Discrete Dynamic Causal Modeling and Its Relationship with Directed Information

TL;DR

This paper establishes a theoretical link between Discrete Dynamic Causal Modeling and Directed Information, demonstrating their equivalence in causal analysis of brain regions, supported by empirical fMRI data.

Contribution

It proves the conditional equivalence between DDCM and DI in causal characterization, bridging two important methods in neuroscience.

Findings

Theoretical proof of DDCM and DI equivalence

Empirical validation using fMRI data

Numerical results align with previous studies

Abstract

This paper explores the discrete Dynamic Causal Modeling (DDCM) and its relationship with Directed Information (DI). We prove the conditional equivalence between DDCM and DI in characterizing the causal relationship between two brain regions. The theoretical results are demonstrated using fMRI data obtained under both resting state and stimulus based state. Our numerical analysis is consistent with that reported in previous study.