Help Privacy Policy Disclaimer
  Advanced SearchBrowse


  Test–retest reliability of structural brain networks from diffusion MRI

Buchanan, C. R., Pernet, C. R., Gorgolewski, K. J., Storkey, A. B., & Bastin, M. E. (2014). Test–retest reliability of structural brain networks from diffusion MRI. NeuroImage, 86, 231-243. doi:10.1016/j.neuroimage.2013.09.054.

Item is


show Files




Buchanan, Colin R.1, 2, Author
Pernet, Cyril R.3, 4, Author
Gorgolewski, Krzysztof J.5, Author           
Storkey, Amos B.2, Author
Bastin, Mark E.3, Author
1Doctoral Training Centre in Neuroinformatics and Computational Neuroscience, School of Informatics, University of Edinburgh, United Kingdom, ou_persistent22              
2Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, United Kingdom, ou_persistent22              
3Centre for Clinical Brain Sciences, University of Edinburgh, United Kingdom, ou_persistent22              
4Brain Research Imaging Centre, Neuroimaging Sciences, University of Edinburgh, United Kingdom, ou_persistent22              
5Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              


Free keywords: Connectome; Diffusion MRI; Human brain; Network; Test–retest; Tractography
 Abstract: Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions.

Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences.

Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability.


Language(s): eng - English
 Dates: 2013-09-202013-10-022014-02-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2013.09.054
PMID: 24096127
Other: Epub 2013
 Degree: -



Legal Case


Project information


Source 1

Title: NeuroImage
Source Genre: Journal
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 86 Sequence Number: - Start / End Page: 231 - 243 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166