Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization

By Stefan J. Kiebel, Olivier David, Karl J. Friston. NeuroImage, 2006

301 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/54ea848071c2b0176f4d7451d512660be2dee448

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Citations in the corpus

Ten Simple Rules for Dynamic Causal Modelling (2009, unknown): “For example, the GLM for fMRI data and source reconstruction methods for EEG/MEG can be thought of as special cases of DCMs (for details, see Daunizeau et al., 2009b; Kiebel et al., 2006; Stephan et al., 2007a).”
Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG (2008, methodology): “Operationally, summarising distributed activity with a small number of sources simplifies analyses of connectivity among those sources (e.g., dynamic casual modelling of evoked or induced responses (Kiebel et al., 2006)).”
Dynamic causal modeling for EEG and MEG (2009, methodology): “DCMis not limited to the neural mass model [Jansen and Rit, 1995], or the ECD model described in this review.”
EEG and MEG Data Analysis in SPM8 (2011, background): “The two key technical contributions for DCM-ERP can be found in [20, 52].”
The mismatch negativity: A review of underlying mechanisms (2009, background): “Dynamic causal modelling is a generic approach to modelling the neuronal mechanisms that underlie measured neuroimaging (Friston, 2003; Stephan et al., 2007; Marreiros et al., 2008) and electrophysiological data (David et al., 2006; Kiebel et al., 2006, 2007; Garrido et al., 2007).”
Variational free energy and the Laplace approximation (2007, methodology): “, 2006), MEG (Kiebel et al., 2006) and mean-field models of neuronal activity (Harrison et al.”
The functional anatomy of the MMN: A DCM study of the roving paradigm (2008, methodology): “DCM furnishes spatiotemporal, generative or forward models for evoked responses as measured with EEG/MEG (David et al., 2006; Kiebel et al., 2006), and provides an important advance over conventional analyses of evoked responses because it places natural constraints on the inversion; namely, activit”
Population dynamics: Variance and the sigmoid activation function (2008, methodology): “We will start with the following ordinary differential equation (ODE) modelling the dynamics of a single neuron from the neuralmass model for EEG/MEG (David and friston, 2003, 2006a,b; Kiebel et al., 2006; Garrido et al., in press; Moran et al., 2007); for example, the i-th neuron in a population of”
Functional and Effective Connectivity: A Review (2011, unknown): “More recent efforts have focused on DCMs for electromagnetic (EEG and MEG) data (Chen et al., 2008; Clearwater et al., 2008; David et al., 2006; Garrido et al., 2007a,b, 2008; Kiebel et al., 2006, 2007), with related developments to cover local ﬁeld potential recordings (Moran et al., 2007, 2008).”