Bayesian M/EEG source reconstruction with spatio-temporal priors

Nelson J Trujillo-Barreto, Eduardo Aubert-Vázquez, William D Penny

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)

Abstract

This article proposes a Bayesian spatio-temporal model for source reconstruction of M/EEG data. The usual two-level probabilistic model implicit in most distributed source solutions is extended by adding a third level which describes the temporal evolution of neuronal current sources using time-domain General Linear Models (GLMs). These comprise a set of temporal basis functions which are used to describe event-related M/EEG responses. This places M/EEG analysis in a statistical framework that is very similar to that used for PET and fMRI. The experimental design can be coded in a design matrix, effects of interest characterized using contrasts and inferences made using posterior probability maps. Importantly, as is the case for single-subject fMRI analysis, trials are treated as fixed effects and the approach takes into account between-trial variance, allowing valid inferences to be made on single-subject data. The proposed probabilistic model is efficiently inverted by using the Variational Bayes framework under a convenient mean-field approximation (VB-GLM). The new method is tested with biophysically realistic simulated data and the results are compared to those obtained with traditional spatial approaches like the popular Low Resolution Electromagnetic TomogrAphy (LORETA) and minimum variance Beamformer. Finally, the VB-GLM approach is used to analyze an EEG data set from a face processing experiment.

Original languageEnglish
Pages (from-to)318-335
Number of pages18
JournalNeuroImage
Volume39
Issue number1
Early online date22 Aug 2007
DOIs
Publication statusPublished - 1 Jan 2008

Keywords

  • Bayes Theorem
  • Brain Mapping
  • Computer Simulation
  • Computer-Assisted Diagnosis
  • Electroencephalography
  • Visual Evoked Potentials
  • Face
  • Humans
  • Magnetoencephalography
  • Neurological Models
  • Automated Pattern Recognition
  • Visual Pattern Recognition

Cite this