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Reproducibility of Graph-Theoretic Brain Network Metrics: A Systematic Review

Welton, Thomas; Kent, Daniel; Auer, Dorothee P.; Dineen, Robert A.


Thomas Welton

Daniel Kent

Dorothee P. Auer


This systematic review aimed to assess the reproducibility of graph-theoretic brain network metrics. Primary research studies of test-retest reliability conducted on healthy human subjects were included that quantified test-retest reliability using either the intraclass correlation coefficient (ICC) or the coefficient of variance. The MEDLINE, Web of Knowledge, Google Scholar, and OpenGrey databases were searched up to February 2014. Risk of bias was assessed with 10 criteria weighted toward methodological quality. Twenty-three studies were included in the review (n=499 subjects) and evaluated for various characteristics, including sample size (5–45), retest interval (1 year), acquisition method, and test-retest reliability scores. For at least one metric, ICCs reached the fair range (ICC 0.40–0.59) in one study, the good range (ICC 0.60–0.74) in five studies, and the excellent range (ICC>0.74) in 16 studies. Heterogeneity of methods prevented further quantitative analysis. Reproducibility was good overall. For the metrics having three or more ICCs reported for both functional and structural networks, six of seven were higher in structural networks, indicating that structural networks may be more reliable over time. The authors were also able to highlight and discuss a number of methodological factors affecting reproducibility.


Welton, T., Kent, D., Auer, D. P., & Dineen, R. A. (2015). Reproducibility of Graph-Theoretic Brain Network Metrics: A Systematic Review. Brain Connectivity, 5(4), 193-202.

Journal Article Type Article
Acceptance Date Dec 1, 2014
Publication Date 2015-05
Deposit Date May 11, 2017
Journal Brain Connectivity
Print ISSN 2158-0014
Electronic ISSN 2158-0022
Publisher Mary Ann Liebert
Peer Reviewed Peer Reviewed
Volume 5
Issue 4
Pages 193-202
Public URL
Publisher URL