Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG

By Stefan J. Kiebel, Jean Daunizeau, Christophe L. M. Phillips, Karl J. Friston. 2008

115 citations recorded by the source corpus.

In magneto- and electroencephalography (M/EEG), spatial modelling of sensor data is necessary to make inferences about underlying brain activity. Most source reconstruction techniques belong to one of two approaches: point source models, which explain the data with a small number of equivalent current dipoles and distributed source or imaging models, which use thousands of dipoles. Much methodological research has been devoted to developing sophisticated Bayesian source imaging inversion schemes, while dipoles have received less such attention. Dipole models have their advantages; they are often appropriate summaries of evoked responses or helpful first approximations. Here, we propose a variational Bayesian algorithm that enables the fast Bayesian inversion of dipole models. The approach allows for specification of priors on all the model parameters. The posterior distributions can be used to form Bayesian confidence intervals for interesting parameters, like dipole locations. Furthermore, competing models (e.g., models with different numbers of dipoles) can be compared using their evidence or marginal likelihood. Using synthetic data, we found the scheme provides accurate dipole localizations. We illustrate the advantage of our Bayesian scheme, using a multi-subject EEG auditory study, where we compare competing models for the generation of the N100 component.

Subfields: Cognitive Neuroscience, Signal Processing

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “…to neuro-science include hierarchical models of multiple subjects (Wool-rich et al. 2004), spatial models (Sato et al. 2004; Zumer et al. 2007; Kiebel et al. 2008; Wipf and Nagarajan 2009; Lashkari et al. 2012; Nathoo et al. 2014), brain-computer interfaces (Sykacek, Roberts, and Stokes…”
Ten Simple Rules for Dynamic Causal Modelling (2009, unknown): “In other neuroimaging domains, BMS is not just being used for DCMs but is also routinely employed to decide between alternative source reconstructions procedures for EEG/MEG data (e.g., Friston et al., 2008; Henson et al., 2009; Kiebel et al., 2008).”
A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers (2017, background): “It is well known that, in the sensorimotor system, movement and sensory stimulation is linked to changes in neural oscillations in the beta band (see Cheyne (2013) and Kilavik et al. (2013) for reviews).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “Using a variational Bayes approach, such an approach would then permit inversion of generative network models from empirical data and allow investigators to disambiguate between competing hypotheses that each embody — an approach that has been very successfully employed in other neuroimaging areas (”
EEG and MEG Data Analysis in SPM8 (2011, methodology): “This section describes source reconstruction based on Variational bayesian equivalent current dipoles (VB-ECDs) [19].”
Dynamic causal modeling for EEG and MEG (2009, methodology): “This evidence-based approach accounts for model complexity and enables comparisons of M/EEG models with different parameters (e.g., with different numbers of sources or connections) [Fastenrath et al., 2009; Kiebel et al., 2008a].”