Stress during pregnancy, for example because of mental or physical disorders, can have long-term effects on child development. Epidemiological studies have shown that individuals exposed to stress in the womb are at higher risk of developmental and mood conditions, such as ADHD and depression. This effect is different between the sexes, and the biological mechanisms that underpin these observations are poorly understood.

One possibility is that a baby’s developing amygdala, the part of the brain that processes emotions, is affected by a signal known as cortisol. This hormone is best known for its role in coordinating the stress response, but it also directs the growth of a fetus. Tracking fetal amygdala changes as well as cortisol levels in the pregnant individual could explain how stress during pregnancy affects development.

To investigate, Stoye et al. recruited nearly 80 volunteers and their newborn children. MRI scans were used to examine the structure of the amygdala, and how it is connected to other parts of the brain. In parallel, the amount of cortisol was measured in hair samples collected from the volunteers around the time of birth, which reflects stress levels during the final three months of pregnancy.

Linking the brain imaging results to the volunteers’ cortisol levels showed that being exposed to higher cortisol levels in the womb affected babies in different ways based on their sex: boys showed alterations in the fine structure of their amygdala, while girls displayed changes in the way that brain region connected to other neural networks.

The work by Stoye et al. potentially reveals a biological mechanism by which early exposure to stress could affect brain development differently between the sexes, potentially informing real-world interventions.

Prenatal exposure to maternal stress is estimated to affect 10–35% of children worldwide, which is a major concern because early life stress may contribute to impaired cognitive development, negative affectivity, autism spectrum disorder (ASD), and psychiatric diagnoses including attention deficit hyperactivity disorder (ADHD), addiction, depression, and schizophrenia (Van den Bergh et al., 2017). Neural correlates of prenatally stressed children include alternations in brain structural and functional connectivity, especially in networks involving the amygdala and prefrontal cortex (Scheinost et al., 2017).

Adaptation of the maternal hypothalamic-pituitary-adrenal (HPA) axis is a key mechanism by which maternal stress modulates offspring neurodevelopment (Moisiadis and Matthews, 2014), and there is evidence that this mechanism operates in a sexually dimorphic manner (Sutherland and Brunwasser, 2018). For example, higher waking maternal salivary cortisol in pregnancy is associated with increased internalising behaviours in female infants and reduced internalising behaviours in males (Braithwaite et al., 2017a; Braithwaite et al., 2017b). Higher maternal salivary cortisol in pregnancy is also associated with stronger amygdala functional connectivity with networks involved in sensory processing and integration in newborn girls, with weaker connectivity to these brain regions in boys (Graham et al., 2019); and in childhood, with larger amygdalae (Buss et al., 2012) and reduced segregation of structural networks in girls but not boys (Kim et al., 2016). The amygdala is further implicated as a neural target of prenatal stress exposure by observations from studies that have characterised maternal stress by symptomatology of depression and/or anxiety, which report alterations in amygdala volume (Wen et al., 2017), microstructure (Rifkin-Graboi et al., 2013), and functional and structural connectivity among offspring (Posner et al., 2016).

Candidacy of the amygdala as an important neural target of prenatal stress exposure comes from the following observations in pre-clinical and clinical studies. First, the amygdala develops early in embryonic life (Humphrey, 1968) and contains a high concentration of glucocorticoid receptors (Wang et al., 2014); second, increased maternal glucocorticoids modulate amygdala development and anxiety-like behaviours in experimental models (Welberg et al., 2000; Barbazanges et al., 1996); third, lesion studies in non-human primates support its critical role in early development of emotion regulation (Schumann et al., 2011); fourth, newborn amygdala functional connectivity is consistently linked with internalising behaviours in children up to the age of 2 years (Graham et al., 2019; Rogers et al., 2017); fifth, early disruption to cell composition of the amygdala is reported in a model of early life stress (Kraszpulski et al., 2006), and in children with autism (Avino et al., 2018); and sixth, in pre-clinical models, stress and glucocorticoid exposure induce dendritic arborisation, amygdala hypertrophy and induce anxiety-like behaviours (Vyas et al., 2003; Mitra and Sapolsky, 2008).

Neonatal magnetic resonance imaging (MRI) serves as an intermediate phenotype for investigating the impact of early life exposures on brain and health because it is distal to the aetiological process, in this case prenatal stress, and is proximal to cognitive, behavioural and disease outcomes. Structural and diffusion MRI (dMRI) have been used to characterise brain structural maturation and emerging network connectivity during the perinatal period, and to investigate pathways to atypical development (Batalle et al., 2018; Boardman and Counsell, 2020). It is a suitable tool to investigate the impact of prenatal stress exposure on the amygdala because age-specific templates enable accurate parcellation of the amygdala and associated structures (Alexander et al., 2017); and diffusion tensor imaging and neurite orientation and dispersion density imaging (NODDI) support inference about tissue microstructure and network connectivity, modelled by fractional anisotropy (FA), mean diffusivity (MD), orientation dispersion index (ODI) and neurite density index (NDI) (Galdi et al., 2020; Zhang et al., 2012).

Hair cortisol concentration (HCC) measured in 3 cm hair samples collected from close to the scalp reflects basal HPA axis activity over the 3 months prior to sampling, and in contrast to single measures from saliva or blood, it is not influenced by short-term activation of the HPA axis in response to acute stressors (Staufenbiel et al., 2013). Studies in pregnant women have shown HCC to be an efficient method of retrospective assessment of long-term cortisol secretion, and thus long-term HPA axis activity (Kirschbaum et al., 2009; D'Anna-Hernandez et al., 2011).

Previous studies have reported sex-specific differences between maternal stress and amygdala functional connectivity and behavioural outcomes among children (Braithwaite et al., 2017a; Braithwaite et al., 2017b; Graham et al., 2019; Buss et al., 2012; Kim et al., 2016), but study designs leave uncertainty about the mechanism linking maternal stress with amygdala development, the potential confounding role of events and environmental exposures during childhood, and the impact of stress on structural connectivity. Resolving these uncertainties is necessary for developing strategies designed to improve socio-emotional development of children born to women who are stressed during pregnancy. Based on studies of the imaging, biochemical, and clinical phenotype of prenatal stress exposure, we hypothesised that higher levels of maternal HPA activity in the final months of pregnancy ascertained from maternal HCC would impact amygdala development and structural connectivity of offspring infants in a sexually-dimorphic manner, and that these effects would be apparent around the time of birth.

We report a mechanism that could explain the impact of maternal stress on infant brain development. We found that maternal HCC, a stable marker of chronic maternal HPA axis activity in pregnancy, is associated with microstructure and structural connectivity of the newborn amygdala, a region of functional importance for early social development and emotion regulation. Specifically, HCC interacts with infant sex to modify amygdala FA, ODI, and NDI, which supports the inference that maternal chronic HPA activity has an impact on dendritic structure, axonal configuration, and the packing density of neurites, in a sexually dimorphic manner (Jespersen et al., 2012; Grussu et al., 2017; Sato et al., 2017; Nazeri et al., 2020).

