On the use of correlation as a measure of network connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 60; no. 4; pp. 2096 - 2106
• Main Authors: Zalesky, Andrew, Fornito, Alex, Bullmore, Ed
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2012; Elsevier Limited
• Subjects: Algorithms; Benchmarking; Brain; Brain - physiology; Brain connectivity; Brain Mapping; Clustering; Connectome; Correlation; Correlation analysis; Humans; Magnetic Resonance Imaging; Networks; Neural Pathways - physiology; Organizations; Partial correlation; Small-world network; Topology; Transitivity; Small-world network; Correlation; Connectome; Brain connectivity; Transitivity; Partial correlation
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.02.001

Numerous studies have demonstrated that brain networks derived from neuroimaging data have nontrivial topological features, such as small-world organization, modular structure and highly connected hubs. In these studies, the extent of connectivity between pairs of brain regions has often been measured using some form of statistical correlation. This article demonstrates that correlation as a measure of connectivity in and of itself gives rise to networks with non-random topological features. In particular, networks in which connectivity is measured using correlation are inherently more clustered than random networks, and as such are more likely to be small-world networks. Partial correlation as a measure of connectivity also gives rise to networks with non-random topological features. Partial correlation networks are inherently less clustered than random networks. Network measures in correlation networks should be benchmarked against null networks that respect the topological structure induced by correlation measurements. Prevalently used random rewiring algorithms do not yield appropriate null networks for some network measures. Null networks are proposed to explicitly normalize for the inherent topological structure found in correlation networks, resulting in more conservative estimates of small-world organization. A number of steps may be needed to normalize each network measure individually and control for distinct features (e.g. degree distribution). The main conclusion of this article is that correlation can and should be used to measure connectivity, however appropriate null networks should be used to benchmark network measures in correlation networks. ► Correlation networks more clustered than random networks. ► Correlation as a measure of connectivity induces nontrivial topological structure. ► Null networks proposed to normalize for structure induced by correlation.