Time-resolved resting-state brain networks

Abstract

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using highresolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and frontoparietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.network efficiency | dynamic connectivity | time-dependent network T he coordination of brain activity between disparate neural populations is a dynamic and context-dependent process (1-3). Although dynamic patterns of neural synchronization may be evident in time-dependent measures of functional connectivity (4, 5), the temporal stability of high-level topological properties is unknown. The topology of large-scale cortical activity-such as its efficient network layout (6), community structure (7), network hubs (8), rich-club organization (9, 10), and small worldness (11, 12)-may reflect fundamental aspects of cortical computation. Temporal fluctuations in these graph-theoretic measures may hence speak to adaptive properties of neuronal information processing.With international connectome mapping consortia such as the Human Connectome Project (HCP) (13) and the developing Human Connectome Project in full swing, resting-state functional magnetic resonance imaging (rsfMRI) data of unprecedented temporal resolution are now available to map the time-resolved properties of functional brain networks. Imaging the brain at rest reveals spontaneous low-frequency fluctuations in brain activity that are temporally correlated between functionally related regions (14-17). Interregional correlations are referred to as functional connections, and they collectively form a complex network (18). Functional brain networks are typically mapped in a timeaveraged sense, based on the assumption that functional connections remain relatively static (stationary)...

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

“…5. This interpretation echoes recent work (Zalesky et al 2014) in which the most dynamic connections are shown to be inter-modular, and support the emergence of temporary phases of high functional efficiency. More generally, the spatial distribution of the level of fluctuation of dFC (Allen et al 2012) and its link with cognitive tasks (Bassett et al 2011) have also been explored.…”

Accelerated Resting-State Functional Magnetic Resonance Imaging Using Multiband Echo-Planar Imaging with Controlled Aliasing

“…These results therefore suggest that multiband accelerated EPI can provide BOLD EPI data for an rs-fMRI study with a much shorter acquisition time or can provide more data volumes in the same acquisition time than conventional EPI. Recent studies have demonstrated that the total volume can be increased using multiband accelerated EPI with short TR (13,14). High rs-fMRI data volume and high frequency with short TR are important for tracking dynamic resting-state networks at higher frequencies (12).…”

“…High rs-fMRI data volume and high frequency with short TR are important for tracking dynamic resting-state networks at higher frequencies (12). Dynamic fluctuations in the functional connectivity of resting-state networks at higher frequencies might represent a balance between optimizing information processing and minimizing metabolic expenditure (14).…”

Stable long-range interhemispheric coordination is supported by direct anatomical projections

“…For instance, interhemispheric functional interactions between different communities are highly variable over the course of a resting-state scan. Meanwhile, interhemispheric connections within the same community, especially homotopic connections, are temporally stable (12)(13)(14). Together, these findings suggest that interhemispheric coordination may occur predominantly via homotopic functional connections.…”