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Related Experiment Video

Updated: Feb 8, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Small-world indices via network efficiency for brain networks from diffusion MRI.

Lan Lin¹, Zhenrong Fu², Cong Jin³

¹Biomedical Research Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China. lanlin@bjut.edu.cn.

Experimental Brain Research

|July 8, 2018

Summary

New metrics, small-world efficiency (SWE) and small-world angle (SWA), accurately quantify small-worldness in brain diffusion networks. These metrics show promise for age classification and cognitive correlation in diffusion tensor imaging (DTI) studies.

Keywords:

Brain network Connectome DTI Small world

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Last Updated: Feb 8, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Area of Science:

Neuroscience
Network Science
Biophysics

Background:

Small-world architecture is crucial in anatomical brain connectivity.
Quantifying small-worldness in diffusion networks presents challenges.
Existing measures have limitations in diffusion network analysis.

Purpose of the Study:

To address limitations in current small-worldness quantification.
To introduce novel metrics: small-world efficiency (SWE) and small-world angle (SWA).
To validate SWE and SWA using network models and diffusion tensor imaging (DTI) data.

Main Methods:

Defined SWE and SWA based on global and local efficiency trade-offs.
Tested SWE and SWA on Watts-Strogatz network models.
Applied SWE and SWA to DTI data from 75 healthy older adults (50-70 years).
Evaluated metric sensitivity using network attack strategies.

Main Results:

SWE and SWA demonstrated validity in network models.
SWE successfully classified subjects into different age groups.
SWE showed correlation with individual performance on the WAIS-IV test.
The new indices outperformed previous measures in DTI data analysis.

Conclusions:

SWE and SWA offer improved quantification of small-worldness in diffusion networks.
These metrics have potential applications in aging research and cognitive assessment.
The proposed indices provide a more sensitive analysis of diffusion tensor imaging data.