Abstract
Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity. Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging, as well as its time-resolved fluctuations on a time scale of tens of seconds. However, comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity, such as fluctuations between segregated and integrated topology or between high and low modularity topology. Since these global network-level fluctuations have been shown to be related to human cognition and behavior, there is an emerging need for clarifying their reproducibility with computational models. To address this problem, we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data, and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations. Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity. By performing model parameter search, we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data. Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible, although their spatial patterns in functional connectivity did not perfectly matched. These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration, whereas additional factors, such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity, would be necessary for fully reproducing the spatial patterning associated with these fluctuations.
Highlights
Neural elements in the brain are structurally connected and functionally coupled heterogeneously to form complex networks, in which neurons, neuronal populations, or brain regions can be viewed as nodes linked by edges of structural connectivity and functional connectivity [1, 2]
Our results suggest that stationary models can explain many empirical properties in the fluctuations in global network topology, while modeling of non-stationary dynamics and/or greater estimation accuracy of anatomical connections underlying the simulation would be required for complete replication
Functional connectivity refers to a pattern of statistical dependence among activities of neural elements [4], which, in human neuroimaging, has typically been assessed by the blood oxygenation level dependent (BOLD) signal measured over several minutes of resting-state functional magnetic resonance imaging [5]
Summary
Neural elements in the brain are structurally connected and functionally coupled heterogeneously to form complex networks, in which neurons, neuronal populations, or brain regions can be viewed as nodes linked by edges of structural connectivity and functional connectivity [1, 2]. Recent advancements in measurement and analysis of rs-fMRI data allow tracking fluctuations in functional connectivity on a time scale of tens of seconds [6,7,8,9]. Fluctuations in such time-resolved functional connectivity have been found at the individual edge level, and at the global network level; for example, fluctuations between segregated and integrated network topology [10] and fluctuations between high and low modularity topology [11, 12]. Fluctuations in global network topology of time-resolved functional connectivity have been associated with various types of human behavior, e.g., pupil dilation [10] and eyelid closures [13] during rest, as well as cognitive performance [10] and decoding accuracy [14] during tasks
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