Individualized event structure drives individual differences in whole-brain functional connectivity

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.

High-amplitude network co-fluctuations linked to variation in hormone concentrations over menstrual cycle

“…S5). Interestingly, the spatial patterns of these clusters closely recapitulate those reported in previous studies [14,18]. Community 1, for instance, was characterized by opposed fluctuations between regions in the default mode network with those in dorsal and salience/ventral attention networks (Fig.…”

“…Previous applications of edge time series analysis to functional imaging data have reported brief, intermittent, and high-amplitude "events" [14,[16][17][18]33]. These studies have shown that the co-fluctuation patterns expressed during events contribute disproportionately to the time-averaged pattern of FC, are subject-specific, can be clustered into a small number of putative "states", and strengthen brain-behavior associations.…”

Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder

“…In the previous section, we examined the presence of rapid and bursty fluctuations in ETS, highlighting this property as one of its main ways it differs from sw-tvFC. These high-amplitude fluctuations – referred to as “events” in previous papers [25] – are infrequent and, in previous work, were shown to be uncorrelated with in-scanner head motion [25, 26]. Therefore, they may be important in providing insights into the ongoing cognitive processes at rest and movie-watching conditions.…”

“…Previous studies have examined edge time series and characterized some of their basic properties [25, 26], speculating that these properties might serve as potent biomarkers for comparing individuals in terms of their cognitive or clinical states. However, with limited exceptions, these speculations have not been investigated.…”

“…One way to broaden our findings is to extend the analysis of network states reported in the first part of the paper and compare the control and ASD groups in terms of these states. Previous studies have shown that these states undergo individualization and may present useful and subject-specific information for comparing groups [26]. Additionally, the framework applied here to an ASD population could be extended to other clinical populations.…”