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Updated: May 7, 2026
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Test-retest reliability of structural brain networks from diffusion MRI.
Colin R Buchanan1, Cyril R Pernet2, Krzysztof J Gorgolewski3
1Doctoral Training Centre in Neuroinformatics and Computational Neuroscience, School of Informatics, University of Edinburgh, Edinburgh, UK; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
This study evaluated the reproducibility of structural brain networks derived from diffusion MRI (dMRI) tractography. Optimal network construction involves white matter seeding and probabilistic tractography, improving reliability for analyzing brain connectivity differences.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Diffusion MRI
Background:
- Structural brain networks from diffusion MRI (dMRI) and tractography are increasingly used in neuroscience research.
- Reproducibility of these networks across different construction parameters remains under-investigated.
Purpose of the Study:
- To assess the test-retest reliability of structural brain networks.
- To identify factors influencing the reproducibility of network construction from dMRI data.
Main Methods:
- Ten healthy volunteers underwent dMRI scans on two separate occasions.
- T1-weighted MRI was used for parcellation into 84 regions-of-interest.
- Network connections were derived using dMRI with varying tractography algorithms, seeding strategies, waypoint constraints, and weighting methods.
- Graph-theoretic measures were analyzed using intraclass correlation coefficients (ICC) and within-subject vs. between-subject differences.
Main Results:
- Test-retest performance improved with white matter seeding and probabilistic tractography (two-fibre model) over grey matter seeding and deterministic tensor tractography.
- Streamline density weighting showed better reproducibility than tract-averaged diffusion anisotropy.
- The best configuration yielded global within-subject differences of 3.2–11.9% (ICC 0.62–0.76) and nodal differences of 5.2–24.2% (ICC 0.46–0.62).
- Within-subject differences were smaller than between-subject differences for 83.3% of nodes.
Conclusions:
- Current dMRI tractography methods can characterize genuine between-subject differences in brain connectivity.
- Further research is needed to enhance the reliability and reproducibility of structural brain network construction.
