Pregnancy alters the organization of the structural brain network
Poster No:
2169
Submission Type:
Abstract Submission
Authors:
Milou Straathof1, Damiaan Denys1, Elseline Hoekzema1
Institutions:
1Amsterdam UMC, Amsterdam, the Netherlands
First Author:
Co-Author(s):
Introduction:
Pregnancy is a monumental phase in many women's lives, which is orchestrated by unparalleled endocrine and physiological changes. We have previously shown that this period is also characterized by unique brain plasticity, with volumetric reductions in gray matter structures (Hoekzema et al., 2017, 2022). However, to fully understand the organization and plasticity of the brain during this period, we also have to consider the connections between gray matter regions and study the brain from a network perspective (Bassett & Sporns, 2017). Therefore, in this study we aimed to investigate whether pregnancy induces alterations in the organization of the structural brain network, as measured with diffusion-weighted imaging.
Methods:
We used a pre-conceptive prospective cohort study to investigate the influence of pregnancy on the organization of the structural brain network. We followed first-time mothers (n=40) and nulliparous control women (n=40) longitudinally: from pre-conception (PRE) to the early (POST) and late postpartum period (POST+1). Anatomical T1-weighted imaging and diffusion-weighted imaging were performed at every time point. The T1-weighted image was parcellated with Freesurfer to identify 82 brain regions (34 cortical and 7 subcortical per hemisphere) for each subject based on the Desikan-Killiany atlas. We reconstructed the structural brain network with whole-brain single shell constrained spherical deconvolution tractography in MRtrix3. The streamline count between brain regions was corrected for underlying white matter density and used as a measure of structural connectivity. For each participant, we removed the 5% weakest connections to reduce the influence of spurious connections. To study the organization of the brain network, we determined several global network measures, including the normalized weighted shortest pathlength (l), normalized weighted clustering coefficient (C), small-worldness (s = l / C) and density (d) for the PRE and POST timepoints.
Results:
Each group contained two outliers in the network density which were excluded from the analyses. An example of whole brain tractography and a subsequently reconstructed structural brain network are shown in Figure 1A. Mixed linear models, corrected for age, education and time between the PRE and POST scan, showed statistically significant group (pregnant vs. control) * time (PRE vs. POST) interaction effects for clustering (F(73) = 5.47; p = 0.022), small-worldness (F(73) = 6.09; p = 0.016) and density (F(73) = 5.36; p = 0.023). Subsequent paired t-tests only showed significant effects from PRE to POST in the pregnancy group, who showed increased clustering (PRE: C=1.78 ± 0.13; POST: 1.84 ± 0.15; t(37) = -2.98; p = 0.005) and small-worldness (PRE: s = 2.43 ± 0.21; POST: 2.52 ± 0.24; t(37) = -2.67; p = 0.011) and decreased density across pregnancy (PRE: d = 0.61 ± 0.03; POST: 0.60 ± 0.03; t(37) = 3.23; p = 0.003) (Figure 1B). To check whether the density difference across pregnancy was influencing the results, we performed all analyses again with a set density (d = 0.3), which yielded highly similar results.
Conclusions:
In this study, we present, for the first time, changes in the organization of the structural brain network across pregnancy in first-time mothers. These changes are characterized by an increase in global clustering and, as a result, small-worldness (calculated by dividing the weighted shortest pathlength by the clustering coefficient). As the clustering coefficient represents how neighboring regions cluster together (Bullmore & Sporns, 2009), an increase in the global clustering represents more segregation of the network. This indicates that in first-time mothers information may be processed more locally. Since previous studies have shown that changes in global clustering may relate to cognitive abilities (Chen et al., 2021), we aim to further investigate relationships between structural brain network organization and behavioral measures in first-time mothers.
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Keywords:
Data analysis
MRI
Plasticity
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - connectome; graph theory; pregnancy
1|2Indicates the priority used for review
Provide references using author date format
Bullmore, E, & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. https://doi.org/10.1038/nrn2575
Chen, Q, Baran, TM, Turnbull, A, Zhang, Z, Rebok, GW, & Lin, FV. (2021). Increased segregation of structural brain networks underpins enhanced broad cognitive abilities of cognitive training. Human Brain Mapping, 42(10), 3202–3215. https://doi.org/10.1002/hbm.25428
Hoekzema, E, Barba-Müller, E, Pozzobon, C, Picado, M, Lucco, F, García-García, D, Soliva, JC, Tobeña, A, Desco, M, Crone, EA, Ballesteros, A, Carmona, S, & Vilarroya, O. (2017). Pregnancy leads to long-lasting changes in human brain structure. Nature Neuroscience, 20(2), 287–296. https://doi.org/10.1038/nn.4458
Hoekzema, E, van Steenbergen, H, Straathof, M, Beekmans, A, Freund, IM, Pouwels, PJW, & Crone, EA. (2022). Mapping the effects of pregnancy on resting state brain activity, white matter microstructure, neural metabolite concentrations and grey matter architecture. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-33884-8