A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration

We review recent methodological developments within a parametric empirical Bayesian (PEB) framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG) and magnetoencephalographic (MEG) data under linear Gaussian assumptions. The PEB framework offers a natural way t...

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Autores principales: Henson, Richard N., Wakeman, Daniel G., Litvak, Vladimir, Friston, Karl J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3160752/
https://www.ncbi.nlm.nih.gov/pubmed/21904527
http://dx.doi.org/10.3389/fnhum.2011.00076
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author Henson, Richard N.
Wakeman, Daniel G.
Litvak, Vladimir
Friston, Karl J.
author_facet Henson, Richard N.
Wakeman, Daniel G.
Litvak, Vladimir
Friston, Karl J.
author_sort Henson, Richard N.
collection PubMed
description We review recent methodological developments within a parametric empirical Bayesian (PEB) framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG) and magnetoencephalographic (MEG) data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors) on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI) or from multiple replications (e.g., subjects). Using variations of the same basic generative model, we illustrate the application of PEB to three cases: (1) symmetric integration (fusion) of MEG and EEG; (2) asymmetric integration of MEG or EEG with fMRI, and (3) group-optimization of spatial priors across subjects. We evaluate these applications on multi-modal data acquired from 18 subjects, focusing on energy induced by face perception within a time–frequency window of 100–220 ms, 8–18 Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects) of cortical responses to faces.
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spelling pubmed-31607522011-09-08 A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration Henson, Richard N. Wakeman, Daniel G. Litvak, Vladimir Friston, Karl J. Front Hum Neurosci Neuroscience We review recent methodological developments within a parametric empirical Bayesian (PEB) framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG) and magnetoencephalographic (MEG) data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors) on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI) or from multiple replications (e.g., subjects). Using variations of the same basic generative model, we illustrate the application of PEB to three cases: (1) symmetric integration (fusion) of MEG and EEG; (2) asymmetric integration of MEG or EEG with fMRI, and (3) group-optimization of spatial priors across subjects. We evaluate these applications on multi-modal data acquired from 18 subjects, focusing on energy induced by face perception within a time–frequency window of 100–220 ms, 8–18 Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects) of cortical responses to faces. Frontiers Research Foundation 2011-08-24 /pmc/articles/PMC3160752/ /pubmed/21904527 http://dx.doi.org/10.3389/fnhum.2011.00076 Text en Copyright © 2011 Henson, Wakeman, Litvak and Friston. http://www.frontiersin.org/licenseagreement This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
spellingShingle Neuroscience
Henson, Richard N.
Wakeman, Daniel G.
Litvak, Vladimir
Friston, Karl J.
A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration
title A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration
title_full A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration
title_fullStr A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration
title_full_unstemmed A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration
title_short A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration
title_sort parametric empirical bayesian framework for the eeg/meg inverse problem: generative models for multi-subject and multi-modal integration
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3160752/
https://www.ncbi.nlm.nih.gov/pubmed/21904527
http://dx.doi.org/10.3389/fnhum.2011.00076
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