The effects of a second pregnancy on women’s brain structure and function

Domain: chemistry
Published: 2026-02-19
Editorial outcome: minor_revision

Abstract
While we have previously shown that a first pregnancy changes women’s brain structure and resting-state brain activity, it is currently unknown how a woman’s brain is transformed when she undergoes another pregnancy. Therefore, we performed a prospective pre-conception cohort study involving 110 women, including women who became pregnant of their second (PRG2) or first child (PRG1) and nulliparous women. Multimodal MRI data were acquired and differential changes between PRG2 and PRG1 were observed in grey matter volume, white matter tracts and functional neural network organization. Together, these results show similar but less pronounced structural and functional changes in the default mode and frontoparietal network in PRG2, suggesting a primary adaptation of these networks in first-time mothers that is further fine-tuned across a second pregnancy. Furthermore, stronger alterations were found in PRG2 in the dorsal attention and somatomotor network including the corticospinal tract, pointing to an enhanced plasticity within these externally-oriented networks. Neurostructural changes in both groups related to mother-infant attachment and peripartum depression. These findings show that a second pregnancy uniquely changes a woman’s brain, entailing both convergent and distinct neural transformations.

Introduction
Pregnancy represents a monumental phase in many women’s lives, orchestrated by unparallelled physiological and neuroendocrine changes that influence all major bodily systems. We have previously shown that reproduction is associated with widespread changes in women’s brain structure1,2, which has since been replicated in various other studies across the world3–5. Additionally, we have shown that pregnancy also affects functional neural network organization in the default mode network2. While research is beginning to uncover pregnancy-induced brain plasticity, all studies so far have focused solely on first-time mothers, leaving it unclear whether similar brain changes occur as a result of subsequent pregnancies.

Animal studies point to an association between subsequent reproductive experiences and neuroanatomical variations in the brain. Hippocampal dendritic morphology, neurogenesis and protein levels have been shown to differ between primiparous and multiparous female rodents6–8. Additionally, parity has lasting effects on synaptic plasticity and inflammation in the hippocampus and the response to ovarian hormones in middle-aged rodents9–12. While these findings provide evidence for structural variations in the rodent brain on the basis of parity, research on this topic in human mothers is still in its infancy.

Recent studies have suggested that parity may involve long-lasting neural changes in human mothers measured beyond the postpartum period. In middle-aged women, parity has been linked to brain age, with multiparous women having younger-looking brains compared to primiparous and nulliparous women13,14. Additionally, parity has been associated with cortical thickness, functional connectivity and gray matter volume in women during late life15–17. Parity may also modulate the risk for and effects of various brain disorders that may occur later in life, such as Alzheimer’s Disease, stroke and traumatic brain injury18–21. Furthermore, links between parity and cognition have been shown22, characterized by a U-shaped effect of parity on cognition, with the highest scores on the Mini Mental State Examination in women with one to four pregnancies, compared to nulliparous women and women with grand parity23. Although these findings provide evidence for long-lasting differences in neural anatomy in relation to multiple childbirths in human mothers, it remains unclear whether a first and second pregnancy differentially affect the human brain.

Therefore, we performed a prospective pre-conception cohort study involving second-time mothers, first-time mothers and nulliparous control women to investigate whether a second pregnancy is associated with structural and functional changes in the brain. We acquired multimodal 3T magnetic resonance imaging (MRI) data, involving high-resolution anatomical MRI, resting-state functional MRI, diffusion-weighted MRI and magnetic resonance spectroscopy (MRS), before pregnancy and in the early and late postpartum period in 30 women undergoing a second pregnancy (PRG2; becoming multiparous between the scans) and in 40 women undergoing a first pregnancy (PRG1; becoming primiparous between the scans). We also included a control group of 40 nulliparous control women (CTR), who underwent MRI scanning at a similar time interval. We assessed the differential effects of a second pregnancy on brain structure, functional neural networks, white matter organization and neural metabolite concentrations. This study demonstrates that a second pregnancy changes a woman’s brain and uniquely impacts its gray matter structure, neural network organization and white matter tracts.

Results
Vertex-wise volumetric brain changes
To investigate anatomical brain changes across a first and second pregnancy compared to control women, we performed vertex-wise longitudinal analyses of cortical volume, thickness and surface area in Freesurfer. Generalized linear models (GLMs) comparing the volume change from pre-pregnancy (PRE) to the early postpartum period (POST) between multiparous and control women demonstrated widespread volumetric decreases across a second pregnancy (Fig. 1a, b). The median percentage volume decrease across the significant vertices in the left and right hemisphere was 2.8%. Similarly, women who were pregnant for the first time showed significant volumetric decreases from PRE to POST compared to control women (Fig. 1c, d). The areas of significant change covered a 79% larger part of the brain than in second-time mothers, and the median percentage of change was 3.1% within the significant vertices. Effect sizes for these volumetric decreases across a first and second pregnancy were large (Cohen’s D > 1.0; Supplementary Table 1). In both groups, there were no significant increases in cortical volume across pregnancy compared to control women. Similar results were found when investigating cortical thickness (Supplementary Fig. 1a) and surface area (Supplementary Fig. 1b), showing decreases in cortical thickness and surface area across a first and second pregnancy when compared to control women.

When directly comparing cortical volume changes across a first and second pregnancy using a permutation-based correction for multiple comparisons, we found several areas of significantly different volumetric change between PRG1 and PRG2 (Supplementary Fig. 2), although it should be noted that these effects do not surface when applying an FDR correction as in the comparisons with CTR women. Areas with a larger decrease in volume in PRG1 were mainly localized in the default mode network, whereas areas with a larger decrease in volume in PRG2 were localized in the dorsal and ventral attention network and the sensorimotor network. Effect sizes for these differences between a first and second pregnancy were smaller than the very large effect size of a pregnancy itself, but the contrast for PRG1 vs. PRG2 still showed a moderate to high effect size (Cohen’s D > 0.5; Supplementary Table 1). A visual inspection of the results also shows a divergence in both the extent and localization of brain changes between these groups (Fig. 1a, c).

This divergence between brain changes across a first and second pregnancy was supported by a multivariate classification approach. We performed a multivariate pattern classification analysis in PRonTo24,25 (v3.0) on the gray matter volume difference maps resulting from the longitudinal symmetric diffeomorphic modeling pipeline implemented in SPM1226. This analysis demonstrated that, based on the gray matter volumetric changes in the brain, 80% of women could be correctly classified as becoming primiparous or multiparous in between the PRE and POST session (PRG1 vs. PRG2; p = 0.0001 after 10,000 permutations; Fig. 2a). When classifying women getting pregnant and women not getting pregnant in between these sessions, the accuracy is even higher (PRG2 vs. CTR: 87%, p = 0.0001, Fig. 2b; PRG1 vs. CTR: 94%; p = 0.0001, Fig. 2c). Using k-folds cross-validation with ten folds to reduce the risk of overfitting, classification accuracy dropped slightly, but was still significant for all three combinations of groups (PRG1 vs. PRG2: accuracy = 70%, p = 0.009; PRG2 vs. CTR: accuracy = 87%, p = 0.0001; PRG1 vs. CTR: accuracy = 91%, p = 0.0001). Adding age as a regressor to the models involving groups with age differences rendered highly similar results, both with leave-one-out cross-validation (PRG1 vs. PRG2: accuracy = 81.43%, p < 0.0001; PRG2 vs. CTR: accuracy = 84.29%, p < 0.0001) and with ten-folds cross-validation (PRG1 vs. PRG2: accuracy = 74.29%, p = 0.0008; PRG2 vs. CTR: accuracy = 82.86%, p < 0.0001.)

In the affected regions, brain volumes slightly increased in the late postpartum period (POST1) but had not returned to pre-pregnancy levels in both multiparous and primiparous women (Supplementary Fig. 3 and Supplementary Table 2). Correlation analyses between the volumetric change across pregnancy and age of the first child at the PRE session in the PRG2 group suggested that time since birth of the first child does not influence the amount of volumetric brain changes across a second pregnancy (Supplementary Fig. 4). We also tested whether there were baseline differences in cortical volumes between groups, showing no significant differences between PRG1, PRG2 and CTR women in cortical volumes at the pre-pregnancy baseline. Extracting gray matter volumes inside the area of change across a first pregnancy, suggests a partial volume reversal across the postpartum period that seems to continue after the first postpartum year (Supplementary Fig. 5).

Associations with maternal behavior and mental health
We then assessed the relationship between the volumetric brain changes across a second or first pregnancy and maternal behavior and mental health status. When comparing PRG1 and PRG2, we did not detect any significant differences in maternal behavior and mental health status (Supplementary Table 3), although we found a trend towards more depressive feelings during pregnancy in the PRG2 group. Correlation analyses showed that maternal behavior during pregnancy and in the early postpartum period was negatively correlated with the amount of volumetric change across pregnancy, except for the postpartum bonding questionnaire (PBQ) which showed a positive correlation due to its reversed scoring where higher values represent lower maternal behavior (Supplementary Fig. 6), with stronger changes in brain structure thus being associated with higher levels of maternal behavior on the employed measures. These correlations between brain volumetric changes and maternal behavior seem to be more widespread across a first compared to a second pregnancy (Supplementary Fig. 6 and Supplementary Table 4). While the volumetric changes across a first pregnancy were associated with maternal-fetal attachment as measured with the maternal antenatal attachment score (MAAS) and prenatal attachment inventory (PAI), this was not evident in second-time mothers. Associations between volumetric changes and measures of postpartum mother-infant attachment and impairments in the mother-infant relationship as measured with the maternal postnatal attachment score (MPAS) and PBQ were observed in both PRG1 and PRG2, but these were more widespread in PRG1. Nesting behavior was similarly associated with mothers’ brain changes across a first or a second pregnancy.

Regarding maternal mental health, we found associations between the observed brain changes with peripartum depression, measured with the Edinburgh Postnatal Depression Score (EPDS) and psychological distress measured with the K10 in both PRG groups, with less pronounced brain changes linked to more depressive complaints for most significant vertices. However, more widespread correlations were observed between volumetric change and depression and psychological distress in the PRG2 group during pregnancy, but in the PRG1 group in the early postpartum period (Supplementary Fig. 7 and Supplementary Table 5).

Network organization
To further investigate the regional distribution of changes in gray matter structure occurring across a first and second pregnancy, we assessed the differences and similarities between the changes in these groups in relation to the brain’s functional networks using a cluster-wise approach (for cluster-wise results, see Supplementary Tables 6–9). Therefore, a combination of clusters resulting from the two significant contrasts (PRG2 vs. CTR and PRG1 vs. CTR) were applied on the volumetric GLM results described above. This resulted in three different areas of interest: Areas that are affected in both first and second pregnancies (overlap), areas that are only affected during a first pregnancy (only CTR-PRG1) and areas that are only affected during a second pregnancy (only CTR-PRG2) (Fig. 3a).

To acquire more information on the localization of these areas across the brain, we then determined their intersection with the seven resting-state networks of Yeo27 (Fig. 3b). These analyses showed that the overlapping areas in first and second pregnancies were mostly located in the default mode network (DMN), followed by the frontoparietal and ventral attention network. For these networks, the intersection between the overlapping areas and the networks was larger than expected based on a random distribution across the gray matter in the brain (Supplementary Table 10 and Fig. 3c). Interestingly, the additional areas that were specifically affected only across a first pregnancy were localized in those same networks (Fig. 3c and Supplementary Table 11). In comparison, the areas affected in a second pregnancy only were not localized in the default mode network but instead were mainly located in the somatomotor and dorsal attention network (Supplementary Table 12 and Fig. 3c, d).

To study functional network organization, we also analyzed resting-state functional MRI data, but we did not find significant changes in within-network coherence between a first or second pregnancy in any of the identified resting-state networks. Subsequent analyses using a default mode network (DMN) region that represents the area of significant change in functional coherence across a first pregnancy2, showed a significant group*session interaction effect between PRG1 and PRG2 in the DMN (MNI coordinates (xyz) = 12 −84 15, T = 2.85, p = 0.006 FWE-corrected; Supplementary Fig. 8), characterized by an increase in DMN network coherence only across a first pregnancy. Results for between-network connectivity are presented in the supplement (Supplementary Tables 13–15).

White matter tract organization
We also investigated the effects of a first and second pregnancy on the organization of white matter tracts in the brain, measured with diffusion-weighted MRI. Based on these diffusion-weighted images, we extracted mean fractional anisotropy (FA) and mean diffusivity (MD in 10−5 mm2/s) in 11 large white matter tracts across the brain (Fig. 4a) and compared PRE- and POST-pregnancy measurements between PRG2 and CTR subjects, PRG1 and CTR subjects, and between PRG2 and PRG1 subjects.

General linear models showed significant group*session interaction effects in the MD of the right corticospinal tract (CST) between PRG2 and CTR (F(68) = 6.28, p = 0.03, η2 = 0.15, 95% CI [0.02, 0.29]) and in the FA of the left superior longitudinal fasciculus (temporal part; SLFT) between PRG1 and CTR (F(78) = 7.86, p = 0.02, η2 = 0.16, 95% CI [0.03, 0.30]) (Fig. 4b and Supplementary Tables 16 and 17). These effects were also visible when directly comparing subjects undergoing a first or second pregnancy, although the FA effect in the left SLFT did not survive the FDR correction for multiple testing (MD CST: F(68) = 7.44, p = 0.02, η2 = 0.18, 95% CI [0.03, 0.32]; FA SLFT: F(68) = 4.09, p = 0.09, η2 = 0.10, 95% CI [0.001, 0.23]; Supplementary Tables 16 and 17). Subsequent paired t-tests/Wilcoxon signed rank tests revealed that these effects were driven by decreases in the MD of the right CST in the PRG2 group (V = 386.5, p = 0.02, r = 0.58, 95% CI [0.28, 0.78]) and decreases in the FA of the left SLFT in the PRG1 group (t(39) = 3.74, p = 0.01, d = 0.59, 95% CI [0.26, 0.93]). We did not observe any differences in the organization of white matter tracts at the PRE session between all three groups.

To examine if these changes were maintained across the postpartum period, we also analyzed the MD in the subset of PRG2 participants of which we had complete PRE, POST and POST1 measurements (n = 14). These analyses showed that the MD in the right CST was still decreased 1 year postpartum compared to the pre-pregnancy baseline (t(13) = 4.39; p = 0.02, d = 1.17, 95% CI [0.49, 1.85]), although in this subgroup of women there was no significant difference between the pre-pregnancy baseline and the early postpartum period after correction for multiple testing (t(13) = 2.45, p = 0.18, d = 0.65, 95% CI [0.08, 1.23]).

Neural metabolites
To assess the influence of a second pregnancy on neural metabolite concentrations, we measured the concentration of five major metabolites, in the precuneus/posterior cingulate cortex VOI, an area showing strong volume reductions across a first pregnancy1: total NAA (tNAA: N-acetylaspartate including contributions from N-acetylaspartylglutamate), total creatine (tCr: creatine and phosphocreatine), total choline (tCho: phosphorylcholine and glycerophosphorylcholine), Glu (Glutamate), and Ins (myo-Inositol) (Fig. 5a). At the baseline measurement, there were no significant differences in metabolite concentration between the three groups.

We compared the PRE and POST measurement between multiparous and control women, and between multiparous and primiparous women (Fig. 5b). When examining the changes in metabolite concentration between PRG2 and CTR with general linear models, we observed a group*session interaction effect for tCr only, although this effect did not survive the correction for multiple testing (F(62) = 4.24, p = 0.08, βstd = 0.31, 95% CI [0.01, 0.61]) (Fig. 5b). Subsequent one sample-tests showed that this effect was driven by significant increases in tCr across a second pregnancy (t(26) = −5.07, p < 0.0001, d = −0.98, 95% CI [−0.85, −0.36]). Results for the other metabolites can be found in Supplementary Table 18. Additionally, directly comparing PRG2 and PRG1 did not show any significant differences in metabolite concentration changes across a first and second pregnancy (Supplementary Table 19).

Methods
Study set-up and sample
This research was evaluated and approved by the Ethics Review Board of the Leiden University Medical Center and complies with all relevant ethical regulations. All participants signed the informed consent forms before any study-related measurement and received monetary compensation. We used a prospective pre-conception cohort study, in which we followed women with the intention to become pregnant of their second child in the following year (PRG2: n = 30). Only women (self-reported) were included in this study. We started with a pre-conception research session (PRE session), followed by a session during the third trimester of pregnancy, a session in the early postpartum period (80.32 ± 27.72 days from parturition; POST session) and a session in the late postpartum period (408.67 ± 38.75 days from parturition; POST1 session). Because of the COVID-19 pandemic, only a subset of women could participate in the POST1 session (n = 14). Due to this low sample size, we just included the POST1 timepoint in analyses where we found a significant pregnancy effect from PRE to POST in the PRG2 group, to study the long-term effects of pregnancy. The pregnancy session only included questionnaires, hormone sampling and cognitive testing, since MRI scanning was not allowed during pregnancy by the ethical committee. Additionally, this study included a primiparous group, including women who became pregnant of their first child during the course of the study (PRG1: n = 40, for the POST1 session n = 28) and nulliparous control women (CTR: n = 40), who underwent a similar study set-up (see Fig. 6)2. All groups were scanned in the same time period, and individuals of different groups were scanned intertwined. For more information about the sample included, see Supplementary Table 20.