The findings are consistent with recent reports from the GUSTO (Growing Up in Singapore Towards Health Outcomes) cohort that describe associations between maternal depressive symptoms and alterations in offspring amygdala development (Wen et al., 2017; Rifkin-Graboi et al., 2013). That study highlighted the role of maternal mental health on newborn brain development, and focussed attention on the amygdala. Here, we provide mechanistic insights into the relationship between maternal health and wellbeing and amygdala development by using maternal HCC to characterise chronic HPA activity, and the NODDI model for inference about tissue microstructure. We chose to measure NODDI parameters for assessing microstructure because ODI and NDI in grey matter appear to be functionally tractable. For example, diffusion markers of dendritic density and arborisation in grey matter predict differences in intelligence (Genç et al., 2018), reduced ODI in grey matter is reported in psychosis and in neurodegenerative disease, and reduced grey matter NDI is reported in Parkinson’s disease, Alzheimer’s disease, autism spectrum disorder, and temporal lobe epilepsy (for review see Nazeri et al., 2020).

Maternal HCC was also related to structural connectivity of the amygdala in a sex-discordant manner. Higher maternal HCC was associated with higher FA in girls than boys in tracts between right amygdala and putamen. These observations were not explained by differences in streamline counts in relation to maternal HCC. Furthermore, in sex-stratified analysis, there were consistent trends for girls born to women with higher HCC to have higher mean FA between the left amygdala and left thalamus, putamen and inferior temporal gyrus, and between right amygdala and right putamen and right inferior temporal gyrus, although these did not survive FDR correction. During the neonatal period, higher FA in white matter tracts is typically taken to imply microstructural maturation, through increased axon diameter, density, or myelination. Therefore, increased mean FA demonstrated in connections between the amygdala and putamen, in girls exposed to higher cortisol, could be interpreted as increased maturation of these connections.

Several plausible biological mechanisms could underlie sex differences in associations between HCC and fetal neurodevelopment. First, the capacity of the placenta to regulate the passage of cortisol from mother to fetus differs according to fetal sex, evidenced by sex-discordant expression of enzymes controlling glucocorticoid metabolism within the placenta (Carpenter et al., 2017; Meakin et al., 2017). Second, evidence from gene expression studies indicate that fetal sex influences the direct actions of cortisol in the placenta (Rosenfeld, 2015). Third, the same level of cortisol exposure likely holds sex-different actions at the level of the fetal brain, given that male and female fetuses have different glucocorticoid and mineralocorticoid expression across development (Owen and Matthews, 2003). Finally, there may be sex differences in placental release of corticotropin-releasing hormone (CRH), which influence fetal cortisol exposure and neurodevelopment (Bangasser and Wiersielis, 2018; Sandman et al., 2013).

To our knowledge, this is the first study to investigate a physiological measure of chronic maternal HPA activity with quantitative biomarkers of brain development, and to include infants born very preterm. The relationships we describe appear to apply across the whole GA range because GA at birth was included as a covariate in regression models, and in sub-group analyses the magnitude and direction of ‘HCC x sex’ interaction effects existed in term and preterm groups. This suggests that maternal health and wellbeing across gestation influences neurodevelopment in preterm and term infants. This is important because preterm birth has previously been associated with both exposure to maternal HPA axis dysregulation (Duthie and Reynolds, 2013), and an increased risk of inattention and affective disorders (Johnson, 2007). Furthermore, the finding that maternal HCC is associated with neonatal amygdala development in preterm and term infants is consistent with the observation that maternal cortisol in early gestation predicts amygdala volume at the age of 7 years (Buss et al., 2012). Given that mothers’ HPA axis shows considerable trait-stability across pregnancy, whereby women with higher cortisol concentrations in the second trimester also tend to have higher cortisol in the third trimester (Graham et al., 2019; Stoye et al., 2020), the data lend further support to strategies designed to optimise pre- and early pregnancy health for optimising fetal neurodevelopment.

Strengths of this study are the use of biophysical tissue modelling (NODDI) to enable inference about neurite density and organisation in the amygdala; and use of a data-driven approach to investigate amygdala structural connectivity. A second strength is use of maternal HCC to operationalise stress because it is a quantitative stable marker of cortisol secretion that represents HPA activity over 3 months; as such HCC is unlikely to reflect transient stresses that can occur in pregnancy, and it overcomes the problems of diurnal variation that occur with plasma and saliva measurements.

The study has some limitations: first it was not powered to detect both sex and birth gestation interactions, but this should be considered in future study design. Second, follow-up studies that include measures of socio-emotional development are needed to understand functional consequences of these findings. Finally, the newborn amygdalae are relatively small anatomical regions so could be susceptible to partial volume effects influencing microstructural characteristics. To mitigate this risk, we used an age-specific atlas for segmentation, and excluded voxels with a υiso < 0.5. Third, due to the close proximity of the amygdala and putamen the corresponding connectivity FA may to some degree reflect microstructure of the regions themselves.

Longitudinal studies that evaluate socio-emotional development are needed to understand the functional consequences of these findings. In addition, as the network architecture underlying socio-emotional function in early life becomes more certain with technological advances, it may be useful to focus image analyses on individual networks. The subcortical-gaze pathway is of specific interest because in adults there is evidence that structural connectivity of the superior colliculus and amygdala is related to processing of facial expressions (McFadyen, 2019), which is one of the earliest social cognitive abilities to develop and is foundational to other trajectories of cognitive development.

In conclusion, dMRI and HCC were used to investigate mechanisms underlying the transmission of prenatal stressors on infant development. Maternal HCC in pregnancy is associated with newborn amygdala microstructure and structural connectivity, in a sex-dimorphic manner. These findings reveal that the amygdala, a structure of known importance for child development, is susceptible to variations in the prenatal stress environment, and that cortisol imparts sex-specific effects on human fetal neurodevelopment.

All data generated or analysed during this study are included in the manuscript and supporting files.

1. 1. Alexander B
   2. Murray AL
   3. Loh WY
   4. Matthews LG
   5. Adamson C
   6. Beare R
   7. Chen J
   8. Kelly CE
   9. Rees S
   10. Warfield SK
   11. Anderson PJ
   12. Doyle LW
   13. Spittle AJ
   14. Cheong JLY
   15. Seal ML
   16. Thompson DK

   (2017) A new neonatal cortical and subcortical brain atlas: the Melbourne children's Regional Infant Brain (M-CRIB) atlas

   NeuroImage 147:841–851.

   https://doi.org/10.1016/j.neuroimage.2016.09.068

   • PubMed
   • Google Scholar
2. 1. Andersson JL
   2. Skare S
   3. Ashburner J

   (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

   NeuroImage 20:870–888.

   https://doi.org/10.1016/S1053-8119(03)00336-7

   • PubMed
   • Google Scholar
3. 1. Andersson JLR
   2. Graham MS
   3. Drobnjak I
   4. Zhang H
   5. Filippini N
   6. Bastiani M

   (2017) Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement

   NeuroImage 152:450–466.

