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Canonical source reconstruction for MEG.

Mattout J, Henson RN, Friston KJ - Comput Intell Neurosci (2007)

Bottom Line: Electromagnetic lead fields are computed using the warped mesh, in conjunction with a spherical head model (which does not rely on individual anatomy).Critically, because anatomical information enters the forward model, there is no need to spatially normalize the reconstructed source activity.Furthermore, it allows the graceful fusion of fMRI and MEG data within the same anatomical framework.

View Article: PubMed Central - PubMed

Affiliation: INSERM U821, Dynamique Cérébrale et Cognition, Lyon, France. jeremiemattout@yahoo.fr

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.

No MeSH data available.


Related in: MedlinePlus

Bayesianinversion scheme.
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fig1: Bayesianinversion scheme.

Mentions: The objective function used by this scheme isequivalent to the ReML objective function, which, as shown in [8], is identical to the(negative) variational free energy(1)F=〈ln p(y∣j,λ)+p(j∣λ)−ln q〉q,where y is the data, j are the sourceactivities, and q(j) is theirconditional or posterior density. Under Gaussian assumptions, when F is maximized; q(j) = p(j ∣ y,λ), and the (negative) free energy becomes the loglikelihood of the model or its log evidence F → Inp(y ∣ λ) [9]. We have shown how the logevidence can be used to compare and adjudicate among different modelscomprising different prior covariance components or different sourceconfigurations [7]. Weuse exactly the same approach below, to compare three different sorts ofanatomical source models, each with slightly different configurations of acortical mesh subtending the lead fields. Figure 1 provides a schematic thatsummarizes this Bayesian inversion scheme.


Canonical source reconstruction for MEG.

Mattout J, Henson RN, Friston KJ - Comput Intell Neurosci (2007)

Bayesianinversion scheme.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2266807&req=5

fig1: Bayesianinversion scheme.
Mentions: The objective function used by this scheme isequivalent to the ReML objective function, which, as shown in [8], is identical to the(negative) variational free energy(1)F=〈ln p(y∣j,λ)+p(j∣λ)−ln q〉q,where y is the data, j are the sourceactivities, and q(j) is theirconditional or posterior density. Under Gaussian assumptions, when F is maximized; q(j) = p(j ∣ y,λ), and the (negative) free energy becomes the loglikelihood of the model or its log evidence F → Inp(y ∣ λ) [9]. We have shown how the logevidence can be used to compare and adjudicate among different modelscomprising different prior covariance components or different sourceconfigurations [7]. Weuse exactly the same approach below, to compare three different sorts ofanatomical source models, each with slightly different configurations of acortical mesh subtending the lead fields. Figure 1 provides a schematic thatsummarizes this Bayesian inversion scheme.

Bottom Line: Electromagnetic lead fields are computed using the warped mesh, in conjunction with a spherical head model (which does not rely on individual anatomy).Critically, because anatomical information enters the forward model, there is no need to spatially normalize the reconstructed source activity.Furthermore, it allows the graceful fusion of fMRI and MEG data within the same anatomical framework.

View Article: PubMed Central - PubMed

Affiliation: INSERM U821, Dynamique Cérébrale et Cognition, Lyon, France. jeremiemattout@yahoo.fr

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.

No MeSH data available.


Related in: MedlinePlus