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Estimation of the Hemodynamic Response in Event-Related Functional MRI: Bayesian Networks as a Framework for Efficient Bayesian Modeling and Inference

G. Marrelec, P. Ciuciu, M. Pelegrini-Issac, H. Benali
2004 IEEE Transactions on Medical Imaging  
ACKNOWLEDGMENT The authors are grateful to Pr. J. Doyon (Institut de Gériatrie, Université de Montréal, Canada) for providing them with the data and to C. Posé for her technical support.  ...  Estimation of the Hemodynamic Response in Event-Related Functional MRI: Bayesian Networks as a Framework for Efficient Bayesian Modeling and Inference Guillaume Marrelec*, Philippe Ciuciu, Member, IEEE  ...  We finally apply this novel technique on both simulations and real data. Index Terms-Bayesian inference, Bayesian networks, functional MRI, hemodynamic response function. I.  ... 
doi:10.1109/tmi.2004.831221 pmid:15338730 fatcat:t4hdvmix2fd63lp7rfj3ydrniu

Application of Spatial Bayesian Hierarchical Models to fMRI Data [chapter]

Kuo-Jung Lee
2016 Assessment of Cellular and Organ Function and Dysfunction using Direct and Derived MRI Methodologies  
In this chapter, a spatial Bayesian hierarchical model is applied to an event-related fMRI study of cognitive control using the Simon test.  ...  Bayesian modelling has attracted great interest in cognitive science and offered a flexible and interpretable way to study cognitive processes using functional magnetic resonance imaging data.  ...  Acknowledgements This research was, in part, supported by the Ministry of Education, Taiwan, R.O.C. and National Cheng Kung University (the Aim for the Top University Project to National Cheng Kung University  ... 
doi:10.5772/64823 fatcat:2tyu76cetvgphffjqxs2btr24y

A deconvolution algorithm for multiecho functional MRI: Multiecho Sparse Paradigm Free Mapping [article]

Cesar Caballero Gaudes, Stefano Moia, Puja Pawar, Peter A. Bandettini, Javier Gonzalez-Castillo
2019 biorxiv/medrxiv   pre-print
This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.  ...  Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R2* changes obtained with ME-SPFM at the times of the stimulus trials show  ...  Penny A. Gowland for helpful discussion regarding the contents of this manuscript, as well as the anonymous reviewers for their valuable comments and feedback.  ... 
doi:10.1101/558288 fatcat:3fymypki2rgu7npddhcaaacsqa

A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping

César Caballero-Gaudes, Stefano Moia, Puja Panwar, Peter A. Bandettini, Javier Gonzalez-Castillo
2019 NeuroImage  
This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.  ...  Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R2⁎ changes obtained with ME-SPFM at the times of the stimulus trials show  ...  Penny A. Gowland for helpful discussion regarding the contents of this manuscript, as well as the anonymous reviewers for their valuable comments and feedback.  ... 
doi:10.1016/j.neuroimage.2019.116081 pmid:31419613 pmcid:PMC6819276 fatcat:sid3nqty2ba67l5jmhsu5aji7m

Partial correlation for functional brain interactivity investigation in functional MRI

Guillaume Marrelec, Alexandre Krainik, Hugues Duffau, Mélanie Pélégrini-Issac, Stéphane Lehéricy, Julien Doyon, Habib Benali
2006 NeuroImage  
In the proposed framework, Bayesian analysis makes it possible to estimate and test the partial statistical dependencies between regions without any prior model on the underlying functional interactions  ...  Partial correlation is more closely related to effective connectivity than marginal correlation and provides a convenient graphical representation for functional interactions.  ...  Acknowledgments The authors are in debt to the two anonymous referees for significantly improving the quality of the original manuscript.  ... 
doi:10.1016/j.neuroimage.2005.12.057 pmid:16777436 fatcat:xpuqam6phjd7nndmlvvukk535y

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

S. Faisan, L. Thoraval, J.-P. Armspach, M.-N. Metz-Lutz, F. Heitz
2005 IEEE Transactions on Medical Imaging  
In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs).  ...  Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function  ...  Classical and Bayesian inference techniques applied to these models allow both to detect neural activation and estimate model parameters and hyperparameters.  ... 
doi:10.1109/tmi.2004.841225 pmid:15707252 fatcat:fhlkgeuciradlbrx2wyt7mg3ii

Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG

Bin He, Zhongming Liu
2008 IEEE Reviews in Biomedical Engineering  
in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording.  ...  In this review, we start with an overview of the physiological origins of EEG/MEG and fMRI, as well as their fundamental biophysics and imaging principles, we proceed with a review of the major advances  ...  Stimulus functions encoding the occurrence of a particular event or experimental state (e.g., boxcar-functions) are convolved with a hemodynamic response function (HRF) to form regressors in the GLM.  ... 
doi:10.1109/rbme.2008.2008233 pmid:20634915 pmcid:PMC2903760 fatcat:6hogffiwvrbonmxn2swkyqstde

Bayesian Treatments of Neuroimaging Data [chapter]

