FILE - In this Friday, Nov. 11, 2011 file photo, a mother holds her newborn baby at a hospital in Corpus Christi, Texas. A study released Monday, Dec. 19, 2016 shows pregnancy affects not only a woman’s... (Associated Press) 
 
 FILE - In this Friday, Nov. 11, 2011 file photo, a mother holds her newborn baby at a hospital in Corpus Christi, Texas. A study released Monday, Dec. 19, 2016 shows pregnancy affects not only a woman’s... (Associated Press) 
 
 NEW YORK (AP) — Pregnancy affects not only a woman's body: It changes parts of her brain too, a new study says. 
 
 When researchers compared brain scans of women before and after pregnancy, they spotted some differences in 11 locations. They also found hints that the alterations help women prepare for motherhood. 
 
 For example, they might help a mother understand the needs of her infant, Elseline Hoekzema, a study author at Leiden University in the Netherlands, explained via email. 
 
 The women were also given memory tests, and they showed no signs of decline. 
 
 Hoekzema, a neuroscientist, began working on the study while at the Autonomous University of Barcelona in Spain. She and colleagues present the results in a paper released Monday by the journal Nature Neuroscience. 
 
 The study includes data on 25 Spanish women scanned before and after their first pregnancies, along with 20 women who didn't get pregnant during the study. The brain changes in the pregnancy group emerged from comparisons of those two groups. 
 
 The results were consistent: A computer program could tell which women had gotten pregnant just by looking at results of the MRI scans. 
 
 And the changes, first documented an average of 10 weeks after giving birth, were mostly still present two years after childbirth. That's based on follow-up with 11 study participants. 
 
 Further work showed they're a motherhood thing: No brain changes were seen in first-time fathers. 
 
 Based on prior research findings, the researchers think the brain changes happened during pregnancy rather than after childbirth. 
 
 What's going on? Hoekzema and colleagues think the differences result from sex hormones that flood the brain of a pregnant woman. In the 11 places, the MRI data indicate reductions in volume of the brain's gray matter, but it's not clear what that means. For example, it could reflect loss of brain cells or a pruning of the places where brain cells communicate, called synapses. 
 
 Losing some synapses is not necessarily a bad thing. It happens during a hormonal surge in adolescence, producing more specialized and efficient brain circuits. The researchers suspect that could be happening in the pregnant women. 
 
 Some study results hint that such upgrades may prepare a woman for motherhood. One analysis linked brain changes to how strongly a woman felt emotionally attached to her infant. And when women viewed pictures of their babies, several brain regions that reacted the most were ones that showed pregnancy-related change. 
 
 In addition, the affected brain areas overlapped with circuitry that's involved in figuring out what another person is thinking and feeling. That's a handy ability for a mother tending to an infant. 
 
 The idea of synapses being pruned in pregnancy makes a lot of sense, commented Bruce McEwen of Rockefeller University in New York, who studies hormonal effects on the brain but didn't participate in what he called a terrific study. 
 
 "The brain is being shaped all the time," he said, and "sex hormones are part of the whole orchestra of processes that change the brain structurally." 
 
 ___ 
 
 Follow Malcolm Ritter at http://twitter.com/malcolmritter His recent work can be found at http://bigstory.ap.org/content/malcolm-ritter ||||| Participants. 
 
