4 nature portfolio  |  reporting summary April 2023 
Normalization template T2-weighted MTL scans:  
Princeton Young Adult 3T ASHS Atlas Template (n=24, mean age = 22.5; Aly & Turk-Browne, 2016).  
 
Diffusion imaging: 
All diffusion images were reconstructed using the ICBM152 template.
Noise and artifact removal Gray Matter Volume & Cortical Thickness: 
All T1-weighted images underwent denoising ('denoiseImage') and N4 bias field correction ('N4BiasFieldCorrection') for field 
inhomogeneity via ANTs.  
 
T2-weighted MTL scans: 
All T2-weighted MTL images underwent denoising ('denoiseImage') via ANTs. 
 
Diffusion: 
All diffusion images underwent denoising, motion and distortion correction using MRtrix3’s dwidenoise and  dwibiascorrect 
with the N4 algorithm. All diffusion images were quality checked using DSI studio’s `QC1: SRC Files Quality Control. All images 
passed QC checks.  
 
Motion: 
Mean framewise displacement (FWD) estimates from gestation sessions with a 10-minute resting state scan (n = 18) were 
used to indirectly assess whether motion increased throughout pregnancy. Average FWD (millimeters) was extremely 
minimal across the entire experiment (M = 0.13, SD = 0.02, range = 0.09–0.17) and varied only slightly by pregnancy stage 
(pre: M = 0.11, SD = 0.004; first: M = 0.11, SD = 0.01; second: M = 0.13, SD = 0.02; third: M = 0.16, SD = 0.007; post: M = 0.13, 
SD = 0.01). While mean FWD did correspond with gestation week (r = 0.88, p < .001), controlling for this did not alter our 
main findings (e.g., total GMV negatively associated with gestation; partial correlation: r = -0.87, p < 0.001) owing to the fact 
that motion differences between stages were minuscule.
Volume censoring Gray Matter Volume & Cortical Thickness: 
All images were visually assessed for QC. Further, we computed quality control (QC) assessments on all T1w images using the 
IQMs pipeline from MRIQC (Esteban et al., 2017). Metrics of interest included 1) coefficient of joint variation (CJV), 2) signal- 
to-noise ratio for gray matter (SNR), and 3) contrast-to-noise ratios (CNR). All QC metrics fell within expected standard 
ranges. We also used FreeSurfer’s Eueler number to evaluate a field-standard quantitative assessment of each T1w structural 
image. We observed no significant relationships between the Euler number and gestation week or summary brain metrics. A 
discrepancy (e.g., 2 SD below average) was noted in session eight; however, again, removing this session did not detract from 
our main findings showing reductions in gray matter volume over gestation. 
 
T2-weighted MTL scans: 
Volumes were visually assessed for QC. Volumes were removed from the analysis if unable to be reliably segmented.  
 
Diffusion imaging:  
All images were assessed using the DSI studio quality control and a visual inspection. DSI studio performed an outlier check, 
labeling images as a “low quality outlier” if the correlation coefficient was greater than 3 standard deviations from the 
absolute mean. No images were labeled as a low quality outlier. 
Statistical modeling & inference
Model type and settings Summary brain metrics: 
To reflect the existing literature, we first explored brain metrics across the entire study duration (pre-conception through 
postpartum). When including all sessions, total brain volume, GMV, CT, global QA, ventricle volume and CSF displayed non-
linear trends over time; therefore, we used generalized additive models (GAM; cubic spline basis, k = 10, smoothing = GCV), a 
method of non-parametric regression analysis (R package: mgcv), 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 6o 1) the choice of basis dimension (k) and 2) the distribution of the model residuals