Filters








19,224 Hits in 6.4 sec

Bayesian Structure Learning for Dynamic Brain Connectivity

Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo
2018 International Conference on Artificial Intelligence and Statistics  
This manuscript proposes a novel Bayesian model for dynamic brain connectivity.  ...  brain connectivity.  ...  Taken together, these ideas result in a novel Bayesian structure learning model for dynamic brain connectivity.  ... 
dblp:conf/aistats/AndersenWHPK18 fatcat:76s2rt52tvah3hksmt2nz2vxpi

Modeling Neuronal Interactivity using Dynamic Bayesian Networks

Lei Zhang, Dimitris Samaras, Nelly Alia-Klein, Nora D. Volkow, Rita Z. Goldstein
2005 Neural Information Processing Systems  
In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models for brain activation patterns  ...  The novelty of our framework from a Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity.  ...  In this paper, we exploit Dynamic Bayesian Networks for modeling dynamic (i.e., connecting and interacting) neuronal circuits from fMRI sequences.  ... 
dblp:conf/nips/ZhangSAVG05 fatcat:vr6ww3ciwnb27iqapayebm2mza

Learning effective brain connectivity with dynamic Bayesian networks

Jagath C. Rajapakse, Juan Zhou
2007 NeuroImage  
We propose to use dynamic Bayesian networks (DBN) to learn the structure of effective brain connectivity from functional MRI data in an exploratory manner.  ...  In our previous work, we used Bayesian networks (BN) to learn the functional structure of the brain (Zheng, X., Rajapakse, J.C., 2006. Learning functional structure from fMR images.  ...  We thank the anonymous reviewers for their valuable comments which have improved the quality of this manuscript.  ... 
doi:10.1016/j.neuroimage.2007.06.003 pmid:17644415 fatcat:htmjd4mf45ezxhwmpxauayjxoy

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling [article]

Amirhossein Jafarian, Peter Zeidman, Vladimir Litvak, Karl Friston
2019 arXiv   pre-print
In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research.  ...  These methods are collectively referred to as dynamical casual modelling (DCM).  ...  The Bayesian methods reviewed here provide a useful basis for addressing profoundly ill posed problems in structure learning of coupled dynamical systems.  ... 
arXiv:1904.03093v2 fatcat:zjfx6zhko5da5psutnfrdjcg6q

Dynamic Bayesian network modeling for intervention mechanism

Yan Sun, Yi-Yuan Tang
2012 BMC Neuroscience  
Third, we used K2 algorithm to learn the structure of the DBNs and adopted the greedy search algorithm to search for the local best optimal connectivity structure from fMRI data [4] .  ...  The greatest advantage of dynamic Bayesian networks (DBNs) is that it could demonstrate the temporal and causal relationships among different brain regions more accurately.  ...  Third, we used K2 algorithm to learn the structure of the DBNs and adopted the greedy search algorithm to search for the local best optimal connectivity structure from fMRI data [4] .  ... 
doi:10.1186/1471-2202-13-s1-p24 pmcid:PMC3403572 fatcat:dztf77eh3ffe7mx7753abf43dq

A Multi-Subject, Dynamic Bayesian Networks (DBNS) Framework for Brain Effective Connectivity

Junning Li, Z. Jane Wang, Martin J. McKeown
2007 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  
As dynamic connectivity is shown essential for normal brain function and is disrupted in disease, it is critical to develop models for inferring brain effective connectivity from non-invasive (e.g., fMRI  ...  Increasingly, (dynamic) Bayesian network (BNs) have been suggested for this purpose due to their exibility and suitability.  ...  networks for each subject as done previously [8] , we learn the common structure from the data utilizing dynamic Bayesian network modeling.  ... 
doi:10.1109/icassp.2007.366708 dblp:conf/icassp/LiWM07 fatcat:ns7xvii2gbbxbox6wm6pzvdvjq

Structure learning in coupled dynamical systems and dynamic causal modelling

Amirhossein Jafarian, Peter Zeidman, Vladimir Litvak, Karl Friston
2019 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research.  ...  These methods are collectively referred to as dynamic causal modelling.  ...  The Bayesian methods reviewed here provide a useful basis for addressing profoundly ill-posed problems in structure learning of coupled dynamical systems.  ... 
doi:10.1098/rsta.2019.0048 pmid:31656140 pmcid:PMC6833995 fatcat:rjgw7ks6wvehxep4s5qejy72ci

