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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\u2010brain 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\u2010effects modeling framework that allows analyzing subnetworks within the context of their whole\u2010brain networks, and show that it can better disentangle global (whole\u2010brain) and local (subnetwork) differences when compared to standard <jats:italic>t<\/jats:italic>\u2010test analyses. T\u2010test 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\u2010level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model\u2014quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks\u2014but for local, subnetwork\u2010level hypotheses.<\/jats:p>","DOI":"10.1002\/hbm.24762","type":"journal-article","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T06:10:30Z","timestamp":1566540630000},"page":"5123-5141","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Analysis of brain subnetworks within the context of their whole\u2010brain networks"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8342-6194","authenticated-orcid":false,"given":"Mohsen","family":"Bahrami","sequence":"first","affiliation":[{"name":"Laboratory for Complex Brain Networks Wake Forest School of Medicine  Winston\u2010Salem North Carolina"},{"name":"Department of Biomedical Engineering Virginia Tech \u2013 Wake Forest School of Biomedical Engineering and Sciences  Winston\u2010Salem North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul J.","family":"Laurienti","sequence":"additional","affiliation":[{"name":"Laboratory for Complex Brain Networks Wake Forest School of Medicine  Winston\u2010Salem North Carolina"},{"name":"Department of Radiology Wake Forest School of Medicine  Winston\u2010Salem North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean L.","family":"Simpson","sequence":"additional","affiliation":[{"name":"Laboratory for Complex Brain Networks Wake Forest School of Medicine  Winston\u2010Salem North Carolina"},{"name":"Department of Biostatistics and Data Science Wake Forest School of Medicine  Winston Salem North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,8,22]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dadm.2017.03.007"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","unstructured":"Bahrami M.andG.\u2010A.Hossein\u2010Zadeh(2015).Assortativity changes in Alzheimer's diesease: A resting\u2010state FMRI study. 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