   https://doi.org/10.1016/j.neuroimage.2017.02.085

   • PubMed
   • Google Scholar
4. 1. Andersson JLR
   2. Sotiropoulos SN

   (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

   NeuroImage 125:1063–1078.

   https://doi.org/10.1016/j.neuroimage.2015.10.019

   • PubMed
   • Google Scholar
5. 1. Avants BB
   2. Epstein CL
   3. Grossman M
   4. Gee JC

   (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

   Medical Image Analysis 12:26–41.

   https://doi.org/10.1016/j.media.2007.06.004

   • PubMed
   • Google Scholar
6. 1. Avino TA
   2. Barger N
   3. Vargas MV
   4. Carlson EL
   5. Amaral DG
   6. Bauman MD
   7. Schumann CM

   (2018) Neuron numbers increase in the human amygdala from birth to adulthood, but not in autism

   PNAS 115:3710–3715.

   https://doi.org/10.1073/pnas.1801912115

   • PubMed
   • Google Scholar
7. 1. Bangasser DA
   2. Wiersielis KR

   (2018) Sex differences in stress responses: a critical role for corticotropin-releasing factor

   Hormones 17:5–13.

   https://doi.org/10.1007/s42000-018-0002-z

   • PubMed
   • Google Scholar
8. 1. Barbazanges A
   2. Piazza PV
   3. Le Moal M
   4. Maccari S

   (1996) Maternal glucocorticoid secretion mediates long-term effects of prenatal stress

   The Journal of Neuroscience 16:3943–3949.

   https://doi.org/10.1523/JNEUROSCI.16-12-03943.1996

   • PubMed
   • Google Scholar
9. 1. Batalle D
   2. Edwards AD
   3. O'Muircheartaigh J

   (2018) Annual research review: not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain

   Journal of Child Psychology and Psychiatry 59:350–371.

   https://doi.org/10.1111/jcpp.12838

   • PubMed
   • Google Scholar
10. 1. Batalle D
    2. O'Muircheartaigh J
    3. Makropoulos A
    4. Kelly CJ
    5. Dimitrova R
    6. Hughes EJ
    7. Hajnal JV
    8. Zhang H
    9. Alexander DC
    10. Edwards AD
    11. Counsell SJ

    (2019) Different patterns of cortical maturation before and after 38 weeks gestational age demonstrated by diffusion MRI in vivo

    NeuroImage 185:764–775.

    https://doi.org/10.1016/j.neuroimage.2018.05.046

    • PubMed
    • Google Scholar
11. 1. Boardman JP
    2. Hall J
    3. Thrippleton MJ
    4. Reynolds RM
    5. Bogaert D
    6. Davidson DJ
    7. Schwarze J
    8. Drake AJ
    9. Chandran S
    10. Bastin ME
    11. Fletcher-Watson S

    (2020) Impact of preterm birth on brain development and long-term outcome: protocol for a cohort study in Scotland

    BMJ Open 10:e035854.

    https://doi.org/10.1136/bmjopen-2019-035854

    • PubMed
    • Google Scholar
12. 1. Boardman JP
    2. Counsell SJ

    (2020) Invited review: factors associated with atypical brain development in preterm infants: insights from magnetic resonance imaging

    Neuropathology and Applied Neurobiology 46:413–421.

    https://doi.org/10.1111/nan.12589

    • PubMed
    • Google Scholar
13. 1. Braithwaite EC
    2. Murphy SE
    3. Ramchandani PG
    4. Hill J

    (2017a) Associations between biological markers of prenatal stress and infant negative emotionality are specific to sex

    Psychoneuroendocrinology 86:1–7.

    https://doi.org/10.1016/j.psyneuen.2017.09.004

    • PubMed
    • Google Scholar
14. 1. Braithwaite EC
    2. Pickles A
    3. Sharp H
    4. Glover V
    5. O'Donnell KJ
    6. Tibu F
    7. Hill J

    (2017b) Maternal prenatal cortisol predicts infant negative emotionality in a sex-dependent manner

    Physiology & Behavior 175:31–36.

    https://doi.org/10.1016/j.physbeh.2017.03.017

    • PubMed
    • Google Scholar
15. 1. Buss C
    2. Davis EP
    3. Shahbaba B
    4. Pruessner JC
    5. Head K
    6. Sandman CA

    (2012) Maternal cortisol over the course of pregnancy and subsequent child amygdala and Hippocampus volumes and affective problems

    PNAS 109:E1312–E1319.

    https://doi.org/10.1073/pnas.1201295109

    • PubMed
    • Google Scholar
16. 1. Carpenter T
    2. Grecian SM
    3. Reynolds RM

    (2017) Sex differences in early-life programming of the hypothalamic-pituitary-adrenal Axis in humans suggest increased vulnerability in females: a systematic review

    Journal of Developmental Origins of Health and Disease 8:244–255.

    https://doi.org/10.1017/S204017441600074X

    • PubMed
    • Google Scholar
17. 1. Caruyer E
    2. Lenglet C
    3. Sapiro G
    4. Deriche R

    (2013) Design of multishell sampling schemes with uniform coverage in diffusion MRI

    Magnetic Resonance in Medicine 69:1534–1540.

    https://doi.org/10.1002/mrm.24736

    • PubMed
    • Google Scholar
18. 1. D'Anna-Hernandez KL
    2. Ross RG
    3. Natvig CL
    4. Laudenslager ML

    (2011) Hair cortisol levels as a retrospective marker of hypothalamic-pituitary Axis activity throughout pregnancy: comparison to salivary cortisol

    Physiology & Behavior 104:348–353.

    https://doi.org/10.1016/j.physbeh.2011.02.041

    • PubMed
    • Google Scholar
19. 1. Duthie L
    2. Reynolds RM

    (2013) Changes in the maternal hypothalamic-pituitary-adrenal Axis in pregnancy and postpartum: influences on maternal and fetal outcomes

    Neuroendocrinology 98:106–115.

    https://doi.org/10.1159/000354702

    • PubMed
    • Google Scholar
20. 1. Galdi P
    2. Blesa M
    3. Stoye DQ
    4. Sullivan G
    5. Lamb GJ
    6. Quigley AJ
    7. Thrippleton MJ
    8. Bastin ME
    9. Boardman JP

    (2020) Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth

    NeuroImage: Clinical 25:102195.

    https://doi.org/10.1016/j.nicl.2020.102195

    • PubMed
    • Google Scholar
21. 1. Gao W
    2. Stalder T
    3. Foley P
    4. Rauh M
    5. Deng H
    6. Kirschbaum C

    (2013) Quantitative analysis of steroid hormones in human hair using a column-switching LC–APCI–MS/MS assay

    Journal of Chromatography B 928:1–8.

    https://doi.org/10.1016/j.jchromb.2013.03.008

    • Google Scholar
22. 1. Genç E
    2. Fraenz C
    3. Schlüter C
    4. Friedrich P
    5. Hossiep R
    6. Voelkle MC
    7. Ling JM
    8. Güntürkün O
    9. Jung RE

