Nature Neuroscience
Resource https://doi.org/10.1038/s41593-024-01741-0
compared to surface-based FreeSurfer55. However, to reproduce our 
findings across software packages, we also ran the T1w data through 
the longitudinal FreeSurfer (v.7) CT pipeline60,61, which corroborated 
our findings using both the Schaefer-400 (Supplementary Fig. 2 and 
Supplementary Tables 1 and 4) and popular Desikan–Killiany62 (Sup-
plementary Table 3) cortical parcellations. Whole-brain T1w-based 
subcortical volume estimates (including cerebellum and lateral ven-
tricles) were also derived using this FreeSurfer pipeline, wherein we 
derived 28 region-of-interest estimates via the commonly used ‘aseg’ 
parcellation scheme63 (Supplementary Fig. 6a). A complete reporting 
of findings can be found in Supplementary Data 1.
Mean framewise displacement (FWD) estimates from gestation 
sessions with a 10-min resting-state scan (n = 18) were used to indirectly 
assess whether motion increased throughout pregnancy. Average FWD 
(mm) was extremely minimal across the entire experiment (M = 0.13, 
s.d. = 0.02, range = 0.09–0.17) and varied only slightly by pregnancy 
stage (pre, M = 0.11 and s.d. = 0.004; first, M = 0.11 and s.d. = 0.01; sec-
ond, M = 0.13 and s.d. = 0.02; third, M = 0.16 and s.d. = 0.007; post, 
M = 0.13 and s.d. = 0.01). While mean FWD did correspond with gesta-
tion week (r = 0.88, P < 0.001), controlling for this did not alter our main 
findings (for example, total GMV remained negatively associated with 
gestation after partial correlation with FWD (r = −0.87 and P < 0.001) 
because motion differences between stages were minuscule (Sup-
plementary Fig. 4a).
As a further test of the robustness of the dataset, we ran QC assess-
ments on all T1w images using the IQMs pipeline64 from MRIQC (ver-
sion 23.1). Assessments 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 ranges65 (Supplementary Fig. 4b–d). Although relationships 
existed between gestation week and QC measures (CJV, r = 0.70 and 
P < 0.001; SNR and CNR, r = −0.83 and P < 0.001), including these vari-
ables in the regression models did not detract from our finding sug-
gesting cortical GMV reductions occur over gestation, especially within 
regions belonging to attention and somatosensory networks (Supple-
mentary Fig. 5). When looking across all MRIQC outputs, discrepancies 
were noted in session seven (gestation week nine, first trimester). 
Removing this day from the analyses only strengthened observed 
relationships between cortical volume and gestation; however, for 
completeness, data from this day is included in the main findings. 
These QC outputs for each session of the experiment can be found 
in Supplementary Data 1. Finally, we used FreeSurfer’s Eueler num-
ber to evaluate a field-standard quantitative assessment of each T1w 
structural image66. We observed no significant relationships between 
the Euler number and gestation week or summary brain metrics. A 
discrepancy (for example, two s.d. below average) was noted in session 
eight; however, again, removing this session did not detract from our 
main findings showing reductions in GMV over gestation.
Hippocampal segmentation. T1- and T2-weighted images (n = 25) were 
submitted to the automatic segmentation of hippocampal subfields 
package (ASHS67, version July 2018) for parcellation of seven MTL 
subregions: CA1, CA2/CA3, dentate gyrus, subiculum, perirhinal cor-
tex, entorhinal cortex and PHC (Supplementary Fig. 6b). The ASHS 
segmentation pipeline automatically segmented the hippocampus in 
the T2w MRI scans using a segmented population atlas, the Princeton 
Young Adult 3T ASHS Atlas template68 (n = 24, mean age = 22.5 years). 
A rigid-body transformation aligned each T2w image to the respective 
T1w scan for each day. Using ANTs deformable registration, the T1w