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) allow one to infer directed synaptic connections (effective connectivity) from neuroimaging or electrophysiological data.
“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
Section 2.1 (p. 6) describes DCMs as modelling directed effective-connectivity edges between brain regions or neuronal populations using fMRI and EEG/MEG data. The Introduction (p. 4), however, distinguishes effective connectivity—directed causal influence—from structural connectivity, which includes anatomical synaptic connections. The source therefore supports inference about directed effective connectivity, but not the claim that DCM infers synaptic connections as such. (LLM, conf=0.88)
Experimental — accuracy not yet measuredModel-based methods depend on the choice of the model and its parameters, so many different parameters need to be tested to ensure unbiased results.
“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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“For example, 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.”
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- 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
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 ›
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