Journal ArticleDOI
Tractography-Based Score for Learning Effective Connectivity From Multimodal Imaging Data Using Dynamic Bayesian Networks
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TLDR
A novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously is proposed that is employed in structure learning of DBN given the data.Abstract:
Objective: Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition [1] EC is “the causal influence exerted by one neuronal group on another” which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure–function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). Method: DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. Results: Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. Conclusion: EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. Significance: Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.read more
Citations
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Journal ArticleDOI
Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input
TL;DR: The experimental results indicate that the proposed variational inference method for the KDBN can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.
Journal ArticleDOI
ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging.
TL;DR: A new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO), which achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules.
Journal ArticleDOI
Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra.
TL;DR: The presented methodology appears to be well-suited for fMRI, particularly given its lack of explicit dependence on temporal lag structure, and is readily applicable to whole-brain effective connectivity estimation.
Journal ArticleDOI
A practical analysis of sample complexity for structure learning of discrete dynamic Bayesian networks
Salih Geduk,Ilkay Ulusoy +1 more
TL;DR: Discrete Dynamic Bayesian Network (dDBN) is used in many challenging causal modelling applications, such as human brain connectivity, due to its multivariate, non-deterministic, and nonlinear capab...
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Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader
TL;DR: Four machine learning tools are applied to address the challenge in the IFDI of cutting arms and the experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods.
References
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Learning Bayesian networks: approaches and issues
TL;DR: This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data, and aims to locate all the relevant publications.
Journal ArticleDOI
Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data.
TL;DR: Findings indicate that regions whose activities are not synchronized interact via time-delayed causal influences, and segregation of different resting state networks is not clear cut but only by soft boundaries.
Journal ArticleDOI
Tractography-based priors for dynamic causal models
TL;DR: This study uses diffusion weighted imaging and probabilistic tractography to specify anatomically informed priors for dynamic causal models of fMRI data and shows that the best model is one in which anatomical probability increases the prior variance of effective connectivity parameters in a nonlinear and monotonic (sigmoidal) fashion.
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Learning effective brain connectivity with dynamic Bayesian networks.
TL;DR: 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.