 [@ref102]). Third, network methods can often explain additional variance in behavioral outcomes beyond what is explained by activation in a single brain region. In some cases dynamic network methods can explain twice as much variance in cognitive functioning as static networks or pairwise connections (Jia, Hu, & Deshpande, [@ref56]). Thus, both static and dynamic network methods have the potential to provide important insights into cognitive, emotional, and social functioning.

In this review, we first summarize common approaches for analyzing brain networks and existing evidence for intrinsic functional brain networks, which serve as a framework for understanding the links between brain networks and cognitive, emotional, and social functioning. Next, we describe existing evidence for both static and dynamic brain networks involved in cognitive functioning. Although research on social and emotional functioning has primarily focused on associations with static brain networks, applying dynamic network methods to examining individual differences in social and emotional functioning is an important next step for understanding the neurobiological underpinnings of social behavior. We discuss existing evidence for static brain networks involved in social and emotional functioning, as well as potential applications of dynamic network methods. Although there is an extensive literature linking individual differences in brain responses to clinical states and outcomes, these relationships are beyond the scope of this paper, and we refer readers interested in these topics to Vaidya and Gordon ([@ref110]) and Cao, Wang, and He ([@ref24]).

2.. Approaches for analyzing brain connectivity {#sec2}
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Approaches for analyzing static and dynamic brain networks build on earlier work that primarily focused on pairwise connections between brain regions. In this section, we first describe these pairwise approaches, as they form a basis for more advanced network approaches and provide insigh