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The magnitude of a given Granger causality interaction can be estimated by the logarithm of the corresponding F-statistic [29].
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Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem.
Specifically, this group has shown that an important analytical technique - Granger Causality - can invent causal interactions that do not actually exist between neurons, and they have figured out why the method sometimes fails.
The application of Granger causality analysis suggested direct causal interactions between activity in the right fronto-insular cortex (rFIC) and other cortical areas across a range of tasks.
The current study addressed several questions of causality and interaction in brain network function.
This result supports our early assertion that, in addition to instantaneous causality, bidirectional interaction may also contribute to near-zero phase-lag.
Granger causality only reveals direct causality between pairwise units with linear interactions, thus conditional and partial Granger causality [ 14, 15] and kernel Granger causality [ 16] have been proposed to deal with indirect causal interactions among multiple variables.
The conditional Granger causality [ 14, 15] aim to deal with the causal interactions among multiple variables.
The performance of both spectral G-causality and its conditional extension for multivariate systems is explored in quantifying causal interactions within ecological networks.
Thus several studies have conducted clinical interventions in humans to prove causality of interactions in humans, as shown by the effect of metformin (Wu et al., 2017).
In contrast to the approaches of functional connectivity, models of effective connectivity facilitate description of the causality of interactions among brain regions.
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