Exploring the relationship between maternal prenatal stress and brain structure in premature neonates.

BACKGROUND: Exposure to maternal stress in utero is associated with a range of adverse outcomes. We previously observed an association between maternal stress and white matter microstructure in a sample of infants born prematurely. In this study, we aimed to investigate the relationship between maternal trait anxiety, stressful life events and brain volumes.
METHODS: 221 infants (114 males, 107 females) born prematurely (median gestational age = 30.43 weeks [range 23.57-32.86]) underwent magnetic resonance imaging around term-equivalent age (mean = 42.20 weeks, SD = 1.60). Brain volumes were extracted for the following regions of interest: frontal lobe, temporal lobe, amygdala, hippocampus, thalamus and normalized to total brain volume. Multiple linear regressions were conducted to investigate the relationship between maternal anxiety/stress and brain volumes, controlling for gestational age at birth, postmenstrual age at scan, socioeconomic status, sex, days on total parenteral nutrition. Additional exploratory Tensor Based Morphometry analyses were performed to obtain voxel-wise brain volume changes from Jacobian determinant maps. RESULTS AND
CONCLUSION: In this large prospective study, we did not find evidence of a relationship between maternal prenatal stress or trait anxiety and brain volumes. This was the case for both the main analysis using a region-of-interest approach, and for the exploratory analysis using Jacobian determinant maps. We discuss these results in the context of conflicting evidence from previous studies and highlight the need for further research on premature infants, particularly including term-born controls.

Introduction

Poor maternal mental health during pregnancy represents a global public health problem, affecting 10–35% of pregnant women [1, 2]. Maternal prenatal psychological distress in the form of maternal depression, anxiety, and/or stress has been associated with adverse obstetrical and early behavioural outcomes, and an increased risk of neurodevelopmental and psychiatric disorders [3-7]. The biological basis of these effects is still poorly understood. However, studies by our group and others suggest prenatal maternal stress modulates the neurodevelopment of brain networks that underpin these disorders [8-11].

The brain regions that appear to be most vulnerable to maternal prenatal stress, other forms of early adversity, and psychopathology include the regions of the frontal lobe, temporal lobe, and limbic system [12-17]. These areas are connected by the uncinate fasciculus, and we recently reported an association between maternal stressful life events and increased diffusivity in this tract, in a sample of premature neonates [18].

However, although there is evidence suggesting that maternal prenatal stress affects the development of white matter tracts, evidence for early changes in structural brain development is inconclusive [12]. A small number of studies have examined this relationship in neonates and infants born at term, suggesting no evidence for differences in brain volumes in relation to maternal psychological distress [10, 19, 20]. Several studies have been conducted on older participants (i.e. childhood, adolescence, and adulthood), with the most commonly reported findings being cortical thinning [21-24], and either reductions [25-27], or increases in regional volumes [28-30].

While human studies so far have been inconclusive, animal studies have provided some limited evidence that maternal distress is related to early volume changes in the limbic system, particularly the hippocampus, amygdala, and thalamus [31-36].

We must also consider biological sex as a potential moderator of risk transmission, as several studies have reported volume changes in females, but not males [28, 30, 37]. In utero stress exposure has been associated with higher rates of mood disorders and anxiety [38-40] in females, and behavioural problems [41] and ADHD [6] in males. High maternal cortisol levels at 15 weeks’ gestation has been associated with increased right amygdala volumes and more affective problems in female, but not male, offspring [41].

In summary, although research has reported differences in brain structure in children, adolescents and adults exposed to maternal psychological distress, evidence in infants is inconclusive. To our knowledge, no studies have investigated this relationship in infants born prematurely. Premature birth is associated with changes in brain development [42] and an increased risk of adverse neurodevelopmental and psychiatric outcomes [43, 44]. In order to improve outcomes in these children, it is important to better understand the role that early adverse experiences such as exposure to prenatal stress could have in moderating these associations.

In this study, we investigated the relationship between maternal trait anxiety and stressful life events, and brain volumes in a large sample of infants born prematurely. We have previously shown differences in white matter microstructure in the uncinate fasciculus in this sample [18]. Based on previous literature, we hypothesized that maternal prenatal stress/trait anxiety would be associated with regional volume differences in areas adjacent to the uncinate fasciculus: frontal and temporal lobe volume, amygdala, hippocampus and thalamus. As the direction of effect in the literature is inconsistent (i.e. volumes found to be normal, enlarged, or decreased), we did not hypothesize a direction of effect. Lastly, given the heterogeneity of outcomes associated with maternal stress, as well as the complexity of functional anatomy in the chosen regions of interest (Text in S1 File), we conducted a whole brain analysis using Tensor Based Morphometry.

Methods and materials

Participants

Participants were mother-infant dyads who took part of the Evaluation of Preterm Imaging Study (ePRIME, [45]). Ethical approval was obtained from the Hammersmith and Queen Charlotte’s Research Ethics Committee (09/H0707/98) and informed written consent was obtained from all participants. Participants were recruited between April 2010 and July 2013 by screening 3619 admissions to level 1,2 and 3 neonatal units at 14 London Hospitals. Eligibility criteria for the main study included: infant born before 33 weeks gestational age, mother aged over 16 years, not a hospital inpatient, no major congenital malformation, no prior MRI, no care in a centre where preterm MRI was routine, no metallic implants, parents able to speak English, parents not subject to child protection proceedings. The ePrime cohort is representative of the UK NICU population in terms of birth weight, ethnicity, and prevalence of cerebral palsy (6%). Additional information is available in [45].

Data was available for n = 511 infants who were born prematurely (before 33 weeks of gestation) and scanned at term equivalent age. We excluded cases where the postmenstrual age at scan was >45 (n = 48), data was not available for all variables of interest (n = 160), women disclosed alcohol and/or drug abuse during pregnancy (n = 5), or the images showed major focal lesions such as periventricular leukomalacia, haemorrhagic parenchymal infarction, and other ischemic or haemorrhagic lesions (n = 40). In cases where a mother had multiple infants enrolled in the study (i.e. twin and triplet pregnancies), only one infant was randomly included in the final analysis. From the remaining sample, segmentation data for the structures of interest were available for n = 221 (Table 1), and a voxel-wise exploratory analysis using Tensor Based Morphometry was performed on the same 221 participants. The sample partially overlapped (n = 191) with a previous study [18]. Maternal socioeconomic status (SES) values were extracted from the Carstairs index, which takes into consideration variables such as unemployment, car ownership, household overcrowding, and social class [46].

Table 1

Obstetric and sociodemographic characteristics (n = 221).

