it was correctly specified with regards 6o 1) the choice of basis dimension (k) and 2) the distribution of the model residuals 
(see mgcv documentation; Wood, 2017). The general pattern of results held after toggling model parameters; however, we 
note the risk of overinterpreting complex models with small sample sizes (see Sullivan et al., 2015). To address overfitting and 
cross-validate our basis type selection, we also fit the data using nonpenalized general linear models (GLM) with both linear 
and polynomial terms for gestation week. We compared the performance of each GLM (i.e., models using only a linear term 
vs. models with polynomial terms) via the Akaike information criterion (AIC), which revealed that cubic models consistently 
outperformed both linear and quadratic models (AICdiff > 3), providing additional evidence for non-linear changes in 
structural brain variables over time.  
 
Gray Matter Volume & Cortical Thickness: 
We first computed Pearson’s product-moment correlation matrices between the following variables (n = 19 pregnancy 
scans): gestation week, estradiol, progesterone, total GMV, and the 17 network-level average GMV values. We then ran a 
multivariate regression analysis predicting ROI-level GMV changes by gestation week. To identify which regions were 
changing at a rate different from the global decrease, we then re-ran the analyses to include total GMV as a variable of non-
interest in the regression model. A similar statistical approach was taken for T1w-derived subcortical volume estimates. We 
ran a multivariate regression analysis predicting GMV changes over gestation in 28 regions-of-interest by gestation week 
(FDR-corrected at q < 0.05).  
 
T2-weighted MTL scans: 
To evaluate the relationship between gestation week and medial temporal lobe (MTL) subregion volume over pregnancy (n = 
7 bilateral subregions; n = 18 MTL scans), we used a combination of linear and non-linear models based on individual 
subregion data patterns. Models were compared for best fit with each subregion via AIC from the GLM output (as described