Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques

Abstract

Characterization of large-scale brain networks using blood-oxygenation-level-dependent functional magnetic resonance imaging is typically based on the assumption of network stationarity across the duration of scan. Recent studies in humans have questioned this assumption by showing that within-network functional connectivity fluctuates on the order of seconds to minutes. Time-varying profiles of resting-state networks (RSNs) may relate to spontaneously shifting, electrophysiological network states and are thus mechanistically of particular importance. However, because these studies acquired data from awake subjects, the fluctuating connectivity could reflect various forms of conscious brain processing such as passive mind wandering, active monitoring, memory formation, or changes in attention and arousal during image acquisition. Here, we characterize RSN dynamics of anesthetized macaques that control for these accounts, and compare them to awake human subjects. We find that functional connectivity among nodes comprising the "oculomotor (OCM) network" strongly fluctuated over time during awake as well as anaesthetized states. For time dependent analysis with short windows (<60 s), periods of positive functional correlations alternated with prominent anticorrelations that were missed when assessed with longer time windows. Similarly, the analysis identified network nodes that transiently link to the OCM network and did not emerge in average RSN analysis. Furthermore, time-dependent analysis reliably revealed transient states of large-scale synchronization that spanned all seeds. The results illustrate that resting-state functional connectivity is not static and that RSNs can exhibit nonstationary, spontaneous relationships irrespective of conscious, cognitive processing. The findings imply that mechanistically important network information can be missed when using average functional connectivity as the single network measure.

On spurious and real fluctuations of dynamic functional connectivity during rest

“…Because fluctuations in xy are low-pass filtered by the convolution with h with cut-off frequency 1/w = f min , the slow modulation term-which in this case is a true fluctuation of dynFC-is recovered as long as f 0 b f min and f − f 0 ≈ f N f min . The influence of the window length on its low-pass filtering effect has previously been noted by Handwerker et al (2012) and less variable dynFC with longer windows is a well documented empirical observation (e.g., Chang and Glover, 2010;Hutchison et al, 2013b;Leonardi et al, 2013). The spectral selectivity of the windowing operation can be improved by using tapering; e.g., Hamming filter (Handwerker et al, 2012), Gaussian filter (Allen et al, 2014), or other windows with smooth roll-off at the edges (Smith et al, 2012).…”

“…Functional connectivity (FC), which is estimated by correlation of BOLD activity, identifies coherent brain activity in distributed and reproducible networks. FC has revealed reorganization of brain networks during cognitive tasks (Ekman et al, 2012;Lewis et al, 2009;Richiardi et al, 2011Richiardi et al, , 2013Shirer et al, 2012), but also at rest (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013b;Kang et al, 2011;Leonardi et al, 2013;Majeed et al, 2011;Smith et al, 2012). To study changes in FC over time sliding-window correlation analysis, where the correlation is estimated for brain activity during multiple, possibly overlapping temporal segments (typically 30-60 s), has been widely deployed (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013a;Sakoglu et al, 2010).…”

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

“…In particular, most task‐based studies focus their connectivity analyses on the regions that activate in response to the studied paradigm, ignoring functional interplays that may occur elsewhere in the brain (Wass, 2011). Also, despite the interest in task‐driven functional changes, the presence of intrinsic FC (iFC) variations (Damoiseaux et al, 2006; van den Heuvel & Pol, 2010; Hutchison, Womelsdorf, Gati, Everling, & Menon, 2013b) is never analytically accounted for; yet, iFC is altered throughout the brain in ASD (see for example Anderson et al, 2011; Cherkassky, Kana, Keller, & Just, 2006; Yahata et al, 2016). In some cases, ASD‐specific FC patterns put forward as caused by the studied task may thus reflect iFC group differences, or task‐driven effects may be overshadowed by iFC fluctuations and fail to be revealed.…”

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

“…This evidence led to the introduction of a new concept in evaluating brain regions connectivity named dynamic FC which has encouraged the introduction of a large number of methods to detect it. Sliding window, for instance, has been a widely used method to assess dynamic FC [12][13][14][15][16]. Dynamic connectivity regression (DCR) is another method recommended by [4,5] to discover FC change points between brain areas.…”