Computational models have suggested that functional connectivity and its associated network characteristics are time-dependent and vary over multiple time scales (Honey et al., 2007; Hansen et al., 2015). Empirical functional connectivity analyses are beginning to move beyond static or time-invariant studies, focusing increasingly on the ongoing temporal dynamics of brain con- nectivity (Hutchison et al., 2013a; Calhoun et al., 2014; Kopell et al., 2014). Biomarkers based on dynamic functional connectivity appear to track behav- iorally relevant variables (Elton and Gao, 2015), including daydreaming (Kucyi and Davis, 2014) and state of arousal (Allen et al., 2012), cognitive state (Betzel et al., 2012; Hutchison et al., 2013b; Tagliazucchi et al., 2014), consciousness (Barttfeld et al., 2015), and developmental level (Hutchison and Morton, 2015). They may also prove useful in differentiating between diseased and healthy populations (Damaraju et al., 2014; Rashid et al., 2014; Miller et al., 2014). Despite these initial successes, there are a number of methodological and in- terpretational issues related to estimating and interpreting dynamic functional connectivity. For example, while the sliding window approach used here is most common, other methods for estimating dynamic functional connectivity exist, such as component (Calhoun and Adali, 2012) and model-based analyses Lindquist et al. (2014). The advantage of one method over the other is not immediately clear. In terms of how we interpret dynamic functional connectiv- ity, it remains unclear whether fluctuations in fMRI connectivity strength over time have a distinct neurobiological basis, i.e. are the expression of fluctuations in the underlying pattern of neural activity, though a number of studies using combined EEG-fMRI acquisition have reported electrophysiological correlates of fMRI fluctuations (Scheeringa et al., 2012; Tagliazucchi et al., 2012b; Chang et al., 2013) . An alternative interpretation is that these fluctuations merely reflect correlated white noise (Hlinka and Hadrava, 2015), or can be accounted 21