Maternal Characteristics	Reported	Values
Stressful life events score	Median (range)	53 (0–270)
Trait anxiety score	Median (range)	36 (20–67)
Maternal age at scan	Mean (SD)	32.94 (5.70)
Maternal SES	Median (range)	17.44 (1.73–60.58)
Maternal education (years)	N (%)
16 or less		24 (10.8%)
17–19		30 (13.5%)
19+		156 (70.6%)
Still in full-time education		8 (3.6%)
Not reported		3 (1.3%)
Infant Characteristics	Reported	Values
Infant sex	N (%)
Male		114 (51.5%)
Female		107 (48.4%)
GA at birth (weeks)	Median (range)	30.43 (23.57–32.86)
PMA at scan (weeks)	Mean (SD)	42.20 (1.60)
Birth weight (grams)	Median (range)	1300 (600–2600)
OFC at birth (cm), n = 192	Median (range)	29.00 (21.80–36)
Number of days on TPN	Median (range)	6 (0–59)
Number of days on ventilation	Median (range)	0 (0–33)

Mean and SD are reported for normally distributed data; median and range are reported for non normally distributed data. GA = gestational age, OFC = Orbitofrontal circumference, PMA = postmenstrual age, SD = standard deviation, SES = socioeconomic status, TPN = Total Parenteral Nutrition, Maternal education = age at leaving formal education. No missing data unless otherwise specified in table.

Trait anxiety

The State Trait Anxiety Inventory (STAI, [47]) which measures levels of anxiety right now (i.e. state) and in general (i.e. trait), was administered at the time of the MRI scan. The analysis was restricted to trait anxiety, as it measures a relatively stable tendency to be prone to experiencing anxiety and thus extends to the period before birth.

For trait anxiety, missing values were imputed for cases in which a maximum of 10% of questions were missing. We imputed missing values by calculating the average response for the questions that were answered. Missing values were imputed for n = 23 (n = 18 missing 1/20 answers and n = 5 missing 2/20 answers).

Stressful life events

Stressful life events were assessed using a questionnaire adapted from the Avon Longitudinal Study of Parents and Children [48], which included yes/no answers to a list of potentially stressful life events the participant may have experienced in the year prior to the study visit (e.g. “Arguments with your partner increased”). Events were ranked according to severity [18] based on the Social Readjustment Rating Scale [49] and summed to form a final score that accounts for the number and severity of events experienced (Table J in S1 File). There were no missing data for this variable.

MR imaging

Magnetic resonance imaging data were acquired using an 8-channel phased array head coil, on a Philips 3T (Philips Medical Systems, Best, The Netherlands) MR system located on the intensive care unit. Imaging data was acquired as follows: Three-dimensional magnetization prepared rapid acquisition gradient echo (repetition time: 17 ms; echo time: 4.6 ms; flip angle: 13°; slice thickness: 0.8 mm; in-plane resolution: 0.82 × 0.82 mm2), T2-weighted turbo spin echo (repetition time: 8670 ms; echo time: 160 ms; flip angle: 90°; slice thickness: 2 mm; in-plane resolution: 0.86 × 0.86 mm2), and single shot echo planar DTI (repetition time: 7536 ms; echo time: 49 ms; flip angle: 90°; slice thickness: 2 mm; in-plane resolution: 2 x 2 mm2, 32 noncollinear gradient directions, b value of 750 s/mm2, 1 non-diffusion-weighted image, b = 0).

An experienced paediatrician supervised all scanning sessions. To enable a successful scan, the majority of infants included in this study were sedated with oral chloral hydrate (25–50 mg/kg) and monitored throughout the scan using pulse oximetry, temperature monitors and electrocardiography. Ear protection was used for all infants, in the form of earplugs molded from a silicone-based putty (President Putty; Coltene Whaledent, Mahwah, NJ) and neonatal earmuffs (MiniMuffs; Natus Medical Inc., San Carlos, CA).

Segmentations

Images were analysed using an automated processing pipeline optimised for neonates. Following motion correction, bias correction and brain extraction, T2w images were segmented using the Draw-EM algorithm, an open-source software optimised for neonatal brain segmentation [50]. Analysing MR images from infants, and especially preterm infants, poses unique challenges, such as motion, lower contrast-to-noise ratio, and partial volume effects; for a discussion of how these were addressed, see [50].

Based on previous literature and considerations of multiple comparisons issues, the following volumes were chosen as variables of interest: amygdala, hippocampus, thalamus, frontal lobe and temporal lobe (Table A in S1 File). To account for inter-individual differences in brain size, all brain volumes included in the analysis were normalized to total brain volume (i.e. dividing each regional volume by total brain volume).

Tensor-based morphometry

Template construction

A multivariate study-specific template was built using images from a subset of 161 participants. In order to reduce computational load, a smaller subset of 161 images meeting inclusion criteria (i.e. PMA at scan <45 weeks, no major lesions, and of good quality) were used to build the population template for this study. Using the Advanced Normalization Tools (ANTS) software to build the template [51], we applied field bias correction and used the Developing Human Connectome Project 40 weeks’ gestational age T2-weighted [52] and T2 tissue labels templates [50] as the target volumes for the template construction inputs. Iteration limit was set to the default (4 iterations).

Registration and log-Jacobian determinants

Images were registered to the study-specific template using the multimodal Symmetric Normalisation (SyN) algorithm from the ANTs software (n = 221) [53]. To improve image registration, two input modalities were used: T2-weighted images and T2-based tissue type segmentation [50]. T2-weighted deformation tensor fields (i.e. warps) from non-linear transformations of the registration process were used to compute a logarithm transformation of Jacobian determinant maps (i.e. deformation tensor field gradients), which reflect volume changes from the template at the voxel-level [54]. Jacobian determinants reflect the degree of transformation (i.e. the expanding or contracting) an image voxel has undergone in order to fit into the template space; therefore, providing information on the relative volumes of brain structures. Smoothing with a 4mm full-width half-maximum Gaussian filter was applied to the log-Jacobian determinants, in order to increase the signal-to-noise ratio. We re-sampled the smoothed log-Jacobian maps from the original isotropic voxel size of 0.5 cm3 to 1 cm3 before running statistical analyses in order to help with computation and memory constraints.

Statistical analysis

Main analysis

Statistical analysis was performed using R [55], with the main packages being psych [56], ggplot2 [57], and hmisc [58]. A minimal dataset and the analysis code including a comprehensive list of packages are available in the (Text in S1 File, S1 Dataset).

We assessed potential covariates using bivariate Spearman’s correlations (Table B in S1 File). Birth weight was excluded as a covariate from the main analysis as it was highly correlated with gestational age (r = .74, p < .001). The number of days on ventilation was also excluded as a covariate in the main analysis as it was highly correlated with the number of days on total parenteral nutrition (r = .60, p = .001), both measures provide information on the health status of the infant, and the distribution of days on total parenteral nutrition was less skewed. Maternal education and number of complications were not correlated with any of the variables of interest and thus were excluded in the main analysis. The regression models used were the same as those used in [18].

Multiple linear regressions were conducted to investigate the relationship between maternal trait anxiety/stress and brain volumes in premature infants. Our models contained the following predictors: stressful life events, trait anxiety, GA, PMA, SES, biological sex, days on total parenteral nutrition. The models were run separately for each dependent variable (frontal lobe grey matter, temporal lobe grey matter, thalamus, amygdala, hippocampus). Correction for multiple comparisons was performed using False Discovery Rate (FDR), and all p values reported below are uncorrected. Unless otherwise specified, all regression models met assumptions for multiple regression (i.e. normality, linearity, homogeneity of variance, uncorrelated predictors, no influential outliers, independence of residuals, [59], Table C in S1 File). One outlier was removed from all regressions due to violating assumptions of normality (days TPN = 59).

