Literature DB >> 16941835

Integrated MEG/EEG and fMRI model based on neural masses.

Abbas Babajani1, Hamid Soltanian-Zadeh.   

Abstract

We introduce a bottom-up model for integrating electroencephalography (EEG) or magnetoencephalography (MEG) with functional magnetic resonance imaging (fMRI). An extended neural mass model is proposed based on the physiological principles of cortical minicolumns and their connections. The fMRI signal is extracted from the proposed neural mass model by introducing a relationship between the stimulus and the neural activity and using the resultant neural activity as input of the extended Balloon model. The proposed model, validated using simulations, is instrumental in evaluating the upcoming combined methods for simultaneous analysis of MEG/EEG and fMRI.

Mesh:

Year:  2006        PMID: 16941835     DOI: 10.1109/TBME.2006.873748

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  15 in total

1.  Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state.

Authors:  Roberto C Sotero; Amir Shmuel
Journal:  J Comput Neurosci       Date:  2011-11-01       Impact factor: 1.621

Review 2.  Model driven EEG/fMRI fusion of brain oscillations.

Authors:  Pedro A Valdes-Sosa; Jose Miguel Sanchez-Bornot; Roberto Carlos Sotero; Yasser Iturria-Medina; Yasser Aleman-Gomez; Jorge Bosch-Bayard; Felix Carbonell; Tohru Ozaki
Journal:  Hum Brain Mapp       Date:  2009-09       Impact factor: 5.038

Review 3.  Endogenous brain oscillations and related networks detected by surface EEG-combined fMRI.

Authors:  Helmut Laufs
Journal:  Hum Brain Mapp       Date:  2008-07       Impact factor: 5.038

4.  Integrated MEG/fMRI model validated using real auditory data.

Authors:  Abbas Babajani-Feremi; Hamid Soltanian-Zadeh; John E Moran
Journal:  Brain Topogr       Date:  2008-05-14       Impact factor: 3.020

5.  MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes.

Authors:  Sergey M Plis; Vince D Calhoun; Michael P Weisend; Tom Eichele; Terran Lane
Journal:  Front Neuroinform       Date:  2010-11-11       Impact factor: 4.081

6.  Adding dynamics to the Human Connectome Project with MEG.

Authors:  L J Larson-Prior; R Oostenveld; S Della Penna; G Michalareas; F Prior; A Babajani-Feremi; J-M Schoffelen; L Marzetti; F de Pasquale; F Di Pompeo; J Stout; M Woolrich; Q Luo; R Bucholz; P Fries; V Pizzella; G L Romani; M Corbetta; A Z Snyder
Journal:  Neuroimage       Date:  2013-05-20       Impact factor: 6.556

7.  Bayesian comparison of neurovascular coupling models using EEG-fMRI.

Authors:  Maria J Rosa; James M Kilner; Will D Penny
Journal:  PLoS Comput Biol       Date:  2011-06-16       Impact factor: 4.475

8.  Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems.

Authors:  Jason F Smith; Ajay Pillai; Kewei Chen; Barry Horwitz
Journal:  Front Syst Neurosci       Date:  2012-01-18

9.  Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI.

Authors:  M J Rosa; J Kilner; F Blankenburg; O Josephs; W Penny
Journal:  Neuroimage       Date:  2009-09-22       Impact factor: 6.556

10.  A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons.

Authors:  Roxana A Stefanescu; Viktor K Jirsa
Journal:  PLoS Comput Biol       Date:  2008-11-14       Impact factor: 4.475

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