
“Mean-field models at the whole-brain scale roughly fall into two categories: coupled neural masses (Gollo and Breakspear, 2014; Sanz-Leon et al., 2015; Schmidt et al., 2015) and neural fields (Atasoy et al., 2016; Jirsa and Haken, 1996; Robinson et al., 2001; Robinson et al., 2016).”
BackgroundStefanovski, L., Triebkorn, P., Spiegler, A., Díaz-Cortés, M.-A., Solodkin, A., Jirsa, V., McIntosh, A. R., Ritter, P., & Initiative, A. D. N. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00054 · Cited by 140 · see citing works
Self-citationLearning function from structure in neuromorphic networks
Suárez, L. E., Richards, B. A., Lajoie, G., & Mišić, B. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00376-1 · Cited by 132 · see citing works
Iturria‐Medina, Y., Carbonell, F., Sotero, R. C., Chouinard-Decorte, F., & Evans, A. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.058 · Cited by 126 · see citing works
“…to take action locally and spread through anatomical connections (Cabral et al., 2012; Friston et al., 2014; Iturria-Medina et al., 2014; Mišić et al., 2015; Raj et al., 2012; Sanz Leon et al., 2013; Sanz-Leon et al., 2015; Sotero et al., 2007; Stam et al., 2016; Valdes-sosa et al., 2011a).”
Transmission time delays organize the brain network synchronization
Petkoski, S., & Jirsa, V. (2019) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2018.0132 · Cited by 131 · see citing works
Self-citationHashemi, M., Vattikonda, A. N., Ší́p, V., Guye, M., Bartoloméi, F., Woodman, M., & Jirsa, V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116839 · Cited by 131 · see citing works
Self-citationSymmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior
Pillai, A. S., & Jirsa, V. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.013 · Cited by 125 · see citing works
Self-citationWang, H., Woodman, M., Triebkorn, P., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.abp8982 · Cited by 118 · see citing works
“The Virtual Brain (TVB) ( 8 ) is a neuroinfor-matics platform for the construction and simulation of virtual brains that can provide the required functionality for EZN estimation and make it possible to perform causal inference on the full brain scale.”
MethodInfluentialSelf-citationBrain simulation as a cloud service: The Virtual Brain on EBRAINS
Schirner, M., Domide, L., Perdikis, D., Triebkorn, P., Stefanovski, L., Pai, R. K., Prodan, P., Valean, B., Palmer, J., Langford, C., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118973 · Cited by 113 · see citing works
Self-citationVirtual brain twins: from basic neuroscience to clinical use
Wang, H., Triebkorn, P., Breyton, M., Dollomaja, B., Lemaréchal, J.-D., Petkoski, S., Sorrentino, P., Depannemaecker, D., Hashemi, M., & Jirsa, V. (2024) · National Science Review · doi:10.1093/nsr/nwae079 · Cited by 111 · see citing works
Self-citationEdges in brain networks: Contributions to models of structure and function
Faskowitz, J., Betzel, R. F., & Sporns, O. (2021) · Network Neuroscience · doi:10.1162/netn_a_00204 · Cited by 102 · see citing works
Zimmermann, J., Perry, A., Breakspear, M. J., Schirner, M., Sachdev, P. S., Wen, W., Kochan, N. A., Mapstone, M., Ritter, P., McIntosh, A. R., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.04.017 · Cited by 97 · see citing works
Brain-Inspired Computing: A Systematic Survey and Future Trends
Li, G., Deng, L., Tang, H., Pan, G., Tian, Y., Roy, K., & Maass, W. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3429360 · Cited by 98 · see citing works
“…to macroscopic scale (e.g., mean-field neural mass models of brain regions [384], [385], and models of entire brain networks [124], [386], [387]), and new properties persistently emerge and previous properties may vanish during the cross-scale transmission of neural dynamics, which can be…”
CoreNEURON : An Optimized Compute Engine for the NEURON Simulator
Kumbhar, P., Hines, M. L., FOURIAUX, J., Ovcharenko, A., King, J. G., Delalondre, F., & Schürmann, F. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00063 · Cited by 90 · see citing works
Next-generation neural mass and field modeling
Byrne, Á., O’Dea, R. D., Forrester, M., Ross, J., & Coombes, S. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00406.2019 · Cited by 96 · see citing works
“Moreover, Wilson–Cowan style neural mass models are a key component of the Virtual Brain project that aims to deliver the first simulation of the human brain based on personalised large-scale connectivity [105].”
