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Updated: Oct 20, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Brain network analyses of diffusion tensor imaging for brain aging.

Song Xu^1,2, Xufeng Yao^1,3,2, Liting Han^1,2

¹College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China.

Mathematical Biosciences and Engineering : MBE

|September 14, 2021

Summary

This summary is machine-generated.

Diffusion tensor imaging (DTI) networks reveal significant brain structural connectivity changes with aging. Older adults show increased path length and clustering, decreased efficiency and strength, indicating white matter degeneration.

Keywords:

brain aging critical nodes diffusion tensor imaging (DTI)network characteristics white matter (WM)

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Last Updated: Oct 20, 2025

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Area of Science:

Neuroimaging
Network Science
Aging Research

Background:

Brain aging involves complex changes in structural connectivity.
Graph-based diffusion tensor imaging (DTI) networks offer a method to study these changes.
Understanding age-related alterations in brain networks is crucial for identifying neurodegenerative processes.

Purpose of the Study:

To quantitatively and qualitatively investigate changes in DTI networks associated with brain aging.
To compare network characteristics between young adults (YA) and older adults (OA).
To explore the correlation between network characteristics and age.

Main Methods:

Utilized graph-based DTI network analysis on 60 volunteers (30 YA, 30 OA).
Evaluated network characteristics: path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small-world attribute (σ).
Analyzed networks at whole-brain, hemispheric, and regional levels.

Main Results:

Significant changes in network configurations were observed between YA and OA groups.
Lp and Cp increased (positive correlation with age), while Eglobal, Elocal, and Sp decreased (negative correlation with age) in OA compared to YA.
The small-world attribute (σ) remained unchanged between groups.

Conclusions:

DTI network analysis effectively evaluates white matter (WM) changes in brain aging.
Network characteristics of critical nodes provide valuable indicators of WM degeneration.
This approach aids in understanding the structural connectivity alterations during the aging process.