The PRG2 group was significantly older (32.03 ± 2.33 years) than the PRG1 (29.35 ± 3.51 years; p = 0.0027) and the CTR group (29.33 ± 3.57 years; p = 0.0025), so age has been used as a covariate in all analyses. There were no significant differences in level of education, time between PRE and POST sessions, time between birth and POST session and time between birth and POST1 session (Supplementary Table 20).

MRI acquisition
All MRI scans were acquired on a Philips 3T MRI system.

Anatomical MRI. High-resolution 3D anatomical T1-weighted images were acquired in transverse orientation, with the following acquisition parameters: repetition time (TR) = 9.8 ms, echo time (TE) = 4.6 ms, Flip Angle = 8°, Field of View (FOV) = 178 × 224 × 168 mm, voxel size = 0.875 × 0.875 × 1 mm. All MRI scans were visually checked for quality control, and no scans had to be excluded. Additionally, we determined Freesurfer’s Euler number as a measure of data quality for each T1w image at each time point (Supplementary Table 21), and did not find a significant group (PRG2, PRG1, CTR) * session (PRE, POST, POST1) interaction effect (F(3, 254) = 0.42, p = 0.74). After processing in Freesurfer, all individual T1w images had Euler numbers of 2, which reflect an accurate surface reconstruction.

Resting-state functional MRI. Functional MRI scans were acquired in all participants during rest, while fixating at a cross hair at the screen to prevent them from falling asleep. A total of 137 T2*-weighted whole-brain echo-planar images (EPIs) and two dummy scans were acquired with the following parameters: TR = 2.2 s; TE = 30 ms, Flip Angle = 80°, FOV = 220 × 220 × 111.65 mm, voxel size = 2.75 × 2.75 mm, 37 descending slices.

Diffusion-weighted MRI. Two sets of transverse diffusion-weighted images (DWI) were acquired with reversed k-space encoding direction, to allow for distortion correction. For both DWI scans, the acquisition parameters were: TR = 7315 ms, TE = 69 ms, Flip Angle = 90°, FOV = 240 × 240 mm, acquisition matrix = 128 × 98, reconstruction matrix 128 × 128, 30 different diffusion directs with b-factor 1000 s/mm2, 5 B0 acquisitions, SENSE factor = 3, 75 slices of 2 mm, no slice gap, no cardiac gating.

MR spectroscopy. Magnetic resonance spectroscopy (MRS) was performed with single-voxel point-resolved spectroscopy (PRESS) localization (TR = 2000 ms; TE = 37 ms; 128 averages, 2 dummy scans, and 16 reference scans without water suppression). Shimming was performed with an automated second order projection-based algorithm. The volumes of interest (VOIs) were positioned in two regions that were found to undergo strong changes in brain structure in our previous study1. One was the precuneus/posterior cingulate cortex (PCC), centered between both hemispheres (Fig. 6a), which had a volume of 8 mL (20 × 20 × 20 mm3). The other VOI was positioned in the right superior temporal gyrus and had a volume of 12 mL (20 × 30 × 20 mm3), but due to low quality spectra, this VOI was excluded from all analyses.

MRI processing and statistics
Anatomical MRI. The anatomical MR images were processed with Freesurfer 7.2.0, using the longitudinal recon-all stream62. We performed vertex-wise analyses using cortical volume, thickness and surface area, for which the subject’s cortical maps were registered to the fsaverage space and smoothed with a 10 mm full-width-at-half-maximum (FWHM) kernel. Cortical change maps were created for each measure, and generalized linear models (GLMs), including age as a covariate, were performed to determine differences in the vertex-wise change between each combination of groups (PRG2 vs. CTR, PRG1 vs. CTR and PRG2 vs. PRG1). These linear models were false discovery rate (FDR) corrected for multiple testing. We also determined Cohen’s D effect sizes for the significant vertices (Supplementary Table 1). In order to allow for slightly less prominent effects to surface, we also used a more lenient approach to correct this contrast for multiple testing than the FDR-correction, using 1000 permutations and a vertex-wise threshold of p < 0.01, corrected for performing analyses in the left and right hemisphere. Cortical volumes were extracted from the total area as output of each GLM, and paired t-tests, or in case of non-normality Wilcoxon signed rank tests, were used to determine the direction of effect in the three groups. These paired t-tests/Wilcoxon signed rank tests were repeated in multiparous (n = 14) and primiparous (n = 28) women with complete datasets, including the late postpartum timepoint (POST1). Differences between groups at the pre pregnancy baseline were determined using linear models corrected for age.

To further investigate differences in brain changes across subsequent pregnancies, we determined whether women undergoing a first or second pregnancy could be separated based on their anatomical MRI scans. To do so, we performed linear support vector machine classification in Pronto (PRoNTo v3.0)24,25 on the smoothed gray matter volume difference maps as output from longitudinal symmetric diffeomorphic modeling pipeline26 in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/) implemented in Matlab 7.8 (Mathworks), using a leave-one-out cross-validation. Because of our relatively small sample size for classification analyses, the leave-one-out cross validation provides the largest training dataset as possible, but at the risk of overfitting and overestimating classifier accuracy. Therefore, we repeated the analyses using a k-fold cross-validation using 10 folds, which reduces the risk of overfitting although it’s better suited for large sample sizes. Details about the longitudinal pipeline in SPM12 can be found in Hoekzema et al.2. Permutation testing using 10,000 permutations was used to determine the significance of the classification accuracy for all three combinations of groups (PRG2 vs. PRG1, PRG2 vs. CTR and PRG1 vs. CTR).

Lastly, we performed a cluster-wise correction on the results from the above described GLMs with a vertex-wise cluster threshold of 0.001 and a cluster-wise p-threshold of 0.05 to determine clusters of significant volumetric differences. We combined these cluster maps of the results from PRG1-CTR and PRG2-CTR, to determine the similarity and difference in areas affected with a first or second pregnancy compared to control women. This resulted in three areas of interest: areas affected in both a first and second pregnancy (Overlap), areas affected only in a first pregnancy (Only CTR-PRG1) and areas affected only in a second pregnancy (Only CTR-PRG2). To localize these areas in the brain, we determined the intersection of those regions with the 7 resting-state networks of Yeo27, relative to the expected volume of the intersection based on a random distribution across the gray matter in the brain.

Resting-state functional MRI. The resting state fMRI images were preprocessed using DPARSF (version 4.5)63, involving slice timing correction, realignment, and co-registration of the anatomical images to the mean functional images. The transformed anatomical images were then segmented64, and DARTEL65 was used to transform the images to MNI space, followed by the application of a 10 mm3 FWHM Gaussian kernel. To account for head motion, we applied the Friston 24-parameter model66 and subjects with any frame-wise displacements (FD) exceeding 2 mm (for translations) or 2° (for rotations) or with a mean FD exceeding 0.2 in any of the sessions were excluded67. Therefore, 3 participants of the PRG2 group and 4 participants of the CTR group had to be excluded. After these exclusions, there were no significant group (PRG1, PRG2 and CTRL) * session (PRE and POST) interaction effects in the mean FD or any of the other motion parameters (maximum translations and rotations in the x, y and z directions) (Supplementary Table 22).

We performed Group spatial independent component analyses (ICA) using the Group ICA for fMRI Toolbox in Matlab (GIFT v4.0b, http://mialab.mrn.org/software/gift), using default options, 20 components and the InfoMax algorithm. Components were selected through automated selection by spatial sorting with the components of Smith et al.68 who defined the major networks in the resting brain using a similar ICA-based approach, using a cutoff value of R > 0.25. We compared within-network coherence across the obtained functional networks, by determining the functional connectivity of each voxel with all the other voxels in the network. After determination of the neural networks, we extracted the correlation between the functional networks using the Functional Network Connectivity toolbox (FNC Toolbox version 2.3, https://trendscenter.org/software/fnc/) for Matlab with a lag-shift algorithm using default options. FNC calculates a constrained maximal lag correlation between each pair of networks by calculating Pearson’s correlations and constraining the lag between the time courses69.

To examine whether there were differences between groups in the within-network coherence change across the PRE and POST sessions, we performed generalized linear models in SPM12 and investigated the group*session interaction effect using the PRG2 and PRG1 group, and the PRG2 and CTR group. As a follow-up analysis, for the model investigating the default mode network, a region of interest representing the significant within-network coherence change across a first pregnancy2 was applied as mask. In these analyses, we used the average functional connectivity of the voxels inside this mask only with the rest of the DMN. Results were considered significant at an FWE-corrected statistical threshold of p < 0.05. When significant interaction results were obtained, these were followed by paired sample t-tests, or in case of non-normality the non-parametric Wilcoxon signed rank test, using the PRE and POST measurement within each group. Baseline differences between groups were determined using linear models corrected for age. For the between-network correlation changes, we performed repeated-measures general linear models in Rstudio (version 23.06.1). In case of significant group*session interaction effects, we performed paired t-tests, or in case of non-normality the non-parametric Wilcoxon signed rank test, using the PRE and POST measurement within each group. Results were considered to be significant at a statistical threshold of p < 0.05, corrected for multiple testing using an FDR correction. To create images, the statistical maps were projected onto the PALS surface provided in Caret software (http://brainvis.wustl.edu/wiki/index.php/Caret). Slice overlays were created using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/).

Diffusion-weighted MRI. Diffusion-weighted images were processed using MRtrix370, unless otherwise stated. We denoised the data, preprocessed the data including eddy current-induced distortion correction, motion correction and susceptibility-induced distortion correction using FSL tools eddy & top-up, and corrected the data for B1 inhomogeneity using dwidenoise, dwifslpreproc and dwibiascorrect, respectively. Subsequently, we fitted the diffusion-tensor and calculated the fractional anisotropy (FA) and mean diffusivity (MD) maps. Diffusion tensors were fitted in two steps using dwi2tensor with default settings: first, using weighted least-squares with weights based on empirical signal intensities, and second, by further iterated WLS with weights determined by the signal predictions from the previous iteration. Mean B0 maps were linearly registered using FSL (version 6.0.3) to one individual B0 map, followed by taking the mean to create a study-specific template. Afterwards, individual mean B0 maps were non-linearly registered to the study-specific template, and subsequently to the JHU-ICBM atlas template71. Registrations were inverted, and masks of 11 large white matter tracts of interest were registered to individual subject space. These included the Forceps Major, Forceps Minor, left and right Thalamic Radiation, left and right Corticospinal Tract, left and right Cingulum bundle (Cingulate part and Hippocampal part), Left and right Inferior Longitudinal Fasciculus, left and right inferior Fronto-Occipital Fasciculus, left and right Superior Longitudinal Fasciculus (Body and temporal part) and the left and right Uncinate Fasciculus. ROI masks in individual space were masked with a fractional anisotropy (FA) mask > 0.25, to ensure inclusion of white matter, and mean FA and MD were calculated for each subject at each timepoint.

General linear models in Rstudio (version 23.06.1) were performed to compare changes in white matter tracts (FA and mean diffusivity (MD)) between the PRE and POST measurements across PRG2 and CTR women, PRG1 and CTR women and PRG2 and PRG1 women. In case of significant group*session interaction effects, we performed paired t-tests, or in case of non-normality the non-parametric Wilcoxon signed rank test, using the PRE and POST measurement within each group. Baseline differences between groups were determined using linear models corrected for age. To correct for multiple testing across all white matter tracts per measure (FA or MD), we performed FDR corrections. Lastly, for individuals with a complete PRE-POST-POST1 dataset (PRG2: N = 14, PRG1: n = 28), we performed paired t-tests/Wilcoxon signed rank tests between the PRE and POST1 measurements to investigate the long-term effects of pregnancy.

MR spectroscopy. MRS data of the PRE and POST session were available of 27 women in the PRG2 group, since the spectroscopy had to be omitted in three participants due to a lack of time. The POST1 data was excluded from the MRS analyses because data was only available of four participants in this group. Additionally, MRS data was acquired from 39 PRG1 women and 37 CTR women at the same timepoints (PRE and POST). Metabolite concentrations were estimated with LCModel, (version 6.3-1M), using a dataset containing seventeen metabolites. For this study, we considered the major metabolites tNAA (N-acetylaspartate including contributions from N-acetylaspartylglutamate), tCr (creatine and phosphocreatine), tCho (phosphorylcholine and glycerophosphorylcholine), Glu (Glutamate), Ins (myo-Inositol). These metabolites (or combinations thereof) can typically be measured with high precision (see next section about spectral quality). Concentrations were expressed using water scaling. Next, we corrected for partial volume contributions of GM, white matter and cerebrospinal fluid in the corresponding VOI, based on Sienax segmentation (FSL 5.0.10) of each subject’s 3D T1 images. Spectral quality was examined based on the full-width half maximum (FWHM), signal-to-noise ratio (SNR), and the estimated Cramer Rao lower bounds of each metabolite. Spectra with FHWM > 0.1 ppm (12 Hz) and/or SNR < 5 were considered poor quality. All PCC spectra in the PRG2 group had high quality, with SNR mean ± sd of 24.83 ± 1.51, FWHM 4.32 ± 0.51 Hz, and Cramer Rao lower bounds of metabolites well below 10%: tCr 2.06 ± 0.23%, tNAA 2.17 ± 0.38%, Cho 4.50 ± 0.50%, Ins 6.63 ± 0.62%, and Glu 8.00 ± 0.34%. Information about spectra quality in the PRG1 and CTR group can be found in our previous paper2. There were no differences in FWHM and SNR ratio across groups and timepoints.

The metabolite concentrations resulting from LCModel were analyzed in Rstudio (version 23.06.1), using repeated measures general linear models to assess whether the change in metabolite concentration from PRE to POST was different in PRG2 women, compared to PRG1 and CTR women. In case of significant group*session interaction effects, we performed paired t-tests, or in case of non-normality the non-parametric Wilcoxon signed rank test, using the PRE and POST measurement within each group. Baseline differences were determined using linear models corrected for age. These analyses were FDR corrected across the five metabolites as correction for multiple testing.

Scales and questionnaires
Maternal behavior. To measure maternal behavior, we used several questionnaires during pregnancy (at the PREG session) and in the early postpartum period (at the POST session). To assess maternal-fetal attachment during pregnancy, women filled in the Prenatal Attachment Inventory (PAI72) and the Maternal Antenatal Attachment Scale (MAAS73). Additionally, to examine nesting behavior, the preparational activities during pregnancy, women filled in the Nesting Behavior Questionnaire74. To assess maternal-child bonding and impairments in the mother-infant relationship in the postpartum period, we asked women to fill in the Maternal Postnatal Attachment Scale (MPAS75) and the Postpartum Bonding Questionnaire (PBQ76) respectively. Questionnaires were filled in by both the PRG1 and PRG2 group, although some subjects had missing data at a specific timepoint (PREG: PRG1: n = 36, PRG2: n = 28; POST: PRG1: n = 39, PRG2: n = 28).

Maternal mental health. We assessed mental health status during pregnancy and in the early postpartum period. To assess psychological distress, women in the PRG1 and PRG2 group filled in the K10 questionnaire77. Additionally, to measure signs of postpartum depression, we acquired data from the Edinburgh Postnatal Depression Scale (EPDS78). For both questionnaires, data were missing from several of subjects (PREG: PRG1: n = 36, PRG2: n = 28; POST: PRG1: n = 39, PRG2: n = 28).

Analyses. We determined whether there was a difference in maternal behavior or maternal mental health between the PRG1 and PRG2 group using ANOVA analyses. Additionally, we performed correlation analyses in Freesurfer, to assess whether the maternal behavior or mental health status was associated with the volumetric changes across a first or second pregnancy. We applied the significant areas of change across a first or second pregnancy (PRG1 vs. CTR or PRG2 vs. CTR) as mask for these analyses, to only use the area of significant volume decrease across a first or second pregnancy. Results were corrected for multiple testing using 1000 permutations, with a vertex-wise p value of 0.01, and corrected for performing the analyses across the left and right hemisphere.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper examined women’s brain changes after a second pregnancy, an important research question given evidence that first pregnancy appears to change the maternal brain. Thirty women undergoing a second pregnancy, 40 women undergoing a first pregnancy, and 40 control women were compared. There was overlap in terms of brain change across the first and second perinatal period, but there were also some differences, with first time mothers showing stronger default mode network changes and second time mothers showing more pronounced changes in somatomotor and dorsal attention network regions. There were also differences in resting state connectivity and white matter connectivity.\nThis is a very impressive study that makes a significant contribution to the research literature. I believe that the paper warrants publication, and just have a few questions or clarifications.\nFirst, one weakness is the lack of measures of parent-child bonding or any other individual differences measures (maternal mental health, parenting stress, amount of time spent with baby, etc). Without these measures, it is unclear whether differences between the first and second pregnancy are driven by hormones or other factors, such as parenting experience and feelings about the baby. If mothers are more bonded to their first child, do they show more or less neural change with the second pregnancy? If they feel stronger antenatal attachment during pregnancy, or stronger postnatal attachment after birth, does that explain larger magnitude of brain change? Even if there are no significant associations between self-report measures and brain changes, it would still be valuable to report null results because it would aid interpretation.\nWas birth spacing (the length of time between the first and second pregnancies) considered in analysis? This would be interesting to measure as a continuous variable to see if brain change is greater in second-time mothers when there is more or less time elapsed after their first birth.\nThe paper would also benefit for more clear explanation and interpretation of what the results mean. For example, the white matter findings are interesting but not explained clearly for readers who are not familiar with metrics such as fractional anisotropy and mean diffusivity. Do the results suggest that white matter is increasing or decreasing in volume or integrity? What does this mean in terms of brain health? Do these changes appear adaptive? In terms of the resting state findings, what are the implications of default mode network coherence? Is DMN coherence associated with stress, mental health, or empathy? Drawing on citations from the human neuroscience literature beyond maternal brain studies would help to contextualize these findings.\nI would also like to see more interpretation of some of the apparent contradictions in these findings. For example, the authors suggest that some brain changes (in the default mode, for example) are of greatest magnitude across the first pregnancy and then are fine-tuned more subtly during the second pregnancy. But if this is the case, and the brain has already done much of its restructuring in the first pregnancy, wouldn’t we see baseline brain differences between first and second time mothers? Instead, it seems like everyone is starting from the same place. How do we reconcile that with the finding that the most profound brain changes seem to occur during the first pregnancy? If the brain reverts back to baseline, then presumably it would need to change in the same way to facilitate the next pregnancy. In a perfect world we could see a longitudinal study that follows the same mothers across a first and then a second pregnancy, but of course that study would be extremely challenging to conduct.", "reviewer_2": "This paper explores the effects of a second pregnancy on brain structure and function using multimodal MRI data. The authors report that a second pregnancy conferred similar though less pronounced changes in the default mode and frontoparietal network, yet stronger changes in the dorsal attentional network and somatomotor network compared to a first pregnancy. The current literature lacks a detailed characterization of how first versus second pregnancies influence brain health, and this paper addresses this gap nicely. This is a high-quality and ambitious study that tackles a critical yet underexplored question. The prospective pre-conception design is a major strength and adds valuable insight. Overall, this paper would make a substantial contribution to the literature on maternal brain plasticity. I have several comments to help temper interpretations of the findings and to acknowledge potential limitations.\nThe introduction is strong and clearly written. However, it is somewhat vague in its description of the effects of multiple pregnancies in both animal and human studies. I recognize there are limited papers examining the effects of previous pregnancies on brain health, but the authors should consider citing additional recent work and outlining the more specific differences between differing levels of parity. In addition, several human studies have assessed parity as a linear/continuous variable, offering relevant insight into how additional pregnancies may shape brain health.\nPlease provide a clearer justification for why the specific neural metabolites were selected for analyses. This would help contextualize the results and clarify their relevance to the broader literature on pregnancy-related brain changes.\nMany studies that have examined the effects of parity on brain health have focused on short-term outcomes. It would strengthen the discussion to explicitly distinguish between short- and long-term effects of pregnancy on the brain. The authors briefly mention longer-term signatures of pregnancy on brain plasticity in humans. However, it would be valuable to integrate evidence from rodent models describing how previous pregnancies influence brain plasticity at middle age to provide a more comprehensive perspective.\nThe reported 80% classification accuracy between PRG1 and PRG2 is promising. However, given the relatively small sample size, there is a risk of overfitting. Please clarify whether any cross-validation procedures were used and discuss this limitation more explicitly. Additionally, the manuscript would benefit from a more detailed discussion of potential confounding variables in the context of pregnancy effects, including interpregnancy interval, age of first pregnancy, breastfeeding, and other relevant factors.", "reviewer_3": "Thank you for inviting me to review the manuscript entitled “The effects of a second pregnancy on women’s brain structure and function” by Straathof and colleagues. This study examines a remarkably rare and valuable cohort, and the questions it addresses are of clear significance to the field. Much of the prospective work to date—most notably Dr. Hoekzema’s groundbreaking studies—has focused on women experiencing their first pregnancy. While these investigations have been foundational, they overlook the fact that most women will experience multiple pregnancies in their lifetime. What happens to the brain in subsequent pregnancies? This study begins to address that critical gap.\nHowever, I have several conceptual and methodological concerns that I would like to see addressed and clarified before I can recommend publication. I have outlined both major and minor considerations below.\n1. My first concern involves the consistency in reporting throughout the Results section, as well as the conceptual meaning behind these findings.\na. The authors find no significant differences when comparing vertex-wise GMV volume between the PRG1 and PRG2 groups, but visual inspection triggered a follow-up analysis (classification of GMV changes) that showed different takeaways. Can the authors speak to how we should reconcile this: are there true differences between PRG1 and PRG2, and/or is it based on the methodological approach?\nb. It’s not always clear when the authors are looking at POST1 in both or just the PRG2 group, and why those changes are not incorporated in all analyses. For example, it looks like POST1 sessions were explored with respect to GMV trajectories, but not incorporated into the classifier?\n2. My second concern involves the nature of the resting-state data. The authors are using incredibly underpowered RS data - 137 images per session at a TR > 2 seconds – to conduct functional brain network analyses (cf. Birn NeuroImage, 2013). I believe they are too underpowered to do this reliably and this section should be preferably 1) removed entirely or 2) moved to supplement & addressed as a significant limitation. If the latter:\na. Can the authors also provide a breakdown of head motion (e.g., FWD) for the different groups? Given the nature of the postpartum sessions in PRG1 and PRG2 (e.g., fatigue, comfort), it’s important to be extra cautious.\nb. Can the authors speak to why they did not use the Schaefer / Yeo atlas utilized in the prior GMV analyses for their functional connectivity analyses? This seems like it would provide the clearest structure / functional comparison.\nc. I’m not sure I follow the framing of the ROI analysis. The authors use a previously defined single ROI that corresponds to the DMN to further explore functional differences. However, they report this finding as an “increase in network coherence”. Isn’t this a comparison of beta activity within a single region – as they report a single MNI coordinate? If the authors are indeed looking at connectivity of this ROI in relation to other regions of the DMN, this should be clarified. Otherwise, this reporting doesn’t seem accurate and their use of “DMN mask” doesn’t fit the traditional nomenclature of the field when referring to networks.\ni. Additionally, the choice to use this ROI from an earlier paper instead of one derived from GMV analyses in the above section is curious. Why not chose an area that showed greatest (or least) amount of overlap between PRG1 and PRG2? Why only focus on DMN, if regions functional belonging to the DAN stood out in the PRG2 – CTR comparisons?\nd. Finally, the authors appear to switch between FWE (within-network) and FDR- correction (between-network) in their fMRI analyses. Can they speak to this?\n3. Third, I believe there is one more covariate that needs to be incorporated in these analyses for the PRG2 group: time since last pregnancy. Throughout the manuscript, the authors mention a partial (not full) recovery of volume in postpartum session (and the literature suggests this is relatively long lasting too). For example, they note “brain volumes slightly increased in the late postpartum period but had not returned to pre-pregnancy levels in both multiparous and primiparous women”. It would be important to both describe and incorporate time since last pregnancy for the PRG2 group. Further analyses exploring GMV change within the PRG2 group alone based on time since last pregnancy could be of value.