 For this prospective cohort study, first-time mothers participated in an MRI acquisition before and after their pregnancy, allowing us to use each woman's pre-pregnancy brain scan as her individual baseline. Data were collected over a total period of 5 years and 4 months. The participants were recruited via the fertility center Instituto Valenciano de Infertilidad (IVI, Barcelona), by flyers and by word of mouth. We sought nulliparous individuals who were planning to try to become pregnant in the near future but were not pregnant yet and nulliparous individuals without such plans. Participants were therefore not randomly assigned to groups. Recruitment and data collection for all groups was initiated at the same time. Although individuals were recruited separately for the pregnancy (PRG) groups (women and men becoming parents between the sessions, hereafter referred to as F PRG and M PRG , respectively) and the control (CTR) groups (women who did not become pregnant within this time frame and men whose partners did not become pregnant, from here on referred to as F CTR and M CTR ) on the basis of their intention to become parents in the near future, the final group allocation depended on the transition from nulliparity into primiparity in between sessions. Women trying to become pregnant were scanned in the early follicular phase of their menstrual cycle or before the insemination or transfer in the fertility-treated group. Only participants who had never experienced a previous pregnancy beyond the first trimester were included in the study. Sixty-five nulliparous women and 56 men without children were scanned for the first time point, including 43 women and 37 of their male partners who wanted to become parents for the first time, aiming for a minimum of 16 participants51 in each group based on fertility statistics52. Pre-established exclusion criteria comprised neurological or psychiatric conditions or a history of substance use disorders as assessed by means of the MINI International Neuropsychiatric Interview53 applied by a clinical psychologist. The main criterion for continuing in the study for participants in the PRG group was achieving pregnancy in the period following the first MRI session. Of the final sample of 25 women who underwent pregnancy between the sessions, the majority (20 women) had an estimated pregnancy onset within 6 months after the session. Five participants became pregnant between 6 and 12 months after their participation in the first MRI session. To ensure that this longer period between the session and conception did not have a significant impact on the results, we also repeated our analysis excluding these 5 women, which rendered very similar results (Supplementary Table 15). Thirty-two participants, comprising 17 women and 15 men, did not achieve pregnancy within this period and did not participate in the follow-up session. Two women and 2 men who were initially recruited for the F PRG and M PRG groups participated as control subjects in the F CTR and M CTR groups when conception was not achieved. In addition, 2 women and 2 men who participated in the first session as control participants were scanned as participants of the F PRG and M PRG groups in the second MRI session following an unexpected pregnancy. In addition, 1 participant became claustrophobic inside the scanner, 4 did not return for the Post session and 3 participants had to be excluded due to poor image quality or neuropathological conditions encountered in the MRI scan. 
 
 Our final sample consisted of the following subject groups with complete Pre and Post data sets: 25 primiparous women, 20 nulliparous control women, 19 first-time fathers and 17 control men without children. Unless explicitly stated otherwise (in case of analyses including other measures only available for a subset of the participants), these represent the sample sizes used in the comparisons. There were no statistically significant differences in Pre-to-Post time interval, age or level of education between the PRG and CTR groups (mean ± s.d.: Pre–Post time interval: M PRG : 459.00 ± 117.46 d, M CTR : 419.17 ± 93.17 d; t = 1.12, P = 0.272. F PRG : 463.52 ± 108.33 d, F CTR : 413.05 ± 106.86 d. t = 1.56, P = 0.126. Age: M PRG : 35.21 ± 4.30 years, M CTR : 31.64 ± 6.41 years. t = 1.94, P = 0.063. F PRG : 33.36 ± 3.97 years, F CTR : 31.10 ± 5.63 years. t = 1.58, P = 0.123. Education: number of participants finishing secondary school/college/university or above: M PRG : 2/4/13, M CTR : 1/3/13, χ2 0.37, P = 0.833. F PRG : 2/4/19, F CTR : 2/3/15, χ2 = 0.06, P = 0.971), but as there was a trend for an age difference in the male groups, we also repeated our model including age as a covariate (Supplementary Tables 16 and 17), which had very little impact on the results. In addition, correlation analyses were performed to further examine potential associations of age and Pre-to-Post time interval with GM volume changes within the observed areas affected by pregnancy (using an explicit mask of the main contrast). These analyses rendered only a trend for stronger volume reductions in the right superior temporal sulcus cluster in the younger women (P = 0.095, FWE-corrected). 
 
 The Post session took place on average at 73.56 ± 47.83 d (mean ± s.d.) after parturition. A model including the time interval between the birth and the Post scan as a covariate rendered results that were highly similar to the main results (Supplementary Table 14). In addition, to further examine the effects of the time between parturition and the Post scan on the GM changes within these regions, we performed correlation analyses with this time interval using the main contrast as an explicit mask. These analyses rendered no significant results (for either a linear, quadratic or cubic positive or negative correlation). 
 
 Nine women achieved pregnancy by natural conception and 16 women by means of fertility treatment. The effect of a natural or assisted conception was further investigated by comparing these groups (Supplementary Table 4) and by separately examining the changes within these groups (Fig. 2, Supplementary Fig. 4 and Supplementary Table 5), revealing no significant impact of the natural versus assisted route to conception on the brain changes of pregnancy. Of the fertility-assisted group, 12 women underwent in vitro fertilization (IVF, 3 involving an egg donation and 5 involving intracytoplasmic sperm injection (ICSI), 4 without egg donation or ICSI), 3 intrauterine insemination (IUI), and 1 a frozen embryo transfer. Albeit negligible in comparison to the hormone surges of pregnancy itself, each of these procedures involves hormone treatment which took place after the Pre session (for IUI: gonadotropins (follicle-stimulating hormone, luteinizing hormone, chorionic gonadotropin, human menopausal gonadotropin) and progesterone; IVF and ICSI: the same plus a gonadotropin-releasing hormone analog; egg donation or embryo transfer: estrogens, progesterone, GnRH analog). To further examine the possible effects of treatment-related hormone therapy, we also repeated these analyses with a more homogeneous group of fertility-assisted women undergoing a procedure with the same approach in terms of hormone therapy (i.e., only women undergoing conventional IVF or IVF involving ICSI, 9 in total). Again, no significant differences were observed between this group and the women who were not exposed to fertility treatment-related hormones (the naturally conceiving group) and similar brain changes were observed in these subgroups (Supplementary Table 18). Future studies involving a larger sample of women undergoing fertility treatments are likely to uncover more subtle changes related to the hormone therapy associated with fertility treatments. 
 
 Ten of the women carried a boy, and 11 a girl. The remaining 4 had twins (2 mixed twins, 1 male twins, 1 female twins). Considering the previously observed effects of fetal sex on cognitive changes in pregnant women54, we additionally compared the women carrying a boy to the women carrying a girl (excluding the four women having twins). No differences in GM volume changes were observed between these groups. 
 
 One woman suffered from eclampsia during labor, 2 had premature deliveries and 2 women suffered from high-risk pregnancies with kidney complications or antiphospholipid syndrome. Leaving out the women with complications during pregnancy or delivery had very little effect on our results (Supplementary Table 19). Twenty of the experimental women gave birth to a singleton and four had twins. Eight of the women gave birth by cesarean section and 17 by vaginal birth. All women except 1 received epidural anesthesia during delivery. Sixteen women practiced exclusive breastfeeding (breast milk as their infant's sole source of nutrition), 2 practiced breastfeeding supplemented by formula feedings, 2 had started breastfeeding their infants but had stopped by the time of the Post scan, and 2 never started breastfeeding. Very similar results were obtained when including variables representing the type of conception, type of delivery, breastfeeding status and number of fetuses as covariates in the model (Supplementary Table 20), suggesting that these factors are not driving the observed neural changes. However, the current study was not designed to further investigate the possible impact of such factors, and future studies investigating these in more detail may reveal specific neural changes associated with these variables. 
 
 In the Post session, the Edinburgh Postnatal Depression Scale55 was administered to the primiparous women to detect symptoms of postpartum depression. One of the mothers showed symptoms of postpartum depression and was being helped by a specialist. Excluding this participant from our analyses did not significantly affect our results (Supplementary Table 21). 
 
 Blood samples were acquired at the sessions before and after pregnancy from a large portion of our participants. Unfortunately, for practical reasons, we could only obtain blood samples from 2 of the women during pregnancy itself. Therefore, we cannot use hormonal data to pinpoint the observed neural changes to specific endocrine changes of pregnancy. 
 
 For the Post + 2 years session, we asked the 25 primiparous women to come back for another MRI acquisition. Of these 25 women, 11 had not yet experienced a (partial) second pregnancy since the last MRI session and were willing and able to participate in this follow-up session (mean time since birth: mean ± s.d.: 2.32 ± 0.50 years, age at Pre scan: 33.72 ± 3.32 years). 
 
 The study was approved by the local ethics committee (Comitè Ètic d'Investigació Clinica de l'Institut Municipal d'Assistència Sanitària), and written informed consent was obtained from all subjects before their participation in the study. 
 
 Data acquisition. 
 
 MRI images were obtained in a Philips 3T scanner. High-resolution anatomical MRI brain scans were acquired using a T1-weighted gradient echo pulse sequence (TR = 8.2 ms, TE = 3.7 ms, NSA = 1, matrix = 256 × 256, FOV = 240 mm, 180 slices, thickness = 1 mm, no gap, FA 8°). Due to an unexpected technical problem, the radio frequency head coil was replaced for some time with another head coil, and 28 scans in total were acquired using the latter coil. There were no significant differences between the groups in the number of scan acquired with this head coil (χ2 = 4.21, P = 0.240). Nonetheless, to err on the side of caution, we also repeated the main analysis without these scans acquired with the temporary head coil, which rendered highly similar results (Supplementary Table 22). Furthermore, direct comparisons of the subjects acquired with the different head coils were performed, rendering no significant results. Finally, the head coil was introduced as a nuisance covariate in all neuroimaging analyses. In the Post + 2 years session, an MRI scan was acquired with both radio frequency head coils for those participants for whom a different coil was used in a previous acquisition, allowing us to match the comparisons on head coil type. Therefore, no covariate for the head coil was included for analyses involving the Post + 2 years session. 
 
 The Post MRI session also included an fMRI paradigm (T2*-weighted gradient echo EPI sequence. TR = 3,000 ms, TE = 35 ms, matrix = 128 × 128, FOV = 230 mm, 30 slices, thickness = 4 mm, gap 0.5 mm, FA 90°) that examined the new mothers' neural responses to their babies. During this MRI session, pictures of the women's own and other unknown babies were shown to the participants using Presentation software (NeuroBehavioral Systems). The images were extracted using Adobe Photoshop CS5 from short movies that were shot by one of the experimenters, or in some cases by the father, at a home visit a few days before the Post session. For the women who had twins, movies were acquired from both babies. The pictures represented cut-out faces on a black background and were matched for size, resolution, brightness and facial expression. For each participant, 28 images of their own baby (14 of each infant in case of twins) and 28 images of other babies were presented in randomized order in an event-related fashion (trial duration 1,500 ms, randomized inter-trial interval 750–1,250 ms), with an average number of trials of (mean ± s.d.) 72.15 ± 6.64 and 72.40 ± 6.99 for the other baby and own baby conditions respectively. Pictures involving sad facial expressions (crying) and neutral facial expressions were acquired from each infant. Additional explorations of the data based on facial expression are provided in Supplementary Table 23. Five participants could not be included in the fMRI analyses due to head motion exceeding 3 mm (for translations) or 3° (for rotations) (1 woman), artifacts in the data (2 women), or incomplete data sets (2 women), rendering a sample of 20 primiparous women for this part of the study (age at Pre session: 32.85 ± 4.13). 
 
 At the Pre and Post session, our participants were also asked to complete several supplementary cognitive tests and questionnaires (the Test de Aprendizaje Verbal España Complutense56, based on the California Verbal Learning test57, the Digits subtest of the Wechsler Adult Intelligence Scale III58, a two-back working memory test, the Interpersonal Reactivity Index59, and a simple reaction time task). Normality of these variables was assessed by Shapiro-Wilk tests, and nonparametric tests were applied as some did not follow a normal distribution. Homoscedasticity was confirmed using a nonparametric Levene's test. No significant changes across sessions were observed in any of these measures (Supplementary Table 10). For completeness, a correlation analysis was performed between the number of correct responses on the verbal word learning paradigm (Post-Pre scores) and the changes in GM volume in the women who underwent pregnancy between sessions. No suprathreshold voxels were observed, either with a whole-brain approach or with an explicit mask representing the areas of GM volume change across pregnancy. 
 
 The women were also asked to retrospectively fill in the Maternal Postnatal Attachment Scale (MPAS)24 for the first 6 months of being a mother. One of the mothers did not complete this measure, and hence these data are available for 24 of the primiparous women (age (at Pre scan): 33.42 ± 4.05). From this scale, the three scores of the MPAS were extracted (mean ± s.d., quality of attachment: 37.11 ± 3.99; absence of hostility: 16.93 ± 4.10; pleasure in interaction: 20.88 ± 3.10). 
 
 Longitudinal symmetric diffeomorphic modeling. 
 