Exploratory analysis of tensor based morphometry

Voxel-wise statistical analyses were performed using FSL’s randomise nonparametric permutation testing [60]. A general linear model tested for relationships between log-Jacobian values at the voxel level and the outcome variables of interest (maternal prenatal stress and trait anxiety). We included gestational age at birth, postmenstrual age at scan, socioeconomic status, sex and days on total parenteral nutrition as covariates in our model. We ran 10,000 permutations of the data and used 3D Threshold-Free Cluster Enhancement (TFCE) and Family Wise Error (FWE) to correct for multiple comparisons [61]. Voxels with FWE-corrected P-values at a threshold of P<0.05 were considered to be significant.

Results

Frontal grey matter volume

The model performed better than expected by chance (p < .001) and accounted for 42% of variance in frontal lobe volume (predicted by PMA, with B = .0058 and SES, with B = .00015). There was no association between frontal grey matter volume and either stressful life events (B = .000018, t = 1.27, p = .204) or trait anxiety (B = -.000024, t = -.304, p = .761, Table D in S1 File). An alternative model removing these two variables performed better (R2 = .42, AIC = -1338.38) than the original model (R2 = .41, AIC = -1336.09), suggesting that the best fit for a model predicting frontal grey matter volume is one without stressful life events or trait anxiety.

Further exploring this relationship with direct Spearman correlations (in the absence of covariates) showed no evidence for a relationship (Fig 1) between frontal grey matter volume and stressful life events (r = .04, p = .593) or trait anxiety (r = .006, p = .929).

Fig 1

Scatterplots for correlations between maternal trait anxiety/stress and volumes for the frontal and temporal lobes.

See Fig A in S1 File for partial regression scatterplots.

Temporal grey matter volume

The model did not meet assumptions of homogeneity of variance, and thus we report the heteroscedasticity corrected covariance matrix (Table F in S1 File). The model accounted for 45% of variance in temporal grey matter volume (predicted by PMA, with B = .0025 and SES, with B = .000051). There was no relationship with stressful life events (B = .0000027, t = .495, p = .621) or trait anxiety (B = .0000047, t = .140, p = .889) (Table E in S1 File).

An alternative model removing these two variables performed better (R2 = .46, AIC = -.1735.3) than the original model (R2 = .46, AIC = -1731.5), suggesting that the best fit for a model predicting temporal grey matter volume is one without stressful life events or trait anxiety.

Further exploring this relationship with direct Spearman correlations (in the absence of covariates) showed no evidence for a relationship (Fig 1) between temporal grey matter volume and stressful life events (r = .04, p = .667) or trait anxiety (r = .05, p = .440).

Hippocampal volume

Hippocampal volume was not accurately predicted by the model (R2 = .06, F(8,211) = 1.58, p = .131), with the only significant predictor being socioeconomic status (B = -.0000040, t = -2.08, p = .039). As the model showed deviations from linearity (Text in S1 File), we repeated the analysis removing 3 outliers (stressful life event scores >250). The new model did not adequately predict hippocampal volume either (R2 = .07, F(8,208) = 2.17, p = .031), but stressful life events was a significant predictor (B = .0000012, t = 2.57, p = .011), alongside socioeconomic status (B = -.0000045, t = -2.36, p = .019) (Table G in S1 File). This result did not survive correction for multiple comparisons and visual inspection of the plot suggests no relationship between the variables. An alternative model excluding trait anxiety and stressful life events performed worse (R2 = .05, p = .111), with a higher AIC of -2840.17 compared with -.2842.99. Further exploring this relationship with direct Spearman correlations (in the absence of covariates), suggested a positive correlation between hippocampal volume and stressful life events (r = .16, p = .020), but not trait anxiety (r = -.004, p = .959)(Fig 2).

Fig 2

Scatterplots for correlations between maternal trait anxiety/stress and volumes for the hippocampus, amygdala, and thalamus.

See Fig B in S1 File for partial regression scatterplots.

Amygdala volume

For amygdala volume, the model performed better than expected by chance and accounted for 27% of variance in outcome measures (predicted by PMA, with B = -.000064 and SES, with B = -.0000023). There was no relationship with stressful life events (B = -.000000028, t = -.114, p = .909) or trait anxiety (B = -.0000013, t = -1.000, p = .319) (Table H in S1 File). An alternative model removing these two variables performed better (R2 = .27, AIC = -3123.95) than the original model (R2 = .27, AIC = -3121.05). Direct Spearman correlations showed no evidence for a relationship between amygdala volume and stressful life events (r = .02, p = .770) or trait anxiety (r = -.05, p = .505) (Fig 2). As the model showed deviations from linearity (Text in S1 File), we repeated the analysis removing 3 outliers (stressful life event scores >250). The new model revealed similar results (Table H in S1 File).

Thalamus volume

Thalamus volume was not accurately predicted by the model (R2 = .08, F(8,210) = 2.40, p = .017). There was no significant relationship between thalamus volume and stressful events (B = -.00000021, t = -.10, p = .920) or trait anxiety (B = -0.00000067, t = -.05, p = .953) (Table I in S1 File). Direct Spearman correlations showed that there was no relationship between thalamus volume and stressful life events (r = -.03, p = .684) or trait anxiety (r = .03, p = .713) (Fig 2).

Exploratory analysis subdividing the sample by sex

As visual inspection of scatterplots suggested that the relationship between maternal trait anxiety/stress and brain volumes may be influenced by infant sex, we repeated our analysis subdividing the sample into males and females. There were no significant relationships between maternal trait anxiety/stressful events and infant volume in frontal lobe, temporal lobe, amygdala, thalamus (Text in S1 File).

In our female sample, hippocampal volume was not accurately predicted by the model (R2 = .12, F(7,95) = 1.79,p = .097), but the only significant predictor was stressful life events (B = .0000017, t = 2.65, p = .009). This did not survive correction for multiple comparisons. The relationship between hippocampal volume and stressful life events was not observed in males.

Voxel wise tensor based morphometry results

In order to explore whether maternal stress or trait anxiety were associated with neonatal brain volumes at the voxel-level, we conducted Tensor Based Morphometry analyses to obtain Jacobian determinant maps which reflect relative voxel-wise volume changes. Tensor Based Morphometry did not reveal any significant relationships between the smoothed log-Jacobian determinants and maternal prenatal stress or trait anxiety at the FWE p<0.05 threshold. The T-statistic maps (Fig 3) show the test statistic at the voxel level before corrections for multiple comparisons were applied. The whole-brain t-stat maps show generally low t-stat values indicating poor associations between maternal trait anxiety (Fig 3a), or stressful life events (Fig 3b) and log-Jacobian determinants. Nifti files for the t-stat maps are available in the S2 File.

Fig 3

T-statistic maps showing the relationships between voxel-wise log-Jacobian determinants and (a) maternal trait anxiety and (b) stressful life events.

Discussion

In this study, we did not find evidence for a relationship between maternal stress (i.e. stressful life events and trait anxiety) and grey matter volumes in a large sample of infants born prematurely. These results were consistent across 2 methodologies, using both a whole-brain voxel-wise approach, as well as a region of interest analysis (i.e. hippocampus, amygdala, thalamus, frontal lobe, and temporal lobe).

