text: Statistical modeling & inference 
  
  Model type and settings 
  
  Summary brain metrics: 
  To reflect the existing literature, we first explored brain metrics across the entire study duration (pre-conception through 
  postpartum). When including all sessions, total brain volume, GMV, CT, global QA, ventricle volume and CSF displayed non- 
  linear trends over time; therefore, we used generalized additive models (GAM; cubic spline basis, k = 10, smoothing = GCV), a 
  method of non-parametric regression analysis (R package: mgcv), to explore the relationship between summary brain metrics 
  (outcome variables) and gestation week (smooth term). Each model underwent examination (gam.check function) to ensure 
  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. 