\n4. Finally, my last comment relates to data quality. The authors report that each T1w image was visually checked for QC. However, given the nature of the population studied (e.g., postpartum women who may be experiencing significant fatigue), quantitative measures of T1w quality are important (e.g., Freesurfer’s Euler number). Can the authors provide this? In the same vein, what was the difference in prospective data collection time between the PRG1/Control groups and the PRG2 groups – was this done at the same time?\nMinor:\n1. A few of the supplementary tables/figures could be clarified further. In Fig S3, what does the dashed line represent? It reads as if the PRG1 population then became the PRG2 group. Additionally, for Table S9, can the authors clarify the comparison shown here? It’d be helpful if these were color coded for strength / significance.\n2. Regarding diffusion methods, can the authors clarify how the tensors were fit?\n3. I believe it would be helpful for the authors to report and show % change in GMV for PRG2 just like they did in their 2022 paper. This is perhaps the most straightforward way to showcase the data and easiest for audiences to comprehend (and to benchmark against PRG1 change)."}, "author_response": "We thank the reviewer for his/her time and efforts to critically evaluate our paper. We appreciate the constructive comments and suggestions that helped us to improve our manuscript. We have responded to the issues/questions raised below, and addressed the specific comments raised by the reviewer as annotations to the manuscript.\nFirst, one weakness is the lack of measures of parent-child bonding or any other individual differences measures (maternal mental health, parenting stress, amount of time spent with baby, etc). Without these measures, it is unclear whether differences between the first and second pregnancy are driven by hormones or other factors, such as parenting experience and feelings about the baby. If mothers are more bonded to their first child, do they show more or less neural change with the second pregnancy? If they feel stronger antenatal attachment during pregnancy, or stronger postnatal attachment after birth, does that explain larger magnitude of brain change? Even if there are no significant associations between self-report measures and brain changes, it would still be valuable to report null results because it would aid interpretation.\nWe thank the reviewer for this suggestion and agree that it is of interest to include these measures to the manuscript. We acquired questionnaire data related to maternal behaviour and mental health status during the third trimester of pregnancy and in the early postpartum period. We have now performed correlation analyses in Freesurfer using the volumetric changes across a first and second pregnancy (in comparison to CTR) with several different measures of maternal behaviour during pregnancy (Maternal Antenatal Attachment Scale (MAAS), Prenatal Attachment Inventory (PAI) and the Nesting Behaviour questionnaire) and in the postpartum period (Maternal Postnatal Attachment Scale (MPAS) and Postpartum Bonding Questionnaire (PBQ)). Additionally we performed similar correlation analyses with the Edinburgh Postpartum Depression Score and the K10 for psychological stress, both measured in the third trimester and the early postpartum period. When comparing these measures between the groups, we did not find any significant differences in maternal behaviour or mental health between the PRG1 and PRG2 groups, although we found a trend towards higher EPDS scores in the PRG2 group. Correlation analyses showed negative correlations between the volumetric change and maternal behaviour across both first and second pregnancies, except for the PBQ that showed a positive correlation. These negative correlations indicate that the more volume loss across pregnancy, the more maternal behaviour, whereas the positive correlation for the PBQ is caused by reverse scoring (lower PBQ scores represent higher maternal behaviour). The correlations are more widespread across a first pregnancy compared to a second pregnancy, especially for the PAI, MPAS and PBQ. For maternal health status, we found more widespread correlations between volumetric change and depression and psychological distress in the PRG2 group during pregnancy, but in the PRG1 group in the early postpartum period.\nWe added these analyses to the results section of the manuscript in lines 142 – 167, Supplementary Fig. 6 and 7 and Supplementary Tables 3, 4 and 5. We also discussed these findings in lines 319 – 334 and lines 490 – 495, and added the information about the scales, questionnaires and analyses in the methods section in lines 725 – 754.\nWas birth spacing (the length of time between the first and second pregnancies) considered in analysis? This would be interesting to measure as a continuous variable to see if brain change is greater in second-time mothers when there is more or less time elapsed after their first birth.\nWe thank the reviewer for this valuable suggestion. We performed a correlation analyses between the age of the first child at the PRE session, and the extracted volumetric brain change across a second pregnancy in the PRG2 group. We did not find a significant correlation for either the left or right hemisphere, suggesting that there is no difference in the amount of brain change across a second pregnancy when more or less time has elapsed since the birth of their first child.\nWe added these results to the results section in lines 133 – 136 and they are visualized in Supplementary Fig. 4.\nThe paper would also benefit for more clear explanation and interpretation of what the results mean. For example, the white matter findings are interesting but not explained clearly for readers who are not familiar with metrics such as fractional anisotropy and mean diffusivity. Do the results suggest that white matter is increasing or decreasing in volume or integrity? What does this mean in terms of brain health? Do these changes appear adaptive? In terms of the resting state findings, what are the implications of default mode network coherence? Is DMN coherence associated with stress, mental health, or empathy? Drawing on citations from the human neuroscience literature beyond maternal brain studies would help to contextualize these findings.\nWe agree with the reviewer that the manuscript benefits from adding more interpretation and explanation about our findings in the diffusion-weighted and resting-state data. We found a decrease in fractional anisotropy (FA) across a first pregnancy in the temporal part of the left longitudinal superior fasciculus. Although a matter of debate, lower FA values are suggested to reflect reduced integrity of white matter tracts, as the diffusion of water in the white matter is less directional with a lower FA. Previous research has linked lower FA values in the superior longitudinal fasciculus to cognitive dysfunction, for example in individuals with Schizophrenia (Peng 2020). In comparison, we found a decrease in mean diffusivity (MD) in the corticospinal tract across a second pregnancy. A decrease in MD indicates less diffusion of water molecules in all directions of the tissue, potentially reflecting an increase in white matter integrity, and has been associated with learning processes (Zatorre 2012, Tavor 2020). Additionally, as aging is associated with decreasing mean diffusivity in the brain (Bennett 2009), the effects of a second pregnancy on the MD of the corticospinal tract might oppose aging effects. Similarly, an increase in DMN network coherence as we measured across a first pregnancy is opposing the effect of aging on the brain, which is characterized by a decrease in connectivity within the DMN (Bagarinao 2019). This decrease in DMN connectivity with aging has been associated with changes in cognitive functioning (Damoiseux 2008). On the other hand, psychiatric disorders have shared and specific phenotypes of within-network connectivity in the DMN, characterized by both increases and decreases across various diagnosis (Doucet 2020), showing that the implications of changed DMN connectivity are not yet completely understood.\nWe added this information in the discussion of the manuscript in lines 304 – 310 (DMN coherence), lines 339 – 345 (reduced FA) and lines 360 – 367 (reduced MD).\nI would also like to see more interpretation of some of the apparent contradictions in these findings. For example, the authors suggest that some brain changes (in the default mode, for example) are of greatest magnitude across the first pregnancy and then are fine-tuned more subtly during the second pregnancy. But if this is the case, and the brain has already done much of its restructuring in the first pregnancy, wouldn’t we see baseline brain differences between first and second time mothers? Instead, it seems like everyone is starting from the same place. How do we reconcile that with the finding that the most profound brain changes seem to occur during the first pregnancy? If the brain reverts back to baseline, then presumably it would need to change in the same way to facilitate the next pregnancy. In a perfect world we could see a longitudinal study that follows the same mothers across a first and then a second pregnancy, but of course that study would be extremely challenging to conduct.\nWe agree that, based on our previous structural findings, we would expect to find significant baseline differences between the women undergoing a first and a second pregnancy in the areas that underwent volume reductions across a first pregnancy.\nWhile we indeed see that the volumetric means are lower in PRG2 women in brain areas that undergo reductions across a first pregnancy, these are not statistically significantly different. This lack of observed differences in grey matter structure are likely related to the reduced power and sensitivity of cross-sectional analyses, especially because the variation in grey matter volumes at the PRE timepoint is very large. It should also be noted that the postpartum recovery process is highly dynamic, with volumetric increases shown across different time points postpartum, but also volumetric decreases from one to two years postpartum. The large range of postpartum time since the first pregnancy in our second-pregnancy group may have masked baseline differences between the PRG2 and other groups in our current study.\nIn lines 378 – 399, we discuss the lack of baseline effects and possible reasons underlying this finding. We have also added the suggestion of the reviewer to the text regarding future studies examining brain changes across a first and second pregnancy within the same women, which could provide a deeper insight into the recovery of brain changes in between subsequent pregnancies and the shared and distinct neural effects of successive pregnancies.\nWe thank the reviewer for his/her time and efforts to critically evaluate our paper. We appreciate the constructive comments and suggestions that helped us to improve our manuscript. We have responded to the issues/questions raised below, and responded within the manuscript to the specific comments raised by the reviewer as annotations to the manuscript.