The brain's functional activation dynamics are associated with female hormone levels
Poster No:
1218
Submission Type:
Abstract Submission
Authors:
Ceren Tozlu1, Louisa Schilling2, Parker Singleton3, Keith Jamison2, Amy Kuceyeski2
Institutions:
1Weill Cornell Medicine, NYC, NY, 2Weill Cornell Medicine, New York City, NY, 3Weill Cornell Medicine, New York, NY
First Author:
Co-Author(s):
Introduction:
Sex hormones play an important role in shaping how the brain's structural and functional architecture evolve across the lifespan [1,2,3]. One of the ways to investigate the brain's structure-function relationship is to analyze how the brain's structural connectivity architecture constrains its dynamic functional activation via Network Control Theory (NCT) [4]. NCT uses the brain's structural connectivity networks to identify the minimum transition energy (TE) required for the transition between the commonly recurring brain activation states. NCT has previously been applied in disease[5] and health[6,7], for example across developing populations[8]. However, no study to date has investigated how TE is associated with female hormones, and more specifically how these relationships are impacted by menopause when striking changes in hormone levels are observed.
Methods:
Four hundred and four females (age: 60.05 ± 15.74) from the Human Connectome Project-Aging (HCP-A) dataset[9] were used in this study. First, k-means was applied to the regional fMRI time series to identify commonly recurring dynamic brain states. The time series were analyzed using regional averages of 86 FreeSurfer-based regions (68 cortical and 18 subcortex/cerebellum), and the structural connectivity matrices were extracted using diffusion MRI and deterministic tractography. Second, NCT was applied to calculate the minimum energy required to transition between each pair of dynamic brain states (or to remain in the same state - i.e. persistence energy). Global TE was calculated as the average of the pairwise TEs between dynamic states and the persistence energy in each state. The regional fMRI time series were also used to calculate their entropy, which quantifies the amount of temporal regularity/unpredictability in brain activity. Global entropy is calculated as the average of regional entropy. Linear models were used to investigate the association of global TE and entropy with hormones such as estradiol, Luteinizing Hormone (LH), and Follicle-Stimulating Hormone (FSH), after controlling for age and framewise displacement (FD). The state-wise TEs were also compared between pre- (n=59), peri- (n=42), and post-menopausal (n=87) females who were between 40-60 years old using ANCOVA where age and FD were used as covariates. Group differences were considered significant when p<0.05 after Benjamini–Hochberg (BH) correction for multiple comparisons.
Results:
Increased global TE was associated with higher levels of estradiol (p=0.03) and lower levels of LH (p=0.01), and FSH (p=0.005) (see Figure 1), while there was no significant association between entropy and hormone levels. Recurrent brain states included those defined by high-amplitude activity (+) or low-amplitude activity (-) in various canonical networks, including visual network (VIS+), dorsal attention (DAN-), fronto-parietal (FP+), and somato-motor (SOM+) (see Figure 2). All state-wise TEs were significantly greater in pre-menopause compared to post-menopause, while the TE between SOM+ and VIS+ networks was greater in peri-menopause compared to post-menopause.
Conclusions:
Our results revealed that the known hormonal changes that occur during menopause may be accompanied with shifts in the brain's dynamic activity landscape. Being able to map how hormonal changes like puberty, pregnancy, postpartum, and menopause affect the brain is necessary for improving the healthcare of females.
Lifespan Development:
Aging
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
FUNCTIONAL MRI
1|2Indicates the priority used for review
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