Automatic fMRI-guided MEG multidipole localization for visual responses

Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, Espoo, Finland.
Human Brain Mapping (Impact Factor: 5.97). 04/2009; 30(4):1087-99. DOI: 10.1002/hbm.20570
Source: PubMed


Previously, we introduced the use of individual cortical location and orientation constraints in the spatiotemporal Bayesian dipole analysis setting proposed by Jun et al. ([2005]; Neuroimage 28:84-98). However, the model's performance was limited by slow convergence and multimodality of the numerically estimated posterior distribution. In this paper, we present an intuitive way to exploit functional magnetic resonance imaging (fMRI) data in the Markov chain Monte Carlo sampling -based inverse estimation of magnetoencephalographic (MEG) data. We used simulated MEG and fMRI data to show that the convergence and localization accuracy of the method is significantly improved with the help of fMRI-guided proposal distributions. We further demonstrate, using an identical visual stimulation paradigm in both fMRI and MEG, the usefulness of this type of automated approach when investigating activation patterns with several spatially close and temporally overlapping sources. Theoretically, the MEG inverse estimates are not biased and should yield the same results even without fMRI information, however, in practice the multimodality of the posterior distribution causes problems due to the limited mixing properties of the sampler. On this account, the algorithm acts perhaps more as a stochastic optimizer than enables a full Bayesian posterior analysis.

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    • "Notice there are vast source models based on multiple penalties; these models can also integrate the virtue of previous extreme source models (Valdes-Sosa et al., 2009b; Wipf & Nagarajan, 2009). Previous studies use fMRI activation to constrain the spatial locations of EEG source (Liu et al., 1998; Dale et al., 2000; Phillips et al., 2002; Liu et al., 2009) or initialize the dipole seeds (Stancak et al., 2005; Auranen et al., 2009). This has undesirable consequences when fMRI was considered the \truth" for spatial information (Dale et al., 2000), since the relative importance of EEG and fMRI is not evaluated (Gonzalez Andino et al., 2001). "
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