    (2018) Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence

    Nature Communications 9:1905.

    https://doi.org/10.1038/s41467-018-04268-8

    • PubMed
    • Google Scholar
23. 1. Graham AM
    2. Rasmussen JM
    3. Entringer S
    4. Ben Ward E
    5. Rudolph MD
    6. Gilmore JH
    7. Styner M
    8. Wadhwa PD
    9. Fair DA
    10. Buss C

    (2019) Maternal cortisol concentrations during pregnancy and Sex-Specific associations with neonatal amygdala connectivity and emerging internalizing behaviors

    Biological Psychiatry 85:172–181.

    https://doi.org/10.1016/j.biopsych.2018.06.023

    • PubMed
    • Google Scholar
24. 1. Greve DN
    2. Fischl B

    (2009) Accurate and robust brain image alignment using boundary-based registration

    NeuroImage 48:63–72.

    https://doi.org/10.1016/j.neuroimage.2009.06.060

    • PubMed
    • Google Scholar
25. 1. Grussu F
    2. Schneider T
    3. Tur C
    4. Yates RL
    5. Tachrount M
    6. Ianuş A
    7. Yiannakas MC
    8. Newcombe J
    9. Zhang H
    10. Alexander DC
    11. DeLuca GC
    12. Gandini Wheeler-Kingshott CAM

    (2017) Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

    Annals of Clinical and Translational Neurology 4:663–679.

    https://doi.org/10.1002/acn3.445

    • PubMed
    • Google Scholar
26. 1. Guerrero JM
    2. Adluru N
    3. Bendlin BB
    4. Goldsmith HH
    5. Schaefer SM
    6. Davidson RJ
    7. Kecskemeti SR
    8. Zhang H
    9. Alexander AL

    (2019) Optimizing the intrinsic parallel diffusivity in NODDI: an extensive empirical evaluation

    PLOS ONE 14:e0217118.

    https://doi.org/10.1371/journal.pone.0217118

    • PubMed
    • Google Scholar
27. 1. Humphrey T

    (1968) The development of the human amygdala during early embryonic life

    The Journal of Comparative Neurology 132:135–165.

    https://doi.org/10.1002/cne.901320108

    • PubMed
    • Google Scholar
28. 1. Jespersen SN
    2. Leigland LA
    3. Cornea A
    4. Kroenke CD

    (2012) Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging

    IEEE Transactions on Medical Imaging 31:16–32.

    https://doi.org/10.1109/TMI.2011.2162099

    • PubMed
    • Google Scholar
29. 1. Jeurissen B
    2. Tournier JD
    3. Dhollander T
    4. Connelly A
    5. Sijbers J

    (2014) Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

    NeuroImage 103:411–426.

    https://doi.org/10.1016/j.neuroimage.2014.07.061

    • PubMed
    • Google Scholar
30. 1. Johnson S

    (2007) Cognitive and behavioural outcomes following very preterm birth

    Seminars in Fetal and Neonatal Medicine 12:363–373.

    https://doi.org/10.1016/j.siny.2007.05.004

    • PubMed
    • Google Scholar
31. 1. Kim D-J
    2. Davis EP
    3. Sandman CA
    4. Sporns O
    5. O'Donnell BF
    6. Buss C
    7. Hetrick WP

    (2016) Prenatal maternal cortisol has Sex-Specific associations with child brain network properties

    Cerebral Cortex 2008:bhw303.

    https://doi.org/10.1093/cercor/bhw303

    • Google Scholar
32. 1. Kirschbaum C
    2. Tietze A
    3. Skoluda N
    4. Dettenborn L

    (2009) Hair as a retrospective calendar of cortisol production-Increased cortisol incorporation into hair in the third trimester of pregnancy

    Psychoneuroendocrinology 34:32–37.

    https://doi.org/10.1016/j.psyneuen.2008.08.024

    • PubMed
    • Google Scholar
33. 1. Kraszpulski M
    2. Dickerson PA
    3. Salm AK

    (2006) Prenatal stress affects the developmental trajectory of the rat amygdala

    Stress 9:85–95.

    https://doi.org/10.1080/10253890600798109

    • PubMed
    • Google Scholar
34. 1. Makropoulos A
    2. Robinson EC
    3. Schuh A
    4. Wright R
    5. Fitzgibbon S
    6. Bozek J
    7. Counsell SJ
    8. Steinweg J
    9. Vecchiato K
    10. Passerat-Palmbach J
    11. Lenz G
    12. Mortari F
    13. Tenev T
    14. Duff EP
    15. Bastiani M
    16. Cordero-Grande L
    17. Hughes E
    18. Tusor N
    19. Tournier JD
    20. Hutter J
    21. Price AN
    22. Teixeira R
    23. Murgasova M
    24. Victor S
    25. Kelly C
    26. Rutherford MA
    27. Smith SM
    28. Edwards AD
    29. Hajnal JV
    30. Jenkinson M
    31. Rueckert D

    (2018) The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction

    NeuroImage 173:88–112.

    https://doi.org/10.1016/j.neuroimage.2018.01.054

    • PubMed
    • Google Scholar
35. 1. McFadyen J

    (2019) Investigating the subcortical route to the amygdala across species and in disordered fear responses

    Journal of Experimental Neuroscience 13:117906951984644.

    https://doi.org/10.1177/1179069519846445

    • Google Scholar
36. 1. Meakin AS
    2. Saif Z
    3. Jones AR
    4. Aviles PFV
    5. Clifton VL

    (2017) Review: placental adaptations to the presence of maternal asthma during pregnancy

    Placenta 54:17–23.

    https://doi.org/10.1016/j.placenta.2017.01.123

    • PubMed
    • Google Scholar
37. 1. Mitra R
    2. Sapolsky RM

    (2008) Acute corticosterone treatment is sufficient to induce anxiety and amygdaloid dendritic hypertrophy

    PNAS 105:5573–5578.

    https://doi.org/10.1073/pnas.0705615105

    • PubMed
    • Google Scholar
38. 1. Moisiadis VG
    2. Matthews SG

    (2014) Glucocorticoids and fetal programming part 1: outcomes

    Nature Reviews Endocrinology 10:391–402.

    https://doi.org/10.1038/nrendo.2014.73

    • PubMed
    • Google Scholar
39. 1. Nazeri A
    2. Schifani C
    3. Anderson JAE
    4. Ameis SH
    5. Voineskos AN

    (2020) In Vivo Imaging of Gray Matter Microstructure in Major Psychiatric Disorders: Opportunities for Clinical Translation

    Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 5:855–864.

    https://doi.org/10.1016/j.bpsc.2020.03.003

    • PubMed
    • Google Scholar
40. 1. Owen D
    2. Matthews SG

    (2003) Glucocorticoids and sex-dependent development of brain glucocorticoid and mineralocorticoid receptors

    Endocrinology 144:2775–2784.

    https://doi.org/10.1210/en.2002-0145

    • PubMed
    • Google Scholar
41. 1. Posner J
    2. Cha J
    3. Roy AK
    4. Peterson BS
    5. Bansal R
    6. Gustafsson HC
    7. Raffanello E
    8. Gingrich J
    9. Monk C

