Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM

ABSTRACT: The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm

• Autores: López, José David; Friston, Karl J.; Espinosa Oviedo, Jairo José; Litvak, Vladimir; Barnes, Gareth Robert
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2014
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/35604
• Acceso en línea: https://hdl.handle.net/10495/35604
• Palabra clave: Algoritmos; Algorithms; Inteligencia Artificial; Artificial Intelligence; Teorema de Bayes; Bayes Theorem; Electroencefalografía - Métodos; Electroencephalography- Métodos; Reproducibilidad de los Resultados; Reproducibility of Results; MEG/EEG inverse problem
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/2.5/co/