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.

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and both brain activity and connectivity. However, how hormonal fluctuations impact fast changes in brain network structure over short timescales remains unknown. Here, we leverage ``edge time series'' analysis to investigate the relationship between high-amplitude network states and quotidian variation in sex steroid and gonadotropic hormones in a single individual sampled over the course of two endocrine states, across a natural menstrual cycle and under a hormonal regimen. We find that the frequency of high-amplitude network states are associated with follicle-stimulating and luteinizing hormone, but not the sex hormones estradiol and progesterone. Nevertheless, we show that scan-to-scan variation in the co-fluctuation patterns expressed during network states are robustly linked with the concentration of all four hormones, positing a network-level target of hormonal control. We conclude by speculating on the role of hormones in shaping ongoing brain dynamics.

No abstract

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and brain connectivity. However, how hormonal fluctuations impact fast changes in brain network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations between the activity of pairs of brain regions at a framewise timescale. In previous studies we showed that timepoints corresponding to high-amplitude co-fluctuations disproportionately contributed to time-averaged functional connectivity pattern and that these co-fluctuation patterns could be clustered into a low-dimensional set of recurring “states”. Here, we assessed the relationship between these network states and quotidian variation in hormone concentrations. Specifically, we were interested in whether the frequency with which network states occurred was related to hormone concentration. We addressed this question using a dense-sampling dataset (N = 1 brain). In this dataset, a single individual was sampled over the course of two endocrine states: a natural menstrual cycle and while the subject underwent selective progesterone suppression via oral hormonal contraceptives. During each cycle, the subject underwent 30 daily resting-state fMRI scans and blood draws. Our analysis of the imaging data revealed two repeating network states. We found that the frequency with which state 1 occurred in scan sessions was significantly correlated with follicle-stimulating and luteinizing hormone concentrations. We also constructed representative networks for each scan session using only “event frames” – those time points when an event was determined to have occurred. We found that the weights of specific subsets of functional connections were robustly correlated with fluctuations in the concentration of not only luteinizing and follicle-stimulating hormones, but also progesterone and estradiol.