Neural Comput - Identification of directed influence: Granger causality, Kullback-Leibler divergence, and complexity.

Tópicos

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Resumo

Detecting and characterizing causal interdependencies and couplings between different activated brain areas from functional neuroimage time series measurements of their activity constitutes a significant step toward understanding the process of brain functions. In this letter, we make the simple point that all current statistics used to make inferences about directed influences in functional neuroimage time series are variants of the same underlying quantity. This includes directed transfer entropy, transinformation, Kullback-Leibler formulations, conditional mutual information, and Granger causality. Crucially, in the case of autoregressive modeling, the underlying quantity is the likelihood ratio that compares models with and without directed influences from the past when modeling the influence of one time series on another. This framework is also used to derive the relation between these measures of directed influence and the complexity or the order of directed influence. These results provide a framework for unifying the Kullback-Leibler divergence, Granger causality, and the complexity of directed influence.

Resumo Limpo

detect character causal interdepend coupl differ activ brain area function neuroimag time seri measur activ constitut signific step toward understand process brain function letter make simpl point current statist use make infer direct influenc function neuroimag time seri variant under quantiti includ direct transfer entropi transinform kullbackleibl formul condit mutual inform granger causal crucial case autoregress model under quantiti likelihood ratio compar model without direct influenc past model influenc one time seri anoth framework also use deriv relat measur direct influenc complex order direct influenc result provid framework unifi kullbackleibl diverg granger causal complex direct influenc

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