The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Dynamic causal modelling: A critical review of the biophysical and statistical foundations
Open accessAbstract
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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Citations
Cites
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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
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Systems Neuroengineering: Understanding and Interacting with the Brain
Edelman, B. J., Johnson, N., Sohrabpour, A., Tong, S., Thakor, N., & He, B. (2015) · Engineering · doi:10.15302/j-eng-2015078 · Cited by 45
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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