Introduction

Preterm-born infants experience a persistently higher risk for anxiety and depressive disorders across the life span [1]. Much of the neural foundation of socio-emotional development is laid down in the fetal and neonatal period with profound morphological changes in brain regions involved in saliency, i.e., perceiving and responding to threat and stress [2, 3]. These changes during “third-trimester” development do not occur in isolation, and there is a high level of developmental coordination or synchronized maturation within and between large-scale brain networks [4]. Stress-provoking early-life experiences following preterm birth might have a programming effect on sensitive, still maturing, neuronal brain networks [2]. Hence, during this critical period of brain development, the third trimester in preterm infants could be viewed as a period of significant adversity which may lay the foundation for a lifelong increased risk for a wide range of psychiatric disorders.

Over recent decades, studies have indicated that brain development is delayed following preterm birth [5,6,7,8,9,10], but an increasing number of studies emphasized the degree of differential vulnerability with the advancement in brain development in a region-specific manner [10,11,12,13]. For example, patterns of accelerated functional development of brain regions may occur that are pivotal for detecting and responding to salient stimuli, including the amygdala and insula [14,15,16]. Similar to sensory regions, the development of which is regulated by activity-dependent modification in cellular events [17, 18], the brain’s salience processing networks appear to be regulated by stressful environmental input [19]. Thus, early environmental input may lead to a reprioritization of developmental trajectories [20]. Such a developmental trade-off may be adaptive in the immediate extrauterine environment, but, as the environment changes, it may become detrimental later in life [20, 21].

The coordinated growth of brain regions—maturational covariance—provides insight into the topographical organization of the develo** brain and, according to previous studies [22, 23], reflects patterns of the functional organization of large-scale brain networks. The accelerated maturation of salience-related brain regions might be detrimental to other networks, providing a potential mechanism for malleability in functional outcome. Examining the developmental trajectory of gray matter covariance and the role of early-life stress exposure provides novel insight on macrostructural properties of large-scale brain networks and may, in turn, help to better understand the developmental origins of resilience and vulnerability following preterm birth.

Therefore, the current study investigates the impact of early-life stress exposure, as indicated by the number of invasive procedures, on ex utero “third-trimester” development of network-oriented covariance patterns in a population of extremely preterm infants (<28 weeks of gestation). During sensitive postnatal periods— specifically the “third-trimester”—the extrauterine brain development enables us to probe the effects of early-life stress on large-scale brain networks’ maturational covariance through structural MRI scans obtained at 30 and 40 weeks of gestation. Specifically, we focused on three canonical brain networks that are pivotal in the central response and regulation of stress: the default mode network (DMN; involved in self-referential and autobiographical memory functions), executive control network (ECN; involved in cognitively demanding tasks such as decision-making), and salience network (SN; important for detecting salient internal and environmental stimuli) [3]. Based on prior studies [21], we anticipated a higher maturational coupling of the salience system, including the amygdala, for infants exposed to more stress, which might come at the cost of other networks.

Materials and methods

Participants

The current study included a total of 180 extremely preterm-born infants. Specifically, preterm-born infants (gestational age <28 weeks) were all admitted to the Neonatal Intensive Care Unit (NICU) at the Wilhelmina Children’s Hospital, Utrecht, The Netherlands, and scanned between 28–32 and 39–42 post-menstrual age (cohort 2008–2019). The data collection was part of standard clinical care. Preterm infants with chromosomal and/or congenital anomalies, a potential confound in brain development studies, were excluded. Clinical information for all included infants is summarized in Table 1. Permission from the medical ethical review committee of the University Medical Center Utrecht (METC Utrecht) was obtained.

Table 1 Sample demographic and neonatal clinical details (n = 180).
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MRI acquisition and preprocessing

MRI data included the examination of 3T structural T2 images (3T Achieva MR scanner). Images were obtained during a 35-minute scanning session, using a Turbo Spin Echo (TSE) sequence, using parameters: TR = 6112 ms, TE = 120 ms, voxel resolution in millimeters 0.53 × 0.64 × 2 for 30 weeks and TR = 4851 ms, TE = 150 ms, voxel resolution in millimeters 0.78 × 0.89 × 1.2 for 40 weeks. Infants were immobilized by wrap** them into a vacuum cushion. MiniMuffs (Natus Europe, Münich, Germany) and earmuffs (EM’s kids Everton Park, Australia) were used to reduce noise and the infant’s propensity to move during image acquisition. Prior to scanning, preterm-born infants scanned at 30 weeks were either sedated with 30 mg/kg oral chloral hydrate or not sedated at all, whereas infants scanned at 40 weeks were all sedated with 50–60 mg/kg oral chloral hydrate. If the infant woke up, scanning was halted, and attempts were made to re-settle the infant without taking them out of the patient immobilization system. A neonatologist was present at all times during the examination.

Volumetric segmentation of MRI data was performed using the structural pipeline from the developmental human connectome project (dHCP; http://www.develo**connectome.org/). Briefly, structural scans were pre-processed by first running bias correction using the N4 algorithm [24]. These images were then brain extracted using BET 2 from FSL. Segmentation of the T2 images was performed using the DRAW-EM algorithm [25]. More specifically, manually labeled atlases, annotated by an expert neuroanatomist [26], were registered to the volume, and their labels were fused to the subject space to provide structure priors. Segmentation was then performed with an Expectation-Maximization scheme that combines the structure priors and an intensity model of the volume. The current study included 32 out of 87 labels.

Early-life stress

Early-life stress was quantified according to prior published studies [27,28,29]. Specifically, the total number of invasive procedures was summated during the first weeks after birth up until the first scan at 30 weeks of gestation. The importance of the first weeks is emphasized because the majority of neonatal invasive procedures occur within the first weeks, with the greatest average daily exposure in the first 14 days after birth. In addition, some studies reported a more potent effect of early stress (i.e., first few weeks after birth) versus late stress (i.e., around term equivalent age) on brain development [30,31,32]. We, therefore, collected all clinical factors and treatments that were recorded throughout the first weeks of an infants’ NICU stay, such as the number of skin-breaking procedures, including heel lance, intravenous and central line insertion, intramuscular injection, and chest tube insertion, as well as days of cerebral monitoring and mechanical ventilation, and suctioning of mouth and nose. The cumulative sum of invasive procedures was weighted on the duration of NICU stay. See Supplementary Fig. S1 for the distribution of stressful procedures. The distribution of NICU-related invasive procedures observed in the current study are is in line with previously reported numbers (e.g., skin-breaking procedures [33, 34], days of mechanical ventilation [31, 35, 36]). For subsequent analyses, infants were grouped into low (lowest 50%) or high (highest 50%) stress exposure (for cut-off see Table 1). To ascertain the robustness of findings, we additionally (1) split the dataset on zero-mean so that infants with a stress score below zero were considered low stress, while infants with a stress score above zero were considered as high stress, and (2) looked at dose-dependent effects by dividing the sample into low (<33.3%), mild (33.3–66.6%) and high (>66.6%) stress exposure.

Construction of maturational covariance network

Maturational covariance networks (MCNs) were constructed using previously published procedures (see for example [37, 38]). First, 32 volume measurements, 16 regions per hemisphere, were extracted from the segmented structural images. The regions of interest are all key regions of either the SN (i.e., amygdala, anterior cingulate cortex, insula, thalamus, subthalamic and lentiform nucleus [39, 40]), ECN (i.e., frontal lobe, parietal lobe, cerebellum, and caudate nucleus [41,42,43]), or DMN (i.e., posterior cingulate gyrus, hippocampus, anterior/posterior parahippocampal gyrus, and anterior/posterior fusiform [44, 45]). Second, a linear regression was used to remove the effects of covariates, including gender, total brain volume, gestational age, age at scan, degree of brain injury (i.e., intraventricular hemorrhage), surgeries, administration of pre- and postnatal corticosteroids (i.e.,, accelerates lung maturation), and days of morphine, using the package brainGraph in R (version 3.0.2 [46]). The subsequent studentized residuals were used for the construction of the MCNs. More specifically, the difference in volumetric values between t2 and t1 was computed by dividing the difference, normalized on t1 volumes, by the difference in weeks between the two-time points (i.e., 30 and 40 weeks of gestation). Then, the annualized difference scores were used to generate a 32 × 32 association matrix across subjects, with each entry rij defined as the inter-regional Pearson’s correlation coefficient between the studentized trajectory of gray matter volumes of region i and j. The diagonal cells were set to zero. Raw correlations were all transformed to z-scores by Fisher’s z-transformation. In line with prior studies [47], maturational coupling was defined as the similarity in the trajectory of gray matter development.

Data analysis

Network-level comparison

The degree of maturational coupling was quantified within and between regions of three well-established networks of functionally and structurally connected (sub)cortical brain regions: SN, DMN, and ECN.

Nonparametric permutation testing was used to examine differences in maturational coupling between low and high-stress groups (median split, see Table 1). Subjects were shuffled 5000 times between each group, and each time a new association matrix was obtained between randomized stress labels and volumetric scores. The association matrices were thresholded (i.e., ensuring equal network density by selecting the strongest connections [48]) and binarized. Edges with a weight below the threshold are binarized to 1 and edges with a weight above the threshold are binarized to 0. Then, the total number of within- and between-network connections were computed. The differences in the number of network connections between randomized stress labels represented a permutation distribution of difference under the null hypothesis. The actual between‐group differences in network connections (Delta, hereafter ∆) were tested against the obtained permutation distribution, and a two‐tailed P value was calculated based on its percentile position. As there is no gold standard for density threshold, we applied a range of thresholds (0.01 ≤ K ≤ 0.5, 0.01 increments) to each association matrix. Importantly, with increasing density levels, covariance patterns become more random (i.e., modularity, small-worldness) and are likely non-biological. Therefore, we limited the range of density levels to 0.10 ≤ K ≤ 0.30, 0.01 increments. We report findings at a single network density (20%) and show robustness by replicating findings across density levels. All comparisons were corrected for multiple testing using false discovery rate correction [p-FDR < 0.05] (R base package stats, version 4.1.0). Positive values indicate a higher coupling for high-stress-exposed infants relative to low-stress-exposed infants.

Secondary analyses were conducted to determine which regions drive network-level stress effects. We converted the maturational covariance networks to Z-scores using Fisher’s r-to-z-transformation (R package cocor, version 1.1–3 [49]). The MCN maps for the high- and low-stress-exposed group were then statistically compared at group-level using the Z-statistic (corrected for multiple testing using FDR [p-FDR < 0.05] (R base package stats, version 4.1.0)). Spurious correlations (r < 0.10) were removed prior to transformation.

Results

The effects of early-life stress on network-level maturational covariance

Stress exposure during the first weeks of life resulted in a significant lower within-network maturational coupling (i.e., their difference in network organization as measured from 30 to 40 weeks of gestation) of the DMN in high-stress-exposed infants (n = 90) compared to low-stress-exposed infants (n = 90) (∆ = −12, p-FDR < 0.01, see Fig. 1). These findings suggest that stress may lower similarity in the rate of volumetric development between DMN regions. In contrast, stress resulted in the higher within-network maturational coupling of regions of the SN (∆ = 12, p-FDR < 0.01) and ECN (∆ = 7, p-FDR < 0.05), suggesting that stress may accelerate the maturational coupling of brain regions of these other higher-order networks. Notably, the ECN effects were not consistently replicated across density levels.

Fig. 1: Difference in within-network maturational coupling between preterm-born infants exposed to high versus low stress (median split) across density levels.
figure 1

Positive values show high stress >low stress and negative values show low stress >high stress. Gray diamonds depict the permutation distribution of average maturational coupling, gray bands depict 95% confidence interval, and orange diamonds depict the group-level difference score.

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We next examined between-network coupling. Our analysis did not show significant differences in DMN-ECN maturational coupling between the low and high-stress groups (∆ = −1, p-FDR = 0.896). However, infants exposed to high stress exhibited decreased between-network covariance between the SN and DMN (∆ = −18, p-FDR < 0.01), and between the SN and ECN (∆ = 7, p-FDR < 0.05). These findings suggest that “third-trimester” changes in MCNs are dependent on stress exposure, with high-stress exposure leading to developmental fluctuations in a network-specific manner (see Figs. 2 and 3). The between-network findings for DMN-SN were replicated across several density levels, but not all, and grou** criteria while the SN-ECN findings were less consistent (see Supplementary Figs. 1 and 2).

Fig. 2: Difference in between-network maturational coupling between preterm-born infants exposed to high versus low stress (median split) across density levels.
figure 2

Positive values show high stress >low stress and negative values show low stress >high stress. Gray diamonds depict the permutation distribution of average maturational coupling, gray bands depict 95% confidence interval, and orange diamonds depict the group-level difference score.

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Fig. 3: Graphical representation of network-level differences in maturational coupling.
figure 3

Delta constitutes the difference score in the number of covariance connections between high-stress-exposed infants and low-stress-exposed infants. Positive values indicate high stress >low stress. DMN default mode network, SN salience network, ECN executive control network. *P < 0.05, **P < 0.01.

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An important confounder is the administration of postnatal corticosteroids (for lung maturation). To ensure that the stress effects could not be attributed to non-stress aspects of the intensive-care environment, we repeated the analyses excluding infants who received postnatal corticosteroids (i.e., hydrocortisone). As shown in Supplementary Figs. 4 and 5, the higher coupling within the SN (∆ = 6, p-FDR < 0.05), the lower coupling within the DMN (∆ = −11, p-FDR < 0.01), and the decoupling between the DMN-SN (∆ = −17, p-FDR < 0.01) following high stress were replicated.

We also explored the potential dose-dependent effects of early-life stress by categorizing infants into low, mild, and high-stress exposure. As shown in Fig. 4, a similar pattern was observed with a lower coupling within DMN and between DMN-SN, and a higher coupling within SN in infants exposed to more stress. Importantly, the decoupling within the DMN and between the DMN-SN showed the steepest decline from low to mild stress exposure (DMN: ∆ = −9, p-FDR < 0.01, DMN-SN: ∆ = −17, p-FDR < 0.01), whereas no changes were observed from mild to high-stress exposure. The increased coupling of the SN is only observed in infants exposed to the highest level of stress (low-high-stress difference: ∆ = 8, p-FDR < 0.01).

Fig. 4: Differences in structural coupling for low (<33.3%), mild (33.3–66.6%), and high-stress-exposed infants (>66.6%).
figure 4

Observations are the number of connections using different density thresholds with mean (black dots) and standard deviation (error bars). The orange line depicts structural coupling at a network sparsity of 20%. DMN default mode network, SN salience network. **P < 0.01, ns non-significant.

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The effects of early-life stress on region-level maturational covariance

We examined specific contributions of brain regions in the networks sensitive to stress-induced alterations. Significantly greater (FDR-corrected, q = 0.05) maturational coupling was observed in high-stress compared to low-stress infants within several homologous regions of the SN, namely between the right insula and bilateral amygdala (left: Z = 4.32, p-FDR < 0.001, right: Z = 3.72, p-FDR < 0.001), between the right subthalamic nucleus and bilateral insula (left: Z = 3.95, p-FDR < 0.001; right: Z = 4.32, p-FDR < 0.001), between interhemispheric amygdala (Z = 15.67, p-FDR < 0.001), and between the right amygdala and bilateral subthalamic nucleus (left: Z = 27.04, p-FDR < 0.001; right: Z = 18.04, p-FDR < 0.001).

Following high stress, maturational coupling was weaker for the DMN regions, specifically between the bilateral hippocampus (Z = −2.03, p-FDR < 0.05), between the bilateral anterior fusiform (Z = −12.09, p-FDR < 0.001), between the right posterior fusiform and bilateral anterior fusiform (left: Z = −11.68, p-FDR < 0.001; right: Z = −15.82, p-FDR < 0.001), and between the right posterior parahippocampal gyrus and bilateral anterior fusiform (left: Z = −12.41; right: Z = −15.23, p-FDR < 0.001), among others.

Significant lower maturational coupling was observed in infants exposed to high stress between several DMN-SN regions. Analyses revealed a lower coupling between the left insula and right posterior parahippocampal gyrus (Z = −16.04, p-FDR < 0.001) and between the left insula and right posterior fusiform gyrus (Z = −17.91, p-FDR < 0.001). In addition, high-stress-exposed infants showed decreased synchronized development between the bilateral anterior cingulate cortex and the right posterior parahippocampal gyrus (left: Z = −3.75, p-FDR < 0.001; right: Z = −3.29, p-FDR < 0.001). A schematic representation of the altered within- and between-network maturational coupling is shown in Fig. 5. For a complete overview of region-level alterations, see Supplementary Table 1.

Fig. 5: A schematic representation of the alterations in within- and between-network maturational covariance of preterm-born infants exposed to low (left) and high (right) stress.
figure 5

Gray; default mode network, yellow; salience network, red; executive control network. HPL hippocampus left, HPR hippocampus right, AML amygdala left, AMR amygdala right, GPAL parahippocampal gyrus anterior left, GPAR parahippocampal gyrus anterior right, LOGAL anterior fusiformis left, LOGAR anterior fusiform right, CBL cerebellum left, CBR cerebellum right, INR insula right, INL insula left, GPPR parahippocampal gyrus posterior right, GPPL parahippocampal gyrus posterior left, CGAR cingulate cortex anterior right, CGAL cingulate cortex anterior left, CGPR cingulate gyrus poster right, CGPL cingulate gyrus posterior left, CNR caudate nucleus right, CNL caudate nucleus left, THR thalamus right, THL thalamus left, SNR subthalamic nucleus right, SNL subthalamic nucleus left, LNR lentiform nucleus right, LNL lentiform nucleus left, FLR frontal lobe right, FLL frontal lobe left, PLL parietal lobe left, PLR parietal lobe right, LOGPL posterior fusiform gyrus left, LOGPR, posterior fusiform gyrus right. Network sparsity at 20%.

