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.

Download full-text


Available from: Aki Vehtari
  • Source
    • "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). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.
    Full-text · Article · Sep 2012 · Journal of Integrative Neuroscience
  • Source
    • "A third approach is to use a Statistical Parametric Map (SPM) obtained from fMRI to improve EEG source estimation. In this approach, SPM information can be used either to constrain the spatial locations of the likely sources of EEG [Dale et al., 2000; Liu et al., 1998], or to initially seed dipoles within the active regions found in the SPM for further dipole fittings [Ahlfors et al., 1999; Auranen et al., 2009; Stancák et al., 2005]. Recently, fMRI SPM information was introduced into a Parametric Empirical Bayesian (PEB) framework for use in EEG source estimation [Friston et al., 2002; Phillips et al., 2002, 2005]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The brain exhibits temporally coherent networks (TCNs) involving numerous cortical and sub-cortical regions both during the rest state and during the performance of cognitive tasks. TCNs represent the interactions between different brain areas, and understanding such networks may facilitate electroencephalography (EEG) source estimation. We propose a new method for examining TCNs using scalp EEG in conjunction with data obtained by functional magnetic resonance imaging (fMRI). In this approach, termed NEtwork based SOurce Imaging (NESOI), multiple TCNs derived from fMRI with independent component analysis (ICA) are used as the covariance priors of the EEG source reconstruction using Parametric Empirical Bayesian (PEB). In contrast to previous applications of PEB in EEG source imaging with smoothness or sparseness priors, TCNs play a fundamental role among the priors used by NESOI. NESOI achieves an efficient integration of the high temporal resolution EEG and TCN derived from the high spatial resolution fMRI. Using synthetic and real data, we directly compared the performance of NESOI with other distributed source inversion methods, with and without the use of fMRI priors. Our results indicated that NESOI is a potentially useful approach for EEG source imaging.
    Full-text · Article · Jul 2011 · Human Brain Mapping
  • Source
    • "Further, we demonstrated that this approach could reveal predictable effects of the eccentricity and size of the stimulus object on the topography of cortical currents, as illustrated with the analysis of the PROXIMATE run in this study. The correspondence with the fMRI retinotopic maps was shown in one subject (see Supplementary Material 1) and strengthens the significance of these results, even though the comparison remains qualitative at this point (Auranen et al., 2009). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Although the spatial organization of visual areas can be revealed by functional Magnetic Resonance Imaging (fMRI), the synoptic, non-invasive access to the temporal characteristics of the information flow amongst distributed visual processes remains a technical and methodological challenge. Using frequency-encoded steady-state visual stimulation together with a combination of time-resolved functional magnetic source imaging from magnetoencephalography (MEG) and anatomical magnetic resonance imaging (MRI), this study evidences maps of visuotopic sustained oscillatory neural responses distributed across the visual cortex. Our results further reveal relative phase delays across responding striate and extra-striate visual areas, which thereby shape the chronometry of neural processes amongst these regions. The methodology developed in this study points at further developments in time-resolved analyses of distributed visual processes in the millisecond range, and to new ways of exploring the dynamics of functional processes within the human visual cortex non-invasively.
    Full-text · Article · Oct 2010 · NeuroImage
Show more