Test–retest reliability of structural brain networks from diffusion MRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 86; pp. 231 - 243
• Main Authors: Buchanan, Colin R., Pernet, Cyril R., Gorgolewski, Krzysztof J., Storkey, Amos J., Bastin, Mark E.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.02.2014; Elsevier; Elsevier Limited
• Subjects: Biological and medical sciences; Brain; Brain - cytology; Brain research; Connectome; Diffusion; Diffusion MRI; Diffusion Tensor Imaging - methods; Female; Fundamental and applied biological sciences. Psychology; Human brain; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Male; Medical imaging; Middle Aged; Nerve Fibers, Myelinated - ultrastructure; Nerve Net - cytology; Network; Network hubs; Neurons - cytology; Reproducibility of Results; Sensitivity and Specificity; Standard deviation; Studies; Test–retest; Tractography; Vertebrates: nervous system and sense organs; Human brain; Test–retest; Diffusion MRI; Connectome; Tractography; Network; Human; Central nervous system; Test―retest; Nuclear magnetic resonance imaging; Encephalon
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.09.054

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 1.5T 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. •dMRI structural networks were constructed from repeat scans of ten volunteers.•Test-retest reliability was measured against factors affecting network construction.•Probabilistic tractography was found to perform better than a deterministic method.•Performance was improved when seeding from white matter, rather than grey.•Our results highlight concerns with reliably measuring nodal network properties.