5 nature portfolio  |  reporting summary April 2023 
above). A linear regression model was most appropriate for PHC (AICdiff < 3), whereas a quadratic model performed best for 
CA1 and CA2/3. As a control, we repeated the analyses with MTL subregion volumes after proportional volume correction of 
total gray matter volume calculated by ASHS. Finally, we evaluated the relationship between endogenous sex hormones 
(estrogen and progesterone) and subregion volumes using linear regression. Relationships were considered significant only if 
they met FDR correction at q < .05. 
 
Diffusion imaging: 
DSI Studio’s correlational tractography (Yeh et al., 2016) 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 gestation. A non-parametric 
Spearman correlation was used to derive the correlation between gestational week and endocrine factors and our metrics of 
interest (QA and MD; see Table S9 and Fig. S10 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 tractography was run with the following parameters: T-score threshold of 2.5, 4 pruning iterations, and a length 
threshold of 25 voxel distance. To estimate the false discovery rate (FDR), a total of 4000 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 10 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 atlas (Yeh et al., 2013).    
 
Effect(s) tested Predicting global, network, and regional volumetric change (GMV, CT, MTL subregion, microstructure) by pregnancy-related 
indicators (gestation week, estrogen, progesterone). 
Specify type of analysis: Whole brain ROI-based Both
Anatomical location(s)
Global measures of gray matter volume, cortical thickness, and cerebrospinal fluid were computed by 
ANTs and validated with FreeSurfer. A whole-brain probabilistic atlas (e.g., Schaefer 400-region 
parcellation) was used for ROI analysis of cortical thickness and volume and the Yeo/Schaefer 17-network 
scheme was used for network-level analyses. The 'aseg' segmentation was used for ROI analysis of 
subcortical gray matter volume. The Princeton Young Adult 3T ASHS Atlas Template was used to examine 
volume among 7 MTL subfields: CA1, CA 2/3, dentate gyrus, subiculum, entorhinal cortex, perirhinal 
cortex, and the parahippocampal gyrus. Whole-brain white matter structure was assessed for the 
diffusion imaging analysis, wherein every tract and bundle was evaluated.
Statistic type for inference
(See Eklund et al. 2016)
N/A; s and diffusion MRI only.
Correction FDR-correction
Models & analysis
n/a Involved in the study
Functional and/or effective connectivity
Graph analysis
Multivariate modeling or predictive analysis
Multivariate modeling and predictive analysis Multivariate regression analyses was used to explore brain structure in relation to gestation. Regional, 
network, and summary brain measures (dependent variables) were examined in relation to gestation week 
(independent variable). In follow-up statistical analyses (noted in Methods), various quality control metrics