Entropic Causal Inference for Neurological Applications

TL;DR

This paper introduces an entropic causal inference method using causation entropy to accurately recover brain structural networks from functional data, outperforming correlation and LASSO techniques in simulations.

Contribution

It develops a novel information-theoretic approach for causal network inference in neuroscience, leveraging real DTI data for improved accuracy.

Findings

Causation entropy outperforms correlation and LASSO in network recovery.

Method works effectively on real DTI and synthetic networks.

Accurately identifies structural connectivity from simulated functional data.

Abstract

The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. Key to this study is understanding structure of both the structural and functional connectivity between anatomical regions. In this paper we follow previous work in developing a simple dynamical model of the brain by simulating its various regions as Kuramoto oscillators whose coupling structure is described by a complex network. However in our simulations rather than generating synthetic networks, we simulate our synthetic model but coupled by a real network of the anatomical brain regions which has been reconstructed from diffusion tensor imaging (DTI) data. By using an information theoretic approach that defines direct information flow in terms of causation entropy (CSE), we show that we can more accurately recover the true structural network than either of the popular correlation or LASSO regression techniques. We demonstrate the effectiveness of our method when applied to data simulated on the realistic DTI network, as well as on randomly generated small-world and Erd\"os-R\'enyi (ER) networks.