Séminaire de Daniel Chicharro

15 mars 2013

Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia (IIT@UniTn), Rovereto (TN), Italy

Distinguishing the inference of causal structure from the analysis of causal effects and of emerging dependencies

invité par Andrea Brovelli

Abstract : Biological systems like the brain often consist of multiple interacting subsystems. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. Based on a standard notion of causal effects, we investigated [1] the extent to which the cause-and-effect framework is applicable to such interacting subsystems, and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We showed that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. This has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM).

Furthermore, even when brain interactions cannot be characterized in terms of causes-and-effects between regions, it is of interest to examine how the dynamic dependencies arising from the interactions determine the emerging properties related to neural processing. Accordingly, we proposed [2] an information-theoretic framework to analyze the dynamic dependencies in multivariate time-evolving systems and to characterize functionally relevant changes in the dynamics. This framework constitutes a fully multivariate extension and unification of previous approaches based on bivariate or conditional mutual information and Granger causality or transfer entropy.

1 : Chicharro D, Ledberg A (2012) When Two become one : The limits of causality analysis of brain dynamics. PLoS ONE 7 : e32466

2. Chicharro D, Ledberg A (2012) Framework to study dynamic dependencies in networks of interacting processes. Phys Rev E 86:041901

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