Brain network dynamics are hierarchically organized in time

By Diego Vidaurre, Stephen M. Smith, Mark William Woolrich. Proceedings of the National Academy of Sciences of the United States of America, 2017

836 citations recorded by the source corpus.

Significance We address the important question of the temporal organization of large-scale brain networks, finding that the spontaneous transitions between networks of interacting brain areas are predictable. More specifically, the network activity is highly organized into a hierarchy of two distinct metastates, such that transitions are more probable within, than between, metastates. One of these metastates represents higher order cognition, and the other represents the sensorimotor systems. Furthermore, the time spent in each metastate is subject-specific, is heritable, and relates to behavior. Although evidence of non–random-state transitions has been found at the microscale, this finding at the whole-brain level, together with its relation to behavior, has wide implications regarding the cognitive role of large-scale resting-state networks. The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/78ec92dccd263033dede2544a810c9577fe99398

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Citations in the corpus

Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, neutral): “Time-resolved fMRI studies previously demonstrated that the RSNs spontaneously fluctuate in and out of spatially and temporally overlapping correlations (Cabral et al., 2017; Cole et al., 2014; Cole et al., 2013; de Pasquale et al., 2017; Hutchinson and Raff, 2014; Karahanoglu and Van De Ville, 2015”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…states include sliding windows + clustering (e.g., Allen et al., 2014), hidden Markov models (HMMs; Shappell, Caffo, Pekar, & Lindquist, 2019; Vidaurre et al., 2017), change-point modeling (Cribben, Haraldsdottir, Atlas, Wager, & Lindquist, 2012; Xu & Lindquist, 2015), and win-dowless…”