    (2016) Alterations in amygdala-prefrontal circuits in infants exposed to prenatal maternal depression

    Translational Psychiatry 6:e935.

    https://doi.org/10.1038/tp.2016.146

    • PubMed
    • Google Scholar
42. 1. Rifkin-Graboi A
    2. Bai J
    3. Chen H
    4. Hameed WB
    5. Sim LW
    6. Tint MT
    7. Leutscher-Broekman B
    8. Chong YS
    9. Gluckman PD
    10. Fortier MV
    11. Meaney MJ
    12. Qiu A

    (2013) Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth

    Biological Psychiatry 74:837–844.

    https://doi.org/10.1016/j.biopsych.2013.06.019

    • PubMed
    • Google Scholar
43. 1. Rogers CE
    2. Sylvester CM
    3. Mintz C
    4. Kenley JK
    5. Shimony JS
    6. Barch DM
    7. Smyser CD

    (2017) Neonatal amygdala functional connectivity at rest in healthy and preterm infants and early internalizing symptoms

    Journal of the American Academy of Child & Adolescent Psychiatry 56:157–166.

    https://doi.org/10.1016/j.jaac.2016.11.005

    • PubMed
    • Google Scholar
44. 1. Rosenfeld CS

    (2015) Sex-Specific placental responses in fetal development

    Endocrinology 156:3422–3434.

    https://doi.org/10.1210/en.2015-1227

    • PubMed
    • Google Scholar
45. 1. Sandman CA
    2. Glynn LM
    3. Davis EP

    (2013) Is there a viability-vulnerability tradeoff? sex differences in fetal programming

    Journal of Psychosomatic Research 75:327–335.

    https://doi.org/10.1016/j.jpsychores.2013.07.009

    • PubMed
    • Google Scholar
46. 1. Sato K
    2. Kerever A
    3. Kamagata K
    4. Tsuruta K
    5. Irie R
    6. Tagawa K
    7. Okazawa H
    8. Arikawa-Hirasawa E
    9. Nitta N
    10. Aoki I
    11. Aoki S

    (2017) Understanding microstructure of the brain by comparison of neurite orientation dispersion and density imaging (NODDI) with transparent mouse brain

    Acta Radiologica Open 6:205846011770381.

    https://doi.org/10.1177/2058460117703816

    • Google Scholar
47. 1. Scheinost D
    2. Sinha R
    3. Cross SN
    4. Kwon SH
    5. Sze G
    6. Constable RT
    7. Ment LR

    (2017) Does prenatal stress alter the developing connectome?

    Pediatric Research 81:214–226.

    https://doi.org/10.1038/pr.2016.197

    • PubMed
    • Google Scholar
48. 1. Schumann CM
    2. Bauman MD
    3. Amaral DG

    (2011) Abnormal structure or function of the amygdala is a common component of neurodevelopmental disorders

    Neuropsychologia 49:745–759.

    https://doi.org/10.1016/j.neuropsychologia.2010.09.028

    • PubMed
    • Google Scholar
49. 1. Smith SM
    2. Jenkinson M
    3. Woolrich MW
    4. Beckmann CF
    5. Behrens TE
    6. Johansen-Berg H
    7. Bannister PR
    8. De Luca M
    9. Drobnjak I
    10. Flitney DE
    11. Niazy RK
    12. Saunders J
    13. Vickers J
    14. Zhang Y
    15. De Stefano N
    16. Brady JM
    17. Matthews PM

    (2004) Advances in functional and structural MR image analysis and implementation as FSL

    NeuroImage 23:S208–S219.

    https://doi.org/10.1016/j.neuroimage.2004.07.051

    • PubMed
    • Google Scholar
50. 1. Smith RE
    2. Tournier JD
    3. Calamante F
    4. Connelly A

    (2012) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information

    NeuroImage 62:1924–1938.

    https://doi.org/10.1016/j.neuroimage.2012.06.005

    • PubMed
    • Google Scholar
51. 1. Smith RE
    2. Tournier JD
    3. Calamante F
    4. Connelly A

    (2015a) SIFT2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography

    NeuroImage 119:338–351.

    https://doi.org/10.1016/j.neuroimage.2015.06.092

    • PubMed
    • Google Scholar
52. 1. Smith RE
    2. Tournier JD
    3. Calamante F
    4. Connelly A

    (2015b) The effects of SIFT on the reproducibility and biological accuracy of the structural connectome

    NeuroImage 104:253–265.

    https://doi.org/10.1016/j.neuroimage.2014.10.004

    • PubMed
    • Google Scholar
53. 1. Staufenbiel SM
    2. Penninx BW
    3. Spijker AT
    4. Elzinga BM
    5. van Rossum EF

    (2013) Hair cortisol, stress exposure, and mental health in humans: a systematic review

    Psychoneuroendocrinology 38:1220–1235.

    https://doi.org/10.1016/j.psyneuen.2012.11.015

    • PubMed
    • Google Scholar
54. 1. Stoye DQ
    2. Andrew R
    3. Grobman WA
    4. Adam EK
    5. Wadhwa PD
    6. Buss C
    7. Entringer S
    8. Miller GE
    9. Boardman JP
    10. Seckl JR
    11. Keenan-Devlin LS
    12. Borders AEB
    13. Reynolds RM

    (2020) Maternal glucocorticoid metabolism across pregnancy: a potential mechanism underlying fetal glucocorticoid exposure

    The Journal of Clinical Endocrinology & Metabolism 105:e782–e790.

    https://doi.org/10.1210/clinem/dgz313

    • Google Scholar
55. 1. Sutherland S
    2. Brunwasser SM

    (2018) Sex differences in vulnerability to prenatal stress: a review of the recent literature

    Current Psychiatry Reports 20:102.

    https://doi.org/10.1007/s11920-018-0961-4

    • PubMed
    • Google Scholar
56. 1. Tournier JD
    2. Smith R
    3. Raffelt D
    4. Tabbara R
    5. Dhollander T
    6. Pietsch M
    7. Christiaens D
    8. Jeurissen B
    9. Yeh CH
    10. Connelly A

    (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation

    NeuroImage 202:116137.

    https://doi.org/10.1016/j.neuroimage.2019.116137

    • PubMed
    • Google Scholar
57. 1. Tustison NJ
    2. Avants BB
    3. Cook PA
    4. Zheng Y
    5. Egan A
    6. Yushkevich PA
    7. Gee JC

    (2010) N4ITK: improved N3 Bias correction

    IEEE Transactions on Medical Imaging 29:1310–1320.

    https://doi.org/10.1109/TMI.2010.2046908

    • PubMed
    • Google Scholar
58. 1. Van den Bergh BRH
    2. van den Heuvel MI
    3. Lahti M
    4. Braeken M
    5. de Rooij SR
    6. Entringer S
    7. Hoyer D
    8. Roseboom T
    9. Räikkönen K
    10. King S
    11. Schwab M