BackgroundSpiegler, A., Hansen, E. C. A., Bernard, C., McIntosh, A. R., & Jirsa, V. (2016) · eNeuro · doi:10.1523/eneuro.0068-16.2016 · Cited by 92 · see citing works
Self-citationIndividual 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 · see citing works
Self-citationNeuronal Cascades Shape Whole-Brain Functional Dynamics at Rest
Rabuffo, G., Fousek, J., Bernard, C., & Jirsa, V. (2021) · eNeuro · doi:10.1523/eneuro.0283-21.2021 · Cited by 83 · see citing works
“36 We use The Virtual Brain (TVB), a neuroinformatics and simulation platform, which allows 37 connectome-based whole brain modeling of multiple species including human [28] and mice [29].”
MethodSelf-citationDynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy
Courtiol, J., Guye, M., Bartoloméi, F., Petkoski, S., & Jirsa, V. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0905-19.2020 · Cited by 85 · see citing works
Self-citationVirtual epileptic patient brain modeling: Relationships with seizure onset and surgical outcome
Makhalova, J., Villalon, S. M., Wang, H., et al. (2022) · Epilepsia · doi:10.1111/epi.17310 · Cited by 85 · see citing works
Self-citationNext Generation Neural Mass Models
Coombes, S., & Byrne, Á. (2018) · PoliTO Springer series · doi:10.1007/978-3-319-71048-8_1 · Cited by 85 · see citing works
“Moreover, they are now an integral part of the Virtual Brain project that aims to deliver the first open simulation of the human brain based on individual large-scale connectivity [14], as well as play a key role in the neurocomputational modelling of neurological and psychiatric disorders [15].”
BackgroundBiophysically detailed forward modeling of the neural origin of EEG and MEG signals
Næss, S., Halnes, G., Hagen, E., Hagler, D. J., Dale, A. M., Einevoll, G. T., & Ness, T. V. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117467 · Cited by 66 · see citing works
Clinical Concepts Emerging from fMRI Functional Connectomics
Matthews, P. M., & Hampshire, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.031 · Cited by 82 · see citing works
“, The Virtual Brain (Sanz-Leon et al., 2015), the Blue Brain Project (Markram et al.”
Proix, T., Spiegler, A., Schirner, M., Rothmeier, S., Ritter, P., & Jirsa, V. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.016 · Cited by 81 · see citing works
“…(BNMs) are networks of neural masses that can provide a first order approximation to explain such brain signals (Bojak and Oostendorp, 2011; Deco et al., 2009; Deco and Jirsa, 2012; Ghosh et al., 2008; Jirsa et al., 2010; Ritter et al., 2013; Sanz-Leon et al., 2015; Spiegler and Jirsa, 2013).”
