text: *White matter microstructure*. DSI Studio’s correlational tractography74 
  was used to analyze the relationship between white matter structure 
  and gestational week (*n*= 16). A truncated model was run to examine the 
  relationship between white matter and sex steroid hormones (*n*= 14) 
  for the subset of diffusion scans with paired endocrine data during ges- 
  tation. A nonparametric Spearman’s correlation was used to derive the 
  correlation between gestational week and endocrine factors and our 
  metrics of interest (QA and MD; see Supplementary Table 9 and Sup- 
  plementary Fig. 10 for MD results) because the data were not normally 
  distributed. Statistical inference was reached using connectometry, 
  a permutation-based approach that tests the strength of coherent 
  associations found between the local connectome and our variables 
  of interest. It provides higher reliability and replicability by correcting 
  for multiple comparisons. This technique provides a high-resolution 
  characterization of local axonal orientation. The correlational trac- 
  tography was run with the following parameters:*t*score threshold of 
  2.5, four pruning iterations and a length threshold of 25 voxel distance. 
  To estimate the FDR, a total of 4,000 randomized permutations were 
  applied to obtain the null distribution of the track length. Reported 
  regions were selected based on FDR cutoff (FDR < 0.2, suggested by 
  DSI Studio), and contained at least ten tracts. For visualization of global 
  and tract QA at each gestational stage, mean QA values were extracted 
  using DSI Studio’s whole-brain fiber tracking algorithm and ROI-based 
  tracking using the default HCP842 atlas78. 
  
  *Summary brain metrics*. To reflect the existing literature, we first 
  explored brain metrics across the entire study duration (prepregnancy 
  through postpartum,*n*= 26 scans). When including all sessions, total 
  brain volume, GMV, CT, global QA, ventricle volume and CSF displayed 
  nonlinear trends over time; therefore, we used generalized additive 
  models (GAM; cubic spline basis,*k*= 10, smoothing = GCV), a method of 
  nonparametric regression analysis (R package, mgcv76), 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 
  to (1) the choice of basis dimension (*k*) and (2) the distribution of model 
  residuals (see mgcv documentation in ref. 76). The general pattern of 
  results held after toggling model parameters; however, we note the 
  risk of overinterpreting complex models with small sample sizes77. 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 per- 
  formance of each GLM (that is, models using only a linear term versus 
  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 nonlinear changes in structural brain variables over time. 
  Determining whether these patterns replicate in larger cohorts and 
  whether complex models are better suited to capture data patterns 
  across individuals will be a necessary next step. 
  
  *Day2Day dataset: measurement variability*. To establish a marker of 
  normative variability over half a year, we computed metrics of meas- 
  urement variability using the Day2Day dataset23, which provided both 
  whole-brain T1 and high-resolution T2 MTL scans. For each region,*j*, of 
  the Schaefer parcellation, we assessed across-session variability,*ε*, as 
  
  *Cortical GMV and CT*. We then narrowed our analyses to the first 19 
  sessions (baseline—36 weeks gestation) to assess novel brain changes 
  occurring over the gestational window. We first computed Pearson’s 
  product-moment correlation matrices between the following vari- 
  ables: gestation week, estradiol, progesterone 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 ran the analyses again to include total GMV in the regression 
  model (Supplementary Table 2). This was extended to the network 
  level, where we ran partial correlations accounting for total GMV. These 
  same analyses were then run with CT measures. Globally-corrected 
  results provided in Supplementary Tables 1–5. Percent change at the 
  network level was computed by subtracting the final pregnancy value 
  (36 weeks pregnant) from the first prepregnancy baseline value, then 
  dividing that difference by said first prepregnancy baseline value. All 
  analyses underwent multiple comparisons testing (false discovery rate 
  (FDR)-corrected at*q*< 0.05). 