Review of Dynamic Resting-State Methods in Neuroimaging: Applications to Depression and Rumination

Large-scale functional brain networks have most commonly been evaluated using static methods that assess patterns of activation or functional connectivity over an extended period. However, this approach does not capture time-varying features of functional networks, such as variability in functional connectivity or transient network states that form and dissolve over time. Addressing this gap, dynamic methods for analyzing functional magnetic resonance imaging (fMRI) data estimate time-varying properties of brain functioning. In the context of resting-state neuroimaging, dynamic methods can reveal spontaneously occurring network configurations and temporal properties of such networks. Patterns of network functioning over time during the resting state may be indicative of individual differences in cognitive-affective processes such as rumination, or the tendency to engage in a pattern of repetitive negative thinking. We first introduce the current landscape of dynamic methods and then review an emerging body of literature applying these methods to the study of rumination and depression to illustrate how dynamic methods may be used to study clinical and cognitive phenomena. An emerging body of research suggests that rumination is related to altered functional flexibility of brain networks that overlap with the canonical default mode network. An important future direction for dynamic fMRI analyses is to explore associations with more specific features of cognition.