BackgroundMethodInfluentialSelf-citationPersonalized brain network models for assessing structure–function relationships
Bansal, K., Nakuci, J., & Muldoon, S. F. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.014 · Cited by 84 · see citing works
Batallé, D., Edwards, A. D., & O’Muircheartaigh, J. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12838 · Cited by 83 · see citing works
A macaque connectome for large-scale network simulations in TheVirtualBrain
Shen, K., Bezgin, G., Schirner, M., Ritter, P., Everling, S., & McIntosh, A. R. (2019) · Scientific Data · doi:10.1038/s41597-019-0129-z · Cited by 83 · see citing works
Petkoski, S., Palva, J. M., & Jirsa, V. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006160 · Cited by 82 · see citing works
Self-citationMultistability in Large Scale Models of Brain Activity
Golos, M., Jirsa, V., & Daucé, E. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004644 · Cited by 79 · see citing works
Self-citationArtificial Intelligence and Computational Approaches for Epilepsy
An, S., Kang, C., & Lee, H. W. (2020) · Journal of Epilepsy Research · doi:10.14581/jer.20003 · Cited by 74 · see citing works
“They have proposed a novel methodology to develop individual brain network models, so called the Virtual Epileptic Patient (VEP),(98) by incorporating multimodal data from each epilepsy patient, and have also developed and distributed the neuroinformatics platform, The Virtual Brain, to enable the model construction.(…”
MethodFunctional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain
Falcon, M. I., Riley, J. D., Jirsa, V., McIntosh, A. R., Chen, E. E., & Solodkin, A. (2016) · eNeuro · doi:10.1523/eneuro.0158-15.2016 · Cited by 70 · see citing works
“…of the system was performed using stochastic Heun’s method (Mannella 2002), with an integration step size of 0.0122 ms. (5) The BOLD signals were derived from the field potentials using a hemodynamic response function implemented with a gamma kernel (Boynton et al., 1996; Sanz-Leon et al., 2015).”
InfluentialSelf-citationFitzgibbon, S. P., DeLosAngeles, D., Lewis, T. W., Powers, D. M. W., Grummett, T. S., Whitham, E. M., Ward, L. M., Willoughby, J. O., & Pope, K. J. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.12.009 · Cited by 67 · see citing works
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 66 · see citing works
Modeling regional changes in dynamic stability during sleep and wakefulness
Ipiña, I. P., Kehoe, P. D., Kringelbach, M. L., Laufs, H., Ibáñez, A. M., Deco, G., Perl, Y. S., & Tagliazucchi, E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116833 · Cited by 67 · see citing works
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 66 · see citing works
On the central role of brain connectivity in neurodegenerative disease progression
Iturria‐Medina, Y., & Evans, A. C. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00090 · Cited by 61 · see citing works
“Similarly, there are promising advances in the modeling and understanding of neuronal/synaptic spreading across structural networks (Sotero et al., 2007; Valdes-Sosa et al., 2009; Sanz-Leon et al., 2013, 2015; Messé et al., 2015), but thesemethodologies also require additional predictive validation.”
BackgroundPutting the “dynamic” back into dynamic functional connectivity
Heitmann, S., & Breakspear, M. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00041 · Cited by 65 · see citing works
Melozzi, F., Woodman, M., Jirsa, V., & Bernard, C. (2017) · eNeuro · doi:10.1523/eneuro.0111-17.2017 · Cited by 66 · see citing works
“, 2000) using the default value implemented in The Virtual Brain (Sanz-Leon et al., 2015).”
BackgroundMethodInfluentialSelf-citationSrivastava, P., Nozari, E., Kim, J. Z., Ju, H., Zhou, D., Becker, C. O., Pasqualetti, F., Pappas, G. J., & Bassett, D. S. (2020) · Network Neuroscience · doi:10.1162/netn_a_00158 · Cited by 63 · see citing works
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 67 · see citing works
“The Virtual Brain (Ritter et al., 2013; Sanz-Leon et al., 2015; Schirner et al., 2021) is an excellent example of a toolbox that affords access to this approach, and has been used to demonstrate important links between structure and function across many spatiotemporal scales.”
MethodA comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics
Goldman, J. S., Kusch, L., Aquilué-Llorens, D., Yalçınkaya, B. H., Depannemaecker, D., Ancourt, K., Nghiem, T.-A. E., Jirsa, V., & Alain Destexhe, A. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.1058957 · Cited by 65 · see citing works
Self-citationLawn, T., Howard, M. A., Turkheimer, F., Mišić, B., Deco, G., Martins, D., & Dipasquale, O. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105193 · Cited by 63 · see citing works
“(Ritter et al., 2013; Sanz-Leon et al., 2015) to model the perturbed network dynamics seen in patients with Alzheimer’s disease (Stefanovski et al.”
