Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring

Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring Elise Turk, Marion I. van den Heuvel, Charlotte Sleurs, Thibo Billiet, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1897872/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jun, 2023 Read the published version in Brain Imaging and Behavior → Version 1 posted 8 You are reading this latest preprint version Abstract Background. The connectome, constituting a unique fingerprint of a person’s brain, may be influenced by its prenatal environment, potentially affecting later-life resilience and mental health. Methods. We conducted a prospective resting-state functional Magnetic Resonance Imaging study in 28-year-old offspring (N=49) of mothers whose anxiety was monitored during pregnancy. Two offspring anxiety subgroups were defined: “High anxiety” (N=13) group versus “low-to-medium anxiety” (N=36) group, based on maternal self-reported state anxiety at 12-22 weeks of gestation. To predict resting-state functional connectivity, maternal state anxiety during pregnancy was included as a predictor in general linear models for both ROI-to-ROI and graph theoretical metrics. Sex, birth weight and postnatal anxiety were included as covariates. Results. Higher maternal anxiety was associated with weaker functional connectivity of medial prefrontal cortex with left inferior frontal gyrus and left lateral prefontal cortex with left somatosensory motor gyrus in the offspring. While our results showed a general pattern of lower functional connectivity in adults prenatally exposed to maternal anxiety, we did not observe significant differences in global brain networks between groups. Conclusions. Weaker (medial) prefrontal cortex functional connectivity in the high anxiety adult offspring group suggests a long-term negative impact of prenatal exposure to high maternal anxiety, extending into adulthood. To prevent mental health problems at population level, universal primary prevention strategies should aim at lowering maternal anxiety during pregnancy. Fetal programming resting-state functional connectivity prospective study Medial Prefrontal Cortex prenatal stress Figures Figure 1 Figure 2 Figure 3 Introduction Worldwide the societal burden of mental health problems is increasing (Vigo et al., 2016 ). Although early stage prevention is more cost-efficient than treatment (Bauer et al., 2016 ), prenatal origins of mental health problems that are often preventable remain understudied (Browne et al., 2020 ; Glover, 2011 ; Monk et al., 2019 ; van den Heuvel, 2022 ). In a UK-based study, it has been estimated that perinatal anxiety and depression combined costs the society about £8500 per woman giving birth. This results in a striking £6.6 billion in total costs (for both mother and child) for the United Kingdom alone (Bauer et al., 2016 ). The majority of these costs were associated with adverse effects of maternal perinatal depression on the children, emphasizing the need for more research on the underlying mechanisms of adverse consequences of maternal psychological distress during pregnancy, especially on the long-term. More than a decade of brain imaging research has shown that maternal psychological distress during pregnancy, including depression and anxiety, affects the developing fetal brain, with later life consequences for offspring’s cognition and mental health (for a review, see Adamson et al., 2018 ; Van den Bergh et al., 2018 ). Recent studies found evidence for changes in offspring structural grey matter (e.g., Acosta et al., 2019 , 2020 ; Donnici et al., 2021 ; Moog et al., 2021 ) and white matter (e.g., Demers et al., 2021 ; Rifkin-Graboi et al., 2015 ) as well as functional brain changes, including resting-state functional connectivity using fMRI (e.g., Humphreys et al., 2020 ; Scheinost et al., 2020 ) and task-based fMRI studies (e.g., Mennes et al., 2020 ; van der Knaap et al., 2018 ). Several pioneering studies have even started to show that the timing of these brain alterations is prenatally, by studying the offspring in utero with fetal resting-state fMRI (De Asis-Cruz et al., 2020 ; Thomason et al., 2021 ; van den Heuvel et al., 2021 ). Such neural alterations potentially underlie the observed behavioral problems and mental health issues of prenatally exposed offspring (Monk et al., 2019 ; Van den Bergh et al., 2018 ). While studies on developmental origins of infant and child brain development are still increasing, only very few studies examined the lasting effect of prenatal exposure to maternal distress into puberty or adulthood. Consequently, we lack knowledge about the persistence of brain developmental alterations in the aftermath of prenatal exposure to maternal distress. Prospective pregnancy cohorts that continue into adulthood may add incredibly valuable information, especially since several researchers have pointed out that neurodegenerative disorders, such as Parkinson’s and Altzheimer’s Disease, may find their origin in fetal life (Faa et al., 2014 ). The limited number of studies that do exist clearly show persistent brain alteration into adulthood, such as presumed accelerated brain aging in young adults prenatally exposed to maternal depression (Mareckova et al., 2020 ), and a deficit in endogenous cognitive control in 20-year-old males as measured with task-based fMRI (Mennes et al., 2020 ). Still, more prospective research with longer follow-up periods are necessary. Additionally, an important gap in neuroimaging research to date is the focus on predetermined brain areas. Most research has focused on structural changes of the amygdala and hippocampus or rsFC of the limbic and/or (pre)frontal region (Scheinost et al., 2017 ). Even though several studies have found important results in changing brain structure and function of these brain regions (Acosta et al., 2019 , 2020 ; Donnici et al., 2021 ; Humphreys et al., 2020 ; Jones et al., 2019 ; Scheinost et al., 2016 ; Scheinost et al., 2020 ; van der Knaap et al., 2018 ), this targeted approach may miss important changes to global brain function and network properties of the prenatally exposed brain (Scheinost et al., 2017 ). Exploration of the adult whole brain network prenatally exposed to maternal distress, with appropriate control for multiple testing, has not been conducted to date. In the current study, we utilize a unique prospective prenatal cohort with a postnatal follow-up of 28-years to study the long-term effects of prenatal exposure to maternal anxiety on whole brain functional connectivity. To this aim, we gathered rs-fMRI scans of the adult offspring to evaluate its association with maternal anxiety at 12–22 weeks of pregnancy. We examined functional connectivity both locally, by studying differences in ROI-to-ROI connectivity using 32 cortical and cerebral ROIs, and by studying the whole-brain network properties with graph metrics, using two different atlases. We expected to observe altered brain functional connectivity associated with prenatal exposure to maternal anxiety. Given the lack of adult offspring research, a data-driven approach was implemented, with no a priori expectations on directions of effects. Methods Study design From the 86 pregnant women that initially participated in 1986 in a prospective longitudinal study, 52 of their offspring participated in our study at age 28 years. Inclusion criteria at the start of the study were Caucasian race, Dutch speaking, aged between 18 and 30 weeks pregnant, nulliparous and without obstetrical complications or medical risks, and not using drugs or medication with risks to the fetus (Van den Bergh, 1990 ; Van den Bergh & Marcoen, 2004 ). Maternal data assessment included standardized psychological distress and lifestyle questionnaires during weeks 12–22, 23–31 and 32–40 of pregnancy and at several waves postnatally. The 28-year-old offspring participated in this study between July 2014 and September 2015 in a University Hospital. The resting-state functional connectivity (rsFC) analyses were performed on the available high-quality imaging data of 49 subjects (after exclusion of one case of whom the T1 image was not available and two cases with Root Mean Square (RMS) motion parameters > 1 in rs-fMRI images; N = 3). Demographic characteristics of the total group of mothers and offspring (N = 49, final sample) are presented in Table 1 and Supplemental demographics, Table S1 . Table 1 Demographics and descriptive statistics between Low-Medium Anxiety group and High Anxiety group Variables Follow-up sample mean (SD) Low-Medium anxiety (LMA) group mean (SD) High-anxiety (HA) group mean (SD) Independent sample t-test* Parents (n = 48) (n = 35) (n = 13) Maternal state anxiety 12–22 weeks of pregnancy 38.84 (8.74) 34.59 (4.73) 50.29 (6.44) -9.24 (p < .001) Postnatal maternal trait anxiety .12 (.99) − .16 (.95) .89 (.61) -3.7 (p < .001) Maternal age at 12–22 weeks, years 26.19 (2.55) 25.89 (2.54) 27 (2.48) -1.36 (p = .18) Paternal age at 12–22 weeks, years 28.32 (4.16) 28.16 (4.44) 28.75 (3.44) 174.5 (p = .65) Social class (based on education both parents) .20 (.99) .3 (.95) − .07 (1.09) 280.50 (p = .21) Months married 36.5 (25.67) 38.03 (25.98) 31.64 (25.21) 232 (p = .31) Cigarettes a day in pregnancy 1.02 (2.05) 1.03 (2.18) 1 (1.73) 221.5 (p = .87) Daily caffeine use (mg) in pregnancy 293.78 (223.01) 322.24 (247.53) 217.15 (111.63) 288.5 (p = .16) Daily alcohol use (mg) in pregnancy 1.92 (2.94) 1.82 (2.88) 2.19 (3.19) 222 (p = .90) N (%) N (%) N (%) Chi square test** Highest level of education mother p = .35 No High-School or Test Equivalent 6 (12.50) 3 (8.57) 3 (23.08) High School or Test Equivalent 11 (22.92) 7 (20.00) 4 (30.77) Undergraduate Level (Associate, Bachelor) 13 (27.08) 10 (28.57) 3 (23.08) Graduate Level (master, PhD.) 18 (37.50) 15 (42.86) 3 (23.08) Highest level education father p = .76 No High-School or Test Equivalent 7 (14.58) 4 (11.43) 3 (23.08) High School or Test Equivalent 12 (25.00) 9 (25.71) 3 (23.08) Undergraduate Level (Associate, Bachelor) 6 (12.50) 5 (14.29) 1 (7.69) Graduate Level (Master, PhD.) 23 (47.92) 17 (48.57) 6 (46.15) Mothers employed or not p = .66 Yes 41 (85.42) 29 (82.86) 12 (92.31) No 7 (14.58) 6 (17.14) 1 (7.69) Fathers employed or not p = 1.00 Yes 46 (95.83) 33 (94.29) 13 (100.00) No 2 (4.17) 2 (5.71) 0 (0.00) Mother level of employment p = .39 Unskilled or low skilled worker 9 (21.95) 5 (17.24) 4 (33.33) Skilled worker (e.g., technician, clerk) 4 (9.76) 2 (6.90) 2 (16.67) Highly skilled worker (e.g., civil servant, primary school teacher) 12 (29.27) 9 (31.03) 3 (25.00) Academic profession (e.g., higher civil servant, academic teacher) 16 (39.02) 13 (44.83) 3 (25.00) Father level of employment p = .63 Unskilled or low skilled worker 15 (32.61) 9 (27.27) 6 (46.15) Skilled worker (e.g., technician, clerk) 3 (6.52) 3 (9.09) 0 ( .00) Highly skilled worker (e.g., civil servant, primary school teacher) 6 (13.04) 5 (15.15) 1 (7.69) Academic profession (e.g., higher civil servant, academic teacher) 22 (47.83) 16 (48.48) 6 (46.15) Married p = .47 Yes 46 (95.83) 34 (97.14) 12 (92.31) No 2 (4.17) 1 (2.86) 1 (7.69) 28 years old offspring Follow-up sample (n = 49) Mean (SD) Low-Medium anxiety (LMA) group (n = 36) Mean (SD) High-anxiety (HA) group (n = 13) Mean (SD) Independent sample t-test* Birth weight 3214.69 (559.03) 3290.28 (490.6) 3005.38 (695.31) -1.60 ( p = .12 ) Gestational age at birth 272.29 (12.8) 273.61 (11.76) 268.62 (15.24) 266 ( p = .48 ) Birth weight adapted for gestational age .01 (1.02) .08 (.87) − .19 (1.4) .82 ( p = .42 ) Age 28 follow-up (age when tested) 27.84 (.43) 27.75 (.39) 28.08 (.45) 136.5 ( p = .03 ) N (%) N (%) N (%) Chi square test** Highest level of education offspring p = .62 No High-School or Test Equivalent 0 (0) 0 (0) 0 (0) High School or Test Equivalent 2 (4.25) 1 (2.86) 1 (8.33) Undergraduate Level (Associate, Bachelor) 18 (38.30) 14 (40.00) 4 (33.33) Graduate Level (Master, PhD.) 27 (57.45) 20 (57.14) 7 (58.33) Offspring level of employment p = .72 Unskilled or low skilled worker 0 (0) 0 (0) 0 (0) Skilled worker (e.g., technician, clerk) 2(4.54) 1 (3.13) 1 (7.69) Highly skilled worker (e.g., civil servant, primary school teacher) 17(38.64) 13 (40.62) 4 (30.77) Academic profession (e.g., higher civil servant, academic teacher) 25 (56.82) 18 (56.25) 7 (53.84) Offspring employed or not p = .56 Yes 44(93.62) 32 (91.43) 12 (100.00) No 3(6.38) 3 (8.57) 0 (0.00) *Non-parametric analysis by Wilcox Rank Sum test was performed if assumptions for t-test were not met. ** Non-parametric analysis by Fisher's exact test was performed if assumptions for Pearson's Chi-squared test were not met. Materials Maternal anxiety during pregnancy To investigate the anxiety level of the mothers during pregnancy, the Dutch version of the State Trait Anxiety Inventory (STAI) (Van der Ploeg et al., 1980 ) was used. Two offspring anxiety subgroups were defined, “High anxiety” (HA; n = 13) group versus “low-to-medium anxiety” (LMA; n = 36) group, based on the mother’s STAI state anxiety subscale total score during week 12–22 week of pregnancy. The threshold is < 43 (i.e., percentile 75) (Koelewijn et al., 2017 ). As expected, mean maternal anxiety was higher in HA group ( M HA = 50.28 ( SD = 6.44) compared to LMA group (M LMA =34.74 ( SD = 4.74) ( t =- 9.18, p < .001) and are situated at respectively decile 9 versus decile 5 of a Dutch STAI female norm population (Van der Ploeg et al., 1980 ). Covariates Postnatally, mothers completed the STAI when the child was 1, 10, and 28 weeks old (postnatal part of first wave), at 8/9 years (second wave), at 14/15 years (third wave), at 17 years (fourth wave) and at 20 years (fifth wave). A principal component analysis conducted on the postnatal trait anxiety measures obtained in wave one to four revealed one component explaining 65% of the variance. This standardized component score was created for each mother, and labeled “postnatal anxiety”. Given that postnatal experience and other potential confounds, i.e., alcohol and caffeine use, smoking, gestational age at birth and birth weight, could affect neurobehavioral outcomes of the child, all of these were recorded and examined. These demographics and potential confounds (alcohol and caffeine use, smoking, and offspring gestational age at birth and birth weight, maternal/paternal age, postnatal anxiety) were not significantly different between LMA and HA group afer correction for multiple testing with Bonferoni correction (Table 1 ). Offspring MRI and fMRI Data Acquisition and Processing Data acquisition MR-scans were acquired using a Philips Achieva 3T scanner (Philips, Best, The Netherlands) with 32 channel head coil and in-coil AC/DC conversion (dStream). Functional images for rs-fcMRI were obtained with a T2*-weighted echo-planar imaging (EPI) sequence (4 x 4 x 4 mm3, TE/TR = 33 ms/1700 ms; FOV = 230x120x230 mm; 4mm slice thickness, 30 slices, 7 minutes acquisition time; 250 volumes). For the acquisition of this scan, participants were requested to relax, but not to fall asleep. For anatomical mapping and optimal registration to standard space, a T1-weighted image was acquired (MPRAGE, resolution 1 × 1 × 1 mm3, TE/TR = 4.6ms/9.6 ms, FOV = 192x250x250, 1.2mm slice thickness, 160 slices, 6 minutes acquisition time). MRI processing Preprocessing followed established procedures for functional imaging using FSL software (Smith et al., 2004 ), see Supplemental materials . After preprocessing, functional connectivity was analyzed based on a matrix of partial correlations between regional BOLD signals. The nodes of interest in this study were the 32 cortical and cerebellar regions (or ROIs) from the network atlas as provided by the Conn toolbox (atlas is based on 497 subjects from the Human Connectome Project, see Table S2 in supplement for a description of the regions). To estimate the functional connectivity between 32 nodes, partial correlations, i.e, corrected for the remaining connections, were calculated using the Conn toolbox (Whitfield-Gabrieli & Nieto-Castanon, 2012 ) implemented in MATLAB ( www.nitrc.org/projects/conn ; version 17.f). Statistical analyses First, rsFC partial correlations between the 32 nodes of interest (as defined by the atlas in the Conn toolbox) were compared between the HA and LMA groups using ANCOVAs, i.e., adjusted for sex, birth weight, and postnatal anxiety, since these variables may influence rsFC. These comparisons were False Discovery Rate (FDR-) corrected for multiple comparisons at p < 0.05 (FDR correction for 32*32 comparisons). Second, Network Based Statistics (NBS; Zalesky et al., 2010 ) as part of the Conn toolbox was implemented to compute more sensitive functional network differences between the HA and LMA groups. The first step in the NBS required a threshold based on a significant test statistic for each connection based on the HA and LMA group difference (i.e., the significant test statistic here was the uncorrected p < 0.05 of the ANCOVA as computed for each ROI-to-ROI connection). The second step included cluster-based permutation testing (randomizing group assignment) to determine whether the subnetwork is significantly larger than chance (NBS seed based threshold, p < 0.05, FDR-corrected). Third, graph theoretical network metrics were computed for each individual participant for two different atlases. For the first parcellation, brain regions were selected using the 32 cortical and cerebellar regions (or ROIs) from the network atlas as provided by the Conn toolbox. For the second parcelation, we used 68 cortical regions of the FreeSurfer’s Desikan Killiany atlas. Individual weighted graphs were thresholded (t > 0.3, only positive connections) for both atlases. Individual whole-brain graph metrics included density, connectivity strength, global clustering, normalized global clustering, global efficiency, normalized global efficiency and normalized small worldness and were computed using the Brain Connecitvity Toolbox implemented in MATLAB (BCT; Rubinov & Sporns, 2010 ). Normalized graph metrics were obtained by taking the ratio of the actual graph metrics and graph metrics observed in the 1000 random networks. LMA and HA group differences of graph metrics were determined using ANCOVAs, i.e., adjusted for sex, birth weight, and postnatal anxiety. Results Group based functional connectomes We examined the functional connectome of the two groups (LMA versus HA) by plotting significant ROI-to-ROI connections ( p <.05, FDR-corrected) per group (see Figure 1 ). Based on this Figure, the HA group shows weaker overall connectivity compared to the LMA group, indicated by the lower number of significant connections in the HA group. More specifically, the medial prefrontal cortex (MPFC) seems at the core of the weaker connectivity in the HA group, showing less significant connections in the HA group as compared to the LMA group. Association between maternal anxiety in pregnancy and offspring rsFC Results of the ANCOVA group comparison, corrected for sex, birth weight (adapted to gestational age), and maternal postnatal anxiety, yielded a significant difference in connectivity between MPFC and left inferior frontal gyrus (IFG) ( p uncorr = 0.0012, p FDR <.05). More specifically, this positive correlation was stronger in the LMA group (for visualization see Figure 2 ). This finding is in line with our observations based on Figure 1 . Network-Based Statistics The ANCOVA group comparison, including a network-based statistic threshold, yielded similar findings with a significant group difference in network-connectivity between medial prefrontal cortex (MPFC) and left inferior frontal gyrus (IFG) ( p uncorr = 0.0012, p FDR <.05). We observed additional effects; i.e., we also observed a significant group difference in network-connectivity between left somatosensory motor gyrus (SMG) and left lateral prefrontal cortex (LPFC) ( p uncorr = 0.0015, p FDR <.05). These positive correlations both showed to be stronger in the LMA group, for visualization see Figure 3 . Group differences in graph metrics In contrast to the ROI-to-ROI analyses, no group (HA versus LMA) differences were found for global network-based density, connectivity strength, global clustering, normalized global clustering, global efficiency, normalized global efficiency and normalized small worldness (all p’s >0.05, see Table S3 for more details). Results remained non-significant when controlled for covariates sex, birth weight (adapted to gestational age), and maternal postnatal anxiety (all p’s >0.05, see Table S3 for more details). Discussion This study demostrated that, in specific networks, adult functional brain connectivity is weaker in adults exposed to higher maternal anxiety at 12–22 weeks of gestation, compared to adults exposed to low to medium maternal anxiety in that period. This association was shown in a prospective prenatal cohort with a postnatal follow-up of 28 years, indicating that the brain changes related to prenatal stress exposure persist at least into early adulthood. In the analyses performed, differences were most pronounced for the medial prefrontal cortex (MPFC), showing weaker functional connectivity in prenatally exposed adult offspring. Specifically, we found weaker functional connectivity between MPFC and the left inferior frontal gyrus (IFG) and between the left lateral prefontal cortex (LPFC) and the left somatosensory motor gyrus (SMG). By contrast, global brain alterations, as measured with graph metrics, did not emerge from our data. This may indicate specific alterations of weaker frontal brain connectivity, instead of a weaker connectivity throughout the brain in adult offspring of mothers with high anxiety in pregnancy. Also of interest is the observed laterality in effect, with all findings presenting in the left hemisphere. Our finding of lower functional connectivity of the MPFC and left PFC with other brain regions is in line with earlier findings of prenatal distress follow-up studies using brain imaging techniques, be it at a general level. Before comparing our specific findings with earlier findings, one should note that we used strict criteria (i.e., comparisons between HA and LMA group were FDR-corrected and corrected for the potential influence of sex, birth weight and postnatal anxiety) and that with less stringent or no corrections, more significant differences in connectivity between the HA and LMA group were encountered. Firstly, our results are in line with the finding that offspring of women (highly) psychologically distressed during pregnancy, show altered structural or functional connectivity of the prefrontal cortex with other brain areas (Hay et al., 2019 ; Humphreys et al., 2020 ; Qiu et al., 2015 ; Soe et al., 2018 ). Most of these studies focus their examination on rsFC of the (pre)frontal region with limbic structures and were conducted in neonates, infants and children only. Second, multiple studies linked prenatal maternal distress to behavioral dysregulation (DiPietro et al., 2002 ), enhanced vigilance (van den Heuvel et al., 2015 ; van den Heuvel et al., 2018 ), and executive dysfunction (Buss et al., 2011 ; Pearson et al., 2016 ), which indirectly suggests frontal neural changes (McKlveen et al., 2015 ). Lastly, autonomic, motor, emotional and neurocognitive problems found in previous waves of our offspring cohort, could indirectly be linked to altered (pre)frontal functional connectivity. They included disturbed sleep-wake patterns in the fetus and neonate and regulation problems (crying, eating and sleep problems, difficult temperament) in infancy (Van den Bergh, 1990 ), ADHD, impulsivity and externalizing problems in childhood and early adolescence (Van den Bergh & Marcoen, 2004 ; Van den Bergh et al., 2005 ) and specific cognitive deficits in late adolescence (Mennes et al., 2009 ). Our previous Gambling task-fMRI study in this population at age 20, revealed more scattered brain activation in the HA group and coherent activation patterns in the LMA group (Mennes et al., 2019 ). The finding of exclusive effects for the left hemisphere could point to lateralization of effect. Some other studies have also reported results in the left hemisphere only. For instance, in a recent study, Moog et al. ( 2021 ) reported smaller volumes of the left hippocampus, but not the right, in infants prenatally exposed to higher levels of maternal perceived distress during pregnancy. Additionally, recent work with fetal imaging showed decreased cerebellar-insular functional connectivity in fetuses of distressed mothers, for the left insula only (van den Heuvel et al., 2021 ). Given that the left hemisphere may develop relatively faster than the right hemisphere during the prenatal period (Andescavage et al., 2017 ), it could be more sensitive to prenatal environmental insults such as maternal distress. Interestingly, Vasung et al. (Vasung et al., 2020 ) specifically reported that the left IFG – a key region that came up in our results – has a faster volume growth than the right IFG, potentially making it more vulnerable. Yet, most human studies do not discuss laterality effects and no human study to date has specifically focused on laterality effects of prenatal stress exposure, nor its potential mechansims. The current study has several strengths and limitations. A first evident strength is the prospective design and the follow-up study spanning almost 29 years with an offspring response rate as high as 60 % at ae 28 years. Second, the proportion of pregnant women experiencing high levels of state anxiety was relatively high; 34% had a score of > 43 at 12–22 weeks of pregnancy, which is a prerequisite for revealing, if any, effects of high anxiety. Third, sex, birth weight and maternal postnatal anxiety were selected as confounds in our main model predicting rsFC differences between LMA and HA group. Nevertheless, our study also has several limitation that should be noted. A first limitation of our study is the relatively small size of the sample (n = 52). This did not allow us to conduct further analyses on specific sex-interactions, which could have been interesting. Secondly, this study did not include any physiological markers of distress/anxiety of the mother. We focused on the subjective, self-reported experience of the mother, rather than biological markers. However, since offspring outcome measures were biological markers (not maternal reported measures), shared method variance inflating the associations is not at stake. Third, no genetic sensitive design was used and, therefore, we cannot rule out genetic mechanisms at play. However, previous research examining the effect of prenatal exposure to objective, random stressors, such as natural disasters, have shown that genetic mechanisms cannot (only) explain the observed effects of stress exposure on the offspring’s brain (Jones et al., 2019 ). Conclusions Our findings confirm that the adult connectome is influenced by the prenatal environment. Athough the brain architecture continues to show plasticity throughout adult life, some biological changes that compromise flexible adaptation and resilience might already be laid down early in life (McEwen et al., 2015 ). Our current rsFC results indicate that individuals exposed to varying levels of maternal anxiety at 12 to 22 weeks of pregnancy show weaker functional brain connectivity of the medial prefrontal cortex (MPFC) and left prefontal cortex (LPFC) with some other brain regions in the left hemisphere, emphasizing an altered frontal neural network in the adults who were prenatally exposed to high maternal anxiety. Such alterations in frontal connectivity may put these adults at higher risk for executive dysfunction, mental health issues, and potentially even neurodegenerative disorders in later life (Faa et al., 2014 ). Future work may seek to replicate our rsFC finding and try to characterize the potential neural vulnerability of prenatally exposed individuals better, e.g., based on properties of dynamic fluctuations in whole-brain rsFC analyses and/or task-related fMRI analyses. Declarations Acknowledgements We would like to thank all participating mothers and their adult children for participating in our study. Additionally, we would like to thank Marijke Braeken for managing data collection and all students that assisted her in gathering psychological and physiological data from the adult offspring in current follow-up phase. Finally, we would like to thank Njeri Kamau and Stijn Vos for putting together and analyzing demographic and life style data. Funding sources The PELS study is supported by the national funding agencies of the European Science Foundation (EuroSTRESS - PELS - 99930AB6-0CAC-423B-9527-7487B33085F3) participating in the Eurocores Program EuroSTRESS programme, i.e., the Brain and Cognition Programme of the Netherlands Organisation for Scientific Research (NWO) for the Netherlands. BVdB is project leader of the PELS study. BVdB was financially supported by European Commission Seventh Framework Programme (FP7—HEALTH. 2011.2.2.2-2 BRAINAGE, grant agreement no: 279281). MvdH is supported by a Veni grant from the Dutch Organization for Scientific Research (NWO; VI.Veni.191G.025). CS received financial support from the Flemish Fonds for Scientific research (FWO; grant no. 12Y6122N). Conflict of interest This manuscript, nor the findings reported, were influenced by any financial source or sponsor. None of the authors have a conflict of interest to declare. Compliance with ethical standards The local ethical committee for experiments on human subjects approved all waves of the study. The work described has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All mothers had given informed consent for participating and publication of the results in all previous waves. Their 28-year-old offspring were clearly informed about the scanning procedures and physiological measures and gave their written informed consent for participation and publication of the results. Availability of data and materials The data that support the findings of this study are available from the corresponding author on reasonable request. Author contributions Author contributions included conception and study design (ET, MvdH, CS, AU, SS and BVdB), preprocessing data and statistical analysis (ET, MvdH, CS, TB, MM and BVdB), interpretation of results (ET, MvdH, CS, BVdB), drafting the manuscript work or revising it critically for important intellectual content (ET, MvdH, CS, and BVdB) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (all authors). References Acosta, H., Tuulari, J. J., Scheinin, N. M., Hashempour, N., Rajasilta, O., Lavonius, T. 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Stress (Amsterdam, Netherlands) , 14 (6), 665–676. https://doi.org/10.3109/10253890.2011.623250 De Asis-Cruz, J., Krishnamurthy, D., Zhao, L., Kapse, K., Vezina, G., Andescavage, N., Quistorff, J., Lopez, C., & Limperopoulos, C. (2020). Association of Prenatal Maternal Anxiety With Fetal Regional Brain Connectivity. JAMA Network Open , 3 (12), e2022349–e2022349. https://doi.org/10.1001/jamanetworkopen.2020.22349 Demers, C. H., Bagonis, M. M., Al-Ali, K., Garcia, S. E., Styner, M. A., Gilmore, J. H., Hoffman, M. C., Hankin, B. L., & Davis, E. P. (2021). Exposure to prenatal maternal distress and infant white matter neurodevelopment. Development and Psychopathology , 33 (5), 1526–1538. https://doi.org/10.1017/S0954579421000742 DiPietro, J. A., Hilton, S. C., Hawkins, M., Costigan, K. A., & Pressman, E. K. (2002). Maternal stress and affect influence fetal neurobehavioral development. 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Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage , 23 Suppl 1 , S208-219. https://doi.org/10.1016/j.neuroimage.2004.07.051 Soe, N. N., Wen, D. J., Poh, J. S., Chong, Y. S., Broekman, B. F., Chen, H., Shek, L. P., Tan, K. H., Gluckman, P. D., Fortier, M. V., Meaney, M. J., & Qiu, A. (2018). Perinatal maternal depressive symptoms alter amygdala functional connectivity in girls. Human Brain Mapping , 39 (2), 680–690. https://doi.org/10.1002/hbm.23873 Thomason, M. E., Hect, J. L., Waller, R., & Curtin, P. (2021). Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery. Neuropsychopharmacology : Official Publication Of The American College Of Neuropsychopharmacology . https://doi.org/10.1038/s41386-021-01066-7 Van den Bergh, B. R. H. (1990). The influence of maternal emotions during pregnancy on fetal and neonatal behavior. 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From the Womb into the World: Protecting the Fetal Brain from Maternal Stress During Pregnancy. Policy Insights from the Behavioral and Brain Sciences , 9 (1), 96–103. https://doi.org/10.1177/23727322211068024 van den Heuvel, M. I., Donkers, F. C., Winkler, I., Otte, R. A., & Van den Bergh, B. R. (2015). Maternal mindfulness and anxiety during pregnancy affect infants' neural responses to sounds. Soc Cogn Affect Neurosci , 10 (3), 453–460. https://doi.org/10.1093/scan/nsu075 van den Heuvel, M. I., Hect, J. L., Smarr, B. L., Qawasmeh, T., Kriegsfeld, L. J., Barcelona, J., Hijazi, K. E., & Thomason, M. E. (2021). Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth. Scientific Reports , 11 (1), 2228. https://doi.org/10.1038/s41598-021-81681-y van den Heuvel, M. I., Henrichs, J., Donkers, F. C. L., & Van den Bergh, B. R. H. (2018). 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Swets & Zeitlinger Vasung, L., Rollins, C. K., Yun, H. J., Velasco-Annis, C., Zhang, J., Wagstyl, K., Evans, A., Warfield, S. K., Feldman, H. A., Grant, P. E., & Gholipour, A. (2020). Quantitative In vivo MRI Assessment of Structural Asymmetries and Sexual Dimorphism of Transient Fetal Compartments in the Human Brain. Cerebral Cortex , 30 (3), 1752–1767. https://doi.org/10.1093/cercor/bhz200 Vigo, D., Thornicroft, G., & Atun, R. (2016). Estimating the true global burden of mental illness. Lancet Psychiatry , 3 (2), 171–178. https://doi.org/10.1016/s2215-0366(15)00505-2 Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connectivity , 2 (3), 125–141. https://doi.org/10.1089/brain.2012.0073 Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks. Neuroimage , 53 (4), 1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041 Additional Declarations No competing interests reported. Supplementary Files RSCONNSupplementalMaterialsFINAL.pdf Cite Share Download PDF Status: Published Journal Publication published 29 Jun, 2023 Read the published version in Brain Imaging and Behavior → Version 1 posted Editorial decision: Major revision 16 Dec, 2022 Reviews received at journal 23 Nov, 2022 Reviewers agreed at journal 21 Nov, 2022 Reviewers agreed at journal 01 Sep, 2022 Reviewers invited by journal 26 Aug, 2022 Editor assigned by journal 04 Aug, 2022 Submission checks completed at journal 27 Jul, 2022 First submitted to journal 26 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1897872","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":124255524,"identity":"2eb2c9ed-2141-446b-8152-8a18ac64623b","order_by":0,"name":"Elise Turk","email":"data:image/png;base64,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","orcid":"","institution":"Tilburg University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Elise","middleName":"","lastName":"Turk","suffix":""},{"id":124255525,"identity":"d51765f5-3155-45b5-adbf-ad632ee9a1b4","order_by":1,"name":"Marion I. van den Heuvel","email":"","orcid":"","institution":"Tilburg University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marion","middleName":"I. van den","lastName":"Heuvel","suffix":""},{"id":124255526,"identity":"f43a0228-02a0-4966-88bc-f1dcdd0cdc18","order_by":2,"name":"Charlotte Sleurs","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Sleurs","suffix":""},{"id":124255527,"identity":"da2c98d1-e6bb-467c-915b-d687219abd18","order_by":3,"name":"Thibo Billiet","email":"","orcid":"","institution":"Icometrix (Belgium)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thibo","middleName":"","lastName":"Billiet","suffix":""},{"id":124255528,"identity":"4652b33f-8c31-47dd-bf5f-5e657500fdc5","order_by":4,"name":"Anne Uyttebroeck","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Uyttebroeck","suffix":""},{"id":124255529,"identity":"22bfe818-0eea-4ef4-b468-9bf70f57a945","order_by":5,"name":"Stefan Sunaert","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Sunaert","suffix":""},{"id":124255530,"identity":"df609d6b-c79e-496c-a5eb-35b028ca1910","order_by":6,"name":"Maarten Mennes","email":"","orcid":"","institution":"Radboud University Nijmegen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maarten","middleName":"","lastName":"Mennes","suffix":""},{"id":124255531,"identity":"5c3f57ee-972f-4015-855c-e7f394395856","order_by":7,"name":"Bea Van den Bergh","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bea","middleName":"Van den","lastName":"Bergh","suffix":""}],"badges":[],"createdAt":"2022-07-26 13:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1897872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1897872/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11682-023-00787-1","type":"published","date":"2023-06-29T21:24:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":24614575,"identity":"c368c214-84ac-4854-b7c2-a997bfa54fac","added_by":"auto","created_at":"2022-08-01 17:47:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":207155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup-based functional connectomes.\u003c/strong\u003e Figure displays the functional group-based connectome rings of offspring of low-medium (left) and high (right) prenatal anxiety. Significant ROI-to-ROI connections (p \u0026lt;.05, FDR-corrected) of both groups are displayed in a color ranging from blue (negative) to red (positive) and represent the T-statistics (see color-bar below the connectome graphs for specific values). The arrow points to the MPFC ROI to indicate that this region (visually) shows most difference between groups, with less significant connections to other areas in the HA group as compared to the LMA group. ROI labels and descriptions for the abbreviations can be found in the supplemental materials, \u003cstrong\u003eTable S2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1897872/v1/35cdaae1225f9be9aee388b3.jpg"},{"id":24614577,"identity":"69d22401-fc3e-4318-953d-92e024f8f3a0","added_by":"auto","created_at":"2022-08-01 17:47:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional connectivity group-comparison.\u003c/strong\u003e Significant group-differences of adult offspring exposed to low-medium maternal anxiety (LMA) and high maternal anxiety (HA) in functional ROI-to-ROI connectivity (\u003cem\u003ep\u003c/em\u003e \u0026lt;.05, FDR-corrected). Connections and nodes are displayed in red (positive), and represent the T-statistics (see color-bar for specific values).\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1897872/v1/d34cda715d8182325ee82331.jpg"},{"id":24614576,"identity":"7fd2143b-2419-49ff-b35c-f3e818be2d58","added_by":"auto","created_at":"2022-08-01 17:47:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional connectivity group-comparison using network-based statistics.\u003c/strong\u003e Significant functional ROI-to-ROI connectivity (p\u003csub\u003euncorr\u003c/sub\u003e \u0026lt;.01, and NBS, seed-based threshold of p \u0026lt;.05\u003csub\u003eFDR\u003c/sub\u003e) group-differences of adult offspring that exposed to low-medium maternal anxiety (LMA) and high maternal anxiety (HA). Connections are displayed in red (positive) and color of the nodes represent the T-statistics (see color-bar below the plot for specific values). Connections are presented on an axial view of an average T1 brain. Significant clusters can be found between medial prefrontal cortex (MPFC) and left inferior frontal gyrus (L IFG) and between left prefrontal cortex (L PFC) and left supramarginal gyrus (L SMG).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1897872/v1/af04d1a75e105dea8701309d.jpg"},{"id":44732059,"identity":"55495715-f6e6-4511-a194-69c0ec1eb640","added_by":"auto","created_at":"2023-10-16 21:52:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":786694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1897872/v1/8b37bee8-157d-4cb8-9be9-c80989cfc4d6.pdf"},{"id":24614578,"identity":"fecdffdc-d33e-4659-b9f4-f6064759e2d2","added_by":"auto","created_at":"2022-08-01 17:47:30","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":364227,"visible":true,"origin":"","legend":"","description":"","filename":"RSCONNSupplementalMaterialsFINAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1897872/v1/1e69ca7b448bb88a90d6e2d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWorldwide the societal burden of mental health problems is increasing (Vigo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although early stage prevention is more cost-efficient than treatment (Bauer et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), prenatal origins of mental health problems that are often preventable remain understudied (Browne et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Glover, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Monk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; van den Heuvel, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In a UK-based study, it has been estimated that perinatal anxiety and depression combined costs the society about \u0026pound;8500 per woman giving birth. This results in a striking \u0026pound;6.6\u0026nbsp;billion in total costs (for both mother and child) for the United Kingdom alone (Bauer et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The majority of these costs were associated with adverse effects of maternal perinatal depression on the children, emphasizing the need for more research on the underlying mechanisms of adverse consequences of maternal psychological distress during pregnancy, especially on the long-term.\u003c/p\u003e \u003cp\u003eMore than a decade of brain imaging research has shown that maternal psychological distress during pregnancy, including depression and anxiety, affects the developing fetal brain, with later life consequences for offspring\u0026rsquo;s cognition and mental health (for a review, see Adamson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Van den Bergh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent studies found evidence for changes in offspring structural grey matter (e.g., Acosta et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Donnici et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moog et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and white matter (e.g., Demers et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rifkin-Graboi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as well as functional brain changes, including resting-state functional connectivity using fMRI (e.g., Humphreys et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Scheinost et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and task-based fMRI studies (e.g., Mennes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van der Knaap et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several pioneering studies have even started to show that the timing of these brain alterations is prenatally, by studying the offspring \u003cem\u003ein utero\u003c/em\u003e with fetal resting-state fMRI (De Asis-Cruz et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thomason et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; van den Heuvel et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such neural alterations potentially underlie the observed behavioral problems and mental health issues of prenatally exposed offspring (Monk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van den Bergh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile studies on developmental origins of infant and child brain development are still increasing, only very few studies examined the lasting effect of prenatal exposure to maternal distress into puberty or adulthood. Consequently, we lack knowledge about the persistence of brain developmental alterations in the aftermath of prenatal exposure to maternal distress. Prospective pregnancy cohorts that continue into adulthood may add incredibly valuable information, especially since several researchers have pointed out that neurodegenerative disorders, such as Parkinson\u0026rsquo;s and Altzheimer\u0026rsquo;s Disease, may find their origin in fetal life (Faa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The limited number of studies that do exist clearly show persistent brain alteration into adulthood, such as presumed accelerated brain aging in young adults prenatally exposed to maternal depression (Mareckova et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and a deficit in endogenous cognitive control in 20-year-old males as measured with task-based fMRI (Mennes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Still, more prospective research with longer follow-up periods are necessary.\u003c/p\u003e \u003cp\u003eAdditionally, an important gap in neuroimaging research to date is the focus on predetermined brain areas. Most research has focused on structural changes of the amygdala and hippocampus or rsFC of the limbic and/or (pre)frontal region (Scheinost et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Even though several studies have found important results in changing brain structure and function of these brain regions (Acosta et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Donnici et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humphreys et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Scheinost et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Scheinost et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van der Knaap et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this targeted approach may miss important changes to global brain function and network properties of the prenatally exposed brain (Scheinost et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Exploration of the adult whole brain network prenatally exposed to maternal distress, with appropriate control for multiple testing, has not been conducted to date.\u003c/p\u003e \u003cp\u003eIn the current study, we utilize a unique prospective prenatal cohort with a postnatal follow-up of 28-years to study the long-term effects of prenatal exposure to maternal anxiety on whole brain functional connectivity. To this aim, we gathered rs-fMRI scans of the adult offspring to evaluate its association with maternal anxiety at 12\u0026ndash;22 weeks of pregnancy. We examined functional connectivity both locally, by studying differences in ROI-to-ROI connectivity using 32 cortical and cerebral ROIs, and by studying the whole-brain network properties with graph metrics, using two different atlases. We expected to observe altered brain functional connectivity associated with prenatal exposure to maternal anxiety. Given the lack of adult offspring research, a data-driven approach was implemented, with no a priori expectations on directions of effects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eFrom the 86 pregnant women that initially participated in 1986 in a prospective longitudinal study, 52 of their offspring participated in our study at age 28 years. Inclusion criteria at the start of the study were Caucasian race, Dutch speaking, aged between 18 and 30 weeks pregnant, nulliparous and without obstetrical complications or medical risks, and not using drugs or medication with risks to the fetus (Van den Bergh, \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Van den Bergh \u0026amp; Marcoen, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Maternal data assessment included standardized psychological distress and lifestyle questionnaires during weeks 12\u0026ndash;22, 23\u0026ndash;31 and 32\u0026ndash;40 of pregnancy and at several waves postnatally. The 28-year-old offspring participated in this study between July 2014 and September 2015 in a University Hospital. The resting-state functional connectivity (rsFC) analyses were performed on the available high-quality imaging data of 49 subjects (after exclusion of one case of whom the T1 image was not available and two cases with Root Mean Square (RMS) motion parameters\u0026thinsp;\u0026gt;\u0026thinsp;1 in rs-fMRI images; N\u0026thinsp;=\u0026thinsp;3). Demographic characteristics of the total group of mothers and offspring (N\u0026thinsp;=\u0026thinsp;49, final sample) are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cstrong\u003eSupplemental demographics, Table S1\u003c/strong\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographics and descriptive statistics between Low-Medium Anxiety group and High Anxiety group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up sample mean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow-Medium anxiety (LMA)\u003c/p\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh-anxiety (HA) group\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndependent sample t-test*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;13)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal state anxiety 12\u0026ndash;22 weeks of pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.84 (8.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.59 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.29 (6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e-9.24 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostnatal maternal trait anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.12 (.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.16 (.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89 (.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\"\u003e-3.7 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal age at 12\u0026ndash;22 weeks, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.19 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.89 (2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e-1.36 (p\u0026thinsp;=\u0026thinsp;.18)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaternal age at 12\u0026ndash;22 weeks, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.32 (4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.16 (4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.75 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e174.5 (p\u0026thinsp;=\u0026thinsp;.65)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial class (based on education both parents)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.20 (.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.3 (.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.07 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e280.50 (p\u0026thinsp;=\u0026thinsp;.21)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonths married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.5 (25.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.03 (25.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.64 (25.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e232 (p\u0026thinsp;=\u0026thinsp;.31)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCigarettes a day in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e221.5 (p\u0026thinsp;=\u0026thinsp;.87)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily caffeine use (mg) in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293.78 (223.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322.24 (247.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217.15 (111.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e288.5 (p\u0026thinsp;=\u0026thinsp;.16)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily alcohol use (mg) in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92 (2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19 (3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222 (p\u0026thinsp;=\u0026thinsp;.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi square test**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest level of education mother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.35\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo High-School or Test Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School or Test Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026nbsp;(22.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026nbsp;(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026nbsp;(30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndergraduate Level (Associate, Bachelor)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026nbsp;(27.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026nbsp;(28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate Level (master, PhD.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026nbsp;(37.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026nbsp;(42.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest level education father\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.76\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo High-School or Test Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026nbsp;(14.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026nbsp;(11.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School or Test Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026nbsp;(25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026nbsp;(25.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndergraduate Level (Associate, Bachelor)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026nbsp;(14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate Level (Master, PhD.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u0026nbsp;(47.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026nbsp;(48.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMothers employed or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.66\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026nbsp;(85.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u0026nbsp;(82.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026nbsp;(92.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026nbsp;(14.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(17.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026nbsp;(7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFathers employed or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;1.00\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026nbsp;(95.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u0026nbsp;(94.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026nbsp;(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026nbsp;(4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026nbsp;(5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMother level of employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnskilled or low skilled worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026nbsp;(21.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026nbsp;(17.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026nbsp;(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkilled worker (e.g., technician, clerk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026nbsp;(9.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026nbsp;(6.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026nbsp;(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly skilled worker (e.g., civil servant, primary school teacher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026nbsp;(29.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026nbsp;(31.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcademic profession (e.g., higher civil servant, academic teacher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u0026nbsp;(39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026nbsp;(44.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFather level of employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.63\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnskilled or low skilled worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026nbsp;(32.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026nbsp;(27.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkilled worker (e.g., technician, clerk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;( .00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly skilled worker (e.g., civil servant, primary school teacher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(13.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026nbsp;(15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026nbsp;(7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcademic profession (e.g., higher civil servant, academic teacher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u0026nbsp;(47.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u0026nbsp;(48.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.47\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026nbsp;(95.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u0026nbsp;(97.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026nbsp;(92.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026nbsp;(4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026nbsp;(2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026nbsp;(7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28 years old offspring\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up sample (n\u0026thinsp;=\u0026thinsp;49)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-Medium anxiety (LMA) group (n\u0026thinsp;=\u0026thinsp;36)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-anxiety (HA) group (n\u0026thinsp;=\u0026thinsp;13)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent sample t-test*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBirth weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3214.69 (559.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3290.28 (490.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3005.38 (695.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.60 (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.12\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGestational age at birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272.29 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273.61 (11.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268.62 (15.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266 (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.48\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBirth weight adapted for gestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.01 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08 (.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.19 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.82 (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.42\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge 28 follow-up (age when tested)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.84 (.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.75 (.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.08 (.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e136.5 (\u003c/strong\u003e\u003cspan class=\"BoldItalic\"\u003ep\u0026thinsp;=\u0026thinsp;.03\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi square test**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest level of education offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.62\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo High-School or Test Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School or Test Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026nbsp;(2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndergraduate Level (Associate, Bachelor)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (38.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026nbsp;(40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate Level (Master, PhD.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (57.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026nbsp;(57.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (58.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOffspring level of employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.72\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnskilled or low skilled worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkilled worker (e.g., technician, clerk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly skilled worker (e.g., civil servant, primary school teacher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(38.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (40.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcademic profession (e.g., higher civil servant, academic teacher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (56.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (56.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (53.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOffspring employed or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;.56\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(93.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (91.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(6.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*Non-parametric analysis by Wilcox Rank Sum test was performed if assumptions for t-test were not met.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e** Non-parametric analysis by Fisher\u0026apos;s exact test was performed if assumptions for Pearson\u0026apos;s Chi-squared test were not met.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ch2\u003eMaterials\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eMaternal anxiety during pregnancy\u003c/h2\u003e\n \u003cp\u003eTo investigate the anxiety level of the mothers during pregnancy, the Dutch version of the State Trait Anxiety Inventory (STAI) (Van der Ploeg et al., \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) was used. Two offspring anxiety subgroups were defined, \u0026ldquo;High anxiety\u0026rdquo; (HA; n\u0026thinsp;=\u0026thinsp;13) group versus \u0026ldquo;low-to-medium anxiety\u0026rdquo; (LMA; n\u0026thinsp;=\u0026thinsp;36) group, based on the mother\u0026rsquo;s STAI state anxiety subscale total score during week 12\u0026ndash;22 week of pregnancy. The threshold is \u0026lt;\u0026thinsp;43 (i.e., percentile 75) (Koelewijn et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). As expected, mean maternal anxiety was higher in HA group (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eHA\u003c/sub\u003e= 50.28 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.44) compared to LMA group (M\u003csub\u003eLMA\u003c/sub\u003e=34.74 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.74) (\u003cem\u003et\u003c/em\u003e=- 9.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and are situated at respectively decile 9 versus decile 5 of a Dutch STAI female norm population (Van der Ploeg et al., \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003ePostnatally, mothers completed the STAI when the child was 1, 10, and 28 weeks old (postnatal part of first wave), at 8/9 years (second wave), at 14/15 years (third wave), at 17 years (fourth wave) and at 20 years (fifth wave). A principal component analysis conducted on the postnatal trait anxiety measures obtained in wave one to four revealed one component explaining 65% of the variance. This standardized component score was created for each mother, and labeled \u0026ldquo;postnatal anxiety\u0026rdquo;. Given that postnatal experience and other potential confounds, i.e., alcohol and caffeine use, smoking, gestational age at birth and birth weight, could affect neurobehavioral outcomes of the child, all of these were recorded and examined. These demographics and potential confounds (alcohol and caffeine use, smoking, and offspring gestational age at birth and birth weight, maternal/paternal age, postnatal anxiety) were not significantly different between LMA and HA group afer correction for multiple testing with Bonferoni correction (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003ch2\u003eOffspring MRI and fMRI Data Acquisition and Processing\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eData acquisition\u003c/h2\u003e\n \u003cp\u003eMR-scans were acquired using a Philips Achieva 3T scanner (Philips, Best, The Netherlands) with 32 channel head coil and in-coil AC/DC conversion (dStream). Functional images for rs-fcMRI were obtained with a T2*-weighted echo-planar imaging (EPI) sequence (4 x 4 x 4 mm3, TE/TR\u0026thinsp;=\u0026thinsp;33 ms/1700 ms; FOV\u0026thinsp;=\u0026thinsp;230x120x230 mm; 4mm slice thickness, 30 slices, 7 minutes acquisition time; 250 volumes). For the acquisition of this scan, participants were requested to relax, but not to fall asleep. For anatomical mapping and optimal registration to standard space, a T1-weighted image was acquired (MPRAGE, resolution 1 \u0026times; 1 \u0026times; 1 mm3, TE/TR\u0026thinsp;=\u0026thinsp;4.6ms/9.6 ms, FOV\u0026thinsp;=\u0026thinsp;192x250x250, 1.2mm slice thickness, 160 slices, 6 minutes acquisition time).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eMRI processing\u003c/h2\u003e\n \u003cp\u003ePreprocessing followed established procedures for functional imaging using FSL software (Smith et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), see \u003cstrong\u003eSupplemental materials\u003c/strong\u003e. After preprocessing, functional connectivity was analyzed based on a matrix of partial correlations between regional BOLD signals. The nodes of interest in this study were the 32 cortical and cerebellar regions (or ROIs) from the network atlas as provided by the Conn toolbox (atlas is based on 497 subjects from the Human Connectome Project, see \u003cstrong\u003eTable S2\u003c/strong\u003e in supplement for a description of the regions). To estimate the functional connectivity between 32 nodes, partial correlations, i.e, corrected for the remaining connections, were calculated using the Conn toolbox (Whitfield-Gabrieli \u0026amp; Nieto-Castanon, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) implemented in MATLAB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.nitrc.org/projects/conn\" target=\"_blank\"\u003ewww.nitrc.org/projects/conn\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e; version 17.f).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eStatistical analyses\u003c/h2\u003e\n\u003cp\u003eFirst, rsFC partial correlations between the 32 nodes of interest (as defined by the atlas in the Conn toolbox) were compared between the HA and LMA groups using ANCOVAs, i.e., adjusted for sex, birth weight, and postnatal anxiety, since these variables may influence rsFC. These comparisons were False Discovery Rate (FDR-) corrected for multiple comparisons at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (FDR correction for 32*32 comparisons).\u003c/p\u003e\n\u003cp\u003eSecond, Network Based Statistics (NBS; Zalesky et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) as part of the Conn toolbox was implemented to compute more sensitive functional network differences between the HA and LMA groups. The first step in the NBS required a threshold based on a significant test statistic for each connection based on the HA and LMA group difference (i.e., the significant test statistic here was the uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 of the ANCOVA as computed for each ROI-to-ROI connection). The second step included cluster-based permutation testing (randomizing group assignment) to determine whether the subnetwork is significantly larger than chance (NBS seed based threshold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR-corrected).\u003c/p\u003e\n\u003cp\u003eThird, graph theoretical network metrics were computed for each individual participant for two different atlases. For the first parcellation, brain regions were selected using the 32 cortical and cerebellar regions (or ROIs) from the network atlas as provided by the Conn toolbox. For the second parcelation, we used 68 cortical regions of the FreeSurfer\u0026rsquo;s Desikan Killiany atlas. Individual weighted graphs were thresholded (t\u0026thinsp;\u0026gt;\u0026thinsp;0.3, only positive connections) for both atlases. Individual whole-brain graph metrics included density, connectivity strength, global clustering, normalized global clustering, global efficiency, normalized global efficiency and normalized small worldness and were computed using the Brain Connecitvity Toolbox implemented in MATLAB (BCT; Rubinov \u0026amp; Sporns, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Normalized graph metrics were obtained by taking the ratio of the actual graph metrics and graph metrics observed in the 1000 random networks. LMA and HA group differences of graph metrics were determined using ANCOVAs, i.e., adjusted for sex, birth weight, and postnatal anxiety.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGroup based functional connectomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the functional connectome of the two groups (LMA versus HA) by plotting significant ROI-to-ROI connections (\u003cem\u003ep\u003c/em\u003e \u0026lt;.05, FDR-corrected) per group (see \u003cstrong\u003eFigure 1\u003c/strong\u003e). Based on this Figure, the HA group shows weaker overall connectivity compared to the LMA group, indicated by the lower number of significant connections in the HA group. More specifically, the medial prefrontal cortex (MPFC) seems at the core of the weaker connectivity in the HA group, showing less significant connections in the HA group as compared to the LMA group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between maternal anxiety in pregnancy and offspring rsFC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of the ANCOVA group comparison, corrected for sex, birth weight (adapted to gestational age), and maternal postnatal anxiety, yielded a significant difference in connectivity between MPFC and left inferior frontal gyrus (IFG) (\u003cem\u003ep\u003csub\u003euncorr\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.0012, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt;.05). More specifically, this positive correlation was stronger in the LMA group (for visualization see \u003cstrong\u003eFigure 2\u003c/strong\u003e). This finding is in line with our observations based on \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork-Based Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ANCOVA group comparison, including a network-based statistic threshold, yielded similar findings with a significant group difference in network-connectivity between medial prefrontal cortex (MPFC) and left inferior frontal gyrus (IFG) (\u003cem\u003ep\u003csub\u003euncorr\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.0012, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt;.05). We observed additional effects; i.e., we also observed a significant group difference in network-connectivity between left somatosensory motor gyrus (SMG) and left lateral prefrontal cortex (LPFC) (\u003cem\u003ep\u003csub\u003euncorr\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.0015, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt;.05). These positive correlations both showed to be stronger in the LMA group, for visualization see \u003cstrong\u003eFigure 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGroup differences in graph metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to the ROI-to-ROI analyses, no group (HA versus LMA) differences were found for global network-based density, connectivity strength, global clustering, normalized global clustering, global efficiency, normalized global efficiency and normalized small worldness (all\u003cem\u003e\u0026nbsp;p\u0026rsquo;s\u003c/em\u003e\u0026gt;0.05, see \u003cstrong\u003eTable S3\u0026nbsp;\u003c/strong\u003efor more details). Results remained non-significant when controlled for covariates sex, birth weight (adapted to gestational age), and maternal postnatal anxiety (all\u003cem\u003e\u0026nbsp;p\u0026rsquo;s\u003c/em\u003e\u0026gt;0.05, see \u003cstrong\u003eTable S3\u0026nbsp;\u003c/strong\u003efor more details).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demostrated that, in specific networks, adult functional brain connectivity is weaker in adults exposed to higher maternal anxiety at 12\u0026ndash;22 weeks of gestation, compared to adults exposed to low to medium maternal anxiety in that period. This association was shown in a prospective prenatal cohort with a postnatal follow-up of 28 years, indicating that the brain changes related to prenatal stress exposure persist at least into early adulthood. In the analyses performed, differences were most pronounced for the medial prefrontal cortex (MPFC), showing weaker functional connectivity in prenatally exposed adult offspring. Specifically, we found weaker functional connectivity between MPFC and the left inferior frontal gyrus (IFG) and between the left lateral prefontal cortex (LPFC) and the left somatosensory motor gyrus (SMG). By contrast, global brain alterations, as measured with graph metrics, did not emerge from our data. This may indicate specific alterations of weaker frontal brain connectivity, instead of a weaker connectivity throughout the brain in adult offspring of mothers with high anxiety in pregnancy. Also of interest is the observed laterality in effect, with all findings presenting in the left hemisphere.\u003c/p\u003e \u003cp\u003eOur finding of lower functional connectivity of the MPFC and left PFC with other brain regions is in line with earlier findings of prenatal distress follow-up studies using brain imaging techniques, be it at a general level. Before comparing our specific findings with earlier findings, one should note that we used strict criteria (i.e., comparisons between HA and LMA group were FDR-corrected and corrected for the potential influence of sex, birth weight and postnatal anxiety) and that with less stringent or no corrections, more significant differences in connectivity between the HA and LMA group were encountered. Firstly, our results are in line with the finding that offspring of women (highly) psychologically distressed during pregnancy, show altered structural or functional connectivity of the prefrontal cortex with other brain areas (Hay et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Humphreys et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Qiu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Soe et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most of these studies focus their examination on rsFC of the (pre)frontal region with limbic structures and were conducted in neonates, infants and children only. Second, multiple studies linked prenatal maternal distress to behavioral dysregulation (DiPietro et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), enhanced vigilance (van den Heuvel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; van den Heuvel et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and executive dysfunction (Buss et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pearson et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which indirectly suggests frontal neural changes (McKlveen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Lastly, autonomic, motor, emotional and neurocognitive problems found in previous waves of our offspring cohort, could indirectly be linked to altered (pre)frontal functional connectivity. They included disturbed sleep-wake patterns in the fetus and neonate and regulation problems (crying, eating and sleep problems, difficult temperament) in infancy (Van den Bergh, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), ADHD, impulsivity and externalizing problems in childhood and early adolescence (Van den Bergh \u0026amp; Marcoen, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Van den Bergh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and specific cognitive deficits in late adolescence (Mennes et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our previous Gambling task-fMRI study in this population at age 20, revealed more scattered brain activation in the HA group and coherent activation patterns in the LMA group (Mennes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe finding of exclusive effects for the left hemisphere could point to lateralization of effect. Some other studies have also reported results in the left hemisphere only. For instance, in a recent study, Moog et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported smaller volumes of the left hippocampus, but not the right, in infants prenatally exposed to higher levels of maternal perceived distress during pregnancy. Additionally, recent work with fetal imaging showed decreased cerebellar-insular functional connectivity in fetuses of distressed mothers, for the left insula only (van den Heuvel et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given that the left hemisphere may develop relatively faster than the right hemisphere during the prenatal period (Andescavage et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), it could be more sensitive to prenatal environmental insults such as maternal distress. Interestingly, Vasung et al. (Vasung et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) specifically reported that the left IFG \u0026ndash; a key region that came up in our results \u0026ndash; has a faster volume growth than the right IFG, potentially making it more vulnerable. Yet, most human studies do not discuss laterality effects and no human study to date has specifically focused on laterality effects of prenatal stress exposure, nor its potential mechansims.