Analysis of brain subnetworks within the context of their whole‐brain networks

• Published in: Human brain mapping Vol. 40; no. 17; pp. 5123 - 5141
• Main Authors: Bahrami, Mohsen, Laurienti, Paul J., Simpson, Sean L.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2019
• Subjects: Brain; Brain - diagnostic imaging; brain connectivity; brain networks; Complex systems; Computer simulation; Connectome; Empirical analysis; Functional anatomy; Humans; Hypotheses; Magnetic Resonance Imaging; mixed models; Models, Neurological; Multivariate analysis; multivariate models; Nerve Net - diagnostic imaging; Networks; Neural networks; Neuroimaging; Phenotypes; Regional analysis; regional subnetworks; Structure-function relationships; Subsystems; Utilities; brain connectivity; regional subnetworks; multivariate models; mixed models; brain networks
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.24762

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole‐brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed‐effects modeling framework that allows analyzing subnetworks within the context of their whole‐brain networks, and show that it can better disentangle global (whole‐brain) and local (subnetwork) differences when compared to standard t‐test analyses. T‐test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system‐level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model—quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks—but for local, subnetwork‐level hypotheses.