A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain
Falcon, M. I., Jirsa, V., & Solodkin, A. (2016) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000344 · Cited by 58 · see citing works
Self-citationHeterogeneity of time delays determines synchronization of coupled oscillators
Petkoski, S., Spiegler, A., Proix, T., et al. (2016) · Physical review. E · doi:10.1103/physreve.94.012209 · Cited by 58 · see citing works
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 59 · see citing works
Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools
Siettos, C. I., & Starke, J. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1348 · Cited by 55 · see citing works
Becker, R., Knock, S. A., Ritter, P., & Jirsa, V. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004352 · Cited by 53 · see citing works
Self-citationKuramoto Model for Excitation-Inhibition-Based Oscillations
Montbrió, E., & Pazó, D. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.244101 · Cited by 54 · see citing works
Papadopoulos, L., Lynn, C. W., Battaglia, D. A., & Bassett, D. S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008144 · Cited by 54 · see citing works
NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics
Sanz‐Leon, P., Robinson, P. A., Knock, S. A., Drysdale, P. M., Abeysuriya, R., Fung, F., RENNIE, C. J., & Zhao, X. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006387 · Cited by 55 · see citing works
“125 A few software packages are available to model neural masses and neural fields: [7] 126 developed a neuroinformatics platform for large-scale brain modeling in terms of a 127 network of linked neural masses with anatomically specified cortical geometry [55], 128 long-range connectivity, and local short-range conne…”
BackgroundMethodSelf-citationThe Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke
Falcon, M. I., Riley, J. D., Jirsa, V., McIntosh, A. R., Shereen, A. D., Chen, E. E., & Solodkin, A. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00228 · Cited by 51 · see citing works
Self-citationHashemi, M., Vattikonda, A. N., Jha, J., Ší́p, V., Woodman, M., Bartoloméi, F., & Jirsa, V. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.03.040 · Cited by 51 · see citing works
Self-citationPartial synchronization patterns in brain networks
Schöll, E. (2021) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/ac3b97 · Cited by 50 · see citing works
Tesler, F., Linne, M., & Alain Destexhe, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-32618-0 · Cited by 47 · see citing works
“Furthermore, our framework can be combined with different neuronal models and has the potential of being incorporated in whole brain simulators such as The Virtual Brain 44,69 .”
MethodYu, Y., Fan, Y., Han, F., Luan, G., & Wang, Q. (2023) · Science China Technological Sciences · doi:10.1007/s11431-022-2341-x · Cited by 49 · see citing works
Stefanovski, L., Meier, J. M., Pai, R. K., Triebkorn, P., Lett, T. A. P., Martin, L., Bülau, K., Hofmann‐Apitius, M., Solodkin, A., McIntosh, A. R., et al. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.630172 · Cited by 46 · see citing works
Synchronization patterns: from network motifs to hierarchical networks
Krishnagopal, S., Lehnert, J., Poel, W., Zakharova, A., & Schöll, E. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0216 · Cited by 45 · see citing works
Dubois, J., Adibpour, P., Poupon, C., Hertz-Pannier, L., & Dehaene-Lambertz, G. (2016) · Brain Plasticity · doi:10.3233/bpl-160031 · Cited by 40 · see citing works
“Thus accurate anatomo-functional models of networks would require to integer all these properties and account for spatial and temporal constraints [150].”
BackgroundArbabyazd, 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 46 · see citing works
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 42 · see citing works
Data-driven brain network models differentiate variability across language tasks
Bansal, K., Medaglia, J. D., Bassett, D. S., Vettel, J. M., & Muldoon, S. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006487 · Cited by 42 · see citing works
“Using experimentally obtained structural brain network data combined with biologically motivated computational models of brain dynamics [36], one can build personalized brain network models of human brain activity [37, 38, 39].”
MethodHashemi, M., Vattikonda, A. N., Ší́p, V., Diaz-Pier, S., Peyser, A., Wang, H., Guye, M., Bartoloméi, F., Woodman, M., & Jirsa, V. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009129 · Cited by 44 · see citing works
“The Virtual Brain (TVB) is a computational framework to simulate large-scale brain network models based on individual subject data [59, 60].”
