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.
Highlights
IntroductionPET and fMRI, is usually into a standard anatomical space (e.g., that defined by the Atlas of [1])
Source reconstruction in neuroimaging, PET and fMRI, is usually into a standard anatomical space
In this paper we have described a simple solution to the problem of reconstructing electromagnetic sources in a canonical anatomical space
Summary
PET and fMRI, is usually into a standard anatomical space (e.g., that defined by the Atlas of [1]). Reconstruction into a canonical space facilitates the formal or informal metaanalysis of findings in imaging neuroscience and provides a useful framework within which to define structure-function relationships. In PET and fMRI the construction of spatially normalized images comprises two distinct steps. The raw data are reconstructed into images of source activity within the subject’s own anatomical space. These data are spatially normalized into a standard space using a template matching approach (e.g., [2]). For EEG and MEG, source reconstruction and spatial or anatomical normalization cannot be separated because the reconstruction depends upon the spatial configuration of sources
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