Interpretation of these findings raises important questions for a field that, to date, has been complicated by inconsistencies between studies along multiple dimensions. These include differences in the samples studied (e.g. age, gender), imaging protocols, definitions of stress, and sample size [12, 62]. Our findings are in line with [10] who reported no difference in right amygdala volume in a large sample of neonates (n = 157) exposed to maternal depression in the second trimester of pregnancy. Similarly, [20] reported no difference in hippocampal volume at birth, but suggested that the hippocampal volume exhibits slower growth in response to exposure to maternal trait anxiety in utero, with smaller volumes being observed at 6 months of age. In a study of exposure to selective serotonin reuptake inhibitors, differences in volume were reported in the right amygdala and right insula [19], but the authors reported no differences in limbic system volumes between untreated depression and controls. Further, [27], in a study of young adults, reported no association between maternal prenatal stress and hippocampal volume, which was instead associated with postnatal anxiety. Studies that have reported associations with maternal distress, primarily regarding cortical thinning in regions of the frontal and temporal lobes [21-24] have been conducted on children rather than infants. Overall, at present, there seems to be no consistent evidence that maternal prenatal stress is associated with neonatal brain volumes, in line with our findings.

This is in stark contrast to the diffusion MRI literature, where studies have consistently reported alterations in limbic and prefrontal microstructure in neonates and infants exposed to maternal psychological distress in utero [9–11, 18, 63]. Further, given that diffusion MRI studies have reported also collecting T2-weighted images, we need to consider whether the lack of studies reporting structural MRI analyses may be driven by the failure to report non-significant findings (i.e. the “file-drawer” problem, [64]). In a recent study published on an overlapping sample [18], we showed differences in white matter microstructure in the uncinate fasciculus in relation to maternal stressful life events. Interestingly, a few of the studies which failed to observe differences in brain structure in relation to maternal psychological distress, reported alterations in white matter microstructure. For example, [10], observed lower fractional anisotropy in the right amygdala of neonates exposed to maternal depression, with no evidence for differences in amygdala volume. Converging evidence suggests that maternal prenatal stress can alter the developing connectome, with differences being most commonly reported in fronto-limbic brain networks (using fMRI and dMRI), with limited evidence for differences in brain structure [62]. Further studies conducted on term-born and preterm infants and reporting on both structural and diffusion MRI are required in order to clarify whether white matter is especially vulnerable to maternal prenatal stress. This is of particular importance given that white matter injury is the most common neuropathology in infants born prematurely [65-67] and white matter may therefore be more vulnerable to additional stressors.

The current study also raised the possibility that the relationship between maternal distress and early brain development may be at least partly influenced by sex differences in the vulnerability to maternal stress in utero. Maternal stressful life events were associated with increased hippocampal volume in the whole sample and in females, but not males; however, these findings were not found to be statistically significant after correction for multiple comparisons.

It is important to highlight that our sample consisted of preterm infants, a population known to have regional brain volume abnormalities [42] and adverse neuropsychiatric and developmental outcomes [44, 68]. We caution against generalizing these findings to infants born at term, and suggest that further studies with term-born controls are needed to further clarify the role that early adverse experiences such as maternal stress may have in moderating the association between preterm birth and adverse outcomes in this vulnerable population.

Although in this study we have examined mean bilateral volumes, several studies of children have reported unilateral differences in volume, such as increased left amygdala volume in girls exposed to pregnancy-specific anxiety, but not boys [37] and greater right amygdala volume in girls exposed to maternal depression, but not boys [30]. Although our analysis was based on mean volumes, the whole-brain analysis did not suggest lateralized differences in volume associated with maternal stress or trait anxiety. Further, other studies that have reported differences in volumes in areas such as the frontal lobe, reported these in very specific areas, such as the mid-dorsolateral frontal cortex [27] or left medial temporal lobe [26]. This may mean that any changes associated with maternal prenatal stress may be more subtle, and thus not affect the overall volume of the frontal or temporal lobes. However, our findings using a voxel-wise whole-brain analysis did not suggest any volume differences associated with maternal stress.

Our findings are not in line with those of [26], who reported decreased amygdala volume, or [28], who reported increased amygdala volume in girls. However, both of these studies were conducted on adult samples, and measures of maternal stress were acquired retrospectively. The biological basis of these potential sex differences is unclear, but may include sex differences in placental functioning, fetal exposure to adrenal hormones and testosterone, as well as various epigenetic mechanisms [69].

Further, there is some evidence to suggest that the child’s development may be more susceptible to maternal pregnancy-specific anxiety, rather than generalized anxiety or stress, as well as that the timing of stress exposure is an important factor to consider [20]. A study [25] suggested that pregnancy anxiety is associated with differences in gray-matter volume at age 6–9, and later reported that neither state anxiety nor depression explained any additional variance in developmental outcomes after accounting for pregnancy-specific anxiety [70]. Future studies should include measures of pregnancy-specific anxiety and assess stress exposure during early, mid, and late gestation.

Although not one of the measures of interest in this study, socio-economic status (which was entered into the regression models as a covariate) was consistently associated with differences in brain volume in our sample of infants born prematurely. Based on these findings, we recommend that future studies should investigate the relationship between socioeconomic status and early brain development, particularly given that low SES is known to be associated with adverse mental health, underreporting of mental health concerns, as well as lack of access to mental health services [71].

It is important to note that although this study was based on subjective self-report measures, the reliability of maternal recall for pregnancy and birth related events appears to be high [72-74], false positive reports of adverse life events are rare [75], and self-reported trait anxiety scores are relatively stable in the perinatal period [76] (See Text in S1 File for further discussion). Future studies should consider including both subjective and laboratory-based measures of stress or anxiety, such as autonomic function, or blood cortisol.

In conclusion, based on our previous findings in an overlapping sample [18], we expected an association between maternal stress and brain volumes in areas adjacent to the uncinate fasciculus tract. To our knowledge, the current study is the first one to examine this relationship in premature infants. In our sample, there is no credible evidence that maternal prenatal stressful life events or trait anxiety influence volumes in the hippocampus, amygdala, thalamus, frontal grey matter or temporal grey matter volume in preterm infants. Our findings are strengthened by an exploratory voxel-wise analysis, and in line with previous literature. Our findings are of particular interest in the context of having reported differences in white matter microstructure in an overlapping sample, using the same statistical methods [18]. It is important to highlight the proximity of our findings to birth, as this minimises the potential confounding influences within the postnatal environment on brain development, which has been a limitation of most prior human studies. We hope that these findings can contribute to a more balanced view of the literature and inform further research into maternal stress and early brain development.

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26 Jan 2021

PONE-D-20-37926

Exploring the relationship between maternal prenatal stress and structural brain development in premature neonates

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b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this study the authors aim to find an association between maternal stress and brain volumes in areas adjacent to the uncinate fasciculus tract. They found no evidence that maternal prenatal stressful life events or trait anxiety influence volumes in the hippocampus, amygdala, thalamus, frontal grey matter or temporal grey matter volume.

The study is well designed and even if complex to understand it is well written and it is interesting to the field.

This reviewer would want to raise some concerns:

The authors would maybe want to discuss a bit further how studying trait anxiety instead of state anxiety could have driven the findings of non-association. While the authors acknowledge this in the discussion (page 17, lines 424) the first line of the introduction talks about poor maternal health during pregnancy and could prone the reader, non-expert in STAI, to confusion.

