2013
DOI: 10.1098/rsta.2011.0610
|View full text |Cite
|
Sign up to set email alerts
|

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Abstract: Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, w… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

4
75
0

Year Published

2013
2013
2022
2022

Publication Types

Select...
4
2
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 64 publications
(81 citation statements)
references
References 34 publications
(46 reference statements)
4
75
0
Order By: Relevance
“…8c,d). Also, conditional GC influence analysis 21 left the pattern of results unchanged for gamma and beta, and suggested the involvement of larger networks for theta (Extended Data Fig. 9).…”
Section: Bastos Et Al Feedforward and Feedback Frequency Channelsmentioning
confidence: 99%
“…8c,d). Also, conditional GC influence analysis 21 left the pattern of results unchanged for gamma and beta, and suggested the involvement of larger networks for theta (Extended Data Fig. 9).…”
Section: Bastos Et Al Feedforward and Feedback Frequency Channelsmentioning
confidence: 99%
“…A limitation on the use of bivariate Granger causality is that if the process in question contains more than two variables, then a conclusion of having an influence from one to another variable could come from the other mediate variables [81,82]. Therefore, in the estimation formulation, we should regress out the effect from other variables, here denoted by z(t ).…”
Section: Therefore the Z Transform Of The Ar Equation Is A(z)y (Z) =mentioning
confidence: 99%
“…It is clear that once we compute the regression estimates of model parameters, if y has an influence on x, it should help predict x and the variance of the residual error on the second model should be decreased. It is stated in [81,82] that the bivariate Granger causality from y to x is quantified by the log-likelihood ratio…”
Section: Therefore the Z Transform Of The Ar Equation Is A(z)y (Z) =mentioning
confidence: 99%
See 1 more Smart Citation
“…Faes et al [18] address in the frequency domain the theoretically challenging issue of the dependence of causality on the canonical form of the multivariate model necessary to interpret instantaneous links resulting from the inadequate temporal resolution in relation to the latencies among signals. Wen et al [19] propose an efficient method for estimating Granger causality among a subgroup of signals present in Ω starting from the spectral density matrix describing all the causal interactions in Ω. Finally, Ramb et al [20] examine in the frequency domain the effects of the latent confounders on the renormalized partial directed coherence.…”
mentioning
confidence: 99%