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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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Self-citation
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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