Based on the number of days on parenteral nutrition being correlated with the number of days on mechanical ventilation the authors exclude the latter which is surprising for this reviewer , as based on the theoretical background and the literature on the impact of duration of mechanical ventilation and bronchopulmonary dysplasia on cognitive outcomes one might speculate that duration on mechanical ventilation would have a bigger effect size on brain volumes than that of duration of parenteral nutrition. The argument the authors describe as to exclude this “both provide information on the health status of the infant” seems to reflect an arbitrary decision instead of a decision based on some preliminary analysis of the effect of both variables.

Maternal education is excluded but SES is included in the models. The authors should explain how maternal SES was measured and classified.

Reviewer #2: PONE-D-20-37926

This study examines the associations between maternal stress and brain volumes in a group of 221 premature-born infants scanned around term age. They find weak associations between stressful life events and hippocampal volume, but these do not survive multiple comparison correction. An exploratory tensor-based morphometry analysis of the whole brain showed similar findings, suggesting no meaningful relationship between maternal stress and brain volumes in premature infants. Strengths of this study include the large sample and the two types of analysis to help confirm findings. The manuscript is well-written, and I think this study provides a valuable contribution to the literature to help understand the relationships between maternal stress and infant/child brain outcomes. I do have a few (mostly minor) suggestions to improve the manuscript.

1. The title of the manuscript refers to “structural brain development”, and this language appears elsewhere in the paper (e.g., p. 17 “associated with changes in brain volume”). However, this was a cross-sectional study that examined brain volumes at a single time point and did not measure changes over time, so I’d suggest the authors refer instead to “brain structure” or “brain volume” rather than brain development.

2. The stress and anxiety measures were administered after the child’s birth. While the authors do point to the stability of these reports over time, more information would be useful. Can you provide some citations for the stability of trait anxiety during pregnancy and the postpartum period? Having a preterm baby would be quite stressful; does this influence retrospective reports? It would be valuable to have more information about the stressful life events measure. Was the time period of reporting restricted to pregnancy, or did it include the time before pregnancy? What is the full range of possible scores for this stressful life events measure? Finally, it would be useful to briefly describe what you mean by “maternal anxiety/stress” in the abstract.

3. Why was the study-specific template for analysis based on 161 infants instead of the full sample? Note that I’m not suggesting you need to redo this, but some rationale would be helpful.

4. Women who disclosed alcohol and/or drug abuse were excluded. Were women excluded for any use at all? (in which case, should say “use” instead of “abuse”) If not, please define what you mean by alcohol/drug abuse, and what the cutoffs were.

5. Please define units in Table 1 (e.g., for maternal education, birthweight, GA, PMA, OFC).

6. Preterm infants have brain differences from full-term infants, and it is possible that associations between maternal stress/anxiety and brain volumes are masked by differences caused by preterm birth. Therefore, while this study represents an important contribution to the literature, the findings cannot necessarily be generalized to full-term born children. I think the differences between preterm infant brains and term-born infant brains, and the implications for these findings merits some discussion.

7. It is disappointing that the data is not publicly available. Ethics constraints may restrict data sharing, but directing inquiries to your local ethics board is not helpful. Perhaps interested parties could contact the authors instead?

8. Typo/word errors:

a. In the methods, “we inputted missing values”; do you mean imputed?

b. P. 9, line 202 should likely say “constraints” instead of “restraints”

c. P. 4, line 98 “women disclosed alcohol” needs “who” inserted

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

3 Mar 2021

Response to Reviewers

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Author’s response: Journal requirements #1

We have now updated our manuscript to ensure it meets PLOS ONE’s style requirements.

2. In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as:

a) the recruitment date range (month and year),

b) a statement as to whether your sample can be considered representative of a larger population, and

c) a description of how participants were recruited.

Author’s response: Journal requirements #2

In response to this comment, we have added the following information to the Methods section:

“Participants were recruited between April 2010 and July 2013 by screening 3619 admissions to level 1,2 and 3 neonatal units at 14 London Hospitals. Eligibility criteria for the main study included: infant born before 33 weeks gestational age, mother aged over 16 years, not a hospital inpatient, no major congenital malformation, no prior MRI, no care in a centre where preterm MRI was routine, no metallic implants, parents able to speak English, parents not subject to child protection proceedings. The ePrime cohort is representative of the UK NICU population in terms of birth weight, ethnicity, and prevalence of cerebral palsy (6%). Additional information is available in Edwards et al. (2009)”

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Author’s response: Journal requirements #3

As our data contained sensitive and potentially identifiable information, we were unable to share the full anonymised dataset necessary to replicate the study findings. However, we have now shared a dataset (S3 Dataset) containing the following variables: stressful life events score, trait anxiety score, gestational age at birth, postmenstrual age at scan, socioeconomic status, sex, and brain volumes for the regions of interest.

The variables above represent the vast majority of variables included in the regression models reported in this manuscript. We were unable to share the variable “maternal age”, as this combination of variables would increase the risk of the data being identifiable. We have conducted a sensitivity analysis removing “maternal age” from the regression models, with no major differences in the results (i.e. there was still no significant relationship between maternal stress or anxiety and any of the volumes of interest).

We have now updated our data availability statement as follows:

“Relevant data have been shared within the Supporting Information files. This data includes the majority of the variables included in the regression models reported in the manuscript. As this data contained sensitive and potentially identifiable information, we were unable to share the full anonymised dataset necessary to replicate the study findings. Data for the variable "maternal age" could not be shared, as this combination of variables would increase the risk of the data being identifiable. Requests related to data access can be directed to the Hammersmith and Queen Charlotte’s Research Ethics Committee (westlondon.rec@hra.nhs.uk).”

4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Author’s response: Journal requirements #4

We thank the editor for this comment. We have now updated the captions for our supporting information files at the end of the manuscript, and have updated all in-text citations to match accordingly.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Author’s response #1

We thank the reviewers for their comments.

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Author’s response #2

We thank the reviewers for their comments.

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Author’s response #3

We thank the reviewers for their comments

We have now shared a minimal de-identified data set (S3. Dataset). Please see the section “Additional information regarding data and code” in “S1 Supplement” for details regarding this dataset.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Author’s response #3

We thank the reviewers for their comments.

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this study the authors aim to find an association between maternal stress and brain volumes in areas adjacent to the uncinate fasciculus tract. They found no evidence that maternal prenatal stressful life events or trait anxiety influence volumes in the hippocampus, amygdala, thalamus, frontal grey matter or temporal grey matter volume.

The study is well designed and even if complex to understand it is well written and it is interesting to the field.

This reviewer would want to raise some concerns:

The authors would maybe want to discuss a bit further how studying trait anxiety instead of state anxiety could have driven the findings of non-association. While the authors acknowledge this in the discussion (page 17, lines 424) the first line of the introduction talks about poor maternal health during pregnancy and could prone the reader, non-expert in STAI, to confusion.

Author’s response: Reviewer 1 Comment 1

We thank the reviewer for this insightful comment.