 The anatomical MRI images were processed in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), implemented in Matlab 7.8 (MathWorks), using the longitudinal symmetric diffeomorphic modeling pipeline60. The images of each participant were first processed using the longitudinal registration tool provided within this framework, which incorporates rigid-body registration, intensity inhomogeneity correction and nonlinear diffeomorphic registration in an interleaved fashion. Considering the bias associated with asymmetry in pairwise registration, this approach registers both time points to a within-subject average image. These midpoint average images were segmented into tissue classes using the unified segmentation algorithm61. The jacobian determinants resulting from the longitudinal registration were subsequently multiplied by each subject's GM segment, creating maps of volumetric change in GM tissue. To bring these images into MNI space, the product images were normalized using DARTEL tools62 and smoothed with a 12-mm full-width half-maximum smoothing kernel63, 64, 65. The individual smoothed GM volume difference maps were entered into general linear models. 
 
 To quantify the overlap between our results and other functional maps such as the neural activity of the mothers in response to pictures of their infant (the 'own baby > baby' contrast in the fMRI task) and the theory-of-mind network as defined by the large-scale meta-analysis of Schurz et al.22, we computed the intersection between these maps and our map of GM volume changes across pregnancy. A further assessment of the localization of these GM changes of pregnancy with respect to functional networks was performed by quantifying the overlap between our map and the 12 functional networks of Yeo et al.23, who investigated the functional specialization of the cerebral cortex with a meta-analysis of 10,449 experimental contrasts and confirmed intrinsic network organization using a resting-state fMRI data set of 1,000 individuals. The overlap of our results with these functional maps was extracted by computing the intersections between each of these maps and the map of GM volume changes of pregnancy, and defining the fraction of the observed intersection relative to the expected volume of the intersection based on a random distribution across the gray matter of the brain (see Supplementary Table 6). 
 
 For completeness, although these MRI images are not optimal for investigating white matter, we also multiplied the individual jacobian difference maps with the white matter segments of the midpoint average images to obtain an indication of the changes in white matter signal across pregnancy. These maps were further processed and analyzed in the same manner as the images obtained by multiplying the jacobian maps with the gray matter segments. 
 
 A cross-sectional voxel-based morphometric approach was also applied to the baseline images to confirm the absence of pre-existing baseline differences between the PRG and CTR groups. This approach included a segmentation of the baseline images using the unified segmentation algorithm61, a DARTEL normalization of the GM segments62 and the application of a 12-mm full-width half-maximum smoothing kernel. A two-sample t-test was performed to test for the presence of baseline group differences. Plots depicting the signal values extracted from this approach are provided in Supplementary Figure 10. 
 
 The Post + 2 years images were processed using the same longitudinal approach described above, rendering volume difference maps between the Post + 2 years images and the two other sessions. To examine whether GM volumes within the regions affected by pregnancy underwent further changes across the first 2 years postpartum relative to the Pre and early Post sessions, we performed one-sample t-tests on the Pre – Post + 2 years and the Post – Post + 2 years difference maps, using an explicit mask of the Pre – Post changes across pregnancy. 
 
 Regarding the main comparisons, each of the primiparous groups was first compared to their nulliparous control group in the framework of the general linear model. Maps of GM volume change were compared using two-sample t-tests. If a significant group difference was found, we then proceeded to separately examine the increases and decreases in GM volume across time within the relevant groups by means of one-sample t-tests to determine which changes were driving this group difference. 
 
 The statistical maps were constructed by applying a stringent voxel-level Gaussian random field theory–based threshold of P < 0.05, FWE-corrected across the whole brain. A minimum cluster size of 10 contiguous voxels was imposed to discard very small clusters and restrict table sizes. 
 
 Multivariate analyses. 
 
 In addition to the above-described mass-univariate analyses, we also performed multivariate pattern recognition analyses using the analysis pipeline provided by PRoNTo 2.0 (http://www.mlnl.cs.ucl.ac.uk/pronto/)66 implemented in Matlab. This pipeline can be used to automatically search for regularities in the data and train a classifier function that models the relation between spatial signal patterns and experimental factors on the basis of a training data set66. This classifier can then be used to predict the group a new image belongs to using the spatial distribution of the signal within the image and to compute the accuracy with which groups can be discriminated from one another on the basis of whole-brain spatial signal patterns. 
 
 To examine the degree to which the experimental women could be discriminated from the control women on the basis of the distribution of GM volume changes across the brain, we applied a linear support vector machine classification to the Post – Pre difference maps. A sample-specific GM mask was created using the SPM Masking Toolbox (http://www0.cs.ucl.ac.uk/staff/g.ridgway/masking/) to serve as the mask image. To evaluate model performance, we applied a leave-one-out cross-validation scheme. Using this cross-validation strategy, the classifier's accuracy is computed by leaving one subject out at a time and predicting this subject's group label on the basis of a training set including all remaining subjects. This procedure is then repeated for each subject, and the accuracy of the discriminant function is computed using all these runs. Permutation testing was used to estimate the null distribution and examine the statistical significance of the classification accuracy (N permutations = 10,000; P < 0.05)67. 
 
 In addition, to further examine the regional contribution to the decision function and determine the areas with greatest relative prediction power, we built multiple kernels based on the regions of the Automated Anatomical Labeling Atlas (http://www.gin.cnrs.fr/AAL-217), using an L1 Multiple Kernel Learning algorithm as implemented in PRoNTo68 with the same cross-validation scheme and significance testing. 
 
 Furthermore, to investigate whether the GM volume changes across pregnancy could significantly predict measures of maternal attachment, we performed kernel ridge regression analyses using the three dimensions of the MPAS24. Kernel ridge regression represents a form of support vector regression using a squared-error loss function combined with l2 regularization (see ref. 69 for a description of this approach). Using kernel ridge regression, MPAS scores were predicted from the changes in GM volume, and the correlation between true and predicted MPAS values was subsequently examined. A leave-one-out cross-validation was applied; that is, in every fold a participant was left out for whom the MPAS score was predicted and examined in relation to the actual MPAS score. As in the classification models, we used a leave-one-out cross-validation scheme and permutation testing (N permutations = 10,000; P < 0.05). 
 
 Since the current version of PRoNTo does not yet allow the inclusion of covariates and we could therefore not include the radio frequency coil covariate in the models, the residuals were written in SPM12 and the multivariate classification and regression analyses were repeated on these images, rendering very similar results (SVM classification on residuals: balanced accuracy = 100%, P < 0.0001; kernel ridge regression with MPAS scores on residuals: quality of attachment: R = 0.38, P = 0.043, normalized mean squared error (nMSE) = 0.90, p nMSE = 0.034; absence of hostility: R = 0.42, P = 0.031, nMSE = 0.87, p nMSE = 0.023). 
 
 Additional measures based on previous related results: manual regions of interest of the pituitary gland and total tissue volumes. 
 
 As a supplementary analysis, we wanted to further explore our data on the basis of previous findings related to structural brain changes in human pregnancy. Therefore, we also examined total tissue volumes and pituitary gland volume in our sample. 
 
 Total brain volumes were extracted in SPM12. To obtain measures of pituitary gland volume, this structure was manually delineated by two raters who were blind to any identifying subject or group information on coronal slices of the Pre, Post and Post + 2 years sessions of the primiparous women using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/), according to the delineation criteria described in MacMaster et al.70. Inter-rater reliability was determined on the basis of ten repeated ROI delineations (intraclass correlation coefficient: 0.935). 
 
 For 5 MRI scans, the pituitary gland could not reliably be delineated due to local inhomogeneity or contrast issues, and these scans were therefore excluded, rendering a total of 23 Pre, 23 Post and 10 Post + 2 years volumes. As the missing volumes did not correspond to the same individuals across sessions, that left 22 Pre-Post pairs, 9 Pre–Post + 2 years pairs and 10 Post–Post + 2 years pairs for the longitudinal comparisons. 
 
 These measures were analyzed in SPSS 23 (IBM). A normal distribution of the data and equal variances were confirmed using Shapiro-Wilk and Levene's tests, respectively. The results are described in Supplementary Figure 3 and Supplementary Tables 2 and 3. 
 
 Surface-based analyses. 
 
 To examine changes in surface area and cortical thickness, surface-based morphometry was conducted in FreeSurfer 5.3 (http://surfer.nmr.mgh.harvard.edu/). The images were reprocessed from raw data for this approach. To investigate changes in these surface-based measures across sessions, the images were processed with the longitudinal stream implemented in FreeSurfer71, 72. The longitudinal preprocessing pipeline involves an initial cross-sectional processing of the images of each of the time points, which includes motion correction, removal of nonbrain tissue, transformation into stereotaxic (MNI) space, intensity correction, volumetric segmentation and cortical surface reconstruction73, 74, and parcellation75. The extraction of the brain for surface-based processing was based on the segmentation algorithm implemented in SPM8. All further steps were performed in FreeSurfer 5.3. Individual surfaces were inspected for accuracy, and minor manual edits were performed where needed, usually involving the removal of sections of nonbrain tissue. The next step in the longitudinal stream was the creation of a probabilistic individual base template based on the cross-sectional images for each participant, which is unbiased with respect to any of the time points. Subsequent processing of each time point was then initialized using the processed results from the unbiased template71, 72. Surface maps were resampled, mapped to a common surface, and smoothed using a full-width at half-maximum kernel of 15 mm. A cross-sectional approach was also applied on the baseline images to confirm the absence of pre-existing baseline differences between the PRG and CTR groups. 
 
 Longitudinal change in cortical surface area and thickness in each hemisphere was calculated as symmetrized percent change (i.e., the rate of change between the time points with respect to the average thickness or area across the time points), and examined using one-sample t-tests. Cluster statistics were obtained using Monte Carlo simulations with a vertex-wise −log 10 (P) of 4 (corresponding to P < 0.0001) and a cluster-wise threshold of P < 0.05. 
 
 Discriminant analyses with leave-one-out cross-validation were performed in SPSS 23 (IBM) using the changes in average cortical thickness and surface area values across the regions of GM volume change to examine the predictive value of these surface-based measures for group classification. 
 
 Functional MRI analyses. 
 
 Analyses of the functional MRI data were performed in SPM12. The functional images were first corrected for differences in slice acquisition timing and realigned to the first volume. Subjects with head motion exceeding 3 mm (for translations) or 3° (for rotations) were excluded from the analyses (1 woman). Then, the anatomical images were co-registered to the mean functional image and normalized into MNI (ICBM) space using nonlinear registration61. Finally, the normalization parameters and a full-width at half-maximum smoothing kernel of 12 mm were applied to the functional images. 
 
 At the first level of analysis, general linear models were used to model voxel-wise changes in BOLD response for the conditions of interest, also including the movement parameters extracted during the realignment and regressors based on temporal basis functions. The first-level parameter estimates for the linear contrast 'own baby pictures > other baby pictures' were entered into a second-level model and one-sample t-tests were performed to examine whether new mothers show a differential pattern of neural activity in response to pictures of their own or other babies. For completeness, the reverse contrast ('other baby pictures > own baby pictures') was also examined. Additional explorations of the data based on facial expression are provided in Supplementary Table 23. In addition to the whole-brain FWE-corrected threshold, the fMRI results were also investigated and are reported at an uncorrected threshold of P < 0.0001 and an extent threshold of 10 voxels to allow a further inspection of the similarity of the regions of strongest neural responsiveness to the women's babies to the pattern of GM volume changes across pregnancy. 
 
 To create images, the statistical maps were projected onto the PALS surface provided in Caret software (http://brainvis.wustl.edu/wiki/index.php/Caret:Download). Slice overlays were created using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/). 
 
 Data availability. 
 
 Source files for the figures are provided in FigShare (http://dx.doi.org/10.6084/m9.figshare.4216809).
– Pregnancy affects not only a woman's body: It changes parts of her brain, too, a new study published in Nature Neuroscience says. The study includes data on 25 Spanish women scanned before and after their first pregnancies, along with 20 women who didn't get pregnant during the study. The brain changes in the pregnancy group emerged from comparisons of those two groups. The results were consistent: A computer program could tell which women had gotten pregnant just by looking at results of the MRI scans. And the changes, first documented an average of 10 weeks after giving birth, were mostly still present two years after childbirth. (Based on prior research findings, the researchers think the brain changes happened during pregnancy). No brain changes were seen in first-time fathers, reports the AP. What's going on? Elseline Hoekzema, a study author at Leiden University in the Netherlands, and colleagues think the differences result from sex hormones that flood the brain of a pregnant woman. In 11 places in the brain, the MRI data indicate reductions in volume of the brain's gray matter, but it's not clear what that means. For example, it could reflect a pruning of the places where brain cells communicate, called synapses. Losing some synapses is not necessarily a bad thing. It happens during a hormonal surge in adolescence, producing more specialized and efficient brain circuits. The researchers suspect that could be happening in the pregnant women, perhaps to prepare a woman for motherhood. One analysis linked brain changes to how strongly a woman felt emotionally attached to her infant.