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High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle
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....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473261/ https://www.ncbi.nlm.nih.gov/pubmed/37781152 http://dx.doi.org/10.1162/netn_a_00307 |
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author | Greenwell, Sarah Faskowitz, Joshua Pritschet, Laura Santander, Tyler Jacobs, Emily G. Betzel, Richard F. |
author_facet | Greenwell, Sarah Faskowitz, Joshua Pritschet, Laura Santander, Tyler Jacobs, Emily G. Betzel, Richard F. |
author_sort | Greenwell, Sarah |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10473261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-104732612023-10-01 High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle Greenwell, Sarah Faskowitz, Joshua Pritschet, Laura Santander, Tyler Jacobs, Emily G. Betzel, Richard F. Netw Neurosci Research Article 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. MIT Press 2023-10-01 /pmc/articles/PMC10473261/ /pubmed/37781152 http://dx.doi.org/10.1162/netn_a_00307 Text en © 2023 Massachusetts Institute of Technology https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Article Greenwell, Sarah Faskowitz, Joshua Pritschet, Laura Santander, Tyler Jacobs, Emily G. Betzel, Richard F. High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
title | High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
title_full | High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
title_fullStr | High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
title_full_unstemmed | High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
title_short | High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
title_sort | high-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473261/ https://www.ncbi.nlm.nih.gov/pubmed/37781152 http://dx.doi.org/10.1162/netn_a_00307 |
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