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Discussion

The current study characterized maturational covariance patterns of three large-scale brain networks in a sample of extremely preterm-born infants, scanned at 30 and 40 weeks of gestation, exposed to ex utero “third-trimester” stress. Infants exposed to high stress showed higher maturational covariance for the SN and a reduced covariance for the DMN. In addition, the high-stress-exposed infants showed lower maturational covariance between the SN and DMN. Follow-up analyses showed a more nuanced pattern such that a decoupling within the DMN and between the DMN-SN was already observed in infants exposed to mild stress, whereas an increased coupling of the SN was only observed in infants exposed to the highest level of stress (top 66.6%). The current findings indicate that “third-trimester” stress exposure leads to a reprioritization of developmental trajectories by altering developmental covariance patterns in a network-specific manner.

The association between early-life stress exposure and alterations in brain development has been supported by prior studies [21, 50]. The current study investigates the notion of a developmental trade-off, such that alterations in the brain’s salience processing network may delay the development of other brain networks. Understanding whether this trade-off exists provides a potential pathway to maladaptive behavior following early-life stress and prematurity.

Several studies suggested that maturational covariance patterns reflect regions that functionally coactivate [22, 51], with functional networks guiding the maturation of covariance patterns [23]. Consistent with previous studies on the functional development following early stressful experiences (e.g., trauma, insensitive parenting, prematurity [15, 16, 50, 52,53,54]), with a higher coupling of the amygdala and insula, we found that the maturational coupling of the SN, a network that subserves the processing of emotions, is significantly influenced by “third-trimester” stress exposure. High-stress-exposed infants showed a higher coupling between the amygdala, insula, and subthalamic nucleus. On the contrary, infants exposed to high stress showed a lower covariance within the DMN, specifically a lower coupling between the hippocampus, parahippocampal gyrus, and fusiform.

Animal studies on the potential mechanisms underlying the effects of chronic early-life stress on brain development report profound and region-specific developmental decline of cell proliferation and neurogenesis, as well as an increased cell death (e.g., doublecortin [DBC], Ki-67 [55,56,57]). More recent studies reported that chronic stress also promotes an earlier rise in myelin basic protein expression, increases synaptic maturation (N-methyl-d-aspartate [NMDA] receptor subunits), and accelerates the emergence of interneurons (parvalbumin [PV] cells) [58,59,60]. Interestingly, PV expression has been implicated in the opening and closure of critical periods, also called the “plasticity switch” [18, 61, 62]. This means that in response to external factors, PV cells might mature faster and lead to a precocious onset of a critical period, shifting neural circuits from an immature to a plastic state. During such a critical period, the brain is open to circuit rewiring based on input from the environment. Hence, early-life stress exposure possibly promotes activity-dependent modification in cellular events, and in turn, regulates the rewiring of the brain’s salience processing networks.

Structural and functional aspects of the amygdala, subthalamic nucleus, and insula represent salient features, including perceptual vigilance, novelty, aversion, and arousal [63,64,65]. The higher maturational coupling within SN-specific nodes might reflect heightened arousal, supporting the anticipation and processing of salient stimuli. Indeed, some studies showed an association between heightened insula-amygdala-subthalamic nucleus coupling and higher anxiety-related disorders [66, 67]. Although the behavioral literature in preterm-born individuals is limited, a few studies found evidence of more extensive processing of salient stimuli in preterm-born infants [68, 69]. Speculative, these studies, combined with our present findings, suggest that higher SN maturational coupling following “third-trimester” stress might facilitate a heightened vigilance and arousal, which could be considered an adaptive response considering the environmental salient experiences during NICU admission.

This study also presents lower maturational covariance within the DMN following high stress, specifically between the bilateral hippocampus, parahippocampal gyrus, and fusiform. These findings are in line with prior studies reporting reduced structural and functional coherences of the DMN in preterm-born infants [70,71,72,73], children [74], and adults [75]. Studies on early-life trauma have repeatedly established a link between reduced DMN coupling and future stress-related psychopathologies, including PTSD, anxiety, and depression [76,77,78]. Reduced DMN coherence following early-life stress might reflect an inability to allocate resources properly between internal thoughts and external stimuli and might be related to impaired internal monitoring and less optimal emotional regulation capacity [79,

Change history

References

  1. Montagna A, Nosarti C. Socio-emotional development following very preterm birth: pathways to psychopathology. Front Psychol. 2016;7:1–23.

  2. Lammertink F, Vinkers CH, Tataranno ML, Benders MJNL. Premature birth and developmental programming: mechanisms of resilience and vulnerability. Front Psychiatry. 2021;11:531571.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hermans EJ, Henckens MJAG, Joëls M, Fernández G. Dynamic adaptation of large-scale brain networks in response to acute stressors. Trends Neurosci. 2014;37:304–14.

    Article  CAS  PubMed  Google Scholar 

  4. Mechelli A, Friston KJ, Frackowiak RS, Price CJ. Structural covariance in the human cortex. J Neurosci. 2005;25:8303–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Saeed N, Cowan FM, Rutherford MA, Edwards AD. Reduced development of cerebral cortex in extremely preterm infants. Seven. 2000;356:1999–2000.

    Google Scholar 

  6. Ball G, Boardman JP, Rueckert D, Aljabar P, Arichi T, Merchant N, et al. The effect of preterm birth on thalamic and cortical development. Cereb Cortex. 2012;22:1016–24.

    Article  PubMed  Google Scholar 

  7. Brown NC, Inder TE, Bear MJ, Hunt RW, Anderson PJ, Doyle LW. Neurobehavior at Term and white and gray matter abnormalities in very preterm infants. J Pediatr. 2009;155:32–38.e1.

  8. Mewes AUJ, Hüppi PS, Als H, Rybicki FJ, Inder TE, McAnulty GB, et al. Regional brain development in serial magnetic resonance imaging of low-risk preterm infants. Pediatrics. 2006;118:23–33.

    Article  PubMed  Google Scholar 

  9. Munakata S, Okada T, Okahashi A, Yoshikawa K, Usukura Y, Makimoto M, et al. Gray matter volumetric MRI differences late-preterm and term infants. Brain Dev. 2013;35:10–16.

