Model driven EEG/fMRI fusion of brain oscillations

Pedro A. Valdes-Sosa, Jose Miguel Sanchez-Bornot, Roberto Carlos Sotero, Yasser Iturria-Medina, Yasser Aleman-Gomez, Jorge Bosch-Bayard, Felix Carbonell, Tohru Ozaki

    Research output: Contribution to journalReview article

    121 Citations (Scopus)

    Abstract

    This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of forward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian computations the practical estimation of very large scale EEG/fMRI models shall soon be possible.

    LanguageEnglish
    Pages2701-2721
    Number of pages21
    JournalHuman Brain Mapping
    Volume30
    Issue number9
    DOIs
    Publication statusPublished - 15 Sep 2009

    Fingerprint

    Electroencephalography
    Magnetic Resonance Imaging
    Brain
    Synaptic Potentials
    Occipital Lobe
    Diffusion Magnetic Resonance Imaging
    Nonlinear Dynamics
    Frontal Lobe
    Thalamus
    Hemodynamics
    Population

    Keywords

    • Alpha rhythm
    • EEG
    • fMRI
    • Hemodynamic response
    • Neural mass models
    • Oscillation

    Cite this

    Valdes-Sosa, P. A., Sanchez-Bornot, J. M., Sotero, R. C., Iturria-Medina, Y., Aleman-Gomez, Y., Bosch-Bayard, J., ... Ozaki, T. (2009). Model driven EEG/fMRI fusion of brain oscillations. Human Brain Mapping, 30(9), 2701-2721. https://doi.org/10.1002/hbm.20704
    Valdes-Sosa, Pedro A. ; Sanchez-Bornot, Jose Miguel ; Sotero, Roberto Carlos ; Iturria-Medina, Yasser ; Aleman-Gomez, Yasser ; Bosch-Bayard, Jorge ; Carbonell, Felix ; Ozaki, Tohru. / Model driven EEG/fMRI fusion of brain oscillations. In: Human Brain Mapping. 2009 ; Vol. 30, No. 9. pp. 2701-2721.
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    abstract = "This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of forward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian computations the practical estimation of very large scale EEG/fMRI models shall soon be possible.",
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    Valdes-Sosa, PA, Sanchez-Bornot, JM, Sotero, RC, Iturria-Medina, Y, Aleman-Gomez, Y, Bosch-Bayard, J, Carbonell, F & Ozaki, T 2009, 'Model driven EEG/fMRI fusion of brain oscillations', Human Brain Mapping, vol. 30, no. 9, pp. 2701-2721. https://doi.org/10.1002/hbm.20704

    Model driven EEG/fMRI fusion of brain oscillations. / Valdes-Sosa, Pedro A.; Sanchez-Bornot, Jose Miguel; Sotero, Roberto Carlos; Iturria-Medina, Yasser; Aleman-Gomez, Yasser; Bosch-Bayard, Jorge; Carbonell, Felix; Ozaki, Tohru.

    In: Human Brain Mapping, Vol. 30, No. 9, 15.09.2009, p. 2701-2721.

    Research output: Contribution to journalReview article

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    AU - Valdes-Sosa, Pedro A.

    AU - Sanchez-Bornot, Jose Miguel

    AU - Sotero, Roberto Carlos

    AU - Iturria-Medina, Yasser

    AU - Aleman-Gomez, Yasser

    AU - Bosch-Bayard, Jorge

    AU - Carbonell, Felix

    AU - Ozaki, Tohru

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    KW - fMRI

    KW - Hemodynamic response

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    Valdes-Sosa PA, Sanchez-Bornot JM, Sotero RC, Iturria-Medina Y, Aleman-Gomez Y, Bosch-Bayard J et al. Model driven EEG/fMRI fusion of brain oscillations. Human Brain Mapping. 2009 Sep 15;30(9):2701-2721. https://doi.org/10.1002/hbm.20704