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The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free(More)
MEG/EEG brain imaging has become an important tool in neuroimaging. Current techniques based in Bayesian approaches require an a-priori definition of patch locations on the cortical manifold. Too many patches results in a complex optimisation problem, too few an under sampling of the solution space. In this work random locations of the possible active(More)
There is uncertainty introduced when a cortical surface based model derived from an anatomical MRI is used to reconstruct neural activity with MEG data. This is a specific case of a problem with uncertainty in parameters on which M/EEG lead fields depend non-linearly. Here we present a general mathematical treatment of any such problem with a particular(More)
Estimates of coefficients of a spherical harmonic Fourier decomposition of the cortical surface can be obtained solely using MEG/EEG data and free energy as objective function. A stochastic methodology based on a Metropolis Search followed by a Bayesian Model Averaging is proposed to reconstruct cortical anatomy based functional information.
Histamine (HA) may bind to cytochrome P450 (CYP450) in rat liver microsomes. The CYP450-HA complex seems to regulate some cellular processes such as proliferation. In the present work, it is shown that HA increases the activity and protein level of CYP1A1 and CYP2E1, in vivo. CYP1A1 is associated with polycyclic aromatic hydrocarbon-mediated carcinogenesis(More)
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