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EEG/fMRI fusion based on independent component analysis: Integration of data-driven and model-driven methods
2012
Journal of Integrative Neuroscience
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We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). ...
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal ...
YAO
Fig. 4 . 4 Integration of data-and model-driven fusions. ...
doi:10.1142/s0219635212500203
pmid:22985350
fatcat:qn5ruc7tq5de5ookdzgrwmn3nm
Estimating Directed Connectivity from Cortical Recordings and Reconstructed Sources
2015
Brain Topography
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, using both data-driven (directed transfer function) and biologically grounded (dynamic causal modelling) methods. ...
In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. ...
We wish to thank Pedro Valdés Sosa for the idea of setting up a workshop on EEG source imaging and a special issue connected to it, and for coordinating the effort of collecting and processing the datasets ...
doi:10.1007/s10548-015-0450-6
pmid:26350398
fatcat:2s3hvhnaxfaurn6g3egfp4wbeq
Estimating directed connectivity from cortical recordings and reconstructed sources
[article]
2015
biorxiv/medrxiv
pre-print
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, using both data-driven (directed transfer function; DTF) and biologically grounded (dynamic causal modelling; DCM) methods. ...
In order to infer the contributions and connectivity of underlying neuronal sources within the brain, it is necessary to reconstruct sensor data at the source level. ...
We wish to thank Pedro Valdés Sosa for the idea of setting up a workshop on EEG source imaging and a special issue connected to it, and for coordinating the effort of collecting and processing the datasets ...
doi:10.1101/023523
fatcat:tkzowe6dxjhxdlgujoxbnzmcdy
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
2019
Neural Information Processing Systems
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In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data. ...
Inferring effective connectivity between spatially segregated brain regions is important for understanding human brain dynamics in health and disease. ...
For analysis of the real simultaneous EEG-fMRI data, we applied the state-space model on the EEG data recorded simultaneously with fMRI to infer the induced connectivity change between brain regions activated ...
dblp:conf/nips/TuPHS19
fatcat:verhiuh2grbulgcilrcz66w4eq
Methods for Simultaneous EEG-fMRI: An Introductory Review
2012
Journal of Neuroscience
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The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of ...
human brain function. ...
To estimate the location and activity of active cortical patches in the brain that lead to measurable EEG signal changes on the scalp, forward or head models are constructed from individual MR images. ...
doi:10.1523/jneurosci.0447-12.2012
pmid:22553012
pmcid:PMC6622140
fatcat:fnemhavinvaajalvheiwb46q4i
Incorporating priors for EEG source imaging and connectivity analysis
2015
Frontiers in Neuroscience
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We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences. ...
For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. ...
The symmetric integration is widely applied in EEG and MEG fusion. We can further categorize the symmetrical integration into the model-driven and the data-driven integration. ...
doi:10.3389/fnins.2015.00284
pmid:26347599
pmcid:PMC4539512
fatcat:dslnmr32h5cnzfz5u23eqi37xa
Towards Model-Based Brain Imaging with Multi-Scale Modeling
[chapter]
2012
Neuroimaging - Methods
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brain imaging data. ...
We also argue that even without complete physical models, which bridge the various scales, a data-driven approach using partly phenomenological model can be pursued, but this calls for new Neuroinformatics ...
The function role of the temporoparietal junction Model-based brain imaging with multi-scale models can be performed in a truly data-driven manner once the model classes are defined. ...
doi:10.5772/24693
fatcat:azhnnvybobbpjd47w7iiqjvfcq
Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach
2005
IEEE Transactions on Signal Processing
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As a consequence, the use of functional neuroimaging, for instance, functional Magnetic Resonance Imaging (fMRI), constitutes an appealing way of constraining the solution. ...
In this paper, we propose a Bayesian characterization of the relevance of fMRI-derived prior information regarding the EEG/MEG data. ...
His research interest include wavelets, learning algorithms, statistical modeling, and entropic techniques in signal processing with applications in various domain of image processing and brain imaging ...
doi:10.1109/tsp.2005.853220
fatcat:kuoky6b6wfgndlw3wxho2vd4zm
Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis
2015
Journal of Neuroscience
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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 ...
Using computational neural modeling on our acquired concurrent EEG-fMRI data under a visual evoked task, we found not only a substantial forward propagation toward "higher-order" brain regions but also ...
We followed the following steps for performing EEG-fMRI source localization: (1) source space modeling using a cortical mesh consisting of 8196 vertices, (2) data coregistration, (3) forward modeling using ...
doi:10.1523/jneurosci.2269-15.2015
pmid:26424894
fatcat:axwpsfz3vzb5fn3skca36japnm
Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics
2018
Annual Review of Biomedical Engineering
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Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. ...
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. ...
Dynamic Causal Modeling The aforementioned methods for directional connectivity are all data-driven. ...
doi:10.1146/annurev-bioeng-062117-120853
pmid:29494213
pmcid:PMC7941524
fatcat:xypqgl7snbbnnidrn5ddepj6tu
A parallel framework for simultaneous EEG/fMRI analysis: Methodology and simulation
2010
NeuroImage
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This approach enables information one modality to be utilized as priors for the other and hence improves the spatial (for EEG) or temporal (for fMRI) resolution of the other modality. ...
Simulations under realistic noise conditions indicated that STEFF is a feasible and physiologically reasonable hybrid approach for spatiotemporal mapping of cognitive processing in the human brain. ...
The key strength of data-driven fusion is its ability to remove noise from the data, generate priors and provide group inferences that can serve as constraints for model-driven methods. ...
doi:10.1016/j.neuroimage.2010.01.024
pmid:20083208
fatcat:muwcsd2bkfeezbolvqololjsge
Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
2008
IEEE Reviews in Biomedical Engineering
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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. ...
Fitting the GLM to the data allows for the estimation of model parameters and the statistic inference against a null hypothesis (i.e., the voxel is not activated). ...
doi:10.1109/rbme.2008.2008233
pmid:20634915
pmcid:PMC2903760
fatcat:6hogffiwvrbonmxn2swkyqstde
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
[article]
2021
arXiv
pre-print
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Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. ...
Moreover, it is shown that our algorithm can achieve as much as 5% absolute improvement in performance compared to previous data-driven methods. ...
We then describe the dataset that is used for our hybrid model-based/data-driven algorithm development and evaluation. ...
arXiv:2108.02372v1
fatcat:7xcxuihqpjh5tpafztpbtpwdqu
IN VIVO CONDUCTIVITY ESTIMATION USING SOMATOSENSORY EVOKED POTENTIALS AND CORTICAL CONSTRAINT ON THE SOURCE
2007
2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
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Unlike classical electrical impedance tomography methods, for which the conductivity is inferred from a current injection on the scalp, we use an evoked source inside the brain that comes from a somatosensory ...
A new method for in vivo conductivity estimation of head tissues is proposed, in the case of a realistic piecewise constant model. ...
ACKNOWLEDGMENTS The authors would like to thank Patrick Marquis and Christian Bénar of INSERM U751 for their help during the data acquisition procedure. ...
doi:10.1109/isbi.2007.357032
dblp:conf/isbi/VallagheCB07
fatcat:3sqhjvllezhvroxptjw2jmva4i
Model driven EEG/fMRI fusion of brain oscillations
2009
Human Brain Mapping
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For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. ...
This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. ...
Oscillatory brain activity Strategies for EEG/fMRI data analysis. A: Simplified underlying forward models (FMs) for fusion. ...
doi:10.1002/hbm.20704
pmid:19107753
fatcat:jnvtii22lvdefadwpg26z3k5fa
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