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Bayesian inference for neural electromagnetic source localization: analysis of MEG visual evoked activity

David M. Schmidt, John S. George, C. C. Wood, Kenneth M. Hanson
1999 Medical Imaging 1999: Image Processing  
BAYESIAN INFERENCE APPLIED T O THE EEG/MEG INVERSE PROBLEM Activity Model In applying the methods of Bayesian inference to the EEG/MEG inverse problem we constructed a model for regions of activation  ...  Our Bayesian inference analysis was applied separately to the data for each visual field stimulus at 110 ms poststimulus latency; a latency that should include robust activation of the calcarine region  ... 
doi:10.1117/12.348596 dblp:conf/miip/SchmidtGW99 fatcat:a5wlfubpsbcntjrdwokrkgve2i

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).  ...  conditions, and (ii) whether to accommodate differences in source orientation by using signed or unsigned (absolute) estimates of source activity.  ...  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

A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities [article]

Shiva Asadzadeh, Tohid Yousefi Rezaii, Soosan Beheshti, Azra Delpak,, Saeed Meshgini
2019 arXiv   pre-print
In this review we provide enough evidence that the effects of psychiatric drugs on the activity of brain sources have not been enough investigated, which provides motivation for consideration in the future  ...  Electroencephalography (EEG) is a popular non-invasive electrophysiological technique of relatively very high time resolution which is used to measure electric potential of brain neural activity.  ...  Timsari, "Imaging variation-based methods for the analysis of extended brain sources," neural activity using MEG and EEG," IEEE Engineering in in 2014 22nd  ... 
arXiv:1910.07980v1 fatcat:rxc6u3d3tvhqpnw6lkt22o44tu

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics

Bin He, Abbas Sohrabpour, Emery Brown, Zhongming Liu
2018 Annual Review of Biomedical Engineering  
Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions.  ...  It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales.  ...  SOURCE IMAGING APPROACHES AND APPLICATIONS Neural activity of interest to EEG and MEG includes ongoing activity in the absence of any task, or the responses evoked or induced by various events.  ... 
doi:10.1146/annurev-bioeng-062117-120853 pmid:29494213 pmcid:PMC7941524 fatcat:xypqgl7snbbnnidrn5ddepj6tu

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

Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents

Felix Lucka, Sampsa Pursiainen, Martin Burger, Carsten H. Wolters
2012 NeuroImage  
Our work examines the performance of fully-Bayesian inference methods for HBM for source configurations consisting of few, focal sources when used with realistic, high resolution Finite Element (FE) head  ...  Especially the recovery of brain networks involving deep-lying sources by means of EEG/MEG recordings is still a challenging task for any inverse method.  ...  Kugel (Department of Clinical Radiology, University of Münster, Germany) for the measurement of the MRI and A. Janssen, S.  ... 
doi:10.1016/j.neuroimage.2012.04.017 pmid:22537599 fatcat:vs33kxcpsrgpni77pictrdciei

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

Yoshio Okada
2003 Brain Topography  
A high-performance, robust tool for electromagnetic source localization and visualization, Curry integrates multiple, complementary modalities (EEG and MEG; MRI, fMRI, CT, PET or SPECT) in a single software  ...  The BESA (Brain Electrical Source Analysis) program provides a large variety of tools for the complete analysis of EEG and MEG recordings.  ...  Reference [2] extended their work to a spatiotemporal Bayesian inference analysis of the full spatial-temporal MEG/EEG data set, using their extended region model for neural activity.  ... 
doi:10.1023/b:brat.0000019284.29068.8d fatcat:tpvp3dcojrczjkuzcu3xefyizy

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  
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 formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior.  ...  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

Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG

Bin He, Zhongming Liu
2008 IEEE Reviews in Biomedical Engineering  
Electrophysiological and hemodynamic/metabolic signals reflect distinct but closely coupled aspects of the underlying neural activity.  ...  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

Evoked brain responses are generated by feedback loops

M. I. Garrido, J. M. Kilner, S. J. Kiebel, K. J. Friston
2007 Proceedings of the National Academy of Sciences of the United States of America  
Furthermore, we were able to quantify the contribution of backward connections to evoked responses and to source activity, again as a function of peristimulus time.  ...  This is the theoretical cornerstone of most modern theories of perceptual inference and learning.  ...  We thank David Bradbury for technical support and the volunteers for participating in this study, Oliver Hulme for comments on the manuscript, and Marcia Bennett for preparing the manuscript.  ... 
doi:10.1073/pnas.0706274105 pmid:18087046 pmcid:PMC2409249 fatcat:nzacb3ooyvenhgoqhd2nlnx6hm

Dynamic causal modeling for EEG and MEG

Stefan J. Kiebel, Marta I. Garrido, Rosalyn Moran, Chun-Chuan Chen, Karl J. Friston
2009 Human Brain Mapping  
V C 2009 Wiley-Liss, Inc. r Dynamic Causal Modeling for EEG and MEG r r 1867 r  ...  We present a review of dynamic causal modeling (DCM) for magneto-and electroencephalography (M/EEG) data.  ...  ACKNOWLEDGMENTS We thank Katharina von Kriegstein for helpful comments.  ... 
doi:10.1002/hbm.20775 pmid:19360734 fatcat:tnrjkb42nbernor22ujooszzo4

Rapid Interactions between the Ventral Visual Stream and Emotion-Related Structures Rely on a Two-Pathway Architecture

D. Rudrauf, O. David, J.-P. Lachaux, C. K. Kovach, J. Martinerie, B. Renault, A. Damasio
2008 Journal of Neuroscience  
neural biophysics.  ...  Visual attention can be driven by the affective significance of visual stimuli before full-fledged processing of the stimuli.  ...  Bayesian inversion scheme to estimate the neural parameters of the neural-mass models based on the measured evoked responses.  ... 
doi:10.1523/jneurosci.3476-07.2008 pmid:18337409 pmcid:PMC6670659 fatcat:sw2vpctw3ferzgk322qej77cka

Adaptive neural network classifier for decoding MEG signals

Ivan Zubarev, Rasmus Zetter, Hanna-Leena Halme, Lauri Parkkonen
2019 NeuroImage  
Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification.  ...  We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements.  ...  of an unknown number of simultaneously active neural sources.  ... 
doi:10.1016/j.neuroimage.2019.04.068 pmid:31059799 pmcid:PMC6609925 fatcat:orsoefql25hm7fexgqtakpki6y

Bayesian Modelling of Induced Responses and Neuronal Rhythms

Dimitris A. Pinotsis, Roman Loonis, Andre M. Bastos, Earl K. Miller, Karl J. Friston
2016 Brain Topography  
We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses-and the identification of important sources  ...  of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity.  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s10548-016-0526-y pmid:27718099 fatcat:pswqqxexfjbw7k46qbjxauz2ry

Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis

V. Youssofzadeh, G. Prasad, A. J. Fagan, R. B. Reilly, S. Martens, J. F. Meaney, K. Wong-Lin
2015 Journal of Neuroscience  
Consistent with hierarchical early visual processing, the model disclosed and quantified the neural temporal dynamics across the identified activity sources.  ...  A recurrent forward-backward connectivity model, consisting of multiple interacting brain regions identified by EEG source localization aided by fMRI spatial priors, best accounted for the data dynamics  ...  A, B, Time course source activities obtained from DCM analysis of grand mean VEPs. Results representing neural source activities with one zoomed area.  ... 
doi:10.1523/jneurosci.2269-15.2015 pmid:26424894 fatcat:axwpsfz3vzb5fn3skca36japnm
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