Transfer entropy—a model-free measure of effective connectivity for the neurosciences

TLDR

Effective connectivity, the causal interaction between brain regions, is crucial because much brain activity is internally generated, yet prior methods such as Granger causality and dynamic causal modeling rely on models; transfer entropy offers a model‑free, inherently nonlinear alternative. The study aimed to assess whether transfer entropy can serve as a metric for effective connectivity in electrophysiological data, using simulations and MEG recordings during a simple motor task. Transfer entropy was applied to simulated data and MEG recordings from the motor task to evaluate its performance. Transfer entropy improved detectability of effective connectivity for nonlinear interactions and for sensor‑level MEG signals where linear methods are hampered by volume conduction.

Abstract

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain's activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction.