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).
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).
T o 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.
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. 
T o 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.
Day2Day dataset: measurement variability. T o 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
εj = 100×mean(
||ts − ̂t||
̂t
)
Where ts is the morphometric measurement of a parcel for session s 
and ̂t is the mean of t across sessions55,79. Thus, we defined variability 
as the mean absolute percent difference between each individual and 
the mean across sessions. Across-session variability estimates for all 
400 regions were then averaged across eight participants, and a global