MEG source analysis in SPM

Mattout, J.
Wellcome Department of Imaging Neuroscience, University College London, UK

Recently, large effort has been put towards new developments of MEG data analysis and source reconstruction. In the SPM software (www.fil.ion.ucl.ac.uk/spm) new methods have been made available to the community:
The probabilistic framework (Empirical Bayes) used to constrain the MEG inverse problem and to make inference on both, the underlying sources and the model [Friston06, 08][Mattout06][Kiebel08][Henson09];
A canonical mesh enabling convenient group analysis [Mattout07][Litvak08];
Dynamic causal models [David06][Chen08][Moran09].
Examples including synthetic, healthy volunteer and clinical data will demonstrate these aspects.
[Chen08] Dynamic causal modeling of induced responses, Neuroimage.
[David06] Dynamic causal modeling of evoked responses in EEG and MEG, Neuroimage.
[Friston06] Bayesian estimation of evoked and induced responses, Human Brain Mapping.
[Friston08] Multiple sparse priors for the M/EEG inverse problem, Neuroimage.
[Henson09] Selecting forward models for MEG source-reconstruction using model-evidence, Neuroimage.
[Kiebel08] Variational Bayesian approach inversion of the equivalent current dipole model in EEG/MEG, Neuroimage.
[Litvak08] Electromagnetic source reconstruction for group studies, Neuroimage.
[Mattout06] MEG source localization under multiple constraints: an extended Bayesian framework, Neuroimage.
[Mattout07] Canonical Source Reconstruction for MEG, Computational Intelligence and Neuroscience.
[Moran09] Dynamic causal models of steady-state responses, Neuroimage.

Symposium 7: Novel Source Localisation Approaches in MEG-Data: Basic and Clinical Applications
11.06.2009, 14:30-15:45
Seminarraum 11