Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain’s time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using “forward” models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology. Author Summary The study of dynamical systems offers a powerful framework for interpreting neuroimaging data from a range of different contexts, however, as a field, we have yet to fully embrace the power of this approach. Here, we offer a brief overview of some key terms from the dynamical systems literature, and then highlight three ways in which neuroimaging studies can begin to embrace the dynamical systems approach: by shifting from local to global descriptions of activity, by moving from static to dynamic analyses, and by transitioning from descriptive to generative models of neural activity patterns.