Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter time scales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

No abstract

No abstract

Resting-state functional connectivity is typically calculated as a correlation between regional activity. It is studied widely, both to gain insight into the brain's intrinsic organization but also to develop markers sensitive to changes in an individual's cognitive, clinical, and developmental state. Despite this, the origins and drivers of functional connectivity, especially at the level of densely sampled individuals, remain elusive. Here, we leverage novel methodology to decompose functional connectivity into its precise framewise contributions. Using two dense sampling datasets, we investigate the origins of individualized functional connectivity, focusing specifically on the role of brain network ``events'' -- short-lived patterns of high-amplitude co-fluctuations. Here, we develop a statistical test to identify events in empirical recordings. We show that the patterns of co-fluctuation expressed during events are repeated across multiple scans of the same individual and represent idiosyncratic variants of template patterns that are expressed at the group level. Lastly, we propose a simple model of functional connectivity based on event co-fluctuations, demonstrating that group-averaged co-fluctuations are suboptimal for explaining subject-specific connectivity. Our work complements recent studies implicating brief instants of high-amplitude co-fluctuations as the primary drivers of static, whole-brain functional connectivity. Our work also extends those studies, demonstrating that co-fluctuations during events are individualized, positing a dynamic basis for functional connectivity.