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

TL;DR

This paper extends Granger causality to analyze the correlation effects of specific neuronal influences, distinguishing excitatory and inhibitory interactions in brain signals, validated through neuronal population models.

Contribution

It introduces a novel extension of Granger causality that infers the sign of influence, providing deeper insight into neuronal connectivity mechanisms.

Findings

Successfully differentiates excitatory and inhibitory effects

Accurately infers positive or negative coupling in neuronal models

Enhances understanding of brain circuit interactions

Abstract

The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.