Entropy measures as indicators of connectivity paths in the human brain

TL;DR

This paper uses entropy-based information theory tools to analyze fMRI data, revealing complex, non-linear brain connectivity patterns during various cognitive tasks without prior assumptions.

Contribution

It introduces a parameter-free, entropy-based approach for detecting linear and non-linear brain connectivity patterns in task-based fMRI data.

Findings

Detected non-linear brain interactions during tasks

Identified previously unknown connectivity patterns

Validated the effectiveness of entropy measures in brain analysis

Abstract

How does the information flow between different brain regions during various stimuli? This is the question we aim to address by studying complex cognitive paradigms in terms of Information Theory. To assess creativity and the emergence of patterns from a Shannon perspective, we applied a range of tools, including Entropy Density, Effective Measure Complexity, and the Lempel-Ziv distance. These entropic tools enable the detection of both linear and non-linear dynamics without relying on pre-established parameters, models, or prior assumptions about the data. To identify connections between different brain regions, we analyse task-based fMRI data from subjects during motor, working memory, emotion recognition, and language stimuli to gain insight into these complex cognitive processes. Since this method does not rely on prior knowledge, it is particularly well-suited for exploratory research, facilitating the discovery of previously unidentified connections or patterns in the brain. The capacity to identify non-linear dynamics is especially important for studying brain connectivity, as the brain exhibits significant non-linear interactions across multiple functional levels.