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Updated: Mar 3, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

An information theory framework for dynamic functional domain connectivity.

Victor M Vergara¹, Robyn Miller¹, Vince Calhoun²

¹The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, 87106 NM, United States.

Journal of Neuroscience Methods

|April 27, 2017

Summary

This summary is machine-generated.

This study introduces a novel information theory framework to measure information flow between brain functional domains. The cross-domain mutual information (CDMI) reveals distinct connectivity patterns, particularly highlighting information exchange in subcortical and sensorimotor networks.

Keywords:

Dynamic functional network connectivity Entropy Functional MRI Mutual information

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Area of Science:

Neuroscience
Information Theory
Brain Connectivity

Background:

Dynamic functional network connectivity (dFNC) traditionally analyzes whole-brain time-varying coherent activity.
Existing dFNC methods often focus on large-scale matrices, potentially overlooking domain-specific information exchange.

Purpose of the Study:

To propose and validate an information theory framework for quantifying information flow among functional brain domains.
To introduce Cross-Domain Mutual Information (CDMI) as a novel metric for assessing inter-domain information exchange.

Main Methods:

Estimating dynamic functional states at the level of functional domains.
Calculating information quantity based on the probabilities of observing dynamic states.
Measuring mutual information across domains to derive CDMI.

Main Results:

Significant CDMI values were found between subcortical domains.
A distinct cluster of CDMI was observed among domains involved in sensory input, motor control, and the cerebellum.
Limited information flow was detected among other functional domains.

Conclusions:

CDMI clusters align with known basal ganglia pathways and sensorimotor connectivity patterns.
Higher-level cognitive domains exhibit different connectivity patterns with less information sharing.
This information-theoretic approach reveals novel features of brain functional connectivity beyond traditional dFNC.