Firstly, we would like to draw the reviewer’s attention to S1 Supplement – Supporting Information, where we have included a section on “Additional information on anxiety and stressful life events”, which we hope motivates our decision to study trait anxiety instead of state anxiety.

To further clarify, we have added the following paragraph to S1 Supplement:

“It is important to acknowledge the possibility that focusing on trait anxiety rather than state anxiety could have influenced our findings. While some previous studies in infants have reported differences in brain development related to maternal state anxiety (Dean et al., 2018) or a combined score of state and trait anxiety (Rifkin-Graboi et al., 2015), these studies analysed white matter microstructure using diffusion MRI. To our knowledge, the only other study which has used the STAI to assess differences in brain volume in infants (Qiu et al., 2013), only included trait anxiety measures. Their results were in line with our current findings, as they reported that maternal trait anxiety was not associated with any differences in hippocampal volume at birth.

Our decision to study trait anxiety instead of state anxiety is motivated by the fact that trait anxiety is considered to be a relatively stable personality trait, while state anxiety reflects a transient anxious state. State anxiety scores were not included in the analysis, as this measure was designed to be sensitive to the conditions under which the questionnaire is administered (Spielberger, 1972). It is likely that state anxiety scores would have been influenced by the stress-inducing situations of having a preterm baby, being in a hospital environment, and attending an appointment for an infant MRI scan, and thus would not be representative of prenatal anxiety levels.

In contrast, trait anxiety scores imply a generalized and enduring predisposition to respond to situations in an anxious manner, are less likely to be influenced by situational variables (Spielberger et al., 1972) and are thus more likely to be reflective of the mother’s prenatal anxiety levels. In the perinatal period, studies have reported that self-reported trait anxiety is not a transient state, but is relatively stable between pregnancy and 7 months postpartum (Grant et al., 2008). “

We would also like to draw the reviewer’s attention to our Methods section, where we have included the sentence “The analysis was restricted to trait anxiety, as it measures a relatively stable tendency to be prone to experiencing anxiety and thus extends to the period before birth “. To further emphasize this point, we have now also added the following to the Discussion section of the manuscript: “self-reported trait anxiety scores are relatively stable in the perinatal period (Grant et al., 2008) (See S1 Supplement for further discussion).”

Lastly, to avoid any confusion for the reader, we have added further clarifications in the manuscript and replaced multiple instances of “anxiety” with “trait anxiety”.

Based on the number of days on parenteral nutrition being correlated with the number of days on mechanical ventilation the authors exclude the latter which is surprising for this reviewer , as based on the theoretical background and the literature on the impact of duration of mechanical ventilation and bronchopulmonary dysplasia on cognitive outcomes one might speculate that duration on mechanical ventilation would have a bigger effect size on brain volumes than that of duration of parenteral nutrition. The argument the authors describe as to exclude this “both provide information on the health status of the infant” seems to reflect an arbitrary decision instead of a decision based on some preliminary analysis of the effect of both variables.

Author’s response: Reviewer 1 Comment 2

We thank the reviewer for the opportunity to further clarify our analytical decision.

We would like to reassure the reviewer that our decision to include “days on parenteral nutrition” in the model instead of “days on mechanical ventilation” was not arbitrary, but was based on several reasons which we outline below.

First of all, “days on total parenteral nutrition” was included in the model over “days on ventilation” based on the distribution of scores (Days TPN: median=6, range 0-59, Days Ventilation: median=0, range 0-33), as it was less skewed. To further clarify this, we have now also added additional information to our Methods section (Statistical analysis: Main analysis) where we have followed “both measures provide information on the health status of the infant” with “and the distribution of days on total parenteral nutrition was less skewed”.

Secondly, in our previous paper using diffusion tensor imaging data on an overlapping sample (Lautarescu et al., 2020), we have used days on total parenteral nutrition in the model, based on its association with uncinate fasciculus microstructure. The regression models were the same for the two papers, and our belief is that the consistency in methodology helps strengthen our argument. This is highlighted in our manuscript (“The regression models used were the same as those used in Lautarescu et al., 2020”).

In Lautarescu et al., 2020, we have performed numerous sensitivity analyses to check the robustness of the results, and reported that the pattern of results remained the same when including days of ventilation in the model instead of days on total parenteral nutrition. Because of this, we felt confident in conducting the analysis on brain volumes using the same statistical models (i.e. including total parenteral nutrition in the model).

Following the reviewer’s comment, we have repeated our main analysis using days on ventilation in the model, instead of days on parenteral nutrition. The pattern of results remained the same, with no significant association between maternal stressful life events or maternal trait anxiety, and any of the brain volumes of interest. We report a summary of the results of this sensitivity analysis in S1 Supplement (Sensitivity analysis: Days on ventilation), and the whole sensitivity analysis can be viewed in the RMarkdown document containing the code.

Maternal education is excluded but SES is included in the models. The authors should explain how maternal SES was measured and classified.

Author’s response: Reviewer 1 Comment 3

In response to the reviewer’s helpful comment, we have now added the following sentence to our Methods:

“Maternal socioeconomic status (SES) values were extracted from the Carstairs index, which takes into consideration variables such as unemployment, car ownership, household overcrowding, and social class (Carstairs, 1991).”

Reviewer #2: PONE-D-20-37926

This study examines the associations between maternal stress and brain volumes in a group of 221 premature-born infants scanned around term age. They find weak associations between stressful life events and hippocampal volume, but these do not survive multiple comparison correction. An exploratory tensor-based morphometry analysis of the whole brain showed similar findings, suggesting no meaningful relationship between maternal stress and brain volumes in premature infants. Strengths of this study include the large sample and the two types of analysis to help confirm findings. The manuscript is well-written, and I think this study provides a valuable contribution to the literature to help understand the relationships between maternal stress and infant/child brain outcomes. I do have a few (mostly minor) suggestions to improve the manuscript.

1. The title of the manuscript refers to “structural brain development”, and this language appears elsewhere in the paper (e.g., p. 17 “associated with changes in brain volume”). However, this was a cross-sectional study that examined brain volumes at a single time point and did not measure changes over time, so I’d suggest the authors refer instead to “brain structure” or “brain volume” rather than brain development.

Author’s response: Reviewer 2 Comment 1

We appreciate this helpful comment and agree that using terms such as “development” and “changes” can be misleading to the reader. In response to this comment, we have made changes as outlined below:

We have changed the title of our manuscript from “Exploring the relationship between maternal prenatal stress and structural brain development in premature neonates” to “Exploring the relationship between maternal prenatal stress and brain structure in premature neonates”.

We have changed all instances in which we referred to “changes” or “development” in the context of cross-sectional research. Please see the “track changes” version of the manuscript and S1 Supplement to view the changes.

2. The stress and anxiety measures were administered after the child’s birth. While the authors do point to the stability of these reports over time, more information would be useful. Can you provide some citations for the stability of trait anxiety during pregnancy and the postpartum period? Having a preterm baby would be quite stressful; does this influence retrospective reports? It would be valuable to have more information about the stressful life events measure. Was the time period of reporting restricted to pregnancy, or did it include the time before pregnancy? What is the full range of possible scores for this stressful life events measure? Finally, it would be useful to briefly describe what you mean by “maternal anxiety/stress” in the abstract.

Author’s response: Reviewer 2 Comment 2

We thank the reviewer for this thoughtful comment, and for the opportunity to further clarify and discuss these issues. We are addressing each of the reviewer’s questions in turn, below.

“While the authors do point to the stability of these reports over time, more information would be useful. Can you provide some citations for the stability of trait anxiety during pregnancy and the postpartum period?”

We would like to draw the reviewer’s attention to S1 Supplement – Supporting Information, where we have included a section on “Additional information on anxiety and stressful life events”. In this section, we argue that trait anxiety is considered to be a relatively stable personality trait, while state anxiety reflects a transient anxious state. Here, we reference a study by Grant et al (2008) where self-reported trait anxiety is suggested to be relatively stable between pregnancy and 7 months postpartum. We also point the reader to several studies suggesting high reliability between data collected during pregnancy and self-report measures collected up to 30 years after birth (Tomeo et al., 1999, Quigley et al., 2007).

Having a preterm baby would be quite stressful; does this influence retrospective reports?

We agree with the reviewer that having a preterm baby is a stressful experience which could influence retrospective reports. This is the reason for our choice to use trait anxiety, over state anxiety measures. In response to the reviewer’s comments, we have added the following paragraph to S1 Supplement:

“Our decision to study trait anxiety instead of state anxiety is motivated by the fact that trait anxiety is considered to be a relatively stable personality trait, while state anxiety reflects a transient anxious state. State anxiety scores were not included in the analysis, as this measure was designed to be sensitive to the conditions under which the questionnaire is administered (Spielberger, 1972). It is likely that state anxiety scores would have been influenced by the stress-inducing situations of having a preterm baby, being in a hospital environment, and attending an appointment for an infant MRI scan, and thus would not be representative of prenatal anxiety levels.

In contrast, trait anxiety scores imply a generalized and enduring predisposition to respond to situations in an anxious manner, are less likely to be influenced by situational variables (Spielberger et al., 1972) and are thus more likely to be reflective of the mother’s prenatal anxiety levels. In the perinatal period, studies have reported that self-reported trait anxiety is not a transient state, but is relatively stable between pregnancy and 7 months postpartum (Grant et al., 2008)”

We would also like to draw the reviewer’s attention to our Methods section, where we have included the sentence “The analysis was restricted to trait anxiety, as it measures a relatively stable tendency to be prone to experiencing anxiety and thus extends to the period before birth “. To further emphasize this point, we have now also added the following to the Discussion section of the manuscript: “self-reported trait anxiety scores are relatively stable in the perinatal period (Grant et al., 2008) (See S1 Supplement for further discussion).”

Lastly, to further clarify, we have also added the following information to S1 Supplement:

“It is important to acknowledge the possibility that focusing on trait anxiety rather than state anxiety could have influenced our findings. While some previous studies in infants have reported changes in brain development related to maternal state anxiety (Dean et al., 2018) or a combined score of state and trait anxiety (Rifkin-Graboi et al., 2015), these studies analysed white matter microstructure using diffusion MRI. To our knowledge, the only other study which has used the STAI to assess changes in brain volume in infants (Qiu et al., 2013), only included trait anxiety measures. Their results were in line with our current findings, as they reported that maternal trait anxiety was not associated with any differences in hippocampal volume at birth.”

It would be valuable to have more information about the stressful life events measure. Was the time period of reporting restricted to pregnancy, or did it include the time before pregnancy? What is the full range of possible scores for this stressful life events measure?

To answer the question regarding the time period of reporting, we would like to draw the reviewer’s attention to the following sentence in the manuscript’s Methods section “which included yes/no answers to a list of potentially stressful life events the participant may have experienced in the year prior to the study visit”. This period includes the pregnancy and several months beforehand.

We agree with the reviewer that further information would be helpful. In response to this comment, we have now added an additional section in S1 Supplement, named “Additional information regarding the stressful life events measure”. This details the list of events and the frequency of responses per each item, as well as additional information regarding how the scores were computed.

Finally, it would be useful to briefly describe what you mean by “maternal anxiety/stress” in the abstract.

We have now clarified this in the abstract, by specifying “trait anxiety and stressful life events”

3. Why was the study-specific template for analysis based on 161 infants instead of the full sample? Note that I’m not suggesting you need to redo this, but some rationale would be helpful.

Author’s response: Reviewer 2 Comment 3

We thank the reviewer for the opportunity to clarify this analytical decision. The reason to use a sub-sample of the population was primarily to reduce computational load. Please note that increasing sample size has been reported to have minimal impact in template creation after a certain threshold (Yang et al., 2020).

In response to this comment, we have now clarified this in the manuscript (please see Methods and Materials – Tensor Based Morphometry – Template construction) by adding the following sentence:

“In order to reduce computational load, a smaller subset of 161 images meeting inclusion criteria (i.e. PMA at scan <45 weeks, no major lesions, and of good quality) were used to build the population template for this study.”

4. Women who disclosed alcohol and/or drug abuse were excluded. Were women excluded for any use at all? (in which case, should say “use” instead of “abuse”) If not, please define what you mean by alcohol/drug abuse, and what the cutoffs were.

Author’s response: Reviewer 2 Comment 4

This variable was based on self-disclosure by the mother and thus no further information is available regarding cutoffs. No further information was collected regarding alcohol and drug use.

5. Please define units in Table 1 (e.g., for maternal education, birthweight, GA, PMA, OFC).

Author’s response: Reviewer 2 Comment 5

In response to the reviewer’s comment, we have defined the units in Table 1 as follows: maternal education (years), birth weight (grams), GA at birth (weeks), PMA at scan (weeks), OFC at birth (cm).

6. Preterm infants have brain differences from full-term infants, and it is possible that associations between maternal stress/anxiety and brain volumes are masked by differences caused by preterm birth. Therefore, while this study represents an important contribution to the literature, the findings cannot necessarily be generalized to full-term born children. I think the differences between preterm infant brains and term-born infant brains, and the implications for these findings merits some discussion.

Author’s response: Reviewer 2 Comment 6

We thank the reviewer for these insightful comments. The aim of our study was to address a gap in literature, as studies reporting associations between maternal prenatal stress and early brain development have focused specifically on term-born infants. We believe that it is important to understand whether these findings are also observed in preterm-born children.

In response to the reviewer’s comment, we have added a paragraph to the Discussion, calling for future research to include term-born controls:

“It is important to highlight that our sample consisted of preterm infants, a population known to have regional brain volume abnormalities (Peterson et al., 2000) and adverse neuropsychiatric and developmental outcomes (Allen et al., 2008, Nosarti et al., 2012). We caution against generalizing these findings to infants born at term, and suggest that further studies with term-born controls are needed to further clarify the role that early adverse experiences such as maternal stress may have in moderating the association between preterm birth and adverse outcomes in this vulnerable population. “

In addition, we expanded on this argument in S1 Supplement, by adding the following paragraph in the “Prematurity and brain development section”:

“Preterm infants are known to be at risk for adverse neuropsychiatric and developmental outcomes (e.g. Allen et al., 2008, Nosarti et al., 2012). A large number of research studies have focused on investigating the relationship between brain development and these adverse outcomes (Ment et al., 2009, Counsell et al., 2008, Peterson et al., 2000). However, it is essential to also assess the role that early adverse experiences may have in moderating these associations. In a recent study by Benavente-Fernandez et al (2019), the association between brain injury and cognitive outcomes in a sample of children born preterm (24-32 weeks GA) was mediated by maternal socioeconomic status. Similarly, it is possible that exposure to maternal prenatal stress may exacerbate the risk for negative outcomes in preterm-born children. Our findings in Lautarescu et al. (2020) suggested that maternal prenatal stress is related to alterations in white matter microstructure, and thus provided evidence that maternal stress may impact early brain development not only in term-born children, but also in children born preterm. We believe that the present study represents an important contribution to the literature, by also focusing on brain volumes in an overlapping sample of preterm infants. Further research including term-born controls is required to further clarify the nature of this relationship, in order to develop potential interventions that may dampen or reverse the effects of early adversity.”

