Partial correlation for functional brain interactivity investigation in functional MRI

Marrelec, Guillaume; Krainik, Alexandre; Duffau, Hugues; Pélégrini‐Issac, Mélanie; Lehericy, Stéphane; Doyon, Julien; Benali, Habib

doi:10.1016/j.neuroimage.2005.12.057

Cited by 436 publications

(359 citation statements)

References 44 publications

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“…While both structural connectivity and effective connectivity have been tackled using Bayesian approaches (Hinne et al, 2013;Jbabdi et al, 2007;Daunizeau et al, 2011), research into how whole brain functional connectivity can be estimated using Bayesian approaches has remained scarce. Some notable exceptions are the approach by Venkataraman et al (2010) who used a forward model in which fMRI and DWI data were combined and the approach by (Marrelec et al, 2006), who used a Bayesian approach to estimate a group partial correlation matrix. As advocated in this paper, we propose that a generative model consisting of a G-Wishart prior and a multivariate Gaussian likelihood term serves as an elegant new approach for Bayesian functional connectivity analysis.…”

Section: Discussionmentioning

confidence: 99%

Structurally-informed Bayesian functional connectivity analysis

et al. 2014

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Functional connectivity refers to covarying activity between spatially segregated brain regions and can be studied by measuring correlation between functional magnetic resonance imaging (fMRI) timeseries. These correlations can be caused either by direct communication via active axonal pathways or indirectly via the interaction with other regions. It is not possible to discriminate between these two kinds of functional interaction simply by considering the covariance matrix. However, the non-diagonal elements of its inverse, the precision matrix, can be naturally related to direct communication between brain areas and interpreted in terms of partial correlations. In this paper, we propose a Bayesian model for functional connectivity analysis which allows estimation of a posterior density over precision matrices, and, consequently, allows one to quantify the uncertainty about estimated partial correlations. In order to make model estimation feasible it is assumed that the sparseness structure of the precision matrices is given by an estimate of structural connectivity obtained using diffusion imaging data. The model was tested on simulated data as well as resting-state fMRI data and compared with a graphical lasso analysis. The presented approach provides a theoretically solid foundation for quantifying functional connectivity in the presence of uncertainty.

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Section: Discussionmentioning

confidence: 99%

Structurally-informed Bayesian functional connectivity analysis

et al. 2014

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“…In another example [72], they compare the result in finding interaction among brain regions between using Granger causality and coherency technique in motor system. There is the use of partial correlation analysis for exploring functional connectivity explained in [74,75]. Moreover, [34] has integrated several measures such as cross correlation, cross coherence, partial correlation and partial coherence functions to learn functional connectivity and developed the implementation into a public MATLAB toolbox.…”

Section: Partial Correlation Function and Partial Coherence Functionmentioning

confidence: 99%

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Pruttiakaravanich

Songsiri

2016

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Abstract. Functional magnetic resonance imaging (fMRI) technique allows us to capture activities occurring in a human brain via signals related to cerebral blood flow, oxygen metabolism and blood volume, known as BOLD (blood oxygen level-dependent) signals. Exploring relationships between brain regions inside human brains from fMRI data is an active and challenging research topic. Relationships or associations between brain regions are commonly referred to as brain connectivity or brain network. This connectivity can be divided into three groups; (i) structural connectivity representing physically anatomical connections between regions, (ii) the functional connectivity which describes the statistical information among brain regions and (iii) the effective connectivity which specifies how one region interacts with others by a causal model. This survey paper provides a review on learning brain connectivities via fMRI data where detailed mathematical definitions of dependence measures widely-used for functional and effective connectivities are described. These measures include correlation, partial correlation, coherence, partial coherence, directed coherence, partial directed coherence, Granger causality, and other concepts such as dynamical causal modeling or structural equation modeling. Interpretation and relations of these measures as well as relevant estimation techniques that are widely used in the problems of fMRI modeling are summarized in this paper.

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“…This formula estimates the partial correlations [19] and constructs a conditional independence graph.…”

Section: Construction Of An Undirected Gaussian Graphmentioning

confidence: 99%

Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

Ennouri

Ayed

Hassen

et al. 2015

Acta Microbiologica et Immunologica Hungarica

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Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH 2 PO 4 , K 2 HPO 4 , FeSO 4 , MnSO 4 , and MgSO 4 and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett-Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors infl uenced the production of delta-endotoxins are FeSO 4 , K 2 HPO 4 , starch and soybean meal. Indeed, it was found that soybean meal, K 2 HPO 4 , KH 2 PO 4 and starch also showed positive effect on the delta-endotoxins production. However, FeSO 4 and MnSO 4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett-Burman design) joined with Bayesian networks method could be used for identifi cation of effect variables on delta-endotoxins variation.

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Partial correlation for functional brain interactivity investigation in functional MRI

Cited by 436 publications

References 44 publications

Structurally-informed Bayesian functional connectivity analysis

Structurally-informed Bayesian functional connectivity analysis

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

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