The human brain is an inherently adaptive (Mattar et al., 2016), plastic (Della-Maggiore et al., 2015) organ. Its fundamental malleability supports changes to its architecture and function that are advantageous to human survival. Importantly, such changes can occur on multiple time scales: from the long time scales of evolution (Kirschner and Gerhart, 1998; Clune et al., 2013) to the shorter time scales of multi-year development (Gu et al., 2015b), or even short-term learning (Ellefsen et al., 2015; Hermundstad et al., 2011). Notably, even in the shortest time scales of learning, adaptation can occur over multiple spatial scales (Mattar and Bassett, 2016), from the level of single neurons (Richardson et al., 2012) to the level of large-scale systems (Tunik et al., 2007). Moreover, this adaptation can affect functional dynamics (Heitger et al., 2012; Krakauer et al., 2005, 2004; Grefkes et al., 2004) or can evoke a direct change in the structure of neuroanatomy, driving new dendritic spines (Xu et al., 2009), axon collaterals (Chklovskii et al., 2004), and myelination (Sampaio-Baptista et al., 2013). Malleability, adaptability, and plasticity often manifest as a variability in quantitative statistics that describe the structure or function of a system. In the large-scale human brain, such statistics can include measures of neurophysiological noise (Garrett et al., 2014, 2013; Breakspear and McIntosh, 2011) or changes in patterns of resting state functional connectivity (Deco et al., 2009, 2011, 2013). More recently, dynamic reconfiguration of putative functional modules in the brain – groups of functionally connected areas identified using community detection algorithms (Porter et al., 2009; Fortunato, 2010) – has been used to define a notion of network flexibility (Bassett et al., 2011b), which differs across individuals and is correlated with individual differences in learning (Bassett et al., 2011b), cognitive flexibility (Braun et al., 2015b), and executive function (Braun et al., 2015b). Indeed, in the context of motor skill learning, dynamic network techniques have proven to be particularly advantageous for longitudinal designs, where data is collected from the same participants at multiple time points interspersed throughout the learning process (Bassett et al., 2013b, 2015; Wymbs and Grafton, 2015). Using a 6-week longitudinal design where participants trained motor sequences while undergoing functional magnetic resonance imaging, motor sequence learning was found to be associated with both increasing and decreasing motor system activity, with sequence-specific representations varying across multiple distinct timescales (Wymbs and Grafton, 2015). With a network modeling approach based on coherent activity between brain regions, the same dataset revealed the existence of a core-periphery structure that changes over the course of training and predicts individual differences in learning success (Bassett et al., 2013b). More recently, these changes were shown to reflect a growing autonomy between sensory and motor cortices, and the release of cognitive control hubs in frontal and cingulate cortices (Bassett et al., 2015). Yet despite these promising advances, dynamic network reconfiguration metrics are fundamentally unable to assess changes in the patterns of activity that are characteristic of brain dynamics, as they require the computation of functional connectivity estimates over extended time windows (Telesford et al., 2016; Bassett et al., 2013a).