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

Xuan Guo, Bing Liu, Le Chen, Guantao Chen, Yi Pan, Jing Zhang
2016 Computational and Mathematical Methods in Medicine  
We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition  ...  Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their  ...  Acknowledgments The authors are grateful for support from Georgia State University Brains-Behavior Seed grant.  ... 
doi:10.1155/2016/3279050 pmid:27034708 pmcid:PMC4791514 fatcat:goiphn32andxto2ohh6kw26voq

Learning partially directed functional networks from meta-analysis imaging data

Jane Neumann, Peter T. Fox, Robert Turner, Gabriele Lohmann
2010 NeuroImage  
The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions.  ...  Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method.  ...  Acknowledgments We wish to thank Chris Needham for valuable comments regarding the theory of Bayesian networks.  ... 
doi:10.1016/j.neuroimage.2009.09.056 pmid:19815079 pmcid:PMC2789920 fatcat:dhlwbaxopfaudnkwlr4ds5bjpm

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  
Dynamic Bayesian networks (DBNs) were then proposed [83] to learn the structure of effective brain connectivity in an exploratory way.  ...  Sparse BN for effective connectivity modeling was investigated in [87] , with a novel formulation for the structure learning of BNs.  ... 
doi:10.5772/55262 fatcat:go2r6jruyzdqrp4lqcnt64j7va

A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks

Junning Li, Z. Jane Wang, Martin J. McKeown
2007 IEEE Engineering in Medicine and Biology Society. Conference Proceedings  
FMRI experiments are usually performed to make inferences about groups of subjects, but current group analysis methods for dynamic Bayesian networks (DBNs) do not easily allow incorporation of covariates  ...  The method is performed in two stages: first, deriving a DBN connectivity network among brain regions for each subject separately; second, regressing the connectivity coefficients of DBNs to the factors  ...  Dynamic Bayesian Network A dynamic Bayesian network (DBN) is a graphical model for stochastic processes.  ... 
doi:10.1109/iembs.2007.4353713 pmid:18003379 fatcat:lbknj6ut35e4vn66tw6frqq2l4

Dynamic Connectivity Mapping of Electrocorticographic Data using Bayesian Differential Structural Equation Modeling

Price LR
2016 Biometrics & Biostatistics International Journal  
Using electrocorticographic (ECoG) data, we propose a data-driven approach using ordinary differential equations and Bayesian differential structural equation modeling (BdSEM) to model effective connectivity  ...  Finally, we employed an information-theoretic search strategy to identify the optimal connectivity model within each experimental condition for a single subject.  ...  2016) Dynamic Connectivity Mapping of Electrocorticographic Data using Bayesian Differential Structural Equation Modeling.  ... 
doi:10.15406/bbij.2016.04.00102 fatcat:tqcuv67lvjfepc3tae2dn4frfu

Neuronal Sequence Models for Bayesian Online Inference [article]

Sascha Frölich, Dimitrije Marković, Stefan J. Kiebel
2020 arXiv   pre-print
brain are grounded on generative processes which maintain a sequential structure.  ...  Combining experimental findings with computational concepts like the Bayesian brain hypothesis and predictive coding leads to the interesting possibility that predictive and inferential processes in the  ...  Combining the Bayesian brain hypothesis with predictive coding provides a theoretical basis for computational mechanisms that drive a lifelong learning of the hierarchical causal model of the world (Friston  ... 
arXiv:2004.00930v1 fatcat:m6nodt3xl5adrns3zqdgmqruem

Neuronal Sequence Models for Bayesian Online Inference

Sascha Frölich, Dimitrije Marković, Stefan J. Kiebel
2021 Frontiers in Artificial Intelligence  
We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions  ...  , but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.  ...  Combining the Bayesian brain hypothesis with the hierarchical aspect of predictive coding provides a theoretical basis for computational mechanisms that drive a lifelong learning of the causal model of  ... 
doi:10.3389/frai.2021.530937 pmid:34095815 pmcid:PMC8176225 fatcat:pafghjxk7bgkdgnlxfqidd3bw4

Bayesian networks in neuroscience: a survey

Concha Bielza, Pedro Larrañaga
2014 Frontiers in Computational Neuroscience  
(C) The dynamic BN unfolded in time for three time slices.  ...  In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms.  ...  ACKNOWLEDGMENTS Research partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2013-41592-P), the Cajal Blue Brain Project (Spanish partner of the Blue Brain Project initiative  ... 
doi:10.3389/fncom.2014.00131 pmid:25360109 pmcid:PMC4199264 fatcat:2ip7hztt4fexdj5cw4a2gpmgbu
« Previous Showing results 1 — 15 out of 19,224 results