    (2017) Prenatal developmental origins of behavior and mental health: the influence of maternal stress in pregnancy

    Neuroscience & Biobehavioral Reviews S0149-7634:30734–30735.

    https://doi.org/10.1016/j.neubiorev.2017.07.003

    • Google Scholar
59. 1. Veraart J
    2. Novikov DS
    3. Christiaens D
    4. Ades-Aron B
    5. Sijbers J
    6. Fieremans E

    (2016) Denoising of diffusion MRI using random matrix theory

    NeuroImage 142:394–406.

    https://doi.org/10.1016/j.neuroimage.2016.08.016

    • PubMed
    • Google Scholar
60. 1. Villar J
    2. Ismail LC
    3. Victora CG
    4. Ohuma EO
    5. Bertino E
    6. Altman DG
    7. Lambert A
    8. Papageorghiou AT
    9. Carvalho M
    10. Jaffer YA
    11. Gravett MG
    12. Purwar M
    13. Frederick IO
    14. Noble AJ
    15. Pang R
    16. Barros FC
    17. Chumlea C
    18. Bhutta ZA
    19. Kennedy SH

    (2014) International standards for newborn weight, length, and head circumference by gestational age and sex: the newborn Cross-Sectional study of the INTERGROWTH-21st project

    The Lancet 384:857–868.

    https://doi.org/10.1016/S0140-6736(14)60932-6

    • Google Scholar
61. 1. Vyas A
    2. Bernal S
    3. Chattarji S

    (2003) Effects of chronic stress on dendritic arborization in the central and extended amygdala

    Brain Research 965:290–294.

    https://doi.org/10.1016/S0006-8993(02)04162-8

    • PubMed
    • Google Scholar
62. 1. Wang H
    2. Suh JW
    3. Das SR
    4. Pluta JB
    5. Craige C
    6. Yushkevich PA

    (2013) Multi-Atlas segmentation with joint label fusion

    IEEE Transactions on Pattern Analysis and Machine Intelligence 35:611–623.

    https://doi.org/10.1109/TPAMI.2012.143

    • PubMed
    • Google Scholar
63. 1. Wang Q
    2. Verweij EW
    3. Krugers HJ
    4. Joels M
    5. Swaab DF
    6. Lucassen PJ

    (2014) Distribution of the glucocorticoid receptor in the human amygdala; changes in mood disorder patients

    Brain Structure and Function 219:1615–1626.

    https://doi.org/10.1007/s00429-013-0589-4

    • PubMed
    • Google Scholar
64. 1. Welberg LA
    2. Seckl JR
    3. Holmes MC

    (2000) Inhibition of 11beta-hydroxysteroid dehydrogenase, the foeto-placental barrier to maternal glucocorticoids, permanently programs amygdala GR mRNA expression and anxiety-like behaviour in the offspring

    European Journal of Neuroscience 12:1047–1054.

    https://doi.org/10.1046/j.1460-9568.2000.00958.x

    • PubMed
    • Google Scholar
65. 1. Wen DJ
    2. Poh JS
    3. Ni SN
    4. Chong YS
    5. Chen H
    6. Kwek K
    7. Shek LP
    8. Gluckman PD
    9. Fortier MV
    10. Meaney MJ
    11. Qiu A

    (2017) Influences of prenatal and postnatal maternal depression on amygdala volume and microstructure in young children

    Translational Psychiatry 7:e1103.

    https://doi.org/10.1038/tp.2017.74

    • PubMed
    • Google Scholar
66. 1. Wennig R

    (2000) Potential problems with the interpretation of hair analysis results

    Forensic Science International 107:5–12.

    https://doi.org/10.1016/S0379-0738(99)00146-2

    • PubMed
    • Google Scholar
67. 1. Woodward LJ
    2. Anderson PJ
    3. Austin NC
    4. Howard K
    5. Inder TE

    (2006) Neonatal MRI to predict neurodevelopmental outcomes in preterm infants

    New England Journal of Medicine 355:685–694.

    https://doi.org/10.1056/NEJMoa053792

    • PubMed
    • Google Scholar
68. 1. Zhang H
    2. Schneider T
    3. Wheeler-Kingshott CA
    4. Alexander DC

    (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain

    NeuroImage 61:1000–1016.

    https://doi.org/10.1016/j.neuroimage.2012.03.072

    • PubMed
    • Google Scholar

Acceptance summary:

The work demonstrates that sex-specific differences in the influence of maternal cortisol, previously shown in childhood and adolescence, are present in the neonatal brain. The use of term age neonates allows the exclusion of the confound of childhood experience and exposure, seen in studies looking at older children. This study can therefore specifically implicate prenatal maternal cortisol exposure as an influence on this early maturing and behaviourally important structure.

Decision letter after peer review:

Thank you for submitting your article "Maternal cortisol is associated with neonatal amygdala microstructure and connectivity in a sexually dimorphic manner" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Jonathan O’Muicheartaigh as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Tamar Makin as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but that, as described below, revisions are required before it is published. We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

This study investigates the relationship between maternal cortisol levels (measured from hair) and infant amygdala microstructure in a cohort of 78 mother-infant dyads. The neonates were a mix of those born at term (n=42) and those born prematurely (n=36) but all were scanned at term equivalent age. The authors demonstrate sex-stratified relationships, with a strong relationship between cortisol and amygdala microstructure in males and a relationship between amygdala and other temporal/subcortical regions in females.

The reviewers agreed that the manuscript is both interesting and timely. The imaging methods are well performed. However, we shared a series of concerns that should be addressed before we can consider publication.

Essential revisions:

Sample:

– The incidence of preterm birth is ~10%, while in this cohort the incidence is much higher. Were the women recruited from a high-risk pregnancy clinic (which may be a high stress population) or do the gestational ages largely reflect twins included in the sample?

– All infants were imaged at term for this protocol. However, NICU-related procedures occurring between birth and scan (primarily days of mechanical ventilation and infection) are associated with alterations subcortical development in preterms. Was the preterm cohort a critically-ill cohort? Were these clinical variables available?

– Maternal education is an important predictor of brain development and outcome. Had the authors considered to adjust for this variable in their analyses, particularly for the volumetric analyses?

Stress and cortisol:

– The last line of the Abstract implicates that the amygdala cortisol relationship gives an insight into the relationship between maternal stress and child outcome. Cortisol levels are shown here to covary with microstructure, but do they reflect actual maternal stress in pregnancy in this sample? Are there any maternal stress (anxiety or depression) questionnaires that could be reported to address this?

Sex Divergence:

– It's not just one sex that is affected but rather sex-specific effects dependent on the outcome examined (amygdala microstructure in male babies and microstructure of connecting white matter from the amygdala in female babies). The Abstract doesn't describe this divergence, focusing on results only with respect to female neonates but actually the interaction effects both sexes in different ways/regions. It would also be very helpful to have plots to illustrates the relationships (residualised for covariates).

