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Related Experiment Video

Updated: Jun 23, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

A survey of brain functional network extraction methods using fMRI data.

Yuhui Du¹, Songke Fang¹, Xingyu He¹

¹School of Computer and Information Technology, Shanxi University, Taiyuan, China.

Trends in Neurosciences

|June 21, 2024

Summary

This summary is machine-generated.

This review explores methods for analyzing brain functional networks (FNs) using functional magnetic resonance imaging (fMRI) data. It covers static and dynamic approaches to understand brain function and disorders.

Keywords:

brain disorder brain imaging connectome deep learning functional connectivity machine learning

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Last Updated: Jun 23, 2025

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Area of Science:

Neuroscience
Brain Imaging
Computational Neuroscience

Background:

Functional network (FN) analysis is crucial for understanding brain function and neurological disorders.
Functional magnetic resonance imaging (fMRI) is a primary tool for deriving these networks.
Existing methods for FN extraction vary in their approach and applicability.

Purpose of the Study:

To systematically review classical and advanced methods for deriving brain FNs from fMRI data.
To compare static and dynamic FN extraction techniques, detailing their principles, pros, and cons.
To discuss the applications and future directions in brain FN analysis.

Main Methods:

Review of hypothesis-driven (e.g., ROI-based) and data-driven (e.g., matrix decomposition, clustering, deep learning) static FN extraction methods.
Survey of window-based and windowless dynamic FN extraction methods for time-varying network estimation.
Analysis of methods for computing functional network states from dynamic analyses.

Main Results:

Comprehensive overview of diverse static and dynamic FN extraction methodologies.
Detailed comparison highlighting the strengths and weaknesses of each approach.
Identification of current applications and potential areas for methodological advancement.

Conclusions:

The choice of FN extraction method depends on the specific research question and data characteristics.
Advancements in both static and dynamic analyses offer deeper insights into brain function and disorders.
Future research should focus on refining existing methods and exploring novel approaches for more robust FN analysis.