Nature Neuroscience
Resource https://doi.org/10.1038/s41593-024-01741-0
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.
Day2Day control dataset. T o compare our findings against a control 
group of nonpregnant densely-sampled individuals, we used the Day-
2Day dataset23 which offered comparable whole-brain T1 and T2 MTL 
scans for eight participants (two male) scanned 12–50 times over 2–7 
months. Each participant was run through the ANTs CT and ASHS pro-
cessing pipelines as outlined above (‘Cortical volume and thickness’ 
and ‘Hippocampal segmentation’). T o note, for each participant, we 
created an SST based on their first two sessions for consistency with 
the primary dataset; subfield volumes for the T2 MTL scans did not 
undergo manual retouching. Due to missing header information on 
the publicly available diffusion scans, we were unable to benchmark 
our white matter changes with the Day2Day dataset.
Statistical analysis. Statistical analyses were conducted using R (sMRI; 
version 3.4.4) and DSI Studio (dMRI; Chen-2022-07-31).
Summary brain metrics. T o reflect the existing literature, we first 
explored brain metrics across the entire study duration (prepregnancy 
through postpartum, n = 26 scans). When including all sessions, total 
brain volume, GMV, CT, global QA, ventricle volume and CSF displayed 
nonlinear trends over time; therefore, we used generalized additive 
models (GAM; cubic spline basis, k = 10, smoothing = GCV), a method of 
nonparametric regression analysis (R package, mgcv76), 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 
to (1) the choice of basis dimension (k) and (2) the distribution of model 
residuals (see mgcv documentation in ref. 76). The general pattern of 
results held after toggling model parameters; however, we note the 
risk of overinterpreting complex models with small sample sizes77. T o 
address overfitting and cross-validate our basis type selection, we also 
fit the data using nonpenalized general linear models (GLM) with both 
linear and polynomial terms for gestation week. We compared the per-
formance of each GLM (that is, models using only a linear term versus 
models with polynomial terms) via the Akaike information criterion 
(AIC), which revealed that cubic models consistently outperformed 
both linear and quadratic models (AICdiff > 3), providing additional 
evidence for nonlinear changes in structural brain variables over time. 
Determining whether these patterns replicate in larger cohorts and 
whether complex models are better suited to capture data patterns 
across individuals will be a necessary next step.
Cortical GMV and CT. We then narrowed our analyses to the first 19 
sessions (baseline—36 weeks gestation) to assess novel brain changes 
occurring over the gestational window. We first computed Pearson’s 
product-moment correlation matrices between the following vari-
ables: gestation week, estradiol, progesterone and the 17 network-level 
average GMV values. We then ran a multivariate regression analysis 
predicting ROI-level GMV changes by gestation week. T o identify which 
regions were changing at a rate different from the global decrease, 
we then ran the analyses again to include total GMV in the regression 
model (Supplementary Table 2). This was extended to the network 
level, where we ran partial correlations accounting for total GMV. These 
same analyses were then run with CT measures. Globally-corrected 
results provided in Supplementary Tables 1–5. Percent change at the 
network level was computed by subtracting the final pregnancy value