Interpretation of measures:

– The major conclusions concerning HCC, the interaction with infant biological sex and the diffusion measures revealing evidence for alterations in "dendritic structure, axonal configuration, and the packing density of neurites…" aren't entirely supported by the findings based on the results from volumetric analyses. The alterations in ODI seen with HCC should be paralleled by changes in the volume data.

Given that brain volume differences are also believed to underlie alterations in dendritic structure, the authors' conclusions wouldn't entirely be supported. Modifying the central claims would be recommended but more data aren't required to support the findings.

– The lack of a significant association between macrostructural changes in the amygdala and HCC was surprising. This is in consideration of previous work in the area in the GUSTO cohort, which focused primarily on amygdalar volumes. The discussion of the results related to the volume data should be expanded upon.

– How inter-related were the measures – e.g. is there a negative association between amygdala microstructure and amygdala connections that could explain the split in sex associations and directionality.

Discussion:

– What potential biological mechanism do the authors propose underlies these sex specific results. There are only really two vague sentences on this, but the complex results need more.

– The authors present an extremely comprehensive overview of the connectivity of the amygdala. Given the authors' conclusions regarding future social cognition assessments, it's surprising to see that the subcortical gaze pathway was not examined (amygdala, thalamus, superior colliculus) as this pathway rapidly processing eye/face processing. Examining connectivity with midbrain structures might be infeasible in the neonatal brain; however, including a discussion of this pathway would be useful for future research in the area.

– For the HCC relationships with microstructure, maternal HCC values for the preterm infants reflects exposure during (coarsely) a different trimester to the term-born infants. The subgroup analyses (term/preterm) indicate that the results are largely independent of this so does this implicate that the influences on amygdala development are from early gestation?

Essential revisions:

Sample:

– The incidence of preterm birth is ~10%, while in this cohort the incidence is much higher. Were the women recruited from a high-risk pregnancy clinic (which may be a high stress population) or do the gestational ages largely reflect twins included in the sample?

The incidence of preterm birth is higher in the study group compared of the general population because the study is designed to investigate the impact of perinatal glucocorticoid exposure on brain development, and perinatal factors (including preterm birth) on infant HPA axis regulation. As such, we recruited term and preterm mother-infant dyads. Preterm dyads were recruited when mothers presented with threatened preterm labour (or immediately after birth if preterm delivery happened without time for antenatal informed consent). The analysis included infants born at term in order to understand generalisability of findings, and because existing literature suggests maternal HPA activity impacts fetal development across the whole of pregnancy. No participating woman was recruited from a high risk or multiple pregnancy clinic.

We have added the following clarification to recruitment section of the Materials and methods:

“We recruited mother-infant dyads who presented to NHS services with threatened preterm labour and delivered at ≤32 completed weeks of gestation, and women who delivered ≥37 weeks’ gestation and received routine care with their infant on postnatal wards. No participants were recruited from high risk antenatal clinics.”

– All infants were imaged at term for this protocol. However, NICU-related procedures occurring between birth and scan (primarily days of mechanical ventilation and infection) are associated with alterations subcortical development in preterms. Was the preterm cohort a critically-ill cohort? Were these clinical variables available?

Thank you. We agree that co-morbidities of preterm birth can affect brain development that is manifest on MRI at term equivalent age. Within the preterm group (n=36) there were the following co-morbidities: early onset sepsis (n=1); late onset sepsis (n=5), necrotizing enterocolitis (n=3); histological chorioamnionitis (n=14); mechanical ventilation during NICU stay (n=16, of which the median period of ventilation was 2 days [IQR 1 to 5 days]).

To investigate whether postnatal co-morbidities influenced associations between maternal cortisol and image metrics we have taken the following steps. First, we tested for univariate associations between the perinatal exposure and image metrics. Where significant univariate associations were identified, we performed additional sensitivity analyses by including the relevant perinatal variable in regression models testing associations between maternal cortisol and image metrics.

Univariate analyses are shown in Author response table 1. Bronchopulmonary dysplasia (BPD), chorioamnionitis and late onset sepsis were not associated with amygdala volume, microstructure or the FA of connections to the putamen. However, infants who received mechanical ventilation (coded as any vs. no ventilation) did have lower right amygdala FA (r=-0.332*, p=0.048) and lower FA in connections with the putamen (r=-0.339**, p=0.043).

Author response table 1: Pearson’s correlations between perinatal exposures and amygdala volume, microstructure and connectivity

Therefore, we included mechanical ventilation as a variable in regression models and found it had little influence on the interactive effect of sex and maternal cortisol on left amygdala FA (β=0.660, previously β=0.677), left amygdala ODI (β=-0.584, previously β=-0.597) and the FA of connections between the right amygdala and putamen (β=0.590, β=0.583).

Since perinatal co-morbidities either did not hold univariate associations with amygdala volume, microstructure or connectivity, or change reported associations between HCC and image metrics, we have not amended the analysis reported in the main manuscript but we have added the following lines to the Results section and we have included the analysis detailed above in Supplementary file 3D:

“The preterm group (n=36) had the following co-morbidities: early onset sepsis (n=1); late onset sepsis (n=5), necrotizing enterocolitis (n=3); histological chorioamnionitis (n=14); mechanical ventilation during NICU stay (n=16, median period of ventilation 2 days [IQR, 1 to 5 days]).”

“Bronchopulmonary dysplasia, sepsis, and histological chorioamnionitis did not correlate with any image feature tested. The need for mechanical ventilation correlated with lower FA in the right amygdala and its connections to putamen, but it had little influence on the interactive effect of sex and maternal cortisol on amygdala microstructure or connectivity (Supplementary file 3D).”

– Maternal education is an important predictor of brain development and outcome. Had the authors considered to adjust for this variable in their analyses, particularly for the volumetric analyses?

Maternal education (binarized as university or college educated (yes [56], no [20]) was not associated with left amygdala (r -0.49) or right amygdala (r -0.148) volume.

Stress and cortisol:

– The last line of the Abstract implicates that the amygdala cortisol relationship gives an insight into the relationship between maternal stress and child outcome. Cortisol levels are shown here to covary with microstructure, but do they reflect actual maternal stress in pregnancy in this sample? Are there any maternal stress (anxiety or depression) questionnaires that could be reported to address this?

The literature linking maternal cortisol with psychological stress/psychiatric disease (‘stress’) in pregnancy does not report a consistent relationship between the two. Our own analysis of the ABCD cohort (Reynolds RM), showed that maternal cortisol is mainly associated with biological and lifestyle factors, and not with psychosocial factors (Blekker et al. Psychoneuroendocrinology 2017).

We have clarified this point of terminology by changing the term ‘…stress…’ to ‘…health…’ in the Abstract.

Sex Divergence:

– It's not just one sex that is affected but rather sex-specific effects dependent on the outcome examined (amygdala microstructure in male babies and microstructure of connecting white matter from the amygdala in female babies). The Abstract doesn't describe this divergence, focusing on results only with respect to female neonates but actually the interaction effects both sexes in different ways/regions. It would also be very helpful to have plots to illustrates the relationships (residualised for covariates).

