Edge-centric functional connectivity dynamics across the menstrual cycle

2621 

Abstract Submission 

Brendan Williams¹, Anastasia Christakou¹

¹University of Reading, Reading, Berkshire

Brendan Williams  
University of Reading
Reading, Berkshire

Anastasia Christakou  
University of Reading
Reading, Berkshire

The menstrual cycle is a recurring endocrine event characterized by fluctuations in hormones preparing the female reproductive system for pregnancy. These hormones include sex steroid hormones, which are also able to cross the blood-brain-barrier and have direct effects on brain physiology and neuronal activity. Previous work has begun to characterise functional connectivity changes across the menstrual cycle (1) e.g., default-mode network (DMN) connectivity dynamics are affected by menstrual cycle phase and oral contraceptive use (2), and DMN connectivity strength is positively correlated with estradiol levels (3). However, menstrual cycle research has mostly focused on cross sectional or sparse repeated samples data (for exception, see (4)). Resting state analyses also often focus on distinct 'canonical' resting state networks, yet recent edge-centric functional connectivity work (which provides a temporal decomposition of static correlational-based resting state) has demonstrated that overlapping communities exist within 'canonical' resting state networks (5,6). Here, we use an existing dense sampling dataset and an edge-centric approach to investigate community dynamics in 'canonical' networks across the menstrual cycle.

The Day2Day dataset is a dense sampling neuroimaging dataset collected from eight individuals between July 2013 and February 2014 (median 46.5 sessions, median interval between sessions 4.85 days) (7). rsfMRI was acquired in each session [TR=2000ms, TE=30ms, FOV=216×216×129mm, FA=80°, voxels=3mm3, distance factor=20%, GRAPPA=2, 150 volumes], and concurrent measures of estradiol and menstrual cycle day were taken (menstrual cycle day converted to percentile for analysis). Resting state data were preprocessed using fMRIprep (8), and image postprocessing was performed using the XCP-D pipeline (9). Functional data were parcellated using the Schaefer parcellation (10), and edge functional connectivity and community clustering (using k-means in Matlab) was calculated as in Faskowitz et al (6).

We show – using a 10 cluster solution – that our edge communities, their similarity, and normalised network-level entropies replicate results presented by Faskowitz et al (6) (fig 1A,B & 2A). Next we generated subject-wise edge communities using group community structure as a seed. Similarity between subject-wise and group communities was assessed using variation of information, with community distance ranging from 0.91-1.10 within networks, to 1.61-2.10 across all node pairs. We then used subject-wise communities to generate session-wise communities for each participant. For each 'canonical' network, we correlated measures of session-wise community similarity (fig 1C), entropy (fig 2B), and entropy variability (fig 1E, and 2D, operationalised as the standard deviation nodal entropy in each network), with estradiol levels (fig 1D,F) and menstrual cycle day (fig 2C,E). Estradiol was positively correlated with session-level community similarity in DMN C, and negatively correlated with peripheral visual network similarity (fig 1D). Estradiol was also negatively correlated with DMN A entropy variability (fig 1F). Cycle day was positively correlated with network entropy in DMN and limbic networks and negatively correlated with entropy variability in DMN B (fig 2C,E) (correlations were FDR corrected).

We show that edge community structure within 'canonical' resting state networks varies as a function of the menstrual cycle. In particular, community structure within DMN appears to vary across the menstrual cycle. As estradiol levels increase DMN community structure of individuals more closely reflects the group average, and entropy variability across the network decreases. Additionally, diversity of community membership in DMN (entropy) increases as the menstrual cycle progresses. This work demonstrates changes in brain network configuration across the menstrual cycle, and the potential role of estradiol in shaping network dynamics.

Connectivity (eg. functional, effective, structural) ²

fMRI Connectivity and Network Modeling

Neurophysiology of Imaging Signals ¹

Physiology, Metabolism and Neurotransmission Other

ADULTS

Cortex

FUNCTIONAL MRI

NORMAL HUMAN

Other - Estradiol, menstrual cycle