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Dynamic causal modeling of evoked responses in EEG and MEG
Open accessAbstract
Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.
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“DCM has also been formulated for M/EEG data, serving to explain a variety of data features such as event-related potentials (David et al., 2006), induced responses (Chen et al., 2008), or steady-state responses (Moran et al., 2009).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as David et al., 2006
In the citing paper
“Dynamic causal models (DCMs)1 have also been implemented for a range of measurement techniques other than fMRI, including electroencephalography (EEG), magnetoencephalography (MEG), and local field potentials (LFPs) obtained from invasive recordings in humans or animals, both in the time domain (Daunizeau et al., 2009b; David et al., 2006; Kiebel et al., 2006) and frequency domain (Chen et al., 2”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as David et al., 2006
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Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Numerical Solution of Stochastic Differential Equations
Kloeden, P. E., & Platen, E. (1992) · Springer eBooks · doi:10.1007/978-3-662-12616-5 · Cited by 5,131
Numerical solution of stochastic differential equations
Kloeden, P. E. 1., & Platen, E. (2016) · doi:10.5194/gmd-2016-45-rc1 · Cited by 5,119
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Self-citationDistributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,537
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
A cortical representation of the local visual environment
Epstein, R. A., & Kanwisher, N. (1998) · Nature · doi:10.1038/33402 · Cited by 3,083
Effects of Attention and Emotion on Face Processing in the Human Brain
Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001) · Neuron · doi:10.1016/s0896-6273(01)00328-2 · Cited by 1,664
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Items 201–300 of 618
Empty nose syndrome: Limbic system activation observed by functional magnetic resonance imaging
Freund, W., Wunderlich, A. P., Stöcker, T., Schmitz, B., & Scheithauer, M. (2011) · The Laryngoscope · doi:10.1002/lary.21903 · Cited by 46
Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence
Hu, B., Guan, Z., Chen, G., & Chen, C. L. P. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3071110 · Cited by 44
Al‐Ezzi, A. A. A., Yahya, N., Kamel, N., Faye, I., Alsaih, K., & Ebenezer, E. G. M. (2021) · IEEE Access · doi:10.1109/access.2021.3089358 · Cited by 45
Neurocognitive and Neuroplastic Mechanisms of Novel Clinical Signs in CRPS
Kuttikat, A., Noreika, V., Shenker, N., Chennu, S., Bekinschtein, T. A., & Brown, C. A. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00016 · Cited by 43
Ge, T., Kendrick, K. M., & Feng, J. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000570 · Cited by 43
Farahibozorg, S.-R., Henson, R. N., Woollams, A. M., & Hauk, O. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab501 · Cited by 48
Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling
Cooray, G., Sengupta, B., Douglas, P. K., Englund, M., Wickström, R., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.064 · Cited by 43
Self-citationPhysiology-based modeling of cortical auditory evoked potentials
Kerr, C. C., RENNIE, C. J., & Robinson, P. A. (2007) · Biological Cybernetics · doi:10.1007/s00422-007-0201-1 · Cited by 44
A mesostate-space model for EEG and MEG
Jean Daunizeau, A., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.06.034 · Cited by 44
Self-citationSpatiotemporal brain hierarchies of auditory memory recognition and predictive coding
Bonetti, L., Fernández‐Rubio, G., Carlomagno, F., Dietz, M. J., Pantazis, D., Vuust, P., & Kringelbach, M. L. (2024) · Nature Communications · doi:10.1038/s41467-024-48302-4 · Cited by 48
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Papadopoulou, M., Leite, M., van Mierlo, P., Vonck, K. E. J., Lemieux, L., Friston, K. J., & Marinazzo, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.007 · Cited by 41
Self-citationEmpirical Bayes for Group (DCM) Studies: A Reproducibility Study
Vladimir Litvak, A., Garrido, M. I., Zeidman, P., & Friston, K. J. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00670 · Cited by 43
Self-citationAdams, R. A., Bauer, M., Pinotsis, D. A., & Friston, K. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.055 · Cited by 45
Self-citationPirondini, E., Babadi, B., Obregon-Henao, G., Lamus, C., Malik, W. Q., Hämäläinen, M., & Purdon, P. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2739824 · Cited by 45
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Self-citationWolff, A., & Northoff, G. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02337-z · Cited by 43
Wang, P., & Knösche, T. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077876 · Cited by 43
Dimitriadis, S. I., Sun, Y., Thakor, N. V., & Bezerianos, A. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00454 · Cited by 44
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Ahmadizadeh, S., Karoly, P. J., Nešić, D., Grayden, D. B., Cook, M. J., Soudry, D., & Freestone, D. R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192842 · Cited by 41
Euler, M. J. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.08.013 · Cited by 42
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Ton, R., Deco, G., & Daffertshofer, A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003736 · Cited by 36
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Sato, W., Kochiyama, T., & Uono, S. (2015) · Scientific Reports · doi:10.1038/srep12432 · Cited by 40
Zhang, T., Wu, J., Li, F., Caffo, B. S., & Boatman‐Reich, D. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.988213 · Cited by 37
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Henson, R. N., Abdulrahman, H., Flandin, G., & Vladimir Litvak, A. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00300 · Cited by 39
Hinrichs, H., Noesselt, T., & Heinze, H. (2007) · Human Brain Mapping · doi:10.1002/hbm.20382 · Cited by 39
Laszlo, S., & Armstrong, B. C. (2014) · Brain and Language · doi:10.1016/j.bandl.2014.03.002 · Cited by 40
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Im, C., Jung, Y., Lee, S., Koh, D., Kim, D.-W., & Kim, B.-M. (2010) · Optics Express · doi:10.1364/oe.18.005730 · Cited by 37
Quantifying auditory event-related responses in multichannel human intracranial recordings
Boatman‐Reich, D. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00004 · Cited by 38
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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
Spriggs, M. J., Sumner, R. L., McMillan, R. L., Moran, R., Kirk, I. J., & Muthukumaraswamy, S. D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.060 · Cited by 38
Schendan, H. E., & Ganis, G. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01289 · Cited by 36
Model-based feature construction for multivariate decoding
Brodersen, K. H., Haiss, F., Ong, C. S., Jung, F., Tittgemeyer, M., Buhmann, J. M., Weber, B., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.036 · Cited by 36
Porta, A., Faes, L., Marchi, A., Bari, V., MARIA, B. D., Guzzetti, S., Colombo, R., & Raimondi, F. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00301 · Cited by 36
Hashemi, M., Hutt, A., Buhry, L., & SLEIGH, J. W. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9369-x · Cited by 30
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Gong, M., Yang, F., & Li, S. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13174 · Cited by 40
Uono, S., Sato, W., Kochiyama, T., Kubota, Y., Sawada, R., Yoshimura, S., & Toichi, M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23505 · Cited by 36
Kumar, S., & Schönwiesner, M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3812-12.2012 · Cited by 35
Campo, P., Poch, C., Toledano, R., Igoa, J. M., Carmona, M. B., García‐Morales, I., & Gil‐Nagel, A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0645-13.2013 · Cited by 34
Llinás, R. R., Ustinin, M. N., Rykunov, S. D., Boyko, A. I., Sychev, V. V., Walton, K. D., Rabello, G. M., & Garcia, J. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00373 · Cited by 35
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Martínez‐Cañada, P., Ness, T. V., Einevoll, G. T., Fellin, T., & Panzeri, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008893 · Cited by 39
Freund, W., Wunderlich, A. P., Stuber, G., Mayer, F., Steffen, P. R. P., Mentzel, M., Schmitz, B., & Weber, F. (2011) · Clinical Journal of Pain · doi:10.1097/ajp.0b013e31821d9063 · Cited by 35
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Bénar, C., Velmurugan, J., López‐Madrona, V. J., Pizzo, F., & Badier, J. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100285 · Cited by 36
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Woodman, M., Pézard, L., Domide, L., Knock, S. A., Sanz‐Leon, P., Mersmann, J., McIntosh, A. R., & Jirsa, V. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00036 · Cited by 38
Haueisen, J., Leistritz, L., Süße, T., Curio, G., & Witte, H. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.036 · Cited by 34
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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 29
Kimura, M., & Takeda, Y. (2015) · Brain Research · doi:10.1016/j.brainres.2015.01.013 · Cited by 33
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Wilmer, A., de Lussanet, M. H. E., & Lappe, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044633 · Cited by 32
Corticothalamic dynamics: Structure of parameter space, spectra, instabilities, and reduced model
Roberts, J. A., & Robinson, P. A. (2012) · Physical Review E · doi:10.1103/physreve.85.011910 · Cited by 30
Ihalainen, R., Gosseries, O., de Steen, F. V., Raimondo, F., Panda, R., Bonhomme, V. L., Marinazzo, D., Bowman, H., Laureys, S., & Chennu, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117841 · Cited by 36
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Cooray, G., Garrido, M. I., Brismar, T., & Hyllienmark, L. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.06.026 · Cited by 30
Hashemi, M., Hutt, A., & SLEIGH, J. W. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-015-0569-1 · Cited by 31
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Thai, N. J. (2009) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2008.12.015 · Cited by 33
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Fastenrath, M., Friston, K. J., & Kiebel, S. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.07.041 · Cited by 31
Self-citationA Spatiotemporal Dynamic Solution to the MEG Inverse Problem: An Empirical Bayes ApproachPreprint
Lamus, C., Hämäläinen, M., Temereanca, S., Brown, E. N., & Purdon, P. L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05056 · Cited by 32
Barrès, V., Simons, A., & Arbib, M. A. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.09.021 · Cited by 29
Moran, R., Molholm, S., Reilly, R. B., & Foxe, J. J. (2008) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2008.06187.x · Cited by 31
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Gilbert, J. R., Ballard, E. D., Galiano, C. S., Nugent, A. C., & Zarate, C. A. (2020) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.011 · Cited by 28
Naruse, Y., Matani, A., Miyawaki, Y., & Okada, M. (2009) · Human Brain Mapping · doi:10.1002/hbm.20899 · Cited by 28
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Sato, W., Kochiyama, T., Uono, S., Yoshikawa, S., & Toichi, M. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw036 · Cited by 29
Friston, K. J. (2007) · Elsevier eBooks · doi:10.1016/b978-012372560-8/50001-2 · Cited by 29
Self-citationTMS provokes target-dependent intracranial rhythms across human cortical and subcortical sites
Solomon, E. A., Wang, J. B., Oya, H., Howard, M. A., Trapp, N. T., Uitermarkt, B. D., Boes, A. D., & Keller, C. J. (2024) · Brain stimulation · doi:10.1016/j.brs.2024.05.014 · Cited by 28
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Cope, T. E., Hughes, L. E., Phillips, H. N., Adams, N. E., Jafarian, A., Nesbitt, D., Assem, M., Woolgar, A., Duncan, J., & Rowe, J. B. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1622-21.2022 · Cited by 32
Ganis, G., & Schendan, H. E. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.103 · Cited by 34
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Self-citationZhao, Y., Billings, S. A., Wei, H., & Sarrigiannis, P. G. (2012) · Physical Review E · doi:10.1103/physreve.86.051919 · Cited by 28
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He, W., Garrido, M. I., Sowman, P. F., Brock, J., & Johnson, B. W. (2015) · Human Brain Mapping · doi:10.1002/hbm.22762 · Cited by 27
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Chehelcheraghi, M., van Leeuwen, C., Steur, E., & Nakatani, C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173776 · Cited by 27
Barrès, V., & Lee, J. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9197-y · Cited by 27
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Shaw, A. D., Muthukumaraswamy, S. D., Saxena, N., Sumner, R. L., Adams, N., Moran, R., & Singh, K. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117189 · Cited by 28
Weber, L. A. E., Tomiello, S., Schöbi, D., Wellstein, K. V., Mueller, D. R., Iglesias, S., & Stephan, K. E. (2022) · eLife · doi:10.7554/elife.74835 · Cited by 28
van Wijk, B. C. M., Vladimir Litvak, A., Friston, K. J., & Daffertshofer, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.076 · Cited by 26
Self-citationNon-invasive brain mapping in epilepsy: Applications from magnetoencephalography
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Díez, Á., Ranlund, S. M., Pinotsis, D. A., Calafato, S., Shaikh, M., Hall, M., Walshe, M., Nevado, Á., Friston, K. J., Adams, R. A., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23588 · Cited by 26
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Strauß, D. J., Francis, A. L., Vibell, J. F., & Corona–Strauss, F. I. (2024) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2024.110923 · Cited by 26
Hashemi, M., Hutt, A., Hight, D., & SLEIGH, J. W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179286 · Cited by 27
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He, W., & Johnson, B. W. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.11.010 · Cited by 22
Hodkinson, D., Bungert, A., Bowtell, R., Jackson, S., & Jung, J.-Y. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00670.2020 · Cited by 23
Chen, G., Ward, B. D., Chen, G., & Li, S. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0295 · Cited by 25
Havlíček, M., Roebroeck, A. F., Friston, K. J., Gardumi, A., Ivanov, D., & Uludaǧ, K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.017 · Cited by 25
Self-citationCooray, G., Sundgren, M., & Brismar, T. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.10.029 · Cited by 25
Sotero, R. C. (2015) · BioMed Research International · doi:10.1155/2015/915606 · Cited by 24
Klingner, C. M., Brodoehl, S., Huonker, R., & Witte, O. W. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00103 · Cited by 23
Cam, S. L., Ranta, R., Caune, V., Korāts, G., Koessler, L., Maillard, L. G., & Louis-Dorr, V. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.030 · Cited by 24
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Metadata
- Authors
- David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., & Friston, K. J.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Corresponding author
- Kiebel, Stefan J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2006-05
- Volume
- 30
- Issue
- 4
- Pages
- 1255-1272
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2053773581
- Cited by
- 620
- References
- 53
- Citations by year
- 2026: 15 · 2025: 25 · 2024: 17 · 2023: 18 · 2022: 28 · 2021: 28 · 2020: 28 · 2019: 55 · 2018: 31 · 2017: 38 · 2016: 32 · 2015: 53 · 2014: 33 · 2013: 30 · 2012: 41
- FWCI
- 12.94
- Type
- journal article
- Language
- en
- Open access
- green
- inserm.hal.science
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust
- Centre National de la Recherche Scientifique
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- dynamic causal modeling, event-related potentials, event-related fields, corticocortical connections, source localization, Bayesian model selection, N170, face perception, ventral stream, mismatch negativity, auditory oddball paradigm
- MeSH terms
- Auditory Pathways, Bayes Theorem, Brain Mapping, Cerebral Cortex, Electroencephalography, Evoked Potentials, Humans, Nerve Net, Synaptic Transmission, Neurons, Pattern Recognition, Visual, Pitch Perception, Visual Pathways, Magnetoencephalography, Neural Networks, Computer, Nonlinear Dynamics
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