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Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC

Sung C. Jun, John S. George, Woohan Kim, Juliana Paré-Blagoev, Sergey Plis, Doug M. Ranken, David M. Schmidt
2008 NeuroImage  
We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior.  ...  In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework.  ...  We propose a combined spatiotemporal MEG/fMRI dipole analysis based on Bayesian inference.  ... 
doi:10.1016/j.neuroimage.2007.12.029 pmid:18314351 pmcid:PMC2929566 fatcat:pusjqwqdhbfe5edcoogrziod3e

Variational Bayesian MultimodalEncephaloGraphy (VBMEG): Its Theory and Applications
階層変分ベイズ推定法(VBMEG)の原理と応用

Taku Yoshioka, Masa-aki Sato
2011 The Brain & Neural Networks  
Vol.39, No.2, pp.728-741 8) Jun, S.C., George, J.S., Par-Blagoev, J., Plis, S.M., Ranken, D.M., Schmidt, D.M., Wood, C.C. (2005): Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging  ...  , Neuroimage, Vol.17, No.1, pp.324-343 7) Kiebel, S.J., Daunizeau, J., Phillips, C., Friston, K.J. (2008): Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG, Neuroimage,  ... 
doi:10.3902/jnns.18.214 fatcat:r2doqfzxgneavm6ux3aprpvemm

Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data

Sung C Jun, Sergey M Plis, Doug M Ranken, David M Schmidt
2006 Physics in Medicine and Biology  
To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets.  ...  For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis.  ...  Some figures showing the results of the Bayesian dipole analysis were generated using MRIVIEW (Ranken et al 2002) .  ... 
doi:10.1088/0031-9155/51/21/011 pmid:17047269 fatcat:bg44r65pv5etvkmv5foqvob6za

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics

Bin He, Abbas Sohrabpour, Emery Brown, Zhongming Liu
2018 Annual Review of Biomedical Engineering  
We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic  ...  Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements.  ...  Bayesian Methods Bayesian inference provides a general framework in which many source imaging algorithms can be derived and interpreted.  ... 
doi:10.1146/annurev-bioeng-062117-120853 pmid:29494213 pmcid:PMC7941524 fatcat:xypqgl7snbbnnidrn5ddepj6tu

Neuro-current response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm

Proloy Das, Christian Brodbeck, Jonathan Z. Simon, Behtash Babadi
2020 NeuroImage  
The analysis of experimentally-recorded MEG data without MR scans corroborates existing findings, but also delineates the distinct cortical distribution of the underlying neural processes at high spatiotemporal  ...  In summary, we provide a principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.  ...  To this end, we developed a fast inverse solution under a Bayesian estimation setting, the Champ-Lasso algorithm, for inferring the Neuro-Current Response Functions (as spatiotemporal models of cortical  ... 
doi:10.1016/j.neuroimage.2020.116528 pmid:31945510 pmcid:PMC7717175 fatcat:ywygvlg3dfa5dfm3ol6pbagpzq

Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG

Bin He, Zhongming Liu
2008 IEEE Reviews in Biomedical Engineering  
Combining fMRI and EEG/MEG data allows us to study brain function from different perspectives.  ...  in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording.  ...  Such inference is classic in terms of statistics, as opposed to more recent methods based on the Bayesian inference which provides the posterior probability that the voxel is activated given the data  ... 
doi:10.1109/rbme.2008.2008233 pmid:20634915 pmcid:PMC2903760 fatcat:6hogffiwvrbonmxn2swkyqstde

A spatiotemporal dynamic distributed solution to the MEG inverse problem

Camilo Lamus, Matti S. Hämäläinen, Simona Temereanca, Emery N. Brown, Patrick L. Purdon
2012 NeuroImage  
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution.  ...  In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent  ...  Analysis of experimental data from human subjects We also applied the MNE, FIS, sMAP-EM, and dMAP-EM algorithms to mu-rhythm MEG data from a human subject.  ... 
doi:10.1016/j.neuroimage.2011.11.020 pmid:22155043 pmcid:PMC3432302 fatcat:lz7wamgjuzhc5jp7obsbcvmyga

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions

Wataru Sato, Takanori Kochiyama, Shota Uono
2015 Scientific Reports  
This contrasts with an MEG study, which reported that the current dipole of posterior activity for dynamic facial stimuli during this time period was located in the V5 region 13 .  ...  Consistent with these behavioral data, neuroimaging studies using functional magnetic resonance imaging (fMRI) and positron-emission tomography have shown that several cortical and subcortical regions  ...  Masaki for advice and the ATR Brain Activity Imaging Center for support in data acquisition.  ... 
doi:10.1038/srep12432 pmid:26206708 pmcid:PMC4513292 fatcat:ohx6ar6aszhrdax7xux5udjxgi

