A rigid-body transformation aligned each T2w image to the respective 
T1w scan for each day. Using ANTs deformable registration, the T1w 
was registered to the population atlas. The resulting deformation 
fields were used to resample the data into the space of the left and 
right template MTL ROI. Within each template ROI, each of the T2w 
scans of the atlas package was registered to that day’s T2w scan. The 
manual atlas segmentations were then mapped into the space of the 
T2w scan, with segmentation of the T2w scan computed using joint 
label fusion69. Finally, the corrective learning classifiers contained in 
ASHS were applied to the consensus segmentation produced by joint 
label fusion. The output of this step is a corrected segmentation of 
the T2w scan. Further description of the ASHS protocol can be found 
here67. T2w scans and segmentations were first visually examined using 
ITK-SNAP70 for quality assurance and then subjected to manual editing 
in native space using ITK-SNAP (v.3.8.0-b; C.M.T.). One session (scan 
15, third trimester) was discarded due to erroneous scan orientation. 
The anterior extent of the segmented labels was anchored 4 mm (two 
slices) anterior to the appearance of the limen insulae, and the posterior 
extent was anchored to the disappearance of hippocampal gray matter 
from the trigone of the lateral ventricle. Boundaries between perirhinal, 
entorhinal and parahippocampal cortices were established in keeping 
with the Olsen–Amaral–Palombo (OAP) segmentation protocol71. In 
instances where automatic segmentation did not clearly correspond 
to the underlying neuroanatomy, such as when a certain label was 
missing several gray matter voxels, manual retouching allowed for 
individual voxels to be added or removed. All results are reported 
using the manually retouched subregion volumes to ensure the most 
faithful representation of the underlying neuroanatomy. Scans were 
randomized and segmentation was performed in a random order, 
blind to pregnancy stage. T o assess intrarater reliability for the pre-
sent analyses, two days underwent manual editing a second time. The 
generalized Dice similarity coefficient72 across subregions was 0.87 
and the intraclass correlation coefficient was 0.97, suggesting robust 
reliability in segmentation.
White matter microstructure. Diffusion scans were preprocessed using 
the automation software QSIprep (version 0.16.1) compiled using a 
singularity container73 and run primarily with the default parameters, 
with the exceptions ‘–output resolution 1.8’ , ‘–dwi denoise window 5′, 
–force-spatial-normalization’ , ‘–hmc model 3dSHORE’ , ‘–hmc-  
transform Rigid’ and ‘–shoreline iters 2’ . Twenty-three sessions were 
preprocessed and analyzed, with the remaining three scans excluded 
due to missing DSI scans (sessions 9 and 15) or corresponding field map 
for distortion correction (session 7). Despite passing QC assessments 
during preprocessing, visual inspection of the field maps in session 10 
revealed a slight artifact. However, removal of this session had minimal 
impact on the overall results and remained in the final analyses. T1w 
images were corrected for intensity nonuniformity (N4BiasFieldCorrec-
tion) and skull-stripped (antsBrainExtraction). The images underwent 
spatial normalization and registration to the ICBM 152 Nonlinear Asym-
metric template. Finally, brain tissue segmentation of CSF , GM and WM 
was performed on each brain-extracted T1w using FMRIB’s Automated 
Segmentation T ool (FAST). Preprocessing of diffusion images began by 
implementing MP-PCA denoising with a 5-voxel window using MRtrix3’s 
dwidenoise function. B1 field inhomogeneity was corrected using dwibi-
ascorrect from MRtrix3 with the N4 algorithm. Motion was corrected 
using the SHORELine method. Susceptibility distortion correction was 
based on GRE field maps. Preprocessed Nifti scans were prepared for 
tractography using DSI Studio via singularity container version Chen-