| Abstract: | The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.Structural and functional brain networks exhibit complex topology, and functional brain networks display rich temporal dynamics (1–3). The topological organization of structural connectivity (SC; the connectome) is characterized by broad degree distributions, hubs linked into cores and rich clubs, and multiscale modularity (4–6). Functional connectivity (FC), as measured with resting-state functional MRI (fMRI), displays consistent system-level architecture (7–9) as well as fluctuating dynamics (10–12) and complex spatiotemporal state transitions (13, 14). Resting brain dynamics exhibit metastable behavior. The lack of a fixed attractor allows for exploration of a large repertoire of network states and configurations (15–17).Recent work has uncovered fine-scale dynamics of FC as measured with fMRI during rest and passive movie watching (18, 19). The approach leverages an exact decomposition of averaged FC estimates into patterns of edge cofluctuations resolved at the timescale of single image frames (20). These studies reveal that ongoing activity is punctuated by brief, intermittent, high-amplitude bursts of brain-wide cofluctuations of the blood-oxygenation level–dependent (BOLD) signal. The approach is reminiscent of an earlier approach proposed for electroencephalography (EEG) data in which an exact frame-wise analysis of modeled and human scalp EEG data using the Hilbert transform revealed brief large-scale desynchronous bursts (21). In the BOLD literature, episodes of high-amplitude cofluctuations, referred to as “events,” drive long-time estimates of FC and represent patterns with consistent topography across time and across individuals (18, 19, 22). The occurrence of events appears unrelated to nonneuronal physiological processes, head motion, or acquisition artifacts. A better understanding of how events originate may illuminate the basis for individual differences in FC and its variation across cognitive state, development, and disorders. Here, we aim to provide a generative model for the origin of events in neuronal time series and uncover potential structural bases for their emergence in fine-scale dynamics.The relationship of structure to function has been a central objective of numerous empirical and computational studies, leveraging cellular population recordings (23, 24), electrophysiological (25), and neuroimaging techniques (26–28). While there is broad consensus that “structure shapes function” on long timescales (29, 30), relating specific dynamic features to the topology of the underlying structural network is an open problem. Computational models have made important contributions to understanding how SC (31, 32), time delays, and noisy fluctuations (33) contribute to patterns of FC as estimated over long and short timescales. Model implementations range from biophysically based neural mass models to much simpler phase oscillators such as the Kuramoto model (34). Despite their overt simplicity, phase oscillator models can generate a wide range of complex synchronization and coordination states, and they reproduce patterns of empirical FC (35), including temporal dynamics at intermediate timescales (36). These modeled dynamics reproduce ongoing fluctuations between integrated (less modular) and segregated (more modular) network states (37, 38), a key characteristic of empirical fMRI resting-state dynamics (39).Here, we pursue a computational modeling approach that seeks to relate high-amplitude cofluctuations to whole-brain network structure. We simulate spontaneous BOLD signal dynamics on an empirical SC matrix of the human cerebral cortex using an implementation of a coupled phase oscillator model incorporating phase delays, the Kuramoto–Sakaguchi (KS) model (40). The KS model is well suited for this purpose because its parsimonious parametrization allows for drawing specific links between network structure and synchronization patterns. The KS model also allows simulation focused on a specific frequency band of interest so that it can more closely replicate the oscillatory behavior of neural populations often found in the gamma band (41). We find that over broad parameter ranges, BOLD signals exhibit significant high-amplitude network-wide fluctuations strongly resembling intermittent events observed in empirical data. Model dynamics reproduce several key characteristics of empirical events, including their strong contribution to long-time averages of FC as well as recurrent patterns across time. Simulated events are significantly related to network structure. They fall into distinct clusters aligned with different combinations of modules in underlying SC. Disruption of structural modules largely abolishes the occurrence of events in BOLD dynamics. These findings suggest a modular origin of high-amplitude cofluctuations in fine-scale FC dynamics. |
