Reproducibility of graph-theoretic brain network metrics: a systematic review
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)...
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| Format: | Article |
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Mary Ann Liebert
2015
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| Online Access: | https://eprints.nottingham.ac.uk/42787/ |
| _version_ | 1848796568115740672 |
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| author | Welton, Thomas Kent, Daniel Auer, Dorothee P. Dineen, Robert A. |
| author_facet | Welton, Thomas Kent, Daniel Auer, Dorothee P. Dineen, Robert A. |
| author_sort | Welton, Thomas |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 h to >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. |
| first_indexed | 2025-11-14T19:50:03Z |
| format | Article |
| id | nottingham-42787 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:50:03Z |
| publishDate | 2015 |
| publisher | Mary Ann Liebert |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-427872024-08-12T15:26:57Z https://eprints.nottingham.ac.uk/42787/ Reproducibility of graph-theoretic brain network metrics: a systematic review Welton, Thomas Kent, Daniel Auer, Dorothee P. Dineen, Robert A. 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 h to >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. Mary Ann Liebert 2015-05-12 Article PeerReviewed Welton, Thomas, Kent, Daniel, Auer, Dorothee P. and Dineen, Robert A. (2015) Reproducibility of graph-theoretic brain network metrics: a systematic review. Brain Connectivity, 5 (4). pp. 193-202. ISSN 2158-0022 http://online.liebertpub.com/doi/10.1089/brain.2014.0313 doi:10.1089/brain.2014.0313 doi:10.1089/brain.2014.0313 |
| spellingShingle | Welton, Thomas Kent, Daniel Auer, Dorothee P. Dineen, Robert A. Reproducibility of graph-theoretic brain network metrics: a systematic review |
| title | Reproducibility of graph-theoretic brain network metrics: a systematic review |
| title_full | Reproducibility of graph-theoretic brain network metrics: a systematic review |
| title_fullStr | Reproducibility of graph-theoretic brain network metrics: a systematic review |
| title_full_unstemmed | Reproducibility of graph-theoretic brain network metrics: a systematic review |
| title_short | Reproducibility of graph-theoretic brain network metrics: a systematic review |
| title_sort | reproducibility of graph-theoretic brain network metrics: a systematic review |
| url | https://eprints.nottingham.ac.uk/42787/ https://eprints.nottingham.ac.uk/42787/ https://eprints.nottingham.ac.uk/42787/ |