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

Sarah Greenwell; Joshua Faskowitz; Laura Pritschet; Tyler Santander; Emily G Jacobs; Richard F Betzel

doi:10.1162/netn_a_00307

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

Netw Neurosci. 2023 Oct 1;7(3):1181-1205. doi: 10.1162/netn_a_00307. eCollection 2023.

Authors

Sarah Greenwell¹, Joshua Faskowitz^{1

2}, Laura Pritschet³, Tyler Santander³, Emily G Jacobs^{3

4}, Richard F Betzel^{1

2

5

6}

Affiliations

¹ Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
² Program in Neurosciences, Indiana University, Bloomington, IN, USA.
³ Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA.
⁴ Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA.
⁵ Cognitive Science Program, Indiana University, Bloomington, IN, USA.
⁶ Network Science Institute, Indiana University, Bloomington, IN, USA.

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 time points corresponding to high-amplitude co-fluctuations disproportionately contributed to the 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.

Keywords: Edge-centric; Functional connectivity; Time-varying networks.