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Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC
2008
NeuroImage
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We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. ...
A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. ...
This led us to develop a combined MEG/EEG/fMRI Bayesian inference source analysis. ...
doi:10.1016/j.neuroimage.2007.12.029
pmid:18314351
pmcid:PMC2929566
fatcat:pusjqwqdhbfe5edcoogrziod3e
Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data
2008
NeuroImage
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We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. ...
Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each ...
Acknowledgments The authors would like to thank Kenneth Hild and Ben Inglis for helpful discussions including naming of the algorithm, Sarang Dalal for help with NUTMEG programming, and Anne Findlay and ...
doi:10.1016/j.neuroimage.2008.02.006
pmid:18455439
pmcid:PMC4361188
fatcat:bhtu2cxfrffjhmmpe2z3fhnbna
Automatic fMRI-guided MEG multidipole localization for visual responses
2009
Human Brain Mapping
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close and temporally overlapping sources. ...
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 ...
Marja Balk for assistance in gathering the functional magnetic resonance imaging data. ...
doi:10.1002/hbm.20570
pmid:18465749
fatcat:3o72hithqnaqvcboxy3x6kykce
Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data
2006
Physics in Medicine and Biology
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To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. ...
The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. ...
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
Efficient Posterior Probability Mapping Using Savage-Dickey Ratios
2013
PLoS ONE
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This latter facility is most parsimoniously provided by PPMs based on Bayesian model comparisons. ...
Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. ...
Conceived and designed the experiments: WP GR. Performed the experiments: WP. Analyzed the data: WP. Contributed reagents/ materials/analysis tools: WP. Wrote the paper: WP GR. ...
doi:10.1371/journal.pone.0059655
pmid:23533640
pmcid:PMC3606143
fatcat:3fbec3cmwzbqlf6vsrpaurtnze
Proceedings: ISBET 200 – 14th World Congress of the International Society for Brain Electromagnetic Topography, November 19-23, 2003
2003
Brain Topography
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In a second example, an auditory reaction time experiment, similar multiple source models could be created using EEG alone or location seeds based on fMRI BOLD clusters. ...
By combining the latest techniques for measuring electrical activity in the brain with anatomical and functional imaging, Curry provides a powerful method for accurately localizing electromagnetic sources ...
New approaches to the MEG/EEG inverse problem
based on Bayesian Inference have been recently described
[1, 2, 3
The signal space separation method. -S. Taulu, M. Kajola and J. ...
doi:10.1023/b:brat.0000019284.29068.8d
fatcat:tpvp3dcojrczjkuzcu3xefyizy
A Bayesian approach to the mixed-effects analysis of accuracy data in repeated-measures designs
2017
Journal of Memory and Language
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To fit this model we derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial smoothing. ...
In the second contribution we develop a Bayesian spatial model for imaging genetics developed for analyses examining the influence of genetics on brain structure as measured by MRI. ...
Farouk Nathoo for top-notch research guidance, illuminating chats, buying coffee, providing funding, sharing cookies and frequent advice over the years at University of Victoria. ...
doi:10.1016/j.jml.2017.05.002
fatcat:vxkalbnuwfgdhgutfpdzuvqsty
Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods
2007
NeuroImage
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Several distributed source estimation methods based on different prior assumptions have been suggested for the resolution of this inverse problem. ...
variable and estimated from the data using the variational Bayesian (VB) framework. ...
model averaging in MEG/EEG imaging, see, Trujillo-Barreto et al. (2004) ). ...
doi:10.1016/j.neuroimage.2006.05.001
pmid:17300961
fatcat:qobx6w7wore7hgygth6ppd3j44
Ten simple rules for dynamic causal modeling
2010
NeuroImage
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Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. ...
DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. ...
We are very grateful to our fellow DCM developers in the FIL methods group, particularly Lee Harrison for his comments on the manuscript and the shaping discussions on DCM we have had with him over many ...
doi:10.1016/j.neuroimage.2009.11.015
pmid:19914382
pmcid:PMC2825373
fatcat:nhftacuv5zfstog4xeqpo6it5m
Modeling & Analysis
2003
NeuroImage
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Abstract Background noise in MEG/EEG-measurements is correlated both in space and in time. ...
