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The specific connectivity of a neuronal network is reflected in the dynamicsof the signals recorded on its nodes. The analysis of how the activity in onenode predicts the behaviour of another gives the directionality in theirrelationship. However, each node is composed of many different elements whichdefine the properties of the links. For instance, excitatory and inhibitoryneuronal subtypes determine the functionality of the connection. Classicindexes such as the Granger causality (GC) quantifies these interactions, butthey do not infer into the mechanism behind them. Here, we introduce anextension of the well-known GC that analyses the correlation associated to thespecific influence that a transmitter node has over the receiver. This way, theG-causal link has a positive or negative effect if the predicted activityfollows directly or inversely, respectively, the dynamics of the sender. Themethod is validated in a neuronal population model, testing the paradigm thatexcitatory and inhibitory neurons have a differential effect in theconnectivity. Our approach correctly infers the positive or negative couplingproduced by different types of neurons. Our results suggest that the proposedapproach provides additional information on the characterization of G-causalconnections, which is potentially relevant when it comes to understandinginteractions in the brain circuits.

Inferring correlations associated to causal interactions in brain signals using autoregressive models