Will Penny, Karl Friston
2006 Bayesian Brain  
In summary, temporal basis functions offer useful constraints on the form of the estimated response that retain (i) the flexibility of FIR models and (ii) the efficiency of single regressor models.  ...  In [13] the form of the hemodynamic impulse response function (HRF) was estimated using a least squares deconvolution and a time invariant model, where evoked neuronal responses are convolved with the  ... 
doi:10.7551/mitpress/9780262042383.003.0005 fatcat:iw2fqoge3bfvxpvqtcidvosptm

A Theoretical Investigation of the Relationship between Structural Equation Modeling and Partial Correlation in Functional MRI Effective Connectivity

Guillaume Marrelec, Habib Benali
2009 Computational Intelligence and Neuroscience  
In this paper, we provide theoretical fundaments explaining why and in what measure structural equation modeling and partial correlations are related.  ...  We recently proposed a data-driven method based on the partial correlation matrix that could provide some insight regarding the pattern of functional interaction between brain regions as represented by  ...  Inference of Π can be performed in a Bayesian framework using a numerical sampling scheme ( [11, 13] , see also the appendix).  ... 
doi:10.1155/2009/369341 pmid:19730736 pmcid:PMC2731937 fatcat:yypkvov44bainaqqzftgrvu2su

Decision-making under risk: A graph-based network analysis using functional MRI

Ludovico Minati, Marina Grisoli, Anil K. Seth, Hugo D. Critchley
2012 NeuroImage  
Regional response non-linearity was excluded as an artifactual basis to the observed effects, and directionality inferences were confirmed by comparison of dynamic causal models.  ...  This study also has more general paradigmatic implications for neuroeconomics, demonstrating the value of explicit modeling of inter-regional interactions for understanding the neural substrates of decisional  ...  Acknowledgments LM was wholly funded and employed by the Fondazione IRCCS Istituto Neurologico Carlo Besta (FINCB) during the core period of  ... 
doi:10.1016/j.neuroimage.2012.02.048 pmid:22387471 fatcat:u4rtcjqmand4zkfq5i5t2v3dpm

Independent component analysis of functional MRI: what is signal and what is noise?

M McKeown
2003 Current Opinion in Neurobiology  
Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation.  ...  Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find  ...  grant P20 EB 002013 to L Hansen, and both the Howard Hughes Medical Institute and a National Institutes of Health grant MH61619-03 to T Sejnowski.  ... 
doi:10.1016/j.conb.2003.09.012 pmid:14630228 pmcid:PMC2925426 fatcat:6qpybi2xzvexdi23ghce7tocem

Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging [chapter]

Michelle Yongmei
2013 Functional Brain Mapping and the Endeavor to Understand the Working Brain  
Acknowledgements Preparation of this chapter is supported in part by a grant from the National Institute of Aging, K25AG033725.  ...  Author details Michelle Yongmei Wang * Address all correspondence to: ymw@illinois.edu Departments of Statistics, Psychology, and Bioengineering, Beckman Institute, University of Illinois at Urbana-Champaign  ...  A critical objective of neuroimaging data analysis is the inference of neural processes responsible for the observed data, that is, the estimation of the hemodynamic response functions.  ... 
doi:10.5772/55262 fatcat:go2r6jruyzdqrp4lqcnt64j7va

Incorporating priors for EEG source imaging and connectivity analysis

Xu Lei, Taoyu Wu, Pedro A. Valdes-Sosa
2015 Frontiers in Neuroscience  
Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET).  ...  For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI.  ...  Acknowledgments This research was supported by grants from the 863 project 2015AA020504 and the National Nature Science Foundation of China (31200857).  ... 
doi:10.3389/fnins.2015.00284 pmid:26347599 pmcid:PMC4539512 fatcat:dslnmr32h5cnzfz5u23eqi37xa

Hierarchical Bayesian Modeling of the Relationship between Task Related Hemodynamic Responses and Neuronal Excitability: a Simultaneous fNIRS/TMS Study [article]

Zhengchen Cai, Giovanni Pellegrino, Jean-Marc Lina, Habib Benali, Christophe Grova
2021 bioRxiv   pre-print
Here we proposed hierarchical Bayesian modeling to taking into account variability in the data at the individual and group levels, aiming to provide accurate and reliable statistical inferences on this  ...  Conclusion: Benefiting from this original Bayesian data analysis, our results showed that PAS modulates task-related cortical hemodynamic responses in addition to M1 excitability.  ...  Acknowledgments This work was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant Program (CG and JML), an operating grant from the Canadian  ... 
doi:10.1101/2021.10.22.465452 fatcat:n57w2slr2fef5anjyd5p67p5rm

Large-scale DCMs for resting-state fMRI

Adeel Razi, Mohamed L. Seghier, Yuan Zhou, Peter McColgan, Peter Zeidman, Hae-Jeong Park, Olaf Sporns, Geraint Rees, Karl J. Friston
2017 Network Neuroscience  
Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied  ...  This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity.  ...  The Bayesian framework used by DCM allows for an informed and graceful way of performing "network inference" via Bayesian model reduction to select or threshold out redundant edges .  ... 
doi:10.1162/netn_a_00015 pmid:29400357 pmcid:PMC5796644 fatcat:k5ovly4t2netfkgdjo7joyuc3q
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