Abstract: Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. During my talk, I will present the results of a set of recent neuroimaging analyses, in which we used time-resolved network techniques to interrogate functional magnetic resonance imaging data. In doing so, we are able to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. The extent of global integration differed as a function of task complexity and was associated with faster and more accurate performance on a cognitively challenging N-back task. In the second half of my talk, I will present ongoing work that attempts to understand the factors responsible for driving fluctuations in network topology. Empirically, we show that fluctuations in network architecture are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Finally, we use a computational modeling approach to confirm the relationship between neural gain modulation and network topology. Together, our results confirm a link between cognitive performance, neural gain modulation and the dynamic reorganization of the network structure of the brain.