    Article  PubMed  Google Scholar 

  10. Padilla N, Alexandrou G, Blennow M, Lagercrantz H, Ådén U. Brain growth gains and losses in extremely preterm infants at term. Cereb Cortex. 2015;25:1897–905.

    Article  PubMed  Google Scholar 

  11. Karolis VR, Froudist-Walsh S, Kroll J, Brittain PJ, Tseng CEJ, Nam KW, et al. Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation. Neuroimage. 2017;163:379–89.

    Article  PubMed  Google Scholar 

  12. Nosarti C, Mechelli A, Herrera A, Walshe M, Shergill SS, Murray RM, et al. Structural covariance in the cortex of very preterm adolescents: a voxel-based morphometry study. Hum Brain Mapp. 2011;32:1615–25.

    Article  PubMed  Google Scholar 

  13. Nosarti C, Giouroukou E, Healy E, Rifkin L, Walshe M, Reichenberg A, et al. Grey and white matter distribution in very preterm adolescents mediates neurodevelopmental outcome. Brain. 2008;131:205–17.

    Article  PubMed  Google Scholar 

  14. Callaghan BL, Richardson R. Early experiences and the development of emotional learning systems in rats. Biol Mood Anxiety Disord 2013;3:8.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gee DG, Gabard-Durnam LJ, Flannery J, Goff B, Humphreys KL, Telzer EH, et al. Early developmental emergence of human amygdala-prefrontal connectivity after maternal deprivation. Proc Natl Acad Sci USA. 2013;110:15638–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tottenham N, Hare TA, Quinn BT, McCarry TW, Nurse M, Gilhooly T, et al. Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation. Dev Sci. 2010;13:46–61.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Volpe JJ. Dysmaturation of premature brain: importance, cellular mechanisms, and potential interventions. Pediatr Neurol 2019;95:42–66.

    Article  PubMed  Google Scholar 

  18. Reha RK, Dias BG, Nelson CA, Kaufer D, Werker JF, Kolbh B, et al. Critical period regulation across multiple timescales. Proc Natl Acad Sci USA 2020;117:23242–51.

    Article  Google Scholar 

  19. Poeggel G, Helmeke C, Abraham A, Schwabe T, Friedrich P, Braun K. Juvenile emotional experience alters synaptic composition in the rodent cortex, hippocampus, and lateral amygdala. Proc Natl Acad Sci USA. 2003;100:16137–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Frankenhuis WE, de Weerth C. Does early-life exposure to stress shape or impair cognition? Curr Dir Psychol Sci. 2013;22:407–12.

    Article  Google Scholar 

  21. Herzberg MP, Gunnar MR. Early life stress and brain function: activity and connectivity associated with processing emotion and reward. Neuroimage. 2020;209:116493.

    Article  PubMed  Google Scholar 

  22. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62:42–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Geng X, Li G, Lu Z, Gao W, Wang L, Shen D, et al. Structural and maturational covariance in early childhood brain development. Cereb Cortex. 2017;27:1795–807.

    PubMed  Google Scholar 

  24. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310–20.

  25. Makropoulos A, Gousias IS, Ledig C, Aljabar P, Serag A, Hajnal JV, et al. Automatic whole brain MRI segmentation of the develo** neonatal brain. IEEE Trans Med Imaging. 2014;33:1818–31.

    Article  PubMed  Google Scholar 

  26. Gousias IS, Edwards AD, Rutherford MA, Counsell SJ, Hajnal JV, Rueckert D, et al. Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants. Neuroimage. 2012;62:1499–509.

    Article  PubMed  Google Scholar 

  27. Grunau RE. Neonatal pain in very preterm infants: long-term effects on brain, neurodevelopment and pain reactivity. Rambam Maimonides Med J. 2013;4:1–13.

    Google Scholar 

  28. Grunau RE, Whitfield MF, Petrie-Thomas J, Synnes AR, Cepeda IL, Keidar A, et al. Neonatal pain, parenting stress and interaction, in relation to cognitive and motor development at 8 and 18 months in preterm infants. Pain. 2009;143:138–46.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Schneider J, Duerden EG, Guo T, Ng K, Hagmann P, Bickle Graz M, et al. Procedural pain and oral glucose in preterm neonates: brain development and sex-specific effects. Pain. 2018;159:515–25.

    Article  CAS  PubMed  Google Scholar 

  30. Brummelte S, Chau CMY, Cepeda IL, Degenhardt A, Weinberg J, Synnes AR, et al. Cortisol levels in former preterm children at school age are predicted by neonatal procedural pain-related stress. Psychoneuroendocrinology. 2015;51:151–63.

    Article  CAS  PubMed  Google Scholar 

  31. Duerden EG, Grunau RE, Guo T, Foong J, Pearson A, Au-Young S, et al. Early procedural pain is associated with regionally-specific alterations in thalamic development in preterm neonates. J Neurosci. 2018;38:878–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tortora D, Severino M, Di Biase C, Malova M, Parodi A, Minghetti D, et al. Early pain exposure influences functional brain connectivity in very preterm neonates. Front Neurosci. 2019;13:899.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kassab M, Alhassan AA, Alzoubi KH, Khader YS. Number and frequency of routinely applied painful procedures in university neonatal intensive care unit. Clin Nurs Res. 2019;28:488–501.

    Article  PubMed  Google Scholar 

  34. Vinall J, Miller SP, Chau V, Brummelte S, Synnes AR, Grunau RE. Neonatal pain in relation to postnatal growth in infants born very preterm. Pain. 2012;153:1374–81.

    Article  PubMed  Google Scholar 

  35. Choi YB, Lee J, Park J, Jun YH. Impact of prolonged mechanical ventilation in very low birth weight infants: results from a national cohort study. J Pediatr. 2018;194:34–39.e3.

    Article  PubMed  Google Scholar 

  36. Lavanga M, Bollen B, Caicedo A, Dereymaeker A, Jansen K, Ortibus E, et al. The effect of early procedural pain in preterm infants on the maturation of electroencephalogram and heart rate variability. Pain. 2021;162:1556.

