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Hum Brain Mapp. 2019 Dec 1;40(17):5123-5141. doi: 10.1002/hbm.24762. Epub 2019 Aug 22.

Analysis of brain subnetworks within the context of their whole-brain networks.

Author information

1
Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.
2
Department of Biomedical Engineering, Virginia Tech - Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina.
3
Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina.
4
Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, North Carolina.

Abstract

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.

KEYWORDS:

brain connectivity; brain networks; mixed models; multivariate models; regional subnetworks

PMID:
31441167
DOI:
10.1002/hbm.24762

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