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Updated: Apr 17, 2026

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

MIC as an appropriate method to construct the brain functional network.

Ziqing Zhang¹, Shu Sun², Ming Yi³

¹Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China ; University of Chinese Academy of Sciences, Beijing 100049, China.

Biomed Research International

|February 25, 2015

Summary

This summary is machine-generated.

Maximal Information Coefficient (MIC) offers a robust method for measuring brain functional connectivity. This approach excels in consistency and robustness compared to existing techniques, aiding brain network research.

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Area of Science:

Neuroscience
Network Science
Data Analysis

Background:

Effective measurement of brain functional connectivity is crucial for understanding brain networks.
Existing methods like correlation coefficient (CF) and mutual information (MI) have limitations.

Purpose of the Study:

To evaluate the Maximal Information Coefficient (MIC) as a novel method for constructing brain functional networks.
To compare MIC's performance against established connectivity measures.

Main Methods:

Utilized Maximal Information Coefficient (MIC) for network construction.
Performed comprehensive comparisons of MIC against correlation coefficient (CF), partial correlation coefficient (PCF), mutual information (MI), wavelet correlation coefficient (WCF), and coherence (CH).
Assessed methods based on consistency and robustness.

Main Results:

Maximal Information Coefficient (MIC) demonstrated superior performance in consistency and robustness.
MIC proved to be a relevant and effective measure for brain functional network construction.
The study validates MIC's potential for new functional connection measures.

Conclusions:

Maximal Information Coefficient (MIC) is a highly effective and robust method for measuring brain functional connectivity.
MIC surpasses traditional methods in assessing brain network relationships.
This research supports the use of MIC for validating future functional connectivity measures.