The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Dynamic causal modelling of induced responses
Abstract
This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and between-frequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a face-perception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.
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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 Chen et al., 2008
In the citing paper
“Having said this, DCM is used routinely to invert models with thousands of free parameters (e.g. DCM for induced electromagnetic sources; Chen et al. 2008).”
From Network discovery with DCM (2011) · cites this paper as Chen et al. 2008
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- David, 2003, A theoretical and experimental introduction to the non-invasive study of large-scale neural phase synchronization in human beings, Int. J. Comput. Cogn., 1, 53
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“…further work is required to extend this to a network-level DCM. 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).”
Background
Metadata
- Authors
- Chen, C. C., Kiebel, S. J., & Friston, K. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Chen, C.C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-07
- Volume
- 41
- Issue
- 4
- Pages
- 1293-1312
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2013176407
- Cited by
- 136
- References
- 78
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 9 · 2019: 10 · 2018: 5 · 2017: 10 · 2016: 8 · 2015: 10 · 2014: 6 · 2013: 11 · 2012: 3
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust (088130)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- dynamic causal modeling, induced responses, spectral response, EEG, MEG, Bayesian parameter estimation, model comparison, nonlinear coupling, parameter identifiability, face perception, fusiform gyrus, early visual cortex, distributed source model
- MeSH terms
- Algorithms, Bayes Theorem, Brain, Computer Simulation, Data Interpretation, Statistical, Electroencephalography, Humans, Synapses, Magnetoencephalography, Models, Statistical, Nonlinear Dynamics
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