\u003c/p\u003e \u003cp\u003eThe current study has several strengths and limitations. A first evident strength is the prospective design and the follow-up study spanning almost 29 years with an offspring response rate as high as 60 % at ae 28 years. Second, the proportion of pregnant women experiencing high levels of state anxiety was relatively high; 34% had a score of \u0026gt;\u0026thinsp;43 at 12\u0026ndash;22 weeks of pregnancy, which is a prerequisite for revealing, if any, effects of high anxiety. Third, sex, birth weight and maternal postnatal anxiety were selected as confounds in our main model predicting rsFC differences between LMA and HA group. Nevertheless, our study also has several limitation that should be noted. A first limitation of our study is the relatively small size of the sample (n\u0026thinsp;=\u0026thinsp;52). This did not allow us to conduct further analyses on specific sex-interactions, which could have been interesting. Secondly, this study did not include any physiological markers of distress/anxiety of the mother. We focused on the subjective, self-reported experience of the mother, rather than biological markers. However, since offspring outcome measures were biological markers (not maternal reported measures), shared method variance inflating the associations is not at stake. Third, no genetic sensitive design was used and, therefore, we cannot rule out genetic mechanisms at play. However, previous research examining the effect of prenatal exposure to objective, random stressors, such as natural disasters, have shown that genetic mechanisms cannot (only) explain the observed effects of stress exposure on the offspring\u0026rsquo;s brain (Jones et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings confirm that the adult connectome is influenced by the prenatal environment. Athough the brain architecture continues to show plasticity throughout adult life, some biological changes that compromise flexible adaptation and resilience might already be laid down early in life (McEwen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our current rsFC results indicate that individuals exposed to varying levels of maternal anxiety at 12 to 22 weeks of pregnancy show weaker functional brain connectivity of the medial prefrontal cortex (MPFC) and left prefontal cortex (LPFC) with some other brain regions in the left hemisphere, emphasizing an altered frontal neural network in the adults who were prenatally exposed to high maternal anxiety. Such alterations in frontal connectivity may put these adults at higher risk for executive dysfunction, mental health issues, and potentially even neurodegenerative disorders in later life (Faa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future work may seek to replicate our rsFC finding and try to characterize the potential neural vulnerability of prenatally exposed individuals better, e.g., based on properties of dynamic fluctuations in whole-brain rsFC analyses and/or task-related fMRI analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participating mothers and their adult children for participating in our study. Additionally, we would like to thank Marijke Braeken for managing data collection and all students that assisted her in gathering psychological and physiological data from the adult offspring in current follow-up phase. Finally, we would like to thank Njeri Kamau and Stijn Vos for putting together and analyzing demographic and life style data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PELS study is supported by the national funding agencies of the European Science Foundation (EuroSTRESS - PELS - 99930AB6-0CAC-423B-9527-7487B33085F3) participating in the Eurocores Program EuroSTRESS programme, i.e., the Brain and Cognition Programme of the Netherlands Organisation for Scientific Research (NWO) for the Netherlands. BVdB is project leader of the PELS study. BVdB was financially supported by European Commission Seventh Framework Programme (FP7\u0026mdash;HEALTH. 2011.2.2.2-2 BRAINAGE, grant agreement no: 279281). MvdH is supported by a Veni grant from the Dutch Organization for Scientific Research (NWO; VI.Veni.191G.025). CS received financial support from the Flemish Fonds for Scientific research (FWO; grant no. 12Y6122N).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript, nor the findings reported, were influenced by any financial source or sponsor. None of the authors have a conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe local ethical committee for experiments on human subjects approved all waves of the study. The work described has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All mothers had given informed consent for participating and publication of the results in all previous waves. Their 28-year-old offspring were clearly informed about the scanning procedures and physiological measures and gave their written informed consent for participation and publication of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor contributions included conception and study design (ET, MvdH, CS, AU, SS and BVdB), preprocessing data and statistical analysis (ET, MvdH, CS, TB, MM and BVdB), interpretation of results (ET, MvdH, CS, BVdB), drafting the manuscript work or revising it critically for important intellectual content (ET, MvdH, CS, and BVdB) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (all authors).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta, H., Tuulari, J. 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Network-based statistic: identifying differences in brain networks. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(4), 1197\u0026ndash;1207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2010.06.041\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.06.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fetal programming, resting-state functional connectivity, prospective study, Medial Prefrontal Cortex, prenatal stress ","lastPublishedDoi":"10.21203/rs.3.rs-1897872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1897872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e The connectome, constituting a unique fingerprint of a person’s brain, may be influenced by its prenatal environment, potentially affecting later-life resilience and mental health. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e We conducted a prospective resting-state functional Magnetic Resonance Imaging study in 28-year-old offspring (N=49) of mothers whose anxiety was monitored during pregnancy. Two offspring anxiety subgroups were defined: “High anxiety” (N=13) group versus “low-to-medium anxiety” (N=36) group, based on maternal self-reported state anxiety at 12-22 weeks of gestation. To predict resting-state functional connectivity, maternal state anxiety during pregnancy was included as a predictor in general linear models for both ROI-to-ROI and graph theoretical metrics. Sex, birth weight and postnatal anxiety were included as covariates.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e Higher maternal anxiety was associated with weaker functional connectivity of medial prefrontal cortex with left inferior frontal gyrus and left lateral prefontal cortex with left somatosensory motor gyrus in the offspring. While our results showed a general pattern of lower functional connectivity in adults prenatally exposed to maternal anxiety, we did not observe significant differences in global brain networks between groups. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e Weaker (medial) prefrontal cortex functional connectivity in the high anxiety adult offspring group suggests a long-term negative impact of prenatal exposure to high maternal anxiety, extending into adulthood. To prevent mental health problems at population level, universal primary prevention strategies should aim at lowering maternal anxiety during pregnancy.\u003c/p\u003e","manuscriptTitle":"Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-01 17:47:28","doi":"10.21203/rs.3.rs-1897872/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-12-16T22:48:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-11-23T22:58:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ed4c2638-0274-4f0e-8249-923da32444a4","date":"2022-11-21T14:38:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cdd6ef21-519a-4d8c-85d1-49e1f9b7506d","date":"2022-09-01T16:52:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-26T16:02:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-04T21:57:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-07-27T05:00:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2022-07-26T13:00:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e7e63f3e-4243-4d31-9d00-953d38833ff9","owner":[],"postedDate":"August 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:35:09+00:00","versionOfRecord":{"articleIdentity":"rs-1897872","link":"https://doi.org/10.1007/s11682-023-00787-1","journal":{"identity":"brain-imaging-and-behavior","isVorOnly":false,"title":"Brain Imaging and Behavior"},"publishedOn":"2023-06-29 21:24:52","publishedOnDateReadable":"June 29th, 2023"},"versionCreatedAt":"2022-08-01 17:47:28","video":"","vorDoi":"10.1007/s11682-023-00787-1","vorDoiUrl":"https://doi.org/10.1007/s11682-023-00787-1","workflowStages":[]},"version":"v1","identity":"rs-1897872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1897872","identity":"rs-1897872","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}