Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles

Authors

  • Toni Auranen,

    Corresponding author
    1. Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland
    2. Advanced Magnetic Imaging Centre, Helsinki University of Technology, Espoo, Finland
    • Laboratory of Computational Engineering, Helsinki University of Technology, P.O. Box 9203, FI-02015 TKK, Finland
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  • Aapo Nummenmaa,

    1. Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland
    2. Advanced Magnetic Imaging Centre, Helsinki University of Technology, Espoo, Finland
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  • Matti S. Hämäläinen,

    1. MGH–MIT–HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
    2. Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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  • Iiro P. Jääskeläinen,

    1. Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland
    2. Advanced Magnetic Imaging Centre, Helsinki University of Technology, Espoo, Finland
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  • Jouko Lampinen,

    1. Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland
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  • Aki Vehtari,

    1. Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland
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  • Mikko Sams

    1. Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland
    2. Advanced Magnetic Imaging Centre, Helsinki University of Technology, Espoo, Finland
    3. Brain Research Unit, Low Temperature Laboratory, Helsinki University of Technology, Espoo, Finland
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Abstract

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. Hum Brain Mapp 2007. © 2007 Wiley-Liss, Inc.

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