Bayesian models for functional magnetic resonance imaging data analysis

Linlin Zhang, Michele Guindani, Marina Vannucci
2014 Wiley Interdisciplinary Reviews: Computational Statistics  
We divide methods according to the objective of the analysis. We start from spatiotemporal models for fMRI data that detect task-related activation patterns.  ...  A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data.  ...  ACKNOWLEDGMENTS We wish to thank the Editor and the three referees for their constructive comments.  ... 
doi:10.1002/wics.1339 pmid:25750690 pmcid:PMC4346370 fatcat:jqt3gog5f5h7hcceqnbve2mtye

Neuro-Current Response Functions: A Unified Approach to MEG Source Analysis under the Continuous Stimuli Paradigm [article]

Proloy Das, Christian Brodbeck, Jonathan Z Simon, Behtash Babadi
2019 bioRxiv   pre-print
cortex via source localization, or the neuroimaging data are first mapped to the cortex followed by estimating TRFs for each of the resulting cortical sources.  ...  In summary, we provide a principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.  ...  To this end, we developed a fast inverse solution under a Bayesian estimation setting, the Champ-Lasso algorithm, for inferring the Neuro-Current Response Functions (as spatiotemporal models of cortical  ... 
doi:10.1101/761999 fatcat:izvlv7aslvannb6yl7pidoo3zi

Hierarchical multiscale Bayesian algorithm for robust MEG/EEG source reconstruction

Chang Cai, Kensuke Sekihara, Srikantan S. Nagarajan
2018 NeuroImage  
We then derive a novel Bayesian algorithm for probabilistic inference with this graphical model.  ...  In this paper, we present a novel hierarchical multiscale Bayesian algorithm for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG).  ...  We would also like to thank Julia Owen for the support of MEG data simulation and evaluation, Hagai Attias for inspiration and early discussions, and Inez Raharjo for editing.  ... 
doi:10.1016/j.neuroimage.2018.07.056 pmid:30059734 pmcid:PMC6214686 fatcat:sm4x7k7ri5fzrp3yiirjzsbeea

Automatic fMRI-guided MEG multidipole localization for visual responses

Toni Auranen, Aapo Nummenmaa, Simo Vanni, Aki Vehtari, Matti S. Hämäläinen, Jouko Lampinen, Iiro P. Jääskeläinen
2009 Human Brain Mapping  
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)  ...  On this account, the algorithm acts perhaps more as a stochastic optimizer than enables a full Bayesian posterior analysis.  ...  Marja Balk for assistance in gathering the functional magnetic resonance imaging data.  ... 
doi:10.1002/hbm.20570 pmid:18465749 fatcat:3o72hithqnaqvcboxy3x6kykce

Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles

Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari, Mikko Sams
2007 Human Brain Mapping  
A recently introduced Bayesian model for magnetoencephalographic (MEG) data consistently localized multiple simulated dipoles with the help of marginalization of spatiotemporal background noise covariance  ...  structure in the analysis : Neuroimage 28:84-98].  ...  Recently, Jun et al. [2005] proposed a Bayesian inference technique for multiple dipole analysis of MEG data having a full spatiotemporal sensor space noise covariance structure.  ... 
doi:10.1002/hbm.20334 pmid:17370346 fatcat:grquxtfpmrgntkwwrvsgkfbhyi

Population-level inferences for distributed MEG source localization under multiple constraints: Application to face-evoked fields

R.N. Henson, J. Mattout, K.D. Singh, G.R. Barnes, A. Hillebrand, K. Friston
2007 NeuroImage  
We address some key issues entailed by population inference about responses evoked in distributed brain systems using magnetoencephalography (MEG).  ...  At the within-subject level, we focused on the use of multiple constraints, or priors, for inverting distributed source models.  ...  Introduction This paper is about the analysis of multi-subject MEG data using distributed source estimates.  ... 
doi:10.1016/j.neuroimage.2007.07.026 pmid:17888687 fatcat:zl4nioeoxrey7comjgx7qgbyd4

Proceedings: ISBET 200 – 14th World Congress of the International Society for Brain Electromagnetic Topography, November 19-23, 2003

Yoshio Okada
2003 Brain Topography  
Signal-space separation (SSS) is applied for head movement correction and noise compensation. For method developers, MATLAB interface exists for MEG/EEG, MRI, MCE and dipole data.  ...  The BESA (Brain Electrical Source Analysis) program provides a large variety of tools for the complete analysis of EEG and MEG recordings.  ...  We use a dipole based spatiotemporal Bayesian inference analysis to demonstrate the use of this noise model [3] .  ... 
doi:10.1023/b:brat.0000019284.29068.8d fatcat:tpvp3dcojrczjkuzcu3xefyizy
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