\nThe introduction is strong and clearly written. However, it is somewhat vague in its description of the effects of multiple pregnancies in both animal and human studies. I recognize there are limited papers examining the effects of previous pregnancies on brain health, but the authors should consider citing additional recent work and outlining the more specific differences between differing levels of parity. In addition, several human studies have assessed parity as a linear/continuous variable, offering relevant insight into how additional pregnancies may shape brain health.\nWe thank the reviewer for this valuable comment and added some more literature regarding the long-term effects of pregnancy on the brain in rodents (lines 54 – 56) and also added more literature on how parity may influence brain health and cognition, and the risk for and effects of other brain disorders in human studies in lines 62 – 69.\nPlease provide a clearer justification for why the specific neural metabolites were selected for analyses. This would help contextualize the results and clarify their relevance to the broader literature on pregnancy-related brain changes.\nWe thank the reviewer for pointing this out. We estimated metabolite concentrations using LCModel, using a dataset containing in total seventeen metabolites. Of these metabolites, we considered just the reported metabolites (tNAA, tCr, tCho, Glu and Ins), as these metabolites (or combinations thereof) can typically be measured with high precision, indicated by their Cramer-Rao lower bounds well below 10%.\nThis information is also described in lines 703 – 704.\nMany studies that have examined the effects of parity on brain health have focused on short-term outcomes. It would strengthen the discussion to explicitly distinguish between short- and long-term effects of pregnancy on the brain. The authors briefly mention longer-term signatures of pregnancy on brain plasticity in humans. However, it would be valuable to integrate evidence from rodent models describing how previous pregnancies influence brain plasticity at middle age to provide a more comprehensive perspective.\nWe thank the reviewer for pointing this out. We agree that adding information about long-term effects of parity in rodent studies helps to interpret our findings. Therefore, we added this information in the discussion of the manuscript in lines 406 – 412.\nThe reported 80% classification accuracy between PRG1 and PRG2 is promising. However, given the relatively small sample size, there is a risk of overfitting. Please clarify whether any cross-validation procedures were used and discuss this limitation more explicitly.\nWe apologize for the unclarity. We have performed a leave one subject out cross-validation for the performed classification analyses, due to the relatively low sample size of our classes, making the training dataset for the classifier as large as possible. However, we do agree with the reviewer that the relatively small sample size also means a risk of overfitting, which we now discuss in the discussion in lines 442 – 451. We validated our cross-validation method by repeating our classification analyses using a k-fold cross validation with 10 folds, to reduce the risk of overfitting. Although the accuracy went down slightly as expected, the classifications between all combinations of groups were still significant. We also describe the results of this alternative way of cross-validation in our manuscript in lines 126 – 130 and describe the method in lines 592 – 596.\nAdditionally, the manuscript would benefit from a more detailed discussion of potential confounding variables in the context of pregnancy effects, including interpregnancy interval, age of first pregnancy, breastfeeding, and other relevant factors.\nWe included a correlation analyses between the volumetric change across a second pregnancy and the interpregnancy interval (age of first child at the PRE session in PRG2), and showed that the interpregnancy interval is not statistically significantly correlated to the volumetric changes across a second pregnancy. We added this analysis to the manuscript in lines 133 – 136, and the results are visualized in Supplementary Fig. 4.\nWe acknowledge that other potential confounding factors, such as age of first pregnancy, breastfeeding status, type of delivery, social support and sleep may have influenced our observed brain changes across a second pregnancy. Although our previous findings suggest that several of these factors such as sleep, stress, and breastfeeding do not strongly contribute to pregnancy-induced brain changes, these only represent a subset of potentially relevant variables and it is likely that such factors are associated with more subtle effects that only surface in larger samples. Therefore, studies involving larger samples that track these measurements are likely necessary to be able to acquire more insights into the influence of such factors on pregnancy-induced brain changes. In the revised manuscript, we now discuss the influence of potential confounding variables in lines 466 – 475.\nWe thank the reviewer for his/her time and efforts to critically evaluate our paper. We appreciate the constructive comments and suggestions that helped us to improve our manuscript. We have responded to the issues/questions raised below, and responded within the manuscript to the specific comments raised by the reviewer as annotations to the manuscript.\nHowever, I have several conceptual and methodological concerns that I would like to see addressed and clarified before I can recommend publication. I have outlined both major and minor considerations below.\n1. My first concern involves the consistency in reporting throughout the Results section, as well as the conceptual meaning behind these findings.\na. The authors find no significant differences when comparing vertex-wise GMV volume between the PRG1 and PRG2 groups, but visual inspection triggered a follow-up analysis (classification of GMV changes) that showed different takeaways. Can the authors speak to how we should reconcile this: are there true differences between PRG1 and PRG2, and/or is it based on the methodological approach?\nWe thank the reviewer for mentioning this critical point. We noticed that pregnancy, being either a first or a second, has very large effects on brain volumes, so that the more “subtle” differences, such as differences between a first and second pregnancy, are difficult to pick up. We calculated the effect sizes (as Cohen’s D) for the vertex-wise analyses of all three contrasts (CTR – PRG2, CTR-PRG1 and PRG1-PRG2), and found large to very large effect sizes for the comparisons between the control women and both pregnancy groups (Cohen’s D > 1.0), while we found moderate to large effect sizes for the comparison between PRG1 and PRG2 (Supplementary Table 1). In order to also allow for slightly less prominent effects to surface, we have now also applied a less stringent method to correct for multiple testing (permutation testing using a vertex-wise threshold of p < 0.01, 1000 permutations, and correcting for measuring across both hemispheres) for the PRG1-PRG2 comparison specifically, and found areas in the vertex-wise analyses that significantly changed differently over time across a first and second pregnancy (Supplementary Figure 2). Areas with larger changes across PRG1 (shown in blue in Supplementary Figure 2) were located in the default mode network, whereas areas with larger changes across a second pregnancy (shown in red) were mainly located in the dorsal and ventral attention network and sensorimotor network. This is in line with our subsequent analyses focussing on changes in the different networks. In addition, our classification approach also supports our observation of different brain changes in PRG1 and PRG2.\nWe added the results of these analyses in the manuscript in lines 105 – 115 and Supplementary Figure 2 and Supplementary Table 1. Additionally, we described the methods used in lines 574 – 579.\nb. It’s not always clear when the authors are looking at POST1 in both or just the PRG2 group, and why those changes are not incorporated in all analyses. For example, it looks like POST1 sessions were explored with respect to GMV trajectories, but not incorporated into the classifier?\nWe apologize for the confusion. We used the POST1 timepoint to study the longer-term effects of a second pregnancy. However, since we only had this POST1 timepoint in a small subset of women (n=14), we only included it in analyses where we found a significant pregnancy effect from PRE to POST in PRG2. Therefore, it is included in the vertex-wise volumetric analyses and the diffusion-weighted analyses. The results of the POST1 timepoint across a first pregnancy (PRG1) are discussed in our previously published manuscript (Hoekzema 2022). They were also not included in the classifier, as the sample size for the classification analyses was already relatively small, and this would have reduced it even more.\nWe added this clarification in the manuscript in lines 512 – 514.\n2. My second concern involves the nature of the resting-state data. The authors are using incredibly underpowered RS data - 137 images per session at a TR > 2 seconds – to conduct functional brain network analyses (cf. Birn NeuroImage, 2013). I believe they are too underpowered to do this reliably and this section should be preferably 1) removed entirely or 2) moved to supplement & addressed as a significant limitation. If the latter:\nWe thank the reviewer for this comment. We agree with the reviewer that our resting-state data is not of the highest possible quality. This approach was chosen due to strict time limitations and the difficult population to scan. It should also be noted that this was the first study to apply any form of fMRI measurements in such a cohort, and that this study started over 10 years ago when 5 minutes for resting state acquisitions were still quite standard. Nevertheless, we still believe the data is valuable as a comparison to our earlier published manuscript reporting on changes in resting-state functional connectivity across a first pregnancy, with the same MRI protocol.\nTherefore, we moved the results of the resting-state analyses to the supplement (Supplementary Fig. 8 and Supplementary Tables 13 – 15), and added information about the quality of the resting-state fMRI to the discussion in lines 452 – 459. We also respond to the raised concerns about the resting-state fMRI data analyses point by point below.\na. Can the authors also provide a breakdown of head motion (e.g., FWD) for the different groups? Given the nature of the postpartum sessions in PRG1 and PRG2 (e.g., fatigue, comfort), it’s important to be extra cautious.\nWe thank the reviewer for this important point. We determined the mean frame-wise displacement for the different groups (PRG1, PRG2 and CTRL) at the PRE and POST timepoint. We did not find any statistically significant group*session interaction effects, indicating that there is no difference in the head motion for the different groups at the different time points. We included this information in the manuscript in lines 620 – 623 and Supplementary Table 22.\nb. Can the authors speak to why they did not use the Schaefer / Yeo atlas utilized in the prior GMV analyses for their functional connectivity analyses? This seems like it would provide the clearest structure / functional comparison.