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Updated: Dec 18, 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

Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities.

Chun-Hung Yeh^1,2,3,4, Derek K Jones^5,6, Xiaoyun Liang^3,4,6

¹Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.

Journal of Magnetic Resonance Imaging : JMRI

|June 20, 2020

Summary

This summary is machine-generated.

This study addresses challenges in using diffusion MRI tractography and graph theory to map brain connections. It explores solutions for accurate quantitative tractography and reliable global network metric calculations.

Keywords:

connectomics diffusion MRI graph theoretical analysis network metrics structural connectome tractography

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Last Updated: Dec 18, 2025

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

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Area of Science:

Neuroimaging
Network Science
Computational Neuroscience

Background:

Diffusion MRI tractography is standard for mapping brain structural connectomes.
Graph theory offers powerful tools for analyzing complex brain network data.
Current methods face challenges in methodological and biological plausibility.

Purpose of the Study:

To identify and discuss challenges in quantitative tractography.
To explore potential solutions for these tractography challenges.
To examine difficulties in calculating global network metrics using graph theory.

Main Methods:

Review of existing literature on diffusion MRI tractography.
Analysis of graph theory applications in connectomics.
Discussion of methodological and biological plausibility issues.

Main Results:

Challenges exist in quantitative tractography accuracy and biological validation.
Calculating global network metrics from tractography data presents significant hurdles.
Potential solutions for improving tractography and metric analysis are proposed.

Conclusions:

Addressing tractography and graph theory challenges is crucial for reliable brain connectome analysis.
Improved quantitative methods will enhance insights into brain connectivity patterns.
Further research is needed to refine these analytical frameworks.