title: An exploration of graph metric reproducibility in complex brain networks.
abstract: The application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain's complex organization. More importantly, it has become a useful tool for studying the brain under various states and conditions. With the ever expanding popularity of network science in the neuroimaging community, there is increasing interest to validate the measurements and calculations derived from brain networks. Underpinning these studies is the desire to use brain networks in longitudinal studies or as clinical biomarkers to understand changes in the brain. A highly reproducible tool for brain imaging could potentially prove useful as a clinical tool. In this review, we examine recent studies in network reproducibility and their implications for analysis of brain networks.
authors.affiliation: Laboratory for Complex Brain Networks, Department of Biomedical Engineering, Wake Forest University School of Medicine Winston-Salem, NC, USA.
authors.forename: Qawi K
authors.initials: QK
authors.lastname: Telesford
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Burdette
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Laurienti
keywords: brain networks; complex systems; graph theory; intraclass correlation coefficient; network science; reproducibility