Thank you for these helpful suggestions. The following sentences have been added to the Abstract:

“We found a relationship between maternal HCC and amygdala development that differed according to infant sex.”

“…Furthermore, altered amygdala microstructure was only observed in boys, with connectivity changes restricted to girls.”

Residualised plots showing sex-discordant associations between HCC, and left amygdala FA, left amygdala ODI and the FA of connections between the right amygdala and right putamen have been added to the manuscript, as requested (Figure 1).

Interpretation of measures:

– The major conclusions concerning HCC, the interaction with infant biological sex and the diffusion measures revealing evidence for alterations in "dendritic structure, axonal configuration, and the packing density of neurites…" aren't entirely supported by the findings based on the results from volumetric analyses. The alterations in ODI seen with HCC should be paralleled by changes in the volume data.

Given that brain volume differences are also believed to underlie alterations in dendritic structure, the authors' conclusions wouldn't entirely be supported. Modifying the central claims would be recommended but more data aren't required to support the findings.

The microstructural measures we used (NDI, ODI, FA and MD) are generally considered to support inference about dendritic structure, axonal configuration, and the packing density of neurites (see citations in manuscript). In the neonatal period, it is very common for microstructural alternations to be present without volumetric correlates, and in adulthood, alterations in dMRI parameters in the contexts of disease and cognition and are not necessarily accompanied by changes in macrostructure (Nazari et al., 2020). Therefore, we think there are insufficient data in the literature to be certain that changes in dMRI parameters, including ODI, should necessarily be paralleled by changes in volume.

Further considerations are: first, the neonatal brain has a higher water content than at other times in life, and this is likely to influence the relationship between microstructure and macrostructure; and second, it is plausible that maternal factors influence the developmental trajectory of the amygdala (Tottenham and Sheridan, 2010) by affecting microstructure in the newborn period, which might in turn affect network connectivity through development, manifesting as volumetric alterations in later childhood. Please also see the next response, which is related to this point.

– The lack of a significant association between macrostructural changes in the amygdala and HCC was surprising. This is in consideration of previous work in the area in the GUSTO cohort, which focused primarily on amygdalar volumes. The discussion of the results related to the volume data should be expanded upon.

The publications arising from the GUSTO cohort that are directly relevant to our study are listed below. There are consistencies between our study and the GUSTO studies when timing of neuroimaging is taken into account. Associations between maternal depressive symptoms and amygdala volume in the GUSTO cohort were found when MRI was conducted in childhood (Wen et al., 2017). However, when the relationship was assessed in the newborn period, maternal depression was associated with amygdala FA, but not with amygdala volume (Rifkin-Graboi et al., 2013). Furthermore, the GUSTO cohort provides supportive evidence that volumetric deficits in sub-cortical structures (hippocampus) in the context of maternal depression may not be apparent at birth but emerge over time (Qiu et al., 2013).

Although we do not see contradictions between the results reported in this manuscript and those of the GUSTO study when timing of imaging is considered, we question the expectation that the two studies should necessarily yield similar results because there are major differences between study designs. The GUSTO investigators used maternal depressive symptoms as the predictor variable in the analyses, whereas we used maternal cortisol; as noted in the response to the point ‘Stress and cortisol’, this is determined by biological and lifestyle factors and is only inconsistently correlated with mental health problems in pregnant women.

– How inter-related were the measures – e.g. is there a negative association between amygdala microstructure and amygdala connections that could explain the split in sex associations and directionality.

Measures of microstructure and connectivity are correlated with varying degrees of strength (see Author response image 1). It can be seen that correlations between microstructural measures are broadly similar in boys and girls.

Author response image 1

Download asset Open asset

Discussion:

– What potential biological mechanism do the authors propose underlies these sex specific results. There are only really two vague sentences on this, but the complex results need more.

Thank you. We have expanded the Discussion to include the following:

“Several plausible biological mechanisms could underlie sex differences in associations between HCC and fetal neurodevelopment. […] Finally, there may be sex differences in placental release of corticotropin-releasing hormone (CRH) which influence fetal cortisol exposure and neurodevelopment (Bangasser and Wiersielis, 2018; Sandman, Glynn and Davis, 2013).”

– The authors present an extremely comprehensive overview of the connectivity of the amygdala. Given the authors' conclusions regarding future social cognition assessments, it's surprising to see that the subcortical gaze pathway was not examined (amygdala, thalamus, superior colliculus) as this pathway rapidly processing eye/face processing. Examining connectivity with midbrain structures might be infeasible in the neonatal brain; however, including a discussion of this pathway would be useful for future research in the area.

We agree this is an interesting future direction to discuss. We have added the following to the Discussion:

“Longitudinal studies that evaluate socio-emotional development are needed to understand the functional consequences of these findings. […] The subcortical-gaze pathway is of specific interest because in adults there is evidence that structural connectivity of the superior colliculus and amygdala is related to processing of facial expressions (McFadyen, 2019), which is one of the earliest social cognitive abilities to develop and is foundational to other trajectories of cognitive development.”

– For the HCC relationships with microstructure, maternal HCC values for the preterm infants reflects exposure during (coarsely) a different trimester to the term-born infants. The subgroup analyses (term/preterm) indicate that the results are largely independent of this so does this implicate that the influences on amygdala development are from early gestation?

Yes, the analysis supports this inference. We have emphasised this point for clarity in the Discussion (fifth paragraph).

References:

Nazeri A et al., in vivo Imaging of Gray Matter Microstructure in Major Psychiatric Disorders: Opportunities for Clinical Translation. Biol Psychiatry Cogn Neurosci Neuroimaging. 5, 9, 855-864 (2020).

Qiu A, Rifkin-Graboi A, Chen H, Chong YS, Kwek K, Gluckman PD, et al. Maternal anxiety and infants' hippocampal development: timing matters. Transl psychiatry. 2013;3(9):e306-e306.Rifkin-Graboi A et al., Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth. Biological psychiatry 74, 837-844 (2013).

Tottenham N, Sheridan MA., A review of adversity, the amygdala and the hippocampus: a consideration of developmental timing. Front Hum Neurosci. 3, 68 (2010).

Wen DJ et al., Influences of prenatal and postnatal maternal depression on amygdala volume and microstructure in young children. Transl psychiatry 7, e1103-e1103 (2017).

Author details

1. David Q Stoye

   MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2444-3573

2. Manuel Blesa

   MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

3. Gemma Sullivan

   MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Paola Galdi

   MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Gillian J Lamb

   MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Gill S Black

   MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Alan J Quigley

   Department of Radiology, Royal Hospital for Sick Children, Edinburgh, United Kingdom

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

8. Michael J Thrippleton

   Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Software, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

9. Mark E Bastin

   Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

10. Rebecca M Reynolds

    Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Writing - review and editing

    Contributed equally with

    James P Boardman

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6226-8270

11. James P Boardman

    1. MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
    2. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Methodology, Writing - review and editing

    Contributed equally with

    Rebecca M Reynolds

    For correspondence

    James.boardman@ed.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3904-8960

• 5,090

  Page views

• 413

  Downloads

• 16

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.