Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model

Abbas Babajani-Feremi, Susan Bowyer, John Moran, Kost Elisevich, Hamid Soltanian-Zadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The integrated analysis of the Electroencephalography (EEG), Magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are instrumental for functional neuroimaging of the brain. A bottom-up integrated E/MEG and fMRI model based on physiology as well as a method for estimating its parameters are keys to the integrated analysis. We propose the variational Bayesian expectation maximization (VBEM) method to estimate parameters of our proposed integrated model. VBEM method iteratively optimizes a lower bound on the marginal likelihood. An iteration of the VBEM consists of two steps: a variational Bayesian expectation step implemented using the extended Kalman smoother (EKS) and the posterior probability of the parameters in the previous step, and a variational Bayesian maximization step to estimate the posterior distributions of the parameters. For a given external stimulus, a variety of multi-area models can be considered in which the number of areas and the configuration and strength of connections between the areas are different. The proposed VBEM method can be used to select an optimal model as well as estimate its parameters. The efficiency of the proposed VBEM method is illustrated using simulation and real datasets. The proposed VBEM method can be used to estimate parameters of other non- linear dynamical systems. This study proposes an effective method to integrate E/MEG and fMRI and plans to use these techniques in functional neuroimaging.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2009
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
DOIs
StatePublished - Jun 22 2009
EventMedical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging - Lake Buena Vista, FL, United States
Duration: Feb 8 2009Feb 10 2009

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7262
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityLake Buena Vista, FL
Period2/8/092/10/09

Fingerprint

Magnetoencephalography
magnetic resonance
estimating
Magnetic Resonance Imaging
Functional neuroimaging
Functional Neuroimaging
estimates
Nonlinear dynamical systems
electroencephalography
physiology
Physiology
Electroencephalography
dynamical systems
stimuli
brain
iteration
Brain

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Babajani-Feremi, A., Bowyer, S., Moran, J., Elisevich, K., & Soltanian-Zadeh, H. (2009). Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model. In Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging [72621T] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7262). https://doi.org/10.1117/12.813840

Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model. / Babajani-Feremi, Abbas; Bowyer, Susan; Moran, John; Elisevich, Kost; Soltanian-Zadeh, Hamid.

Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging. 2009. 72621T (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7262).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Babajani-Feremi, A, Bowyer, S, Moran, J, Elisevich, K & Soltanian-Zadeh, H 2009, Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model. in Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging., 72621T, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, Lake Buena Vista, FL, United States, 2/8/09. https://doi.org/10.1117/12.813840
Babajani-Feremi A, Bowyer S, Moran J, Elisevich K, Soltanian-Zadeh H. Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model. In Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging. 2009. 72621T. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.813840
Babajani-Feremi, Abbas ; Bowyer, Susan ; Moran, John ; Elisevich, Kost ; Soltanian-Zadeh, Hamid. / Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model. Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging. 2009. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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