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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Oct 11, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Functional connectivity inference from fMRI data using multivariate information measures.

Qiang Li1

  • 1Image Processing Laboratory, Parc Cientific, University of Valencia, Valencia, Spain.

Neural Networks : the Official Journal of the International Neural Network Society
|November 30, 2021
PubMed
Summary
This summary is machine-generated.

This study clarifies higher-order information theory concepts like interaction information and total correlation for neuroscience. These methods robustly capture complex brain connectivity, revealing new functional connections.

Keywords:
Functional connectivityInformation transmissionInteraction informationMultivariate distributionMutual informationTotal correlation

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Area of Science:

  • Neuroscience
  • Information Theory
  • Computational Neuroscience

Background:

  • Shannon's entropy quantifies information transmission, with mutual information capturing pairwise nonlinear relationships.
  • Interaction information and total correlation are underutilized multivariate generalizations of mutual information in neuroscience.
  • Current neuroscience lacks clear explanations for quantifying information flow using interaction information and total correlation.

Purpose of the Study:

  • To clarify the distinctions between mutual information, interaction information, and total correlation in neuroscience.
  • To propose a novel method for calculating interaction information among three variables.
  • To demonstrate the application of these higher-order information-theoretic approaches in estimating functional brain connectivity.

Main Methods:

  • Comparative analysis of mutual information, interaction information, and total correlation.
  • Development of a novel method using total correlation and conditional mutual information for interaction information calculation.
  • Application of simulation experiments and real neural data analysis to estimate functional connectivity.

Main Results:

  • Interaction information and total correlation are robust in capturing redundancy for multivariate variables.
  • These higher-order methods successfully estimated functional connectivity in simulated and real neural data.
  • The proposed methods identified both known and previously undiscovered functional brain connections.

Conclusions:

  • Interaction information and total correlation offer powerful, underutilized tools for analyzing complex brain networks.
  • The proposed method provides a practical approach for quantifying multivariate information flow in the brain.
  • These information-theoretic approaches enhance the understanding of functional connectivity and redundancy in neural systems.