text: performed an outlier check, labeling images as a ‘low-quality outlier’ if 
  the correlation coefficient was >3 s.d. from the absolute mean. None of 
  our scans were flagged as outliers. The reconstructed participant files 
  were aggregated into one connectometry database per metric.  *Subcortical GMV*. A similar statistical approach was taken for subcorti- 
  cal volume estimates. We ran a multivariate regression analysis predict- 
  ing GMV changes over gestation in 28 ROIs (Supplementary Fig. 6a) by 
  gestation week (FDR-corrected at*q*< 0.05). 
  
  To evaluate the relationship between gestation week and MTL 
  subregion volume over pregnancy (*n*= 7 bilateral subregions and 
  *n*= 18 MTL scans), we used a combination of linear and nonlinear 
  models based on individual subregion data patterns. Models were 
  compared for best fit with each subregion via AIC from the GLM output 
  (as described in ‘Summary brain metrics’). A linear regression model 
  was most appropriate for PHC (AICdiff < 3), whereas a quadratic model 
  performed best for CA1 and CA2/CA3. As a control, we repeated the 
  analyses with MTL subregion volumes after proportional volume cor- 
  rection of total GMV calculated by ASHS. Finally, we evaluated the 
  relationship between endogenous sex hormones (estrogen and proges- 
  terone) and subregion volumes using linear regression. Relationships 
  were considered significant only if they met FDR correction at*q*< 0.05. 