BackgroundSelf-citationStructural and functional, empirical and modeled connectivity in the cerebral cortex of the rat
Díaz‐Parra, A., Osborn, Z. G., Canals, S., Moratal, D., & Sporns, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.046 · Cited by 42 · see citing works
“TVB simulations take structural data about a brain - structural connectivity, spatial coordinates of ROIs, and tract length distances between ROIs - and output simulated neural activity time series for each ROI, using a given model type and set of simulation parameter values (Sanz-Leon et al., 2015).”
MethodIturria‐Medina, Y., Carbonell, F., & Evans, A. C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.028 · Cited by 42 · see citing works
Mean-Field Models for EEG/MEG: From Oscillations to Waves
Byrne, Á., Ross, J., Nicks, R., & Coombes, S. (2021) · Brain Topography · doi:10.1007/s10548-021-00842-4 · Cited by 42 · see citing works
“2020), and are a key ingredient of the Virtual Brain project that aims to deliver the first open simulation of the human brain based on individual large-scale connectivity (Sanz-Leon et al. 2015).”
BackgroundPetkoski, S., Ritter, P., & Jirsa, V. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac500 · Cited by 41 · see citing works
“Time delays and the weights compose the space-time structure of the connectivity that is crucial in shaping its macroscopic activity (Deco et al. 2009; Ghosh et al. 2008; Sanz-Leon et al. 2015).”
BackgroundMethodInfluentialSelf-citationNormalizing the brain connectome for communication through synchronization
Petkoski, S., & Jirsa, V. (2022) · Network Neuroscience · doi:10.1162/netn_a_00231 · Cited by 42 · see citing works
“Models of brain networks based on human connectomes (10) have demonstrated individual predictive power in resting state paradigms (11), cognitive tasks (12) and brain disease such as epilepsy (13, 14).”
Self-citationA Stochastic Version of the Jansen and Rit Neural Mass Model: Analysis and Numerics
Ableidinger, M., Buckwar, E., & Hinterleitner, H. (2017) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-017-0046-4 · Cited by 41 · see citing works
“They have successfully been used to fit neuroimaging data, understanding EEG rhythms [8] or epileptic brain dynamics [9], and are now also a major building block in the Virtual Brain [10].”
MethodModeling seizures: From single neurons to networks
Depannemaecker, D., Alain Destexhe, A., Jirsa, V., et al. (2021) · Seizure · doi:10.1016/j.seizure.2021.06.015 · Cited by 41 · see citing works
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 · see citing works
Sorrentino, P., Petkoski, S., Sparaco, M., Lopez, E. T., Signoriello, E., Baselice, F., Bonavita, S., Pirozzi, M. A., Quarantelli, M., Sorrentino, G., et al. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0938-22.2022 · Cited by 37 · see citing works
“For this reason, estimating a comprehensive map of the delays poses great challenges, and most modeling studies assume homogeneous velocities of conduction (typically ranging from 2 to 10 m/s; Sanz-Leon et al., 2015; Breakspear, 2017).”
BackgroundSelf-citationGerster, M., Taher, H., Škoch, A., Hlinka, J., Guye, M., Bartoloméi, F., Jirsa, V., Zakharova, A., & Olmi, S. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.675272 · Cited by 39 · see citing works
Self-citationNetworks of piecewise linear neural mass models
Coombes, S., Lai, Y. M., Şaylı, M., & Thul, R. (2018) · European Journal of Applied Mathematics · doi:10.1017/s0956792518000050 · Cited by 37 · see citing works
Mills, B. D., Grayson, D. S., Shunmugavel, A., Miranda-Domínguez, Ó., Feczko, E., Earl, E., Neve, K. A., & Fair, D. A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2910-17.2018 · Cited by 36 · see citing works
“Modeling approaches to predict FC networks based on macro- or meso-scale anatomical connectivity networks commonly simulate mass neuronal activity by optimizing parameters that describe local population dynamics as well as the contribution of inter-areal connectivity (Honey et al., 2009; Messé et al., 2014; Sanz-Leon…”
BackgroundDomhof, J. W. M., Jung, K., Eickhoff, S. B., & Popovych, O. V. (2021) · Network Neuroscience · doi:10.1162/netn_a_00202 · Cited by 36 · see citing works
“Previous studies suggested that dynamical whole-brain models are able to simulate the restingstate brain activity on a personalized level (Bansal et al., 2018; Deco et al., 2017; Ritter et al., 2013; Sanz-Leon et al., 2015; Zimmermann et al., 2018).”