\nWe used the Yeo atlas for our volumetric analyses as this atlas is based on surface-based clustering methods in Freesurfer (Yeo 2011), and hereby might best reflect the structural correlates of functional networks. They assume in their paper introducing the atlas that functional connectivity reflects stable properties of cortical architecture. Since we used an ICA analysis to identify functional networks in our resting-state fMRI data, we used the Smith 2009 atlas which is also based on an ICA approach defining networks by their spatiotemporal configuration and functional roles. Using this ICA-based atlas, we expected the most representative functional networks in our resting-state analyses. We added this in line 628 in the manuscript.\nc. I’m not sure I follow the framing of the ROI analysis. The authors use a previously defined single ROI that corresponds to the DMN to further explore functional differences. However, they report this finding as an “increase in network coherence”. Isn’t this a comparison of beta activity within a single region – as they report a single MNI coordinate? If the authors are indeed looking at connectivity of this ROI in relation to other regions of the DMN, this should be clarified. Otherwise, this reporting doesn’t seem accurate and their use of “DMN mask” doesn’t fit the traditional nomenclature of the field when referring to networks.\nWe apologize for the unclarity about the ROI analyses. To determine within-network connectivity, we determined the connectivity for each voxel in the network with all the other voxels inside the specific network, and averages this across the entire network. In line with this, instead of using all voxels in the DMN, we additionally performed an ROI analysis, using the DMN mask that showed a change in within-network coherence across a first pregnancy. We performed a within-network correlation analyses by determining the connectivity of each voxel inside this ROI, with all the other voxels within the DMN. We also clarified this in the manuscript in lines 630 – 631 and 643 – 645.\ni. Additionally, the choice to use this ROI from an earlier paper instead of one derived from GMV analyses in the above section is curious. Why not chose an area that showed greatest (or least) amount of overlap between PRG1 and PRG2? Why only focus on DMN, if regions functional belonging to the DAN stood out in the PRG2 – CTR comparisons?\nWe apologize for the confusion, but we did not solely focus on the DMN in our resting-state fMRI analyses. We identified the ten networks of Smith 2009 and determined the within-network connectivity within these networks, and the between-network connectivity between all combinations of these networks. These analyses did not render significant results (see Supplementary Tables 13-15). As a follow-up analysis, we used the DMN mask from our previous manuscript (Hoekzema 2022) that represented the area of change in DMN coherence across a first pregnancy, and calculated the within-network connectivity of the voxels inside this mask with the rest of the DMN, to identify whether the changes that occur across a first pregnancy also take place across a second pregnancy.\nd. Finally, the authors appear to switch between FWE (within-network) and FDR- correction (between-network) in their fMRI analyses. Can they speak to this?\nWe apologize for the confusion. This difference in the method to correct for multiple testing is due to limitations of the software packages used, and types of analyses performed. For within-network analyses, we used SPM12, and used a FWE-correction across all voxels. For the between-network analyses we used an FDR-correction in Rstudio as these included less tests and FWE-corrections are not standard in Rstudio.\n3. Third, I believe there is one more covariate that needs to be incorporated in these analyses for the PRG2 group: time since last pregnancy. Throughout the manuscript, the authors mention a partial (not full) recovery of volume in postpartum session (and the literature suggests this is relatively long lasting too). For example, they note “brain volumes slightly increased in the late postpartum period but had not returned to pre-pregnancy levels in both multiparous and primiparous women”. It would be important to both describe and incorporate time since last pregnancy for the PRG2 group. Further analyses exploring GMV change within the PRG2 group alone based on time since last pregnancy could be of value.\nWe thank the reviewer for this valuable suggestion. We performed a correlation analyses between the age of the first child at the PRE session, and the volumetric change across a second pregnancy in the PRG2 group. We did not find a significant correlation for either the left or right hemisphere, suggesting that there is no difference in the amount of brain change across a second pregnancy when more or less time has elapsed since the birth of the first child.\nWe added this analysis to the manuscript in lines 133 – 136, and the results are visualized in Supplementary Fig. 4.\n4. Finally, my last comment relates to data quality. The authors report that each T1w image was visually checked for QC. However, given the nature of the population studied (e.g., postpartum women who may be experiencing significant fatigue), quantitative measures of T1w quality are important (e.g., Freesurfer’s Euler number). Can the authors provide this? In the same vein, what was the difference in prospective data collection time between the PRG1/Control groups and the PRG2 groups – was this done at the same time?\nWe are grateful to the reviewer for this comment, and appreciate the suggestion to include Freesurfer’s Euler number as a measure of data quality of our T1w images. We determined the Euler number for each individual T1w image, and calculated the average Euler number per group per time point, which we added in Supplementary Table 21. The mean Euler numbers are lower than Freesurfer’s own exemplar dataset BERT dataset as described in Chalavi et al., 2012. We did not find a statistically significant group (PRG1, PRG2, CTR) * session (PRE, POST, POST1) interaction effect, which is described in the manuscript in lines 536 – 540. Additionally, after processing in Freesurfer all T1w scans had an Euler number of 2, which represents an accurate reconstruction.\nAdditionally, the data for the different groups was collected at the same time, meaning that subjects of the different groups were scanned intertwined, which we also added to the manuscript in lines 519 – 520.\nMinor:\n1. A few of the supplementary tables/figures could be clarified further. In Fig S3, what does the dashed line represent? It reads as if the PRG1 population then became the PRG2 group. Additionally, for Table S9, can the authors clarify the comparison shown here? It’d be helpful if these were color coded for strength / significance.\nWe agree with the reviewer that the dashed line in Supplementary Fig. 3 (old version, now Supplementary Fig. 5) is confusing and reads as if the same population has been scanned across a first and second pregnancy. Therefore, we removed the dashed line.\nAdditionally, we clarified what is shown in Supplementary Table 9 and 10 (old version, now Supplementary Table 13 and 14). These tables show the results of the group*session interaction effect for between-network connectivity between each combination of our identified resting-state networks, with a comparison between PRG2 and CTR in Supplementary Table 9 (now Supplementary Table 13) and between PRG2 and PRG1 in Supplementary Table 10 (now Supplementary Table 14). We also color-coded the tables according to the F-values of the effect, with higher F-values represented by darker red colours.\n2. Regarding diffusion methods, can the authors clarify how the tensors were fit?\nWe apologize for the unclarity, we fitted the diffusion tensors using standard dwi2tensor in MRtriX3, and described the fitting of the tensors in more detail in the manuscript in lines 666 – 669.\n3. I believe it would be helpful for the authors to report and show % change in GMV for PRG2 just like they did in their 2022 paper. This is perhaps the most straightforward way to showcase the data and easiest for audiences to comprehend (and to benchmark against PRG1 change).\nWe thank the reviewer for this valuable comment, and we calculated the %change in GMV for the PRG2 group. The median percentage volume change across a second pregnancy (in regions that significantly differed in the PRG2 vs CTR contrast) was 2.8%, which we added in the manuscript in lines 95 – 96."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This is a revision of a paper that I reviewed earlier. The authors have been very responsive to detailed comments from all three reviewers, including me. They have added more detail to their results, added new correlational results linking brain change to maternal depression and attachment, and included more explanation and context. I was already favorably disposed to this paper and I believe that it now warrants publication in Nature Communications. I have no further requests or concerns.", "reviewer_2": "All my comments were sufficiently addressed in the revised manuscript.", "reviewer_3": "Thank you to the reviewers for providing a thorough and actionable response to our comments. I believe that they have adequately 1) addressed and 2) clarified the text. The new analyses, contextualizations, and additional information has made this manuscript ready for publication. I believe the scientific community, as well as the public, will be quite interested in these findings.\nI have two minor follow-ups:\n1. Can you also provide the % change for this PRG1 group in the Results section to make the comparisons between PRG1 and PRG2 clear and easy to digest?\n2. Was age accounted for in the classifier analyses?\nGreat job to the authors. These data collection efforts are incredibly difficult but so valuable. It'd be amazing to see these participants tracked longitudinally through midlife!"}, "author_response": "We would like to thank the reviewers for their time and their careful evaluation of our manuscript. We are very glad that we were able to address the raised concerns and believe it has allowed us to significantly improve the quality of our work. Below we respond to the minor follow-ups requested by reviewer 3. We have adjusted the manuscript to address these comments as well as all editorial requests outlined in the Author Checklist.\nReviewer #3 (Remarks to the Author):\nThank you to the reviewers for providing a thorough and actionable response to our comments. I believe that they have adequately 1) addressed and 2) clarified the text. The new analyses, contextualizations, and additional information has made this manuscript ready for publication. I believe the scientific community, as well as the public, will be quite interested in these findings.\nI have two minor follow-ups:\n1. Can you also provide the % change for this PRG1 group in the Results section to make the comparisons between PRG1 and PRG2 clear and easy to digest?\nWe have now also computed this for the first-time mothers and added this to the Results section of the revised manuscript (line 92 of the revised manuscript).\n2. Was age accounted for in the classifier analyses?\nThese analyses did not include age as a covariate, we had overlooked this, our apologies. We have now performed these analyses including age as a regressor (with both cross-validation approaches), which rendered highly similar results. These are now reported in lines 123-127 of the Results section of the revised manuscript.\nGreat job to the authors. These data collection efforts are incredibly difficult but so valuable. It'd be amazing to see these participants tracked longitudinally through midlife!\nWe thank the reviewer for these very kind words."}]

Decision letter