Abstract [Background] We have advocated the use of 3D virtual reality Talairach modeling (VRTM) for topographic mapping of EEG/ERP activation and tomographic registration of PET/fMRI responses based on ...
London, UK ) for software support and source codes. We appreciate Zhou Shen for help with data conversion and Dan Fitzgerald for providing fMRI data. ...
doi:10.1016/s1053-8119(05)70006-9
fatcat:zff2suxcofbxvetfrwfwcxi3zm
Joint estimation of neuralsources and their functional connections from MEG data
[article]
2020
bioRxiv
pre-print
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Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering, which does not require anatomical constraints in form of structural connectivity or a-priori ...
not used to inform the estimation of source amplitudes, and iii) the limited spatial resolution of source estimates often leads to spurious connectivity due to spatial leakage. ...
In addition, these locations are in agreement with 533 previous MEG/EEG studies that have utilized either dipole localization [21, 34, 41] or 534 distributed source imaging methods [28] . ...
doi:10.1101/2020.10.04.325563
fatcat:7qdjqwil5vaqdk3gvd4ew6u5be
Matrix-normal models for fMRI analysis
[article]
2017
arXiv
pre-print
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Similarity Analysis and its empirical Bayes variant, RSA and BRSA; Intersubject Functional Connectivity, ISFC), combining multi-subject datasets (Shared Response Mapping; SRM), and mapping between brain ...
Multivariate analysis of fMRI data has benefited substantially from advances in machine learning. ...
In neuroscience, such models have been applied to MEG/EEG data [19] , as well as nonlatent models for fMRI data [12] , with some evidence that a separable covariance is a reasonable approximation to ...
arXiv:1711.03058v2
fatcat:w7pu5mbzpbayjlh6cr4wuofisq
A Potts-Mixture Spatiotemporal Joint Model for Combined MEG and EEG Data
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1710.08269v7 fatcat:g2aopl3ijjc2vpxilyloixefx4Other Versions
We formulate the new spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial ...
We propose a new Bayesian spatial finite mixture model that builds on the mesostate-space model developed by Daunizeau and Friston (2007). ...
use spatial information based on diffusion MRI to solve the MEG/EEG inverse problem, while Nathoo et al. (2014) use spatial spike-and-slab priors to solve the EEG/MEG inverse problem while incorporating ...
arXiv:1710.08269v8
fatcat:y7tfqcgmtjapzmr7e2geowtcfa
Bi-directional audiovisual influences on temporal modulation discrimination
2017
Journal of the Acoustical Society of America
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We would also like to thank Diego Fernandez-Duque and three anonymous reviewers for their comments on an earlier version of this manuscript. ...
We would like to thank Lorraine Delhorne for conducting hearing screenings on the individuals who took part in this study. ...
When using DDM or similar models, such distinctions can be drawn in humans with the aid of functional neuroimaging techniques such as fMRI or MEG/EEG. ...
doi:10.1121/1.4979470
pmid:28464677
fatcat:gk3esmvhbbbmpf5cnzea4dljlm
Abstracts of the 20th European Conference on Eye Movements, 18-22 August 2019, in Alicante (Spain)
2019
Journal of Eye Movement Research
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This document contains all abstracts of the 20th European Conference on Eye Movements, August 18-22, 2019, in Alicante, Spain Video stream "Glimpses at 20th ECEM": https://vimeo.com/user43478756. ...
Reward was calculated as a fraction of the maximum depending on the performance (accuracy and speed). ...
Results: We observed improved performance on incentivized trials (more so for reward than penalty) looking at peak velocity and reaction times. ...
doi:10.16910/jemr.12.7.1
pmid:33828763
pmcid:PMC7917478
fatcat:wqzwn552cndsnablyikzx3og5e
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