BackgroundMethodCombining hypothesis- and data-driven neuroscience modeling in FAIR workflows
Eriksson, O., Bhalla, U. S., Blackwell, K. T., Crook, S. M., Keller, D. X., Kramer, A., Linne, M., Saudargienė, A., Wade, R. C., & Kotaleski, J. H. (2022) · eLife · doi:10.7554/elife.69013 · Cited by 35 · see citing works
Synchronization in Functional Networks of the Human Brain
Hövel, P., Viol, A., Loske, P., Merfort, L., & Vuksanović, V. (2018) · Journal of Nonlinear Science · doi:10.1007/s00332-018-9505-7 · Cited by 36 · see citing works
Vagus nerve stimulation for epilepsy: A narrative review of factors predictive of response
Clifford, H. J., Paranathala, M. P., Wang, Y., Thomas, R. H., Costa, T. D. S., Duncan, J. S., & Taylor, P. N. (2024) · Epilepsia · doi:10.1111/epi.18153 · Cited by 34 · see citing works
Yu, Y., Wang, H., Liu, X., & Wang, Q. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-10132-w · Cited by 34 · see citing works
“The propagation matrix is obtained by solving the boundary element method and is available in the TVB dataset [34,35].”
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 · see citing works
“, (dynamic) FC of RSNs, can be reached by an appropriate parameter calibration (Ritter et al., 2013; Sanz Leon et al., 2013; Sanz-Leon et al., 2015; Falcon et al., 2016).”
BackgroundBrain simulation augments machine‐learning–based classification of dementia
Triebkorn, P., Stefanovski, L., Dhindsa, K., Díaz-Cortés, M.-A., Bey, P., Bülau, K., Pai, R. K., Spiegler, A., Solodkin, A., Jirsa, V., et al. (2022) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12303 · Cited by 31 · see citing works
Self-citationFully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo
Jha, J., Hashemi, M., Vattikonda, A. N., Wang, H., & Jirsa, V. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac9037 · Cited by 33 · see citing works
Self-citationSheheitli, H., & Jirsa, V. (2020) · Network Neuroscience · doi:10.1162/netn_a_00134 · Cited by 31 · see citing works
Self-citationMascaro, A. L. A., Falotico, E., Petkoski, S., Pasquini, M., Vannucci, L., Tort‐Colet, N., Conti, E., Resta, F., Spalletti, C., Ramalingasetty, S. T., et al. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00031 · Cited by 31 · see citing works
“Neural masses formalisms have been used over many years to develop macroscopic models that capture the collective dynamics of large neural assemblies (Deco et al., 2008; Sanz-Leon et al., 2015).”
BackgroundMethodSelf-citation
- Authors
- Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K.
- Affiliations
- QIMR Berghofer Medical Research Institute (AU)
- Inserm (FR)
- Corresponding author
- Jirsa, Viktor K.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 111
- Pages
- 385-430
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2074725578
- Semantic Scholar
- 23be65f2b2fee15432e3086c0d98a6610709848f
- Cited by
- 356
- References
- 240
- Citations by year
- 2026: 27 · 2025: 53 · 2024: 41 · 2023: 34 · 2022: 37 · 2021: 36 · 2020: 29 · 2019: 23 · 2018: 25 · 2017: 21 · 2016: 15 · 2015: 13 · 2014: 2
- FWCI
- 14.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- James S. McDonnell Foundation (FP7-ICT BrainScales - 2009-6)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- The Virtual Brain, whole-brain dynamics, neural mass model, structural connectivity, forward model, EEG, MEG, functional MRI, stereoelectroencephalography, mathematical notation, computational neuroscience simulation
- MeSH terms
- Brain, Humans, Models, Neurological, Nerve Net, Functional Neuroimaging
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
