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with general linear model and standard fMRI softwares and is easily automated. RESULTS: Our stimulation method enabled individual mapping of visual field up to 50 degrees of eccentricity and showed that retinotopic visual areas extended through posterior cerebrum. In addition, we located a separate peripheral upper visual field representation in parieto-occipital (PO) sulcus. CONCLUSIONS: These functional results are in line with earlier histological data, and support recent findings on human V6, a retinotopic area in the medial PO sulcus with an apparent emphasis on peripheral visual field. SIGNIFICANCE: Our projection system and mf-design together enable efficient and robust retinotopic mapping of wide visual field, which can at low cost be adapted to any clinical environment with visual back-projection system.

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Auranen T, Nummenmaa A, Hämäläinen MS, Jääskeläinen IP, Lampinen J, Vehtari A, and Sams M: Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles. Human Brain Mapping 2007, 28: 979–994.

A recently introduced Bayesian model for magnetoencephalographic (MEG) data consistently localized multiple simulated dipoles with the help of marginalization of spatiotemporal background noise covariance structure in the analysis [Jun et al., (2005): Neuroimage 28:84-98]. Here, we elaborated this model to include subject's individual brain surface reconstructions with cortical location and orientation constraints. To enable efficient Markov chain Monte Carlo sampling of the dipole locations, we adopted a parametrization of the source space surfaces with two continuous variables (i.e., spherical angle coordinates). Prior to analysis, we simplified the likelihood by exploiting only a small set of independent measurement combinations obtained by singular value decomposition of the gain matrix, which also makes the sampler significantly faster. We analyzed both realistically simulated and empirical MEG data recorded during simple auditory and visual stimulation. The results show that our model produces reasonable solutions and adequate data fits without much manual interaction. However, the rigid cortical constraints seemed to make the utilized scheme challenging as the sampler did not switch modes of the dipoles efficiently. This is problematic in the presence of evidently highly multimodal posterior distribution, and especially in the relative quantitative comparison of the different modes. To overcome the difficulties with the present model, we propose the use of loose orientation constraints and combined model of prelocalization utilizing the hierarchical minimum-norm estimate and multiple dipole sampling scheme.

Nummenmaa A, Auranen T, Hämäläinen MS, Jääskeläinen IP, Sams M, Vehtari A, and Lampinen J: Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. Neuroimage 2007, 37: 876–889.  

In recent simulation studies, a hierarchical Variational Bayesian (VB) method, which can be seen as a generalisation of the traditional minimum-norm estimate (MNE), was introduced for reconstructing distributed MEG sources. Here, we studied how nonlinearities in the estimation process and hyperparameter selection affect the inverse solutions, the feasibility of a full Bayesian treatment of the hyperparameters, and multimodality of the true posterior, in an empirical dataset wherein a male subject was presented with pure tone and checkerboard reversal stimuli, alone and in combination. An MRI-based cortical surface model was employed. Our results show, with a comparison to the basic MNE, that the hierarchical VB approach yields

Annual Report 2007

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