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Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within\u2010network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big\u2010data framework and a novel application of covariate\u2010adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20095,019, ages\u2009=\u20097\u201390). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). 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