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Updated: Jun 1, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

A comprehensive survey of complex brain network representation.

Haoteng Tang¹, Guixiang Ma², Yanfu Zhang³

¹Department of Computer Science, College of Engineering and Computer Science, University of Texas Rio Grande Valley, 1201 W University Dr, Edinburg, 78539, TX, USA.

|January 20, 2025

Summary

This summary is machine-generated.

This survey explores neuroimaging-derived brain networks, detailing traditional and deep learning graph learning methods for analyzing brain structure and function in neurological conditions.

Keywords:

Brain functional network Brain network analysis Brain structural network Deep learning Network representation learning

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Last Updated: Jun 1, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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Published on: May 12, 2019

Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Neuroimaging data reveals brain structural and functional changes linked to neurodegenerative diseases.
Brain networks derived from neuroimaging offer system-level insights into neurological conditions.
Traditional methods analyze predefined network features against clinical data.

Purpose of the Study:

To provide a comprehensive overview of brain network mining techniques.
To review traditional and state-of-the-art deep learning methods in brain network analysis.
To discuss future research directions in the field.

Main Methods:

Overview of neuroimaging-derived brain networks.
Detailed review of traditional brain network analysis methods.
Comprehensive survey of deep learning and graph learning approaches for brain networks.

Main Results:

Identified major models and objectives in traditional and deep learning brain network analysis.
Highlighted the growing importance of graph learning in the field.
Synthesized current methodologies for mining brain network data.

Conclusions:

Deep learning methods are significantly advancing brain network analysis.
Graph learning offers powerful tools for understanding brain dynamics and abnormalities.
Further research in this area holds promise for clinical applications.