7. It is disappointing that the data is not publicly available. Ethics constraints may restrict data sharing, but directing inquiries to your local ethics board is not helpful. Perhaps interested parties could contact the authors instead?

Author’s response: Reviewer 2 Comment 7

We have now shared a minimal de-identified data set (S3. Dataset). Please see the section “Additional information regarding data and code” in “S1 Supplement” for details regarding this dataset.

We have now also provided contact details for our local ethics board. We have not provided contact details for the authors for data enquiries, as PLOS ONE Data Availability Policy states that “It is not acceptable for an author to be the sole named individual responsible for ensuring data access”.

We have now updated our data availability statement as follows:

“Relevant data have been shared within the Supporting Information files. This data includes the majority of the variables included in the regression models reported in the manuscript. As this data contained sensitive and potentially identifiable information, we were unable to share the full anonymised dataset necessary to replicate the study findings. Data for the variable "maternal age" could not be shared, as this combination of variables would increase the risk of the data being identifiable. Requests related to data access can be directed to the Hammersmith and Queen Charlotte’s Research Ethics Committee (westlondon.rec@hra.nhs.uk).”

8. Typo/word errors:

a. In the methods, “we inputted missing values”; do you mean imputed?

b. P. 9, line 202 should likely say “constraints” instead of “restraints”

c. P. 4, line 98 “women disclosed alcohol” needs “who” inserted

Author’s response: Reviewer 2 Comment 8

We thank the reviewer for pointing out these typos. We have now corrected the mistakes pointed out in 8a. and 8b.

Regarding comment 8c, the full sentence reads “We excluded cases where (...) women disclosed alcohol and/or drug abuse “, and thus does not need a “who”.

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

References

Allen, M. C. (2008). Neurodevelopmental outcomes of preterm infants. Current opinion in neurology, 21(2), 123-128.

Benavente-Fernández, I., Synnes, A., Grunau, R. E., Chau, V., Ramraj, C., Glass, T., ... & Miller, S. P. (2019). Association of socioeconomic status and brain injury with neurodevelopmental outcomes of very preterm children. JAMA network open, 2(5), e192914-e192914.

Carstairs V, Morris R. (1991): Deprivation and Health in Scotland. Aberdeen University Press

Counsell SJ, Edwards AD, Chew AT, Anjari M, Dyet LE, Srinivasan, L. et al. (2008). Specific relations between neurodevelopmental abilities and white matter microstructure in children born preterm. Brain, 131(12), 3201-3208.

Dean DC, Planalp EM, Wooten W, Kecskemeti SR, Adluru N, Schmidt CK et al. (2018): Association of prenatal maternal depression and anxiety symptoms with infant white matter microstructure. JAMA pediatrics, 172(10), 973-981.

Edwards, A. D., Redshaw, M. E., Kennea, N., Rivero-Arias, O., Gonzales-Cinca, N., Nongena, P., ... & Counsell, S. (2018). Effect of MRI on preterm infants and their families: a randomised trial with nested diagnostic and economic evaluation. Archives of Disease in Childhood-Fetal and Neonatal Edition, 103(1), F15-F21.

Grant, K. A., McMahon, C., & Austin, M. P. (2008). Maternal anxiety during the transition to parenthood: a prospective study. Journal of affective disorders, 108(1-2), 101-111.

Lautarescu, A., Pecheva, D., Nosarti, C., Nihouarn, J., Zhang, H., Victor, S., ... & Counsell, S. J. (2020). Maternal prenatal stress is associated with altered uncinate fasciculus microstructure in premature neonates. Biological psychiatry, 87(6), 559-569.

Ment, L. R., Hirtz, D., & Hüppi, P. S. (2009). Imaging biomarkers of outcome in the developing preterm brain. The Lancet Neurology, 8(11), 1042-1055.

Nosarti C, Reichenberg A, Murray RM, Cnattingius S, Lambe MP, Yin L et al. (2012). Preterm birth and psychiatric disorders in young adult life. Archives of general psychiatry, 69(6), 610-617.

Peterson, B. S., Vohr, B., Staib, L. H., Cannistraci, C. J., Dolberg, A., Schneider, K. C., ... & Duncan, C. C. (2000). Regional brain volume abnormalities and long-term cognitive outcome in preterm infants. Jama, 284(15), 1939-1947.

Qiu, A., Rifkin-Graboi, A., Chen, H., Chong, Y. S., Kwek, K., Gluckman, P. D., ... & Meaney, M. J. (2013). Maternal anxiety and infants' hippocampal development: timing matters. Translational psychiatry, 3(9), e306-e306.

Quigley, M. A., Hockley, C., & Davidson, L. L. (2007). Agreement between hospital records and maternal recall of mode of delivery: evidence from 12 391 deliveries in the UK Millennium Cohort Study. BJOG: An International Journal of Obstetrics & Gynaecology, 114(2), 195-200.

Rifkin-Graboi A, Meaney MJ, Chen H, Bai J, Bak W, Thway Tint M et al. (2015): Antenatal Maternal Anxiety Predicts Variations in Neural Structures Implicated in Anxiety Disorders in Newborns. Journal of the American Academy of Child & Adolescent Psychiatry, 54(4), 313–321

Spielberger, C. D. (1972). Anxiety as an emotional state. Anxiety-Current trends and theory, 3-20.

Tomeo, C. A., Rich-Edwards, J. W., Michels, K. B., Berkey, C. S., Hunter, D. J., Frazier, A. L., ... & Buka, S. L. (1999). Reproducibility and validity of maternal recall of pregnancy-related events. Epidemiology, 774-777.

Yang, G., Zhou, S., Bozek, J., Dong, H. M., Han, M., Zuo, X. N., ... & Gao, J. H. (2020). Sample sizes and population differences in brain template construction. NeuroImage, 206, 116318.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

7 Apr 2021

Exploring the relationship between maternal prenatal stress and brain structure in premature neonates

PONE-D-20-37926R1

Dear Dr. Lautarescu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #1: All comments have been addressed

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Reviewer #2: The authors have addressed all of my comments. This is a topic area of great importance, and I look forward to more work by this team.

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Reviewer #1: Yes: Isabel Benavente-Fernandez

Reviewer #2: No

12 Apr 2021

PONE-D-20-37926R1

Exploring the relationship between maternal prenatal stress and brain structure in premature neonates

Dear Dr. Lautarescu:

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