Canonical Source Reconstruction for MEG
Open Access
- 1 January 2007
- journal article
- research article
- Published by Hindawi Limited in Computational Intelligence and Neuroscience
- Vol. 2007, 1-10
- https://doi.org/10.1155/2007/67613
Abstract
We describe a simple and efficient solution to the problem of reconstructing electromagnetic sources into a canonical or standard anatomical space. Its simplicity rests upon incorporating subject-specific anatomy into the forward model in a way that eschews the need for cortical surface extraction. The forward model starts with a canonical cortical mesh, defined in a standard stereotactic space. The mesh is warped, in a nonlinear fashion, to match the subject's anatomy. This warping is the inverse of the transformation derived from spatial normalization of the subject's structural MRI image, using fully automated procedures that have been established for other imaging modalities. Electromagnetic lead fields are computed using the warped mesh, in conjunction with a spherical head model (which does not rely on individual anatomy). The ensuing forward model is inverted using an empirical Bayesian scheme that we have described previously in several publications. Critically, because anatomical information enters the forward model, there is no need to spatially normalize the reconstructed source activity. In other words, each source, comprising the mesh, has a predetermined and unique anatomical attribution within standard stereotactic space. This enables the pooling of data from multiple subjects and the reporting of results in stereotactic coordinates. Furthermore, it allows the graceful fusion of fMRI and MEG data within the same anatomical framework.Funding Information
- Wellcome Trust
This publication has 16 references indexed in Scilit:
- Population-level inferences for distributed MEG source localization under multiple constraints: Application to face-evoked fieldsNeuroImage, 2007
- Variational free energy and the Laplace approximationNeuroImage, 2007
- MEG source localization under multiple constraints: An extended Bayesian frameworkNeuroImage, 2006
- Unified segmentationNeuroImage, 2005
- Comparing dynamic causal modelsNeuroImage, 2004
- Classical and Bayesian Inference in Neuroimaging: TheoryNeuroImage, 2002
- Nonlinear spatial normalization using basis functionsHuman Brain Mapping, 1999
- From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformationsJournal of Mathematical Imaging and Vision, 1995
- Bayes FactorsJournal of the American Statistical Association, 1995
- Basic mathematical and electromagnetic concepts of the biomagnetic inverse problemPhysics in Medicine & Biology, 1987