entropy-brain-connectivity-paths

What problem does it solve?

Large-scale brain connectivity analysis often relies on predefined models, which can miss nonlinear information dynamics in fMRI data. This method provides entropy-based tools to detect both linear and nonlinear information flow between brain regions without requiring preset parameters. It supports task-related and exploratory studies by revealing connectivity paths and key regions.

Core Features & Use Cases

• Entropy density to measure information creation without a model.
• Effective measure complexity to capture structure in time series.
• Lempel-Ziv distance to compare regional activity patterns.
• Applications: task-based fMRI analysis, exploratory connectivity discovery, detection of nonlinear dynamics.

Quick Start

Provide an fmri_data array and region_labels, then run entropy density, EMC, and Lempel-Ziv distance to identify the most significant brain connectivity paths.