Journal ArticleDOI
Learning effective brain connectivity with dynamic Bayesian networks.
TLDR
Dynamic Bayesian networks render more accurate and informative brain connectivity than earlier methods as connectivity is described in complete statistical sense and temporal characteristics of time-series are explicitly taken into account.About:
This article is published in NeuroImage.The article was published on 2007-09-01. It has received 128 citations till now. The article focuses on the topics: Dynamic Bayesian network & Bayesian network.read more
Citations
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Journal ArticleDOI
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.
TL;DR: A systematic review of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls, to provide insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
Journal ArticleDOI
Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation.
Shuai Huang,Jing Li,Liang Sun,Jieping Ye,Adam S. Fleisher,Teresa Wu,Kewei Chen,Eric M. Reiman +7 more
TL;DR: A method based on sparse inverse covariance estimation (SICE) to identify functional brain connectivity networks from PET data that is able to identify both the connectivity network structure and strength for a large number of brain regions with small sample sizes is proposed.
Journal ArticleDOI
Network discovery with DCM
Karl J. Friston,Baojuan Li,Baojuan Li,Jean Daunizeau,Jean Daunizeau,Klaas E. Stephan,Klaas E. Stephan +6 more
TL;DR: A scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity is described that furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks.
Journal ArticleDOI
Altered default mode network connectivity in Alzheimer's disease--a resting functional MRI and Bayesian network study
TL;DR: The altered effective connectivity in patients with AD may reveal more characteristics of the disease and may serve as a potential biomarker.
Proceedings ArticleDOI
Weighted graph comparison techniques for brain connectivity analysis
TL;DR: The design space of applicable visual representations are explored and augmented adjacency matrix and node-link visualizations are presented to suggest that matrices support high-level brain connectivity analysis tasks well, outperforming node- link diagrams.
References
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Journal ArticleDOI
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
Journal ArticleDOI
Statistical parametric maps in functional imaging: A general linear approach
Karl J. Friston,Andrew P. Holmes,Keith J. Worsley,J-B. Poline,Chris D. Frith,Richard S. J. Frackowiak +5 more
TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
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Dynamic causal modelling.
TL;DR: As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling, but unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
Journal ArticleDOI
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
Journal ArticleDOI
The anatomy of language: contributions from functional neuroimaging
TL;DR: From functional imaging results, a new anatomically constrained model of word processing is proposed which reconciles the anatomical ambitions of the 19th Century neurologists and the cognitive finesse of the 20th Century cognitive models.