    Article  CAS  PubMed  Google Scholar 

  37. Alexander-Bloch A, Raznahan A, Bullmore E, Giedd J. The convergence of maturational change and structural covariance in human cortical networks. J Neurosci. 2013;33:2889–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Singh MK, Kesler SR, Hadi Hosseini SM, Kelley RG, Amatya D, Hamilton JP, et al. Anomalous gray matter structural networks in major depressive disorder. Biol Psychiatry. 2013;74:777–85.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang Y, Tang W, Fan X, Zhang J, Geng D, Jiang K, et al. Resting-state functional connectivity changes within the default mode network and the salience network after antipsychotic treatment in early-phase schizophrenia. Neuropsychiatr Dis Treat. 2017;13:397–406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bostan AC, Strick PL. The basal ganglia and the cerebellum: nodes in an integrated network. Nat Rev Neurosci 2018;19:338–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Martín-Signes M, Paz-Alonso PM, Chica AB. Connectivity of frontoparietal regions reveals executive attention and consciousness interactions. Cereb Cortex. 2019;29:4539–50.

    Article  PubMed  Google Scholar 

  43. Shen K, Welton T, Lyon M, McCorkindale AN, Sutherland GT, Burnham S, et al. Structural core of the executive control network: a high angular resolution diffusion MRI study. Hum Brain Mapp. 2020;41:1226–36.

    Article  PubMed  Google Scholar 

  44. Teipel SJ, Bokde ALW, Meindl T, Amaro E, Soldner J, Reiser MF, et al. White matter microstructure underlying default mode network connectivity in the human brain. Neuroimage. 2010;49:2021–32.

    Article  PubMed  Google Scholar 

  45. Öngür D, Lundy M, Greenhouse I, Shinn AK, Menon V, Cohen BM, et al. Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Res - Neuroimaging. 2010;183:59–68.

    Article  Google Scholar 

  46. Watson CG. brainGraph: graph theory analysis of brain MRI data. R package version 3.0.0. 2021. https://github.com/cwatson/brainGraph

  47. Khundrakpam BS, Lewis JD, Jeon S, Kostopoulos P, Itturia Medina Y, Chouinard-Decorte F, et al. Exploring individual brain variability during development based on patterns of maturational coupling of cortical thickness: a longitudinal MRI study. Cereb Cortex. 2019;29:178–88.

    Article  PubMed  Google Scholar 

  48. van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BTT, Schmidt R. Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: issues and recommendations. Neuroimage. 2017;152:437–49.

    Article  PubMed  Google Scholar 

  49. Diedenhofen B, Musch J. Cocor: a comprehensive solution for the statistical comparison of correlations. PLoS ONE. 2015;10:1–12.

    Article  Google Scholar 

  50. Callaghan BL, Tottenham N. The stress acceleration hypothesis: effects of early-life adversity on emotion circuits and behavior. Curr Opin Behav Sci. 2016;7:76–81.

    Article  PubMed  Google Scholar 

  51. Clos M, Rottschy C, Laird AR, Fox PT, Eickhoff SB. Comparison of structural covariance with functional connectivity approaches exemplified by an investigation of the left anterior insula. Neuroimage. 2014;99:269–80.

    Article  PubMed  Google Scholar 

  52. Graham AM, Rasmussen JM, Entringer S, Ben Ward E, Rudolph MD, Gilmore JH, et al. Maternal cortisol concentrations during pregnancy and sex-specific associations with neonatal amygdala connectivity and emerging internalizing behaviors. Biol Psychiatry. 2019;85:172–81.

    Article  CAS  PubMed  Google Scholar 

  53. Thijssen S, Muetzel RL, Bakermans-Kranenburg MJ, Jaddoe VWV, Tiemeier H, Verhulst FC, et al. Insensitive parenting may accelerate the development of the amygdala-medial prefrontal cortex circuit. Dev Psychopathol. 2017;29:505–18.

    Article  PubMed  Google Scholar 

  54. Johns CB, Lacadie C, Vohr B, Ment LR, Scheinost D. Amygdala functional connectivity is associated with social impairments in preterm born young adults. NeuroImage Clin. 2019;21:101626.

    Article  PubMed  Google Scholar 

  55. Joëls M, Karst H, Krugers HJ, Lucassen PJ. Chronic stress: Implications for neuronal morphology, function and neurogenesis. Front Neuroendocrinol. 2007;28:72–96.

    Article  PubMed  Google Scholar 

  56. Lajud N, Torner L. Early life stress and hippocampal neurogenesis in the neonate: sexual dimorphism, long term consequences and possible mediators. Front Mol Neurosci. 2015;8:1–10.

    Article  Google Scholar 

  57. Suri D, Vaidya VA. The adaptive and maladaptive continuum of stress responses—a hippocampal perspective. Rev Neurosci. 2015;26:415–42.

    Article  PubMed  Google Scholar 

  58. Bath KG, Manzano-Nieves G, Goodwill H. Early life stress accelerates behavioral and neural maturation of the hippocampus in male mice. Horm Behav. 2016;82:64–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Goodwill HL, Manzano-Nieves G, LaChance P, Teramoto S, Lin S, Lopez C, et al. Early life stress drives sex-selective impairment in reversal learning by affecting parvalbumin interneurons in orbitofrontal cortex of mice. Cell Rep. 2018;25:2299–307.e4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Nieves GM, Bravo M, Baskoylu S, Bath KG. Early life adversity decreases pre-adolescent fear expression by accelerating amygdala PV cell development. eLife. 2020;9:1–24.

    Google Scholar 

  61. Werker JF, Hensch TK. Critical periods in speech perception: new directions. Annu Rev Psychol. 2015;66:173–96.

    Article  PubMed  Google Scholar 

  62. Hensch TK. Critical period plasticity in local cortical circuits. Nat Rev Neurosci. 2005;6:877–88.

    Article  CAS  PubMed  Google Scholar 

  63. Serranová T, Sieger T, Růžička F, Bakštein E, Dušek P, Vostatek P, et al. Topography of emotional valence and arousal within the motor part of the subthalamic nucleus in Parkinson’s disease. Sci Rep. 2019;9:1–11.

    Article  Google Scholar 

  64. Sullivan RM, Opendak M. Neurobiology of infant fear and anxiety: impacts of delayed amygdala development and attachment figure quality. Biol Psychiatry. 2020;89:641–50.

  65. Tuulari JJ, Kataja EL, Leppänen JM, Lewis JD, Nolvi S, Häikiö T, et al. Newborn left amygdala volume associates with attention disengagement from fearful faces at eight months. Dev Cogn Neurosci. 2020;45:100839.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Yun JY, Boedhoe PSW, Vriend C, Jahanshad N, Abe Y, Ameis SH, et al. Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA consortium. Brain. 2020;143:684–700.

    PubMed  PubMed Central  Google Scholar 

  67. Bruce SE, Buchholz KR, Brown WJ, Yan L, Durbin A, Sheline YI. Altered emotional interference processing in the amygdala and insula in women with post-traumatic stress disorder. NeuroImage Clin. 2013;2:43–49.

    Article  Google Scholar 

  68. Langerock N, van Hanswijck de Jonge L, Bickle Graz M, Hüppi PS, Borradori Tolsa C, Barisnikov K. Emotional reactivity at 12 months in very preterm infants born at <29 weeks of gestation. Infant Behav Dev. 2013;36:289–97.

    Article  CAS  PubMed  Google Scholar 

  69. Cismaru AL, Gui L, Vasung L, Lejeune F, Barisnikov K, Truttmann A, et al. Altered amygdala development and fear processing in prematurely born infants. Front Neuroanat. 2016;10:1–10.

  70. Cui J, Tymofiyeva O, Desikan R, Flynn T, Kim H, Gano D, et al. Microstructure of the default mode network in preterm infants. Am J Neuroradiol. 2017;38:343–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Smyser CD, Inder TE, Shimony JS, Hill JE, Degnan AJ, Snyder AZ, et al. Longitudinal analysis of neural network development in preterm infants. Cereb Cortex. 2010;20:2852–62.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Smyser CD, Snyder AZ, Shimony JS, Mitra A, Inder TE, Neil JJ. Resting-state network complexity and magnitude are reduced in prematurely born infants. Cereb Cortex. 2016;26:322–33.

    Article  PubMed  Google Scholar 

  73. Bouyssi-Kobar M, Brossard-Racine M, Jacobs M, Murnick J, Chang T, Limperopoulos C. Regional microstructural organization of the cerebral cortex is affected by preterm birth. NeuroImage Clin. 2018;18:871–80.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Degnan AJ, Wisnowski JL, Choi S, Ceschin R, Bhushan C, Leahy RM, et al. Alterations of resting state networks and structural connectivity in relation to the prefrontal and anterior cingulate cortices in late prematurity. Neuroreport. 2015;26:22–26.

    Article  PubMed  Google Scholar 

  75. White TP, Symington I, Castellanos NP, Brittain PJ, Froudist Walsh S, Nam KW, et al. Dysconnectivity of neurocognitive networks at rest in very-preterm born adults. NeuroImage Clin. 2014;4:352–65.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Daniels JK, Frewen P, McKinnon MC, Lanius RA. Default mode alterations in posttraumatic stress disorder related to early-life trauma: a developmental perspective. J Psychiatry Neurosci. 2011;36:56–59.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Imperatori C, Farina B, Adenzato M, Valenti EM, Murgia C, Marca GD, et al. Default mode network alterations in individuals with high-trait-anxiety: an EEG functional connectivity study. J Affect Disord. 2019;246:611–8.

    Article  PubMed  Google Scholar 

  78. Sripada RK, King AP, Welsh RC, Garfinkel SN, Wang X, Sripada CS, et al. Neural dysregulation in posttraumatic stress disorder: evidence for disrupted equilibrium between salience and default mode brain networks. Psychosom Med. 2012;74:904–11.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Shi H, Wang X, Yi J, Zhu X, Zhang X, Yang J, et al. Default mode network alterations during implicit emotional faces processing in first-episode, treatment-naive major depression patients. Front Psychol. 2015;6:1198.

    Article  PubMed  PubMed Central  Google Scholar 

  80. **e X, Mulej Bratec S, Schmid G, Meng C, Doll A, Wohlschläger A, et al. How do you make me feel better? Social cognitive emotion regulation and the default mode network. Neuroimage. 2016;134:270–80.

    Article  PubMed  Google Scholar 

  81. Clark CAC, Woodward LJ, Horwood LJ, Moor S. Development of emotional and behavioral regulation in children born extremely preterm and very preterm: biological and social influences. Child Dev. 2008;79:1444–62.

    Article  PubMed  Google Scholar 

  82. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 2010;214:655–67.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Rogers CE, Sylvester CM, Mintz C, Kenley JK, Shimony JS, Barch DM, et al. Neonatal amygdala functional connectivity at rest in healthy and preterm infants and early internalizing symptoms. J Am Acad Child Adolesc Psychiatry. 2017;56:157–66.

    Article  PubMed  Google Scholar 

  84. Papini C, White TP, Montagna A, Brittain PJ, Froudist-Walsh S, Kroll J, et al. Altered resting-state functional connectivity in emotion-processing brain regions in adults who were born very preterm. Psychol Med. 2016;46:3025–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Arpi E, Ferrari F. Preterm birth and behaviour problems in infants and preschool-age children: a review of the recent literature. Dev Med Child Neurol. 2013;55:788–96.

    Article  PubMed  Google Scholar 

  86. Spittle AJ, Treyvaud K, Doyle LW, Roberts G, Lee KJ, Inder TE, et al. Early emergence of behavior and social-emotional problems in very preterm infants. J Am Acad Child Adolesc Psychiatry. 2009;48:909–18.

    Article  PubMed  Google Scholar 

  87. Witt A, Theurel A, Tolsa CB, Lejeune F, Fernandes L, de Jonge L, et al. Emotional and effortful control abilities in 42-month-old very preterm and full-term children. Early Hum Dev. 2014;90:565–9.

    Article  PubMed  Google Scholar 

  88. Hsu HC, Jeng SF. Two-month-olds’ attention and affective response to maternal still face: a comparison between term and preterm infants in Taiwan. Infant Behav Dev. 2008;31:194–206.

    Article  PubMed  Google Scholar 

  89. Yaari M, Rotzak NL, Mankuta D, Harel-Gadassi A, Friedlander E, Eventov-Friedman S, et al. Preterm-infant emotion regulation during the still-face interaction. Infant Behav Dev. 2018;52:56–65.

    Article  PubMed  Google Scholar 

  90. Ellis BJ, Figueredo AJ, Brumbach BH, Schlomer GL. Fundamental dimensions of environmental risk: the impact of harsh versus unpredictable environments on the evolution and development of life history strategies. Hum Nat. 2009;20:204–68.

    Article  PubMed  Google Scholar 

  91. Belsky DW, Caspi A, Houts R, Cohen HJ, Corcoran DL, Danese A, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015;112:E4104–E4110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lobbestael J, Arntz A, Bernstein DP. Disentangling the relationship between different types of childhood maltreatment and personality disorders. J Pers Disord. 2010;24:285–95.

    Article  PubMed  Google Scholar 

  93. Nelson CA, Gabard-Durnam LJ. Early adversity and critical periods: neurodevelopmental consequences of violating the expectable environment. Trends Neurosci. 2020;43:133–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Duncan AE, Auslander WF, Bucholz KK, Hudson DL, Stein RI, White NH. Relationship between abuse and neglect in childhood and diabetes in adulthood: differential effects by sex, national longitudinal study of adolescent health. Prev Chronic Dis. 2015;12:140434.

  95. Gauthier L, Stollak G, Messé L, Aronoff J. Recall of childhood neglect and physical abuse as differential predictors of current psychological functioning. Child Abus Negl. 1996;20:549–59.

    Article  CAS  Google Scholar 

  96. Infurna MR, Reichl C, Parzer P, Schimmenti A, Bifulco A, Kaess M. Associations between depression and specific childhood experiences of abuse and neglect: a meta-analysis. J Affect Disord. 2016;190:47–55.

    Article  PubMed  Google Scholar 

  97. Sullivan TP, Fehon DC, Andres-Hyman RC, Lipschitz DS, Grilo CM. Differential relationships of childhood abuse and neglect subtypes to PTSD symptom clusters among adolescent inpatients. J Trauma Stress. 2006;19:229–39.

    Article  PubMed  Google Scholar 

  98. Parker N, Vidal-Pineiro D, French L, Shin J, Adams HHH, Brodaty H, et al. Corticosteroids and regional variations in thickness of the human cerebral cortex across the lifespan. Cereb Cortex. 2020;30:575–86.

    Article  PubMed  Google Scholar 

  99. Sinclair D, Webster MJ, Wong J, Weickert CS. Dynamic molecular and anatomical changes in the glucocorticoid receptor in human cortical development. Mol Psychiatry. 2011;16:504–15.

    Article  CAS  PubMed  Google Scholar 

  100. Tomalski P, Moore DG, Ribeiro H, Axelsson EL, Murphy E, Karmiloff-Smith A, et al. Socioeconomic status and functional brain development - associations in early infancy. Dev Sci. 2013;16:676–87.

    Article  PubMed  Google Scholar 

  101. Campbell-Yeo M, Johnston CC, Benoit B, Disher T, Caddell K, Vincer M, et al. Sustained efficacy of kangaroo care for repeated painful procedures over neonatal intensive care unit hospitalization: a single-blind randomized controlled trial. Pain. 2019;160:2580–8.

    Article  PubMed  Google Scholar 

  102. Hortensius LM, Van Elburg RM, Nijboer CH, Benders MJNL, De Theije CGM. Postnatal nutrition to improve brain development in the preterm infant: a systematic review from bench to bedside. Front Physiol. 2019;10:961.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Hoftiezer L, Hof MHP, Dijs-Elsinga J, Hogeveen M, Hukkelhoven CWPM, van Lingen RA. From population reference to national standard: new and improved birthweight charts. Am J Obs. 2019;202:383.e1–.e17.

    Article  Google Scholar 

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Acknowledgements

Femke Lammertink was supported by a grant from the Wilhelmina Children’s Hospital (D-17-010007). Martijn P. van den Heuvel was supported by a VIDI (452-16-015) grant from the Netherlands Organization for Scientific Research (NWO) and a European Research Council grant (ERC-2015-CoG 101001062). Erno J. Hermans was supported by a European Research Council grant (ERC-2015-CoG 682591).

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Authors and Affiliations

  1. Department of Neonatology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

    Femke Lammertink, Jeroen Dudink, Maria L. Tataranno & Manon J. N. L. Benders

  2. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije University Amsterdam, Amsterdam, The Netherlands

    Martijn P. van den Heuvel

  3. Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands

    Martijn P. van den Heuvel

  4. Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands

    Erno J. Hermans

  5. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands

    Erno J. Hermans

  6. Department of Anatomy & Neurosciences, Amsterdam UMC (location Vrije University Amsterdam), Amsterdam, The Netherlands

    Christiaan H. Vinkers

  7. Department of Psychiatry, Amsterdam UMC (location Vrije University Amsterdam), Amsterdam, The Netherlands

    Christiaan H. Vinkers

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Contributions

FL, CHV, and MPH carried out the conceptualization of the study. FL and MPH performed the analyses. FL and CHV wrote the manuscript, MPH, EJH, MJNLB, JD, and MLT reviewed and edited the manuscript. FL, JD, and MLT collected the data. CHV and MJNLB funded the project.

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Correspondence to Manon J. N. L. Benders.

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Lammertink, F., van den Heuvel, M.P., Hermans, E.J. et al. Early-life stress exposure and large-scale covariance brain networks in extremely preterm-born infants. Transl Psychiatry 12, 256 (2022). https://doi.org/10.1038/s41398-022-02019-4

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