Dynamic functional connectivity: Promise, issues, and interpretations

By R. Matthew Hutchison, Thilo Womelsdorf, Elena A. Allen, Peter A. Bandettini, V. Calhoun, Maurizio Corbetta, Stefania Della Penna, Jeff H. Duyn, Gary H. Glover, Javier Gonzalez-Castillo and 8 more. NeuroImage, 2013

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The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/610233f0f1aedbaf4306018a6f967bc3a3779e82

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In Cognitive Neuroscience

Citations in the corpus

Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Parsing the contribution of real, brain-derived dynamics underlying variability at shorter timescales remains a challenge (Hutchison et al. 2013; Tagliazucchi and Laufs 2014; Hindriks et al. 2016; Laumann et al. 2016).”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “…(Cole et al., 2014; Gratton et al., 2016), other reports emphasize variability of functional networks (Allen et al., 2014; Calhoun et al., 2014; Hutchison et al., 2013) and the sensitivity of these networks to ongoing cognition (Gonzalez-Castillo et al., 2015; Shine et al., 2016; Shirer et…”
Communication dynamics in complex brain networks (2017, background): “With anatomical networks acting as scaffolds or structural skeletons that enable certain interactions while excluding many others, neuronal signalling (and the ensuing signal transformations; that is, computations) are inherently dynamic and context-or task-dependent 152,153 .”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “For example, censoring prior to sliding-window analysis can result in windows that contain unequal numbers of time points (Hutchison, Womelsdorf, Allen, et al., 2013; Zalesky et al., 2014), and time series with uneven spacing between time points due to censoring are incompatible with most…”
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, background): “Subjects had their eyes closed during the resting state scan.”
Fast transient networks in spontaneous human brain activity (2014, background): “…Jann et al., 2010; Liu et al., 2010; Brookes et al., 2011), and are underpinned by much richer spatiotemporal dynamics that may be better characterized using time-varying measures of interactions (Brookes et al., 2014; Chang et al., 2013; Hutchison et al., 2013; de Pasquale et al., 2010, 2012).”
Network hubs in the human brain. (2013, background): “These statistical estimates are highly time dependent, modulated by stimuli and task context, and exhibit signiﬁ-cant non-stationary ﬂuctuations even at rest [48].”
Network neuroscience (2017, background): “Indeed, this recognition now pervades many areas of neuroscientific investigation, from the changing patterns of functional connectivity 85 to the patterns of genome folding over developmental timescales 86 or genetic coexpression over evolutionary timescales 87 .”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “…emerging literature emphasizing the dynamic properties of functional connections on faster time scales (Allen et al. 2012; Tagliazucchi et al. 2012; Hutchison et al. 2013; Gonzalez-Castillo et al. 2014; 2015; Shen et al. 2015), and the dependence of these fluctuations on anatomical connectivity is…”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, positive): “It is worth noting that recent studies investigating the complex brain functions have observed the phenomenon that functional connectivity spontaneously changes over time (Chang and Glover, 2010; Bassett et al., 2011; Hutchison et al., 2013), i.e., dynamic rather than stationary.”