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Dynamic causal modelling: A critical review of the biophysical and statistical foundations
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The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs.
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“dynamic causal models (DCMs; for reviews see Daunizeau et al., 2011; Friston et al., 2013) allow one to infer directed synaptic connections (effective connectivity) from neuroimaging or electrophysiological data.”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Daunizeau et al., 2011
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“Model-based methods depend on the choice of the model and its parameters; thus many different parameters need to be tested to ensure unbiased results (112).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [112]
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Cites
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
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Bcl-2 gene promotes haemopoietic cell survival and cooperates with c-myc to immortalize pre-B cells
Vaux, D., Cory, S., & Adams, J. (1988) · Nature · doi:10.1038/335440a0 · Cited by 3,355
Psychophysiological and Modulatory Interactions in Neuroimaging
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997) · NeuroImage · doi:10.1006/nimg.1997.0291 · Cited by 3,097
A practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917
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
Dynamics of pattern formation in lateral-inhibition type neural fields
Амари, Ш.-И. (1977) · Biological Cybernetics · doi:10.1007/bf00337259 · Cited by 2,086
(2003) · Cited by 1,680
Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model
Buxton, R. B., Wong, E. C., & Frank, L. R. (1998) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910390602 · Cited by 1,665
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Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., et al. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00550 · Cited by 437
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Mazzoni, A., Lindén, H., Cuntz, H., Lansner, A., Panzeri, S., & Einevoll, G. T. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004584 · Cited by 462
Anatomical insights into the interaction of emotion and cognition in the prefrontal cortex
Ray, R. D., & Zald, D. H. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.08.005 · Cited by 446
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Daunizeau, J., Adam, V., & Rigoux, L. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003441 · Cited by 385
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Rehme, A. K., Eickhoff, S. B., Wang, L. E., Fink, G. R., & Grefkes, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.014 · Cited by 334
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Centeno, M., & Carmichael, D. W. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00093 · Cited by 198
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Wakeman, D. G., & Henson, R. N. (2015) · Scientific Data · doi:10.1038/sdata.2015.1 · Cited by 190
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Friston, K. J., Bastos, A. M., Pinotsis, D. A., & Vladimir Litvak, A. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.05.004 · Cited by 198
Translational 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
Self-citationNonlinear connectivity by Granger causality
Marinazzo, D., Liao, W., Chen, H., & Stramaglia, S. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.099 · Cited by 188
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Gábor, A., & Banga, J. R. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0219-2 · Cited by 182
Piccinini, G. (2020) · doi:10.1093/oso/9780198866282.001.0001 · Cited by 179
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Self-citationFrässle, 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 163
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Vossel, S., Mathys, C., Daunizeau, J., Bauer, M., Driver, J., Friston, K. J., & Stephan, K. E. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs418 · Cited by 146
Self-citationFriston, K. J., Bastos, A. M., Oswal, A., van Wijk, B. C. M., Richter, C. G., & Vladimir Litvak, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.062 · Cited by 133
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Giorgio, J., Adams, J. N., Maass, A., Jagust, W. J., & Breakspear, M. (2024) · Neuron · doi:10.1016/j.neuron.2023.11.014 · Cited by 133
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McFadyen, J., Mermillod, M., Mattingley, J. B., Halász, V., & Garrido, M. I. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3525-16.2017 · Cited by 144
Computational nosology and precision psychiatry
Friston, K. J., Redish, A. D., & Gordon, J. A. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00001 · Cited by 126
Critical comments on dynamic causal modelling
Lohmann, G., Erfurth, K., Müller, K., & Turner, R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.025 · Cited by 125
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Jahnke, K., von Wegner, F., Morzelewski, A., Borisov, S. V., Maischein, M., Steinmetz, H., & Laufs, H. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.013 · Cited by 126
Teipel, S. J., Grothe, M. J., Zhou, J. H., Sepulcre, J., Dyrba, M., Sorg, C., & Babiloni, C. (2016) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715000995 · Cited by 117
Stochastic dynamic causal modelling of fMRI data: Should we care about neural noise?
Jean Daunizeau, A., Stephan, K. E., & Friston, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.061 · Cited by 114
Self-citationA generative model of whole-brain effective connectivity
Frässle, S., Lomakina, E. I., Kasper, L., Manjaly, Z. M., Leff, A. P., Pruessmann, K. P., Buhmann, J. M., & Stephan, K. E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.058 · Cited by 104
Self-citationA short history of causal modeling of fMRI data
Stephan, K. E., & Roebroeck, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.034 · Cited by 112
Self-citationDynamic Modulation of Amygdala–Hippocampal Connectivity by Emotional Arousal
Fastenrath, M., Coynel, D., Spalek, K., Milnik, A., Gschwind, L., Roozendaal, B., Papassotiropoulos, A., & de Quervain, D. J. ‐. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0786-14.2014 · Cited by 116
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Working memory load modulation of parieto‐frontal connections: Evidence from dynamic causal modeling
Ma, L., Steinberg, J. L., Hasan, K. M., Narayana, P. A., Kramer, L. A., & Moeller, F. G. (2011) · Human Brain Mapping · doi:10.1002/hbm.21329 · Cited by 99
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Reid, A., & Evans, A. C. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.11.010 · Cited by 94
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Hindriks, R., & van Putten, M. J. A. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.042 · Cited by 88
Studying Network Mechanisms Using Intracranial Stimulation in Epileptic Patients
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The combination of EEG Source Imaging and EEG‐correlated functional MRI to map epileptic networks
Vulliémoz, S., Lemieux, L., Jean Daunizeau, A., Michel, C. M., & Duncan, J. S. (2009) · Epilepsia · doi:10.1111/j.1528-1167.2009.02342.x · Cited by 92
The Connected Brain: Causality, models, and intrinsic dynamics
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Top‐down versus bottom‐up attention differentially modulate frontal–parietal connectivity
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Dong, G., Wang, M., Zheng, H., Wang, Z., Du, X., & Potenza, M. N. (2020) · Psychological Medicine · doi:10.1017/s003329172000032x · Cited by 84
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Liang, M., Mouraux, A., & Iannetti, G. D. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6207-10.2011 · Cited by 81
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Rosch, R. E., Hunter, P. R., Baldeweg, T., Friston, K. J., & Meyer, M. P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006375 · Cited by 78
Frässle, S., Paulus, F. M., Krach, S., Schweinberger, S. R., Stephan, K. E., & Jansen, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.055 · Cited by 79
Self-citationPhillips, H. N., Blenkmann, A. O., Hughes, L. E., Kochen, S. S., Bekinschtein, T. A., Cam‐CAN, & Rowe, J. B. (2016) · Cortex · doi:10.1016/j.cortex.2016.05.001 · Cited by 77
Herz, D. M., Haagensen, B. N., Christensen, M. S., Madsen, K. H., Rowe, J. B., Løkkegaard, A., & Siebner, H. R. (2015) · Brain · doi:10.1093/brain/awv096 · Cited by 82
Hell, F., Palleis, C., Mehrkens, J. H., Koeglsperger, T., & Bötzel, K. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00314 · Cited by 75
Emergence of Physiological Oscillation Frequencies in a Computer Model of Neocortex
Neymotin, S. A., Lee, H., Park, E. H., Fenton, A. A., & Lytton, W. W. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00019 · Cited by 73
A cross-modal, cross-species comparison of connectivity measures in the primate brain
Reid, A., Lewis, J. D., Bezgin, G., Khundrakpam, B. S., Eickhoff, S. B., McIntosh, A. R., Bellec, P., & Evans, A. C. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.057 · Cited by 73
Reading without the left ventral occipito-temporal cortex
Seghier, M. L., Neufeld, N. H., Zeidman, P., Leff, A. P., Mechelli, A., Nagendran, A., Riddoch, J. M., Humphreys, G. W., & Price, C. J. (2012) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2012.09.030 · Cited by 74
Bielczyk, N., Uithol, S., van Mourik, T., Anderson, P., Glennon, J. C., & Buitelaar, J. K. (2018) · Network Neuroscience · doi:10.1162/netn_a_00062 · Cited by 70
Legon, W., Punzell, S., Dowlati, E. P., Adams, S. E., Stiles, A. B., & Moran, R. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv200 · Cited by 69
A dynamic causal model for evoked and induced responses
Chen, C., Kiebel, S. J., Kilner, J. M., Ward, N., Stephan, K. E., Wang, W. J., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.066 · Cited by 66
Self-citationLiang, M., Mouraux, A., & Iannetti, G. D. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr363 · Cited by 71
Spectral graph theory of brain oscillations
Raj, A., Cai, C., Xie, X., Palacios, E. M., Owen, J. P., Mukherjee, P., & Nagarajan, S. S. (2020) · Human Brain Mapping · doi:10.1002/hbm.24991 · Cited by 66
Campo, P., Garrido, M. I., Moran, R., Maestú, F., García‐Morales, I., Gil‐Nagel, A., del Pozo, F., Dolan, R. J., & Friston, K. J. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr201 · Cited by 64
Hillebrandt, H. F., Friston, K. J., & Blakemore, S. (2014) · Scientific Reports · doi:10.1038/srep06240 · Cited by 66
When Two Become One: The Limits of Causality Analysis of Brain Dynamics
Chicharro, D., & Ledberg, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032466 · Cited by 60
A graph neural network framework for causal inference in brain networks
Wein, S., Malloni, W. M., Tomé, A. M., Frank, S., Henze, G., Wüst, S., Greenlee, M. W., & Lang, E. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-87411-8 · Cited by 66
Estimation of effective connectivity via data-driven neural modeling
Freestone, D. R., Karoly, P. J., Nešić, D., Aram, P., Cook, M. J., & Grayden, D. B. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00383 · Cited by 59
Sohrabpour, A., Ye, S., Worrell, G. A., Zhang, W., & He, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2616474 · Cited by 59
Human cerebellum and corticocerebellar connections involved in emotional memory enhancement
Fastenrath, M., Spalek, K., Coynel, D., Loos, E., Milnik, A., Egli, T., Schicktanz, N., Geissmann, L., Roozendaal, B., Papassotiropoulos, A., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204900119 · Cited by 57
Precision and neuronal dynamics in the human posterior parietal cortex during evidence accumulation
FitzGerald, T. H. B., Moran, R., Friston, K. J., & Dolan, R. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.015 · Cited by 59
Vartanian, O. A., Beatty, E. L., Smith, I., Blackler, K., Lam, Q., & Forbes, S. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.02.024 · Cited by 58
Seizure pathways: A model-based investigation
Karoly, P. J., Kuhlmann, L., Soudry, D., Grayden, D. B., Cook, M. J., & Freestone, D. R. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006403 · Cited by 57
DiQuattro, N. E., Sawaki, R., & Geng, J. J. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht172 · Cited by 57
Bayesian model reductionPreprint
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Self-citationObserving the Observer (II): Deciding When to Decide
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Metadata
- Authors
- Daunizeau, J., David, O., & Stephan, K. E.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- ORCID
- Stephan, K.E.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-09
- Volume
- 58
- Issue
- 2
- Pages
- 312-322
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2133352409
- Cited by
- 331
- References
- 95
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 10 · 2023: 14 · 2022: 16 · 2021: 22 · 2020: 37 · 2019: 19 · 2018: 23 · 2017: 15 · 2016: 25 · 2015: 30 · 2014: 26 · 2013: 20 · 2012: 18
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- HAL (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- University Research Priority Program “Foundations of Human Social Behaviour”
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- dynamic causal modeling, neuronal network dynamics, biophysical model, parameter estimation, model comparison, functional MRI, EEG, MEG, functional integration
- MeSH terms
- Bayes Theorem, Biophysics, Brain Mapping, Data Interpretation, Statistical, Electroencephalography, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Reproducibility